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Regionalisation Based on Basin Characteristics Applied to Flood

Forecasting in the Da River

Master’s thesis S. Foppes University of Twente, August 2005

Title: Regionalisation Based on Basin Characteristics Applied to Flood Forecasting in

the Da River Author: Foppes, Steven Department: Water Engineering and Management, Faculty of Engineering Technology,

University of Twente Committee: dr. D.C.M. Augustijn

dr. M.J. Booij

Regionalisation Based on Basin Characteristics Applied to Flood Forecasting in the Da River

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Preface In front of you lies, I suppose, my last report as a Civil Engineering student. For this project I have worked in Vietnam for two months, and a couple of months before and after on the University of Twente. For offering me a nice project, great working environments and professional assistance I would like to thank dr. D.C.M. Augustijn, dr. M.J. Booij, prof. Diep N.V., dr. Ha T.T. and dr. Lai H.V. (in no specific order). Also, I would like to thank Bob Marley for the audio support. Enjoy reading, Steven Enschede, August 24th, 2005


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Summary Problem The Red River delta, one of the largest deltas in Vietnam, is vulnerable to river flooding. During the monsoon season from June to October, life and property of many people are threatened by disastrous annual flood events. Heavy rainfall is the primary cause of the flooding of the delta. However, the cultivation of low land near the river and deforestation have increased the flood impact and decreased the response time between rainfall and river runoff. To tackle the flooding problem, proper watershed management is required. FLOCODS (FLOod COntrol Decision Support) is a research project on decision support for the Red River. A system including hydrological, hydraulic and socio-economic models will provide decision makers with an analytical tool to assess and evaluate ecosystem upgrading, and flood control measures. The system of the Hoa Binh Dam and Hoa Binh Reservoir in the Da River (tributary of the Red River) is an important tool to mitigate the flood severity. Also, it is used for the generation of hydro-electricity. In the rainy season, the 2 purposes are in conflict because of their optimal reservoir water level: hydro power generation requires a high level, flood protection requires a low level. To meet the 2 purposes as well as possible, forecasting water levels and discharges upstream the Hoa Binh Dam is crucial. The 2 bottle necks for calculating the forecasts are: inaccurate precipitation forecasts; and the absence of observed or forecasted discharges from the Chinese section of the Da River basin. This report focuses on the latter: improving the flood forecasting at the inlet of the Hoa Binh Reservoir, by modelling the ungauged Chinese section. Approach The conceptual HBV-RR model, used for discharge forecasting in this project, resembles the land phase of the hydrological cycle. It consists of 4 routines: the soil moisture routine, the fast flow routine, the slow flow routine and the river routing routine. The river routing routine calculates on a sub-basin scale. Other routines have a 5x5km resolution. The spatial distribution of the input variables precipitation and potential evaporation are modelled by using Thiessen polygons. Because there are not enough discharge data from China available to calibrate HBV-RR in a traditional way, regionalisation is used. Regionalisation is the process of transferring model parameters from comparable basins to the basin of interest. Here, relations between model parameters and basin characteristics form the basis of the regionalisation. HBV-RR is considered to have a sufficient physical basis to be suitable for regionalisation on the basis of basin characteristics. Equifinality, different parameter sets with the same model performance, is a feature of all conceptual models. Equifinality can harm the regionalisation. To avoid this problem, relations between model parameters and basin characteristics are presumed before model calibration. At least it decreases the amount of parameters, which decreases the equifinality problem. Presumptions between model parameters and basin characteristics are based on previous research, HBV-RR model structure and expert opinion. The soil moisture routine and the fast flow routine of HBV-RR are considered the main routines regarding flood forecasting. So, only important parameters from these routines are regionalised, other parameters are kept constant for the whole basin. The soil moisture routine is considered to be influenced by the class of land use (parameter FC) and the size of the sub-basin under


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consideration (parameter �). The fast flow routine is considered to be influenced by the slope (both parameter � and parameter kfast). Calibration is carried out in 2 steps. In the first step the parameters of the soil moisture routine are estimated through the optimisation of the water balance, while other parameters are kept constant. In the second step, the fit of the computed hydrograph to the observed hydrograph results in the estimation of the flow routine parameters, while parameters of the soil moisture routine are kept constant. Results The calibration results of the first calibration step still show non unique parameters sets with the same model performance, the approach, as used in this project, is not able to solve the equifinality problem totally. Also, model sensitivity to the variation of regionalisation parameters that determine �, is low. This makes the regionalisation of � superfluous. Calibration of the flow routines results only in minor improvements of model performance compared to model performance after the first calibration step. Apparently, the soil moisture routine plays an important role in optimising the shape of the hydrograph as well. Validation of the regionalised HBV-RR in the calibrated area for another period results in reasonable flood forecasts. However, the computed overestimation of large discharges indicates an error in the fast flow routine. Probably, the influence of the slope is too large. The second validation run includes the Chinese area of the Da River basin. The performance of the regionalised HBV-RR model is poor, the accuracy of the computed discharges is worse than a mean value of the observed discharges. This is caused by the low density of the network of precipitation stations. Some observed peaks are exaggerated by the model outcomes: this means that a station value is spread out over a too large area. While other peaks are not reproduced at all: there is not any available station in the area of the precipitation event. So, improving the network of precipitation stations in China and the north of Vietnam is the main recommendation. If this is not possible, more effort should be put into modelling the spatial distribution of precipitation.


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Contents 1 Introduction _______________________________________________________ 1

1.1 Scope _______________________________________________________________ 1

1.2 Problem _____________________________________________________________ 3

1.3 Objective and Report Structure _________________________________________ 5

2 The HBV Model ____________________________________________________ 7

2.1 Introduction _________________________________________________________ 7

2.2 Rainfall-Runoff Modelling______________________________________________ 7

2.3 Introduction to the HBV Model _________________________________________ 8

2.4 Description of the HBV-RR Model_______________________________________ 9

2.5 Traditional Calibration of the HBV Model _______________________________ 13

2.6 Conclusions _________________________________________________________ 13

3 The Da River Basin ________________________________________________ 15

3.1 Introduction ________________________________________________________ 15

3.2 Geographical Characteristics __________________________________________ 15

3.3 Meteorological Information____________________________________________ 17

3.4 Hydraulic Information________________________________________________ 20

3.5 Scaling _____________________________________________________________ 21

3.6 Conclusions _________________________________________________________ 23

4 Regionalisation ____________________________________________________ 25

4.1 Introduction ________________________________________________________ 25

4.2 Regionalisation Methods ______________________________________________ 25

4.3 Regionalisation Relations Integrated in the Model Calibration_______________ 26

4.4 Presumed Regionalisation Functions ____________________________________ 27

4.5 Conclusions and Discussion____________________________________________ 29

5 Calibration and Validation___________________________________________ 31

5.1 Introduction ________________________________________________________ 31

5.2 Performance Criteria _________________________________________________ 31

5.3 Calibration of the Soil Moisture Routine _________________________________ 33

5.4 Calibration of the Flow routines ________________________________________ 36

5.5 Validation in Time ___________________________________________________ 38

5.6 Validation in Space: The Da River Basin in China _________________________ 40



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6 Conclusions and Recommendations ___________________________________ 45


6.2 Recommendations____________________________________________________ 47

References ___________________________________________________________ 49

Appendix I: Used parameter values ________________________________________ 1

Appendix II: Calibration of the soil moisture routine __________________________ 4

Appendix III: Calibration of the flow routines________________________________ 9

Appendix IV: Validation in Time with all Available Precipitation Stations ________ 12


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1 Introduction

1.1 Scope The Red River delta (see Figure 1.1 and Figure 1.2), one of the largest deltas in Vietnam, is vulnerable to river flooding. Including the capital Hanoi and the main Red River seaport of Hai Phong, it is of great economic and social importance for northern Vietnam. With almost 17 million people and about 1000 inhabitants/km2, the delta has one of the highest population densities in the world. By comparison, the Mekong delta, another important Vietnamese economic centre, has 400 inhabitants/km2 (Hansson and Ekenberg, 2002). During the rainy season from June to October, life and property of many people are threatened by disastrous annual flood events. Asian Development Bank studies found that the average annual flooding damage for the area protected by the dikes around Hanoi alone amounted to well over US$ 50 million (UNDP Project, 2002).

Figure 1.1: Vietnam (from: Sequoyah Research Center, 2005)

Heavy rainfall in the Red River basin during the monsoons from June to October is the primary cause of flooding the delta, though human influences have increased the flood impact. Rapid population growth and degradation of the existing extensive system of dikes worsen the problems. The majority of the delta consists of cultivated landscape. Approximately 54% is cropland, of which 82% is wet rice agriculture (Hansson and Ekenberg, 2002). With its patchwork of paddies intersected by dikes and channels, it is prone to floods. Further, 60% of the delta area depends heavily on pumping for drainage (Hansson and Ekenberg, 2002), causing problems with high water levels and flooding. Critical high water levels in canals and rivers reduce the possibility to drain water, because extra drainage water increases the water level and


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flood risk downstream. So, critical high water levels in the Red River delta can cause direct inundation problems (dike breaking and overtopping) and indirect inundation problems (reduced possibility to drain water). To mitigate the severity of floods in the delta, the Vietnamese government has been taking several measures. Diep et al. (2000) mention the following: � Strengthening the dike systems; � Reservoir construction and management for flood protection; � Operation of flood diversion Day River system; � Operation of flood retention system; � River dredging and clearance of flood discharge channels; � Dam and dike break emergency action plans. Except for reservoir construction, most of the activities take place in the delta and the core of every activity is to protect the economic valuable delta.

Figure 1.2: The Red River Basin

The river floods are usually formed in sections of the basin upstream of the Red River delta. There, deforestation and other environmental degradation due to uncontrolled development of urban and industrial centres have led to an increasing flood risk in the delta (Ngia, 2000). Forests protect the land from erosion and enlarge the humidity of the basin. Forests store more precipitation and release the water more evenly in time. Thus, forests retard the response time between rainfall and river runoff and reduce flood intensity. Contrary to the retardation function of forests, urban development leads to more impermeable surfaces and drained areas and therefore increases the rain runoff speed, which results in higher water level peaks in the river. To overcome the disadvantages of urbanisation, proper watershed management is required. Reforestation and sustainable urbanisation can help to reduce the flood intensity.


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Estimating the effects and adequacy of the flood mitigating measures and watershed management, requires, among other things, hydrological, hydraulic and social-economic models. ‘FLOCODS (FLOod COntrol Decision Support) is an interdisciplinary long-term research project on the functioning of the Red River System in rainy seasons, facing increasing degradation of the ecosystem and climate changes’ (Institute of Mechanics-Vietnam, 2004). A decision support system including hydrological, hydraulic and social-economic models will provide decision makers with an analytical tool to assess and evaluate ecosystem upgrading, and flood control measures. Modelling the most upstream Chinese section of the Red River tributaries (see Figure 1.2), though valuable for decision support in Vietnam, is not included in the FLOCODS project or in any other project available to the Vietnamese government.

Table 1.1: Basin areas of the Red River’s tributaries.

Name of Red river tributary

Basin area (km2) Percentage of total basin (%)

Da River 52,900 34 Thao River 51,800 33 Lo River 39,000 25

Red River delta 13,000 8 Whole system 155,000 100

The Da River (or Black River, see Figure 1.3 and Figure 1.1) is an important tributary of the Red River system. Its basin area is larger than any other tributary basin of the Red River (see Table 1.1). The yearly discharge of the Da River is approximately half of the total Red River discharge and its influence on the floods in the delta is substantial. Three out of four of the largest recorded floods in the Red River are dominated by Da River discharges: the contribution of the Da River discharge was more than half of the total discharge of three tributaries (Nghia, 2000). To prevent the Red River delta from Da River floods, the Hoa Binh Dam just upstream of the Da-Red River junction was built between 1979 and 1994. With a proper dam gate outflow management, the Hoa Binh Reservoir can function as a buffer to store large Da River discharges. Thus, one of the main purposes for the presence of the Hoa Binh Dam in the Da River is mitigating floods downstream the Red River. The second, and sometimes conflicting, purpose is the production of hydro electricity.

1.2 Problem The 2 purposes for building the Hoa Binh Dam, power generation and flood protection, are in conflict, because optimal reservoir water levels differ. In the rainy season, the Central Committee for Flood Control (controls dam management from the 15th of June to the 15th of September) wants to keep the water level low, increasing the possibilities to temporarily store high discharges from the Da River and so protect the delta against flooding. Though, the electricity company Electricity of Vietnam (controls dam management throughout the rest of the year) requires as much water storage as possible to be able to produce hydro electricity through out the dry season, until the next rainy season. To match these 2 contradicting purposes as much as possible, forecasting water levels and discharges upstream of the Hoa Binh Dam is crucial. The sooner the (accurate) forecasts of a flood are available, the more time dam management has to anticipate, by releasing water from the reservoir to create a larger buffer. The possibility to create a buffer in advance of a flood, reduces the buffer quantity needed during the whole rainy season. The disadvantage of a buffer during the


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whole rainy season is that there is a risk that, especially with low discharges at the end of the rainy season, there is not enough water to fill the reservoir entirely. A not entirely filled reservoir at the end of the rainy season means a loss of potential hydro electricity during the dry season. So, the longer the lead time of the forecasts, the lower the buffer capacity needed during the rainy season, the lower the risk of loss of potential hydro electricity.

Figure 1.3: The Da River basin upstream the Hoa Binh Dam (shaded) (from: Strategic Environment Framework, 2005)

For the Central Committee for Flood Control, the Institute of Mechanics in Hanoi currently makes useful forecasts for the Hoa Binh Reservoir 1 to 2 days in advance. The aim of the Central Committee for Flood Control requires proper forecasts 3 days in advance. For the Institute of Mechanics, the 2 bottle-necks calculating these relatively long term forecasts for the Hoa Binh Reservoir are: � inaccurate precipitation data and precipitation forecasts; � the absence of observed discharges and discharge forecasts in the Chinese section of the

Da River Basin. The first problem relating to relatively long term forecasting of discharges and water levels in the Da River basin, is the lack of accurate precipitation data and precipitation forecasts. Since precipitation is the main input variable for all rainfall-runoff models, the absence of accurate data is a problem. In sub-basins close to the reservoir, the time between the moment of rainfall, and generated discharge by this rainfall at the Hoa Binh Dam, is less than the lead time of three days. In the past, these sub-basins have proven to be crucial for a number of flood events. Therefore, precipitation forecasts are required to forecast Da River discharges and manage water levels of the Hoa Binh reservoir. So, in this case, also inaccurate precipitation forecasts result in a sub optimal reservoir management. This report deals with the second problem related to forecasting of discharges and water levels in the Da River. Nowadays, the Vietnamese government monitors the Da River water levels only by


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seven discharge stations in Vietnam. However, river water travel time from the most upstream discharge station in Vietnam to the Hoa Binh Dam, is usually smaller than the lead time of three days, especially regarding floods with high flow velocities. Therefore, Da River discharge forecasts from China are required to forecast Hoa Binh Reservoir water levels and discharges. However, political issues between China and Vietnam obstruct the exchange of hydrologic information required for hydraulic and hydrological models as used by the Institute of Mechanics. So, at least from a Vietnamese point of view, the upstream Chinese section of the Da River is a poorly gauged area. In this sense, modelling the Chinese section of the Da River basin area relates to the Prediction in Ungauged Basins (PUB) context, which is an initiative of the International Association of Hydrological Sciences (IAHS). According to Sivapalan et al. (2003), PUB is ‘aimed at formulating and implementing appropriate science programmes to engage and energize scientific community, in a coordinated matter, towards achieving major advances in the capacity to make predictions in ungauged basins.’ An ungauged basin or basin area is ‘one with inadequate records (in terms of both data quantity and quality) of hydrological observations to enable computations of hydrological variables of interest (both water quantity and quality) at the appropriate spatial and temporal scales, and to the accuracy acceptable for practical applications’. In this context, prediction means both estimating the frequency of occurrence, and short term forecasting of basin area hydrological responses. The Chinese basin area is ungauged with respect to rainfall-runoff modelling, because too few discharge data are known to do a successful calibration for the hydrological models used by the Institute of Mechanics. The parameters of basins without observed discharge data have to be estimated from other sources of information. Therefore, in this project, a method, called ‘regionalisation’, is used to estimate parameters. Regionalisation is the process of transferring model parameters from (neighbouring) comparable basins to the basin of interest. To transfer parameters from one to another basin, the basins must be, in some way, similar. It involves extrapolation of what is observed or inferred at one relatively well gauged basin area, for example in Vietnam, to the Chinese basin area. The rainfall-runoff model used for this project is the HBV model (Bergström and Forsman, 1973). It is a semi-distributed conceptual model which calculates river discharges on the basis of meteorological input. The concept of the HBV model resembles the concept of the land phase of the hydrological cycle. Its simple structure and the limited amount of parameters make regionalisation applicable to the model (Foppes, 2004).

1.3 Objective and Report Structure Following the scope in Section 1.1 and the problem in Section 1.2, the objective of this project is formulated as follows: To improve flood forecasting at the inlet of the Hoa Binh Reservoir, by regionalising parameters of a HBV rainfall-runoff model of the Da River basin, using the Vietnamese section to estimate the parameters for the ungauged Chinese section. In order to achieve the objective described above the approach is followed. In Chapter 2, the suitability of the conceptual HBV model for this project is discussed. It is investigated whether regionalisation is applicable. Therefore, the structure of a HBV model of Booij (2003), used in this project, is described in detail. Also, some drawbacks and limitations are talked over.


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Chapter 3 starts with a description of the available geographical characteristics, meteorological information and hydraulic information. These available data determine the discretisation of adequate spatial and temporal scales of the model described in Chapter 2. The discretisation of the scales is discussed the last section of Chapter 3. With the HBV model of Booij (2003), its drawbacks and limitations, from Chapter 2, and the available basin characteristics from Chapter 3, a regionalisation method is defined in Chapter 4. Several types of regionalisation methods for the HBV model are discussed and the most suitable is chosen. After that, relations between available basin characteristics and suitable HBV parameters are formulated. In Chapter 5, a description is given of the calibration and validation of the regionalised (Chapter 4) HBV model (Chapter 2) with adequate spatial and temporal scales (Chapter 3). The performance criteria for the calibration and validation are specified. A calibration procedure is set up. With the performance criteria, input series and observed discharge series (Chapter 3), and the calibration procedure, the model is calibrated. A validation, again based on the performance criteria, is performed with other input series and observed discharge series as used for calibration. Final conclusions, reflections and recommendations are given in Chapter 6.


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2 The HBV Model

2.1 Introduction The objective in Section 1.3 mentions ‘a HBV model’. In this chapter, the HBV model is described and discussed. In Section 2.2 the assumption, that a conceptual model is considered more suitable for this research project than other types of rainfall-runoff models, is substantiated. Section 2.3 gives an introduction to the HBV model and Section 2.4 gives a description of the HBV-RR model as used for this project. Traditional calibration of the model and its disadvantages are discussed in Section 2.5. The disadvantages of this traditional calibration are important for the choice calibration method (see Section 4.3). Finally, conclusions and discussion complete this chapter in Section 2.6.

2.2 Rainfall-Runoff Modelling Rainfall-runoff models calculate river discharges on the basis of meteorological input. Based on the extent to which physical processes are taken into account, three categories of hydrological models are recognized: empirical or black box models, conceptual models and physically based models (described by Diermanse, 2001). Empirical Models Diermanse (2001) labels hydrological models as ‘empirical models’ if they are based on mathematical equations that do not take into account the physical processes involved in the hydrological system. The analysis of paired time series of precipitation and evapotranspiration (model input), and river discharges (model output), forms the basis of the parameter calibration. Sherman’s Unit Hydrograph method from 1932 (as referred to by Shaw, 1994), is a well known example of an empirical rainfall-runoff model. Basically, it defines a standard hydrograph generated by a standard effective precipitation event (for example an uniform event of 1 mm/h) over a standard time T, called a T-hour Unit Hydrograph (TUH). Under several assumptions this TUH can be multiplied for precipitation events with a longer duration (multiplication of the time values of the hydrograph), events with a higher precipitation intensity (multiplication of the discharge values of the hydrograph), or both. Conceptual models Conceptual models are models using processes and parameters with a physical meaning, but mostly not measurable on a (sub-) basin scale. Most of the parameters need to be determined by calibration procedures. The advantage of these models is that simplicity and a physical basis go hand in hand. The HBV-RR model (Booij, 2003, described in Section 2.4) is an example of a conceptual rainfall-runoff model. Physically based models Physically based rainfall-runoff models like SHE (Abbott et al., 1986) are based on physical laws, for example the conservation laws of mass, momentum and energy. The distributed character of these models enables the parameters to be, to a certain extent, directly measurable. A disadvantage of the physically based models is the need of extensive data sets to determine parameters. Of course, strict borders between model types are hard to define. Also, different processes within one model can be modelled with different methods. For example, conceptual models can be scaled down to semi-distributed conceptual models with a stronger physical basis.


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Models are further classified from lumped to distributed. This classification depends on the degree of discretisation when describing the basin characteristics, model input and model output. To describe ‘real’ physical processes, physically based models are highly distributed. Most hydrological models are distributed to some degree, called ‘semi-distributed’. Larger basins are regularly geographically split up into sub-basins. Each sub-basin has its typical values for model input, model output and basin characteristics. The Chinese section of the Da River basin is considered poorly gauged. So, modelling this section with an extensive physically based model is impossible. Also meteorological and discharge data are insufficient to calibrate an empirical or a conceptual model. However, with conceptual models, it is possible to transfer parameters from (neighbouring) comparable (sub-) basins to the (sub-)basin of interest. This process is called regionalisation (see Chapter 4). Comparable sub-basins in this case are Da River sub-basins in Vietnam. The absence of a (conceptual) physical basis in empirical models makes these models less suitable for regionalisation. Physically based models cannot be used for regionalisation, the resulting model would not be physically based: it would become a conceptual model (with an excessive high resolution), because parameters are no longer directly measured. The conceptual HBV model, used for this study, has been used for regionalisation in several other studies. Inferring relations between parameter and basin characteristics using the HBV model have been carried out and described by several authors. Seibert (1999) found relations between basin characteristics and HBV parameters for 6 of the 13 investigated parameters in central Sweden. Haberlandt et al. (2001) derived an empirical relationship for the estimation of the average base flow in relation to total flow in the Elbe basin using regionalisation for the HBV model. Booij (2002) used regionalisation for a HBV model of the Meuse basin in order to find appropriate model complexity for climate change impacts on river flooding. Hundecha and Bárdossy (2004) modelled the impact of land-use change on rainfall-runoff processes in the Rhine basin regionalising the parameters of the HBV model. The possibility to use the HBV model for regionalisation makes the model a suitable rainfall-runoff model for this study.

2.3 Introduction to the HBV Model The HBV model is named after the abbreviation of Hydrologiska Byråns Vattenbalansavdelning (Hydrological Bureau Water Balance-section). This was a former section at the Swedish Meteorological and Hydrological Institute (SMHI), where the model was originally developed (SMHI, 2005). The HBV model is a rainfall-runoff model which calculates river discharges on the basis of meteorological input. It was developed in the early 1970s in Norrköping by Bergström and Forsman (1973). Originally it was developed for runoff simulation and hydrological forecasting in Sweden, but its applications in other countries have increased steadily. In more than 50 countries, the HBV model is used for different research topics (Booij, personal communication, 2005). In 1996 the HBV model was re-evaluated and upgraded by Lindström et al. (1997). A firmer physical basis, the possibility to use more spatially distributed data, and model performance improvement are the benefits of the upgrading. This model is called ‘HBV 96’. For the FLOCODS project (Institute of Mechanics, 2004), Booij simplified the HBV 96 model to reduce its amount of parameters and applied it to the Red River basin in Vietnam. The basis of Booij’s HBV model and its implementation in PCRaster (Booij, 2003), is used in this project. In the remainder of the report this model will be referred to as ‘HBV-RR’ (HBV Red River). When the general features of the HBV model are discussed, just ‘HBV model’ is used.


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The HBV model is a ‘conceptual’ model (see also Section 2.2). The concept of the HBV model resembles the concept of the hydrological cycle’s land phase (see Figure 2.1). In Section 2.4 the concept of HBV-RR and the hydrological cycle are compared. HBV-RR is ‘semi-distributed’ (see Section 2.2). It also facilitates the option of an even higher resolution in space (than a sub-basin resolution) for integration of spatially distributed field data in the model, either as model input or for validation and upgrading (Lindström et al., 1997).

2.4 Description of the HBV-RR Model The HBV-RR model is partly based on the land phase of the hydrological cycle. In Figure 2.1, this land phase is presented schematically. Water is temporarily stored in air moisture, snow pack, interception storage, soil moisture, groundwater and in the river. Processes between these storages are rainfall, snowfall, snowmelt, interception, infiltration, percolation, capillary rise, evapotranspiration, interflow, overland flow and groundwater flow.

Figure 2.1: Schematic view of the land phase of the hydrological cycle.

To model the land phase of the hydrological cycle, the HBV-RR model uses four routines: the soil moisture routine, the fast flow routine, the slow flow routine and the river routing routine (see Figure 2.2). Soil moisture, fast flow and slow flow computations are made on a cell by cell basis (left hand side of Figure 2.2). One sub-basin consists of a number of cells on a spatial grid. The whole HBV model consists of a number of sub-basins. Every time step, the HBV-RR model produces for each cell separately: precipitation, evapotranspiration, direct discharges, indirect discharges, storm flow, percolation, and groundwater flow. Storm flow and groundwater flow of each cell are summed and added to the river at the outlet point of the sub-basins on the right hand side of Figure 2.2. Each sub-basin, not located at a most upstream part of the Da River basin has river inflow upstream. Every time step, the river flow at the downstream end of a sub-basin consists of the river flow upstream, delayed by the routing routine plus the accumulated storm

Air moisture

Soil moisture

Ground water

Snow pack Interception

storage

Snowfall

Snowmelt Interception

Infiltration Ground surface

Overland flow River

Evapotranspiration

Percolation Capillary rise

Interflow

Groundwater flow

Rainfall

Surface storage


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and groundwater flow of each cell. Computations in the routines of Figure 2.2 are described below in more detail.

Figure 2.2: Schematisation the HBV-RR model

HBV-RR model is modelled in a PCRaster environment (Utrecht University, 2005). PCRaster is a Geographical Information System which consists of a set of computer tools for storing, manipulating, analysing and retrieving geographic information. Computations are made on a cell by cell basis. Precipitation Measured precipitation values are regarded as input for the HBV-RR model. Booij’s HBV-RR (2003) calculates precipitation P [LT-1] on a certain cell on the basis of precipitation values of several surrounding stations weighed by the distances to these stations. Due to the low amount of stations in China and the large amount of missing values in this project, the differences in the distances between the stations is variable in both time and space. Therefore, the method used by Booij (2003) is less suitable. In this research project the well known and well proved ‘Thiessen polygon’ method (as referred to by Shaw, 1994) is used. Every time step, precipitation P on a cell is determined by the precipitation value of its nearest station. Soil Moisture Routine The soil moisture content in the soil moisture routine is represented by a reservoir. Precipitation P is the input for this reservoir. The direct discharge Qdirect [LT-1] is schematised by the overtopping of the soil moisture reservoir when it is full. Thus, the difference of precipitation P plus the (in the previous time step computed) soil moisture storage SSM [L], and the parameter ‘maximum soil moisture storage FC [L]’, gives the direct discharge Qdirect. When the reservoir is not full, direct discharge is absent:

( )0 FC,SSM Pmax Qdirect −+= (2.1)

Soil Moisture Routine

Direct discharge

Groundwater flow

Storm flow

Indirect discharge

Precipitation

Fast Flow Routine

Percolation

Slow Flow Routine

Computations per cell

Evapo-transpiration

Computations per sub-basin

River flow downstream

Summ

ation of cell values per sub-basin

River Routing Routine

River flow upstream


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The indirect discharge Qindirect [LT-1] through the soil moisture is modelled as an outflow at the bottom of the soil moisture reservoir and depends on the soil moisture reservoir level SSM/FC to the power � [1] (a measure of permeability) and on the infiltrated water (P-Qdirect):

( )directindirect QP*FC

SSM Q −�

�

��

�=β

(2.2)

Actual evapotranspiration ETa [LT-1] is a portion of the potential evapotranspiration ETp [LT-1]. An important parameter is LP [1]: a limit for potential evapotranspiration, between 0 and 1. The potential evapotranspiration ETp is reached when the soil moisture storage SSM is above the threshold LP * FC. Under this threshold evapotranspiration is linearly related to the soil moisture storage SSM: the less soil moisture the lower the actual evapotranspiration ETa, which is physically clear.

��

��

�= ppa ET*FC * LP

SSM,ETmin ET (2.3)

The amount of water that does not run off or does not evaporate is added to the soil moisture storage SSM for the next time step, where t [T] reflects the current time and t+�t [T] reflects the time at the following time step:

(t)ET - (t)Q - (t)Q - P(t) SSM(t) t)SSM(t adirectindirect+=∆+ (2.4)

Flow routines Runoff delay is simulated through the use of 2 reservoirs which represent the surface water storage (in the fast flow routine) and the groundwater storage (in the slow flow routine). Yield from the fast flow routine Qfast [LT-1], represents storm flow, and yield from the slow flow routine Qslow [LT-1], represents groundwater flow. These 2 types of yields contribute directly to the river discharge at the downstream end of the sub-basin following Equation (2.5) and Equation (2.6).

( )α+= 1fastfast SSW*k,SSWmin Q (2.5)

Normally, the storm flow Qfast is defined by the surface water storage SSW [L]to the power 1+�, times recession coefficient kfast [T

-1]. Here, � [1] is a measure of non linearity, typically in the order of 1. Of course, yield from this reservoir is not larger than its storage, so if this term exceeds surface water storage SSW, only SSW is discharged.

SGW*k Q slowslow = (2.6)

The groundwater flow Qslow is defined by the linear function of kslow [T-1] times the groundwater storage SGW [L]. For the next time step, the direct discharge Qdirect and indirect discharge Qindirect from the current time step become available to the flow routines. The first part of these discharges, with a maximum of the constant PERC [LT-1], percolates to the groundwater storage, resulting in a groundwater storage for the next time step of:

( )PERC (t),Q(t)Qmin(t)Q- SGW(t) t)SGW(t indirectdirectslow ++=∆+ (2.7)

If the direct discharge Qdirect and indirect discharge Qindirect together, exceed the constant PERC, the remainder becomes available to the surface water storage SSW, resulting in a surface water storage for the next time step of:


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( )PERC,0-(t)Q(t)Qmax(t)Q - SSW(t) t)SSW(t indirectdirectfast ++=∆+ (2.8)

River Routing Routine In a most upstream Da River sub-basin the sum of Qslow plus Qfast of each cell (converted to river discharge in [L3T-1]) is the computed discharge at the most downstream point of a sub-basin O [L3T-1]. However, inflow from upstream a sub-basin I [L3T-1], if present, contributes to the discharge downstream too. The Muskingum Method (as referred to by Shaw, 1994) derives the current outflow of a sub-basin O(t) on the basis of the outflow at the previous time step O(t-�t), inflow at the previous time step I(t-�t) and current inflow I(t):

t)-O(t*cI(t)*ct)-I(t*c O(t) 321 ∆++∆= (2.9)

The weight factors c1, c2 and c3 are determined as:

2Kx-2Kt2Kxt

c1 +∆+∆=

2Kx-2Kt2Kxt

c3 +∆−∆= (2.10)

2Kx-2Kt2Kxt2K

c3 +∆−∆−=

Here, �t [T] is the time step between 2 calculations. The dimensionless weighting factor x [1] indicates the relative importance of I (and 1-x the importance of O) in determining the river storage in the sub-basin and has typical values of 0.2 to 0.4. Cunge (as referred to by Shaw, 1994) proposed K [T] to be equal to the time of travel of a flood wave through the sub-basin:

vL

K∆= (2.11)

Here, v is the average velocity of the flood peak (LT-1) and �L [L] is the length of the river through the sub-basin. For sub-basins with 2 inflows upstream (I1 and I2) the Muskingum Method is rewritten:

t)-O(t*f)-1(*)(Ic(t)I*)(Ict)-(tI*)(Ic

t)-O(t*f*)(Ic(t)I*)(Ict)-(tI*)(Ic O(t)

23222221

13112111

∆++∆+∆++∆=

(2.12)

The weight factors c1, c2 and c3 are determined for both inflows and are multiplied with the corresponding inflow. However, to avoid the overestimation of the influence of outflow O(t-�t) the dimensionless weighting factor f [1] is used. It determines the relative importance of the upstream inflow I1 to the outflow at the previous time step O(t-�t), and 1-f the relative importance of the upstream inflow I2. When comparing HBV-RR with the land phase of the hydrological cycle (schematically presented in Figure 2.1), several differences are noticed. Processes of evapotranspiration of both models do not entirely correspond. Interception and capillary rise are not modelled at all in HBV-RR. Overland flow and interflow can be compared with direct discharge and indirect discharge, but not only are their names different, also the processes are not totally in agreement. Since there is hardly any snowfall in the Red River basin, the absence of processes incorporating snow is obvious. So, the HBV-RR model is a conceptual model with a physical basis.


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2.5 Traditional Calibration of the HBV Model Since data of groundwater flow, soil moisture contents, percolation, et cetera. are usually not available, the HBV model calibration needs only observed precipitation data, observed potential evapotranspiration data and observed river discharge data. To do a successful calibration, time series of 5 to 10 years with daily values are considered sufficient. The calibration is carried out individually for each sub-basin. Parameters are kept constant over each sub-basin (though, parameters can be adjusted per cell in HBV-RR). Before each time the model is run, parameter values per sub-basin are changed. After the model is run, the computed discharges of each model run are compared with the observed discharges. The parameters values of the run with the best performance for the sub-basin under consideration are taken as model parameter for the sub-basin under consideration. This procedure is carried out for all sub-basins. After the calibration, each sub-basin has its own parameter values and these values remain constant during the model application. However, there is a disadvantage. Calibration of models with more than one parameter often results in a non unique set of parameters, called equifinality (Beven, 2001a,b). Different sets of parameter values give equal model performance. Different sets of parameter values imply different quantities for storm flow, groundwater flow, percolation, et cetera. This fact undermines the physical basis of the conceptual models, or at least, the physical basis of calibrated conceptual models.

2.6 Conclusions Conceptual modelling is useful for flood forecasting of the Da River basin. Sufficient amounts of data are available to calibrate a conceptual model in the gauged Vietnamese section of the basin. With calibration, model parameter values of the Vietnamese sub-basins can be obtained. Then, parameters from Vietnamese sub-basins are transferred to the Chinese sub-basins. This process is called regionalisation. The structure of the conceptual HBV-RR model, as used in this research project, has a physical basis. Storm flow, groundwater flow, soil moisture contents, percolation and evaporation are modelled in cells on a spatial grid. However, the problem of equifinality undermines the physical basis of the conceptual HBV model. A calibrated model might result in proper forecasts of river discharges, but this does not necessarily mean that other modelled quantities are forecasted properly. So, in advance, the HBV-RR model seems to offer a proper tool to use for the flood forecasting of the whole Da River basin upstream of the reservoir inlet at Ta Bu. Regionalisation seems to be practicable, however attention must be paid to the equifinality problem.


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3 The Da River Basin

3.1 Introduction This chapter deals with Da River basin characteristics and other data required for the research project. In Section 3.2, the geographical basin characteristics, river network, topography and elevation, and land use are described. Section 3.3 gives an overview of the meteorological information. Time series of precipitation and potential evaporation serve as input values for the HBV-RR model. For model calibration and validation in Chapter 5, observed discharges are required to assess the performance of the model. Together with the Hoa Binh Reservoir the available time series of observed discharges are discussed in Section 3.4. All characteristics described Section 3.2, 3.3 and 3.4, influence the scaling in Section 3.5. Here, the temporal and spatial scales for HBV-RR (see Section 2.4) are determined. The final Section 3.6 deals with the conclusions.

3.2 Geographical Characteristics

3.2.1 The River Network The Da River springs from the Himalaya spurs Ailao Shan and Wuliang Shan mountains (see Figure 1.3), in Yunnan province, China. From there it flows in south east direction, converging with several rivers like Nam Na, Nam Mu and Nam Po, to Hoa Binh in Vietnam where it is dammed. After being discharged from the reservoir, the flow direction of the Da River turns north-eastward. Finally, after approximately 1000 km, it converges with the other main tributaries of the Red River system close to Viet Tri. The, from here called, Red River flows via Hanoi into the Tonkin Bay (see Figure 1.2). The meandering part and the river delta are called the Red River, so the Da River is considered to be one of the upstream rivers of the Red River system. The upstream area of the Da River has steep narrow beds with lots of falls. Downstream, the river bed generally widens and the slope decreases. The average slope of almost 37% makes the Da River the steepest tributary of the Red River. Georeferenced network maps are available of both the Chinese section and the Vietnamese section of the basin. However, Vietnamese maps only reflect the main tributaries of the Da River basin.

3.2.2 Topography and Elevation With a total area of approximately 52900 km2 and a mean altitude of 1130 m above sea level, the Da River basin is the largest and most elevated tributary of the Red River system. The basin area is quite evenly distributed between China (26100 km2) and Vietnam (26800 km2). It is a narrow basin with an average width of only 80 km (see Figure 1.3). A Digital Elevation Model (DEM) provides georeferenced raster images with (average) ground elevation values assigned to each pixel. The DEM of the Chinese section of the basin, available at the University of Twente, has a spatial resolution of 100 m by 100 m. The DEM of the Vietnamese section of the basin area from the Institute of Mechanics has a spatial resolution of 50 m by 50 m. The combined DEM is displayed in Figure 3.1.


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Figure 3.1: Elevations in the Da River basin

3.2.3 Land Use Major parts of land in the Da River basin are covered by forests (see Table 3.1 and Figure 3.2). If forested, land is often covered by mixed forests. But also deciduous broadleaf forests, evergreen broadleaf forests and evergreen needle leaf forests are present. Next to forests, other natural land, such as natural grassland, savannah and natural shrub land are present in the Da River basin.

Table 3.1: Classes of landuse in the Da River basin

Type of landuse Area (x1000 km2)

Percentage of total basin (%)

Irrigated cropland and pasture 4.79 9.2 Cropland/grassland mosaic 3.79 7.3 Cropland/woodland mosaic 0.09 0.2 Grassland 0.14 0.3 Shrub land 6.78 13.1 Savanna 1.77 3.4 Deciduous broadleaf forest 6.38 12.3 Evergreen broadleaf forest 10.31 19.9 Evergreen needle leaf forest 0.04 0.1 Mixed Forest 17.71 34.2

Agricultural land also occupies large areas of the Da River basin (see Table 3.1 and Figure 3.2), mostly irrigated crop land (mainly wet rice paddies) and pasture or normal cropland and pasture. Also present are shrub land (for example fruit gardens) and grassland. Concentrations of

Demav3100m+2800 - 3099m2500 - 2799m2200 - 1899m1900 - 2199m1600 - 1899m1300 - 1599m1000 - 1299m700 - 999m400 - 699m100 - 399mNo Data

Demav3100m+2800 - 3099m2500 - 2799m2200 - 1899m1900 - 2199m1600 - 1899m1300 - 1599m1000 - 1299m700 - 999m400 - 699m100 - 399mNo Data

Elevation


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agricultural land use are located around the Hoa Binh Reservoir and in the western Chinese/ Vietnamese border area of the Da River basin. Although being one of the most remote areas in Vietnam, the Da River basin has a number of cities with populations of over 50 000. Most populated cities are Moc Chau (113 000 inhabitants) and Tuan Giao (95 000 inhabitants) (Lonely Planet, 2003). A digital georeferenced land use map of both the Vietnamese and Chinese section of the basin is available at the U.S. Geological Survey (USGS, 2005). The absence of urban land on the land use map is odd. Despite the presence of a number of cities with more than 50 thousand inhabitants, the digital land use map claims that there is not one (1 by 1 km) raster cell with a majority of urban land use. The reliability of the digital land use maps in Vietnam is often disputable. Problem with most land use records in Vietnam is that abandoned cultivated close to forests or (illegally) logged land originally covered by forest is often regarded as forest (Hansson and Ekenberg, 2002), despite the fact that it will take years before forest has fully matured again. Or the land has lost is ability to grow forest because of the absence of the nutritious top soil layer, previously held by three roots, now washed away due to the lack of roots after logging.

Figure 3.2: Land use in the Da River basin (USGS, 2005)

3.3 Meteorological Information In this section, the distribution and the availability of precipitation and evaporation are discussed. Evaporation data and Vietnamese precipitation data are provided by the Institute of Mechanics. Chinese precipitation data are available at the U.S. Geological Survey (USGS, 2005).

3.3.1 Precipitation Northwest Vietnam is located in the tropical zone. Despite of mountain peaks up to 3100 metre in the Da River basin the rivers are almost totally rain fed, due to the tropical climate. Especially

Imgrd1Irrigated Cropland and PastureCropland/Grassland MosaicCropland/Woodland MosaicGrasslandShrubland (woody or bushy plants)SavannaDeciduous Broadleaf ForestEvergreen Broadleaf ForestEvergreen Needleleaf ForestMixed Forest

Landuse


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during the monsoon months from June till October it is hot, humid and rainy. During these months, monsoon winds from the South Chinese Sea are forced uphill by several mountain areas in the Da River basin, causing heavy rainfall. Depending on the area, 60- 90% of the total annual precipitation falls in the rainy season from June till October, with its peaks often in July and August. In Figure 3.3, calculations (using Thiessen polygons, see Section 2.4) of mean annual precipitation for the years 1991-2000 are displayed (calculations of the northern half of the basin are, due to the absence of data, incorrect). The region with the largest mean annual precipitation is located around the Chinese Vietnamese border area. The annual total varies from 2200 to 3000 mm. With 90% of annual precipitation falling in the rainy season, these branches’ contributions to floods are often substantial. Around Moc Chau, south of the Hoa Binh Reservoir, mean annual total precipitation is only between 1100 and 1500 mm. Monsoon’s influence is dimmed because its location at the lee side of the Moc Chau mountains, resulting in summer precipitation below 1000 mm. In Figure 3.4 all precipitation stations relevant for this project are displayed. Precipitation stations are unequally distributed over the Da River basin. For the Vietnamese section of the basin 20 precipitation stations with data from the beginning of 1991 to the end of 2000 are available. During the rainy season, precipitation data are available four times a day, otherwise only daily. In China 4 stations relevant for the Da River basin are available with daily measurements for the year 1991 only.

Figure 3.3: Mean annual precipitation 1991-2000 in the Da River basin

Observing Figure 3.3 and Figure 3.4 the area west of station 1 and 5, precipitation is high, despite the fact that this location is on the leeside of the mountains (considering the monsoon from the southeast). Also precipitation between station 7 and 4 is low, despite the fact that this location is on the weather side of the mountains. So, in these areas, estimated precipitation probably does not reflect real precipitation.


19

Figure 3.4: Meteorological stations relevant for the Da River basin

3.3.2 Potential Evaporation

3000 mm/y

688 mm/y 741 mm/y

904 mm/y

814 mm/y 1024 mm/y

993 mm/y

628 mm/y

Figure 3.5: Mean annual potential evaporation in the Da River basin

Differences in mean annual potential evaporation in the Da River basin are large (see Figure 3.5, again Thiessen polygons are used). In Vietnam they are, depending on location, between 680 and 1030 mm. In China, due to the less humid air, potential evaporation is much higher. In Vietnam daily values of 7 stations (station 1, 3, 5, 6, 8, 13 and 20 in Figure 3.4) are available throughout the whole year for the years 1991-2000. For the Chinese stations, only 1 annual average is


20

available. This yearly average is distributed over time using the distribution of potential evaporation throughout the year of the nearest evaporation station (1. Muong Te) in Vietnam. The difference between Vietnamese and Chinese potential evaporation seems to be unreliable.

3.4 Hydraulic Information

3.4.1 Observed Discharges Flood flow in the Da River basin is mainly formed in the Vietnamese territory. Generated discharges from these areas can reach up to 2 m3/s/km2, one of the largest generated discharges in Vietnam. But of course, the contributions from China (upstream Muong Te and Nam Giang, see Figure 3.6) are substantial (see Table 3.2). High discharges in the Da River basin coincide with the rainy seasons. In the main stream, run-off in August occupies approximately 24 percent of the annual runoff. Largest discharges at the Hoa Binh station occurred in August 1945 and in August 1996, respectively 21 600 m3/s and 21 500 m3/s.

Figure 3.6: Discharge stations and sub-basins of the Da River basin

In the Da River, the only discharge that is measured is the one through the Hoa Binh Dam, however, this is not a natural discharge, but a discharge generated by the reservoir management, and therefore not useful in this project. Other discharges are calculated with a relation between discharges and water levels, using characteristics of the river channel. So, discharges are not really observed, but derived from observed water levels. However, in the remainder of this report, the term ‘observed discharges’ will be used for the derived discharges. In the rainy season, water levels (and derived discharges) in the Vietnamese section of the basin are available from 1991 to 2000 four times a day at seven stations (see Figure 3.6). However, natural discharges at the Hoa Binh station are difficult to derive from the water level, due to the complex hydraulic behaviour of the reservoir. Therefore, observed discharges from the Hoa Binh station are too unreliable. Outside the rainy season, discharge or water level data are not available. Chinese measurements are not available at all.


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Table 3.2: Mean observed discharges of the flood seasons

Discharge station

Sub-basin Mean observed discharge (m3/s)

1991

Mean observed discharge (m3/s)

1992-1995

Mean observed discharge (m3/s)

1996-2000 (A) Muong Te 11-16 1462 994 1436 (B) Nam Giang 21,22 659 487 424 (C) Lai Chau 3 2658 1977 2566 (D) Quynh Nhai 4 2878 2235 2994 (E) Ban Cung 5 338 308 342 (F) Ta Bu 6 3956 3154 4311 (G) Hoa Binh 7 4610 3645 4684 Flow velocity of the river between 2 stations is derived from the discharge-time plots of the stations. Discharge peaks are identified and the time of a peak between 2 stations is estimated. From all identified peaks between 2 stations, the mean travel time is taken, and with the distance over the river between the stations flow velocity is calculated (see Table 3.3).

Table 3.3: Flow velocities between the discharge stations

River section Sub-basin Distance (km) Velocity (km/h)

(A) Muong Te- (C) Lai Chau 3 91 11

(B) Nam Giang- (C) Lai Chau 3 21 4

(C) Lai Chau- (D) Quynh Nhai 4 81 13

(D) Quynh Nhai- (F) Ta Bu 6 81 13

(E) Ban Cung- (F) Ta Bu 6 56 8

3.4.2 The Hoa Binh Reservoir The Hoa Binh Reservoir was created by building the Hoa Binh Dam just upstream from Hoa Binh. From there, it goes more than 200 kilometres upstream to Ta Bu. With the designed volume of 9.45x109 m3 (or 9.45 km3) and a water surface of 208 km2, the Hoa Binh Reservoir is the largest artificial lake in Vietnam. Every year, from the 15th of June to the 15th of September, the main function of the reservoir is flood prevention. Throughout the rest of the year, the generation of hydro electricity has priority.

3.5 Scaling

3.5.1 Temporal Discretisation Time Step The selection of the time step for hydrological modelling is subject to at least three considerations (Nemec, 1993):


22

• the purpose of the model; • the storages in the hydrological system; basically part of a general consideration both of

the spatial scaling and temporal scaling; • the availability of data: for flood forecasting daily data are acceptable in most cases and

can be, if necessary, disaggregated in x-hourly values. The purpose of the model is flood forecasting. Considering the non-linear rainfall-runoff flooding processes, the correct time step is between one hour and one day (Nemec, 1993). Regarding the second consideration, steep slopes are present in many parts of the basin, resulting in a short time between rainfall and runoff. Especially the north eastern Vietnamese section of the basin is known for its quick reaction: time between rainfall and Da River main stream discharge for the area around Nam Giang is even less than six hours. Flood waves between the water level measurement stations take six to twelve hours from station to station. For the model regarded in this thesis, the third consideration is determinative. Water levels and thus discharges are only available in the rainy season over a period of ten years. To collect enough observed discharge data to compare the model outcomes with (in the calibration and validation, see Chapter 5), model time step must stay small. Time steps larger than six hours generate not enough comparison data. Comparison data with smaller time steps are not available. Model Lead Time With proper input data the model should be able to forecast discharges at the water level measurement stations every 6 hours with no limitations on the lead time. Then of course, this includes a lead time of 3 days, required by the dam management. In practise, a very large lead time is not possible due to the absence of or unreliable weather forecasts. The shorter the lead time, the more accurate the forecasted discharges. A short lead time means that there is a possibility to take observed discharges upstream as model input, which improves model performance. Also, the shorter the lead time, the more reliable the weather forecasts and the bigger the area with already observed precipitation and evaporation. More reliable weather forecasts and more observed meteorological data improve the model performance.

3.5.2 Spatial Discretisation PCRaster Cells According to Nemec (1993) the following criteria should be taken into account when establishing the spatial discretisation of semi-distributed models like HBV-RR: � the areal distribution of the most important meteorological inputs to hydrological systems,

in particular precipitation and potential evaporation (taking into account the available data collection network);

� the storages in the system; basically part of a general consideration both of the spatial scaling and temporal scaling;

� the areal distribution of basin characteristics which significantly influence general hydrological conditions and regime such as topography, hydrogeology, soil and land use.

A fourth criterion is added here: The PCRaster model run time must be applicable. Considering the amounts of runs needed for the calibration, cells smaller than 5x5km make the model time too slow (with 5x5 cells, a 5 year run takes approximately 1 hour). 5x5km cells also cover the available areal distribution of input variables and basin characteristics.


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Sub-basins Apart from spatial resolution of the cells in PCRaster, the formation of sub-basins is required. A sub-basin is the area between 2 discharge stations. Each cell in a sub-basin will be given the same parameters value. Also, sub-basins are required for the river routing (see Section 2.4). So in the semi-distributed HBV-RR, a basin area is divided in several sub-basins, each containing cells and the river routing routines. The position of discharge stations is important here. The HBV-RR model used in this research project, requires observed discharges at the inlets and outlets of the sub-basins in Vietnam. This determines the maximum number of sub-basins in Vietnam. Considering the low number of discharge stations, in the model all possible stations are used, and thus five sub-basins are created. The size of these sub-basins coincide with the time step of 6h, because water travel time between 2 stations is approximately 6h (see Table 3.3) and time between rainfall and main river runoff is also in that order. Unfortunately, observed discharge data just upstream the Hoa Binh Dam are unreliable, so only 4 sub-basins remain for the calibration procedure (sub-basin 3,4,5 and 6 in Figure 3.6). Observed discharges in China are not available. The characteristics of the Vietnamese sub-basins used for parameter estimations are important for the Chinese sub-basin formation. In order to make the regionalisation relations as applicable as possible, the characteristics of the Chinese sub-basins must resemble the characteristics of the Vietnamese sub-basins. Since the area of sub-basins is used in a regionalisation function (see Section 4.4), Chinese sub-basin areas should be approximately equal to Vietnamese basin sizes. Because model parameters are coupled to basin characteristics it is important that sub-basins coincide with watersheds. Thus, sub-basin borders in the model are determined by the watersheds. The watersheds are derived from the river network and elevation. The map with sub-basins is shown in Figure 3.6.

3.6 Conclusions The available geographical characteristics of the whole Da River basin are elevation, the river network and the land use. The elevation maps are sufficiently detailed while land use and river network maps are poorly detailed. Precipitation is, because of its relative variable nature and large magnitude, the most critical input for a hydrological model. In the Da River basin, precipitation variation in time and space is high, due to the meteorological active tropical climate and elevation differences. In the Vietnamese section of the basin, there are 20 precipitation stations available, with, 0-4 measurements per day, for the period 1991-2000. Especially in the northern upstream sections of Vietnam, the network of precipitation stations has a low density. In China, only 1 precipitation station in the basin is available plus 3 others not so far from the basin area. Chinese stations have daily values of 1991. Another input value is evaporation. Spatial variability of evaporation is considered lower than spatial variability in precipitation. There are 6 stations available in Vietnam, with daily values for 1991-2000. For the Chinese area, only 1 mean annual value is available (period unknown). Reliable 6 hourly data during the flood seasons of 1991-2000 are available for 6 stations. For validation and calibration both meteorological data and discharge data are required. Available time series in Vietnam are then the flood seasons of 1991-2000. For China only meteorological data of the flood season of 1991 are available.


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Spatial and temporal scaling are highly interdependent. Considering the spatial and temporal distribution of the input values, the spatial distribution of the basin characteristics and data availability, the model time step is 6h and the model lead time is a multiple of 6h. Due to computer power, spatial distribution of basin characterisics and meteorological data a PCRaster cell measures 5x5km. The chosen sub-basins, based on the hydrological response and discharge stations, are shown in Figure 3.6. These scales cover the available spatial distribution of input variables and basin characteristics, the hydrological response time, the available time series and the PCRaster run time is reasonable.


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4 Regionalisation

4.1 Introduction As written in Section 2.5 ‘Traditional calibration of the HBV Model’, normally the HBV parameters are calibrated on the basis of a comparison between computed discharges (model output) and observed discharges. However, observed Chinese discharge data are not available. So, a traditional calibration process for the Chinese section of the Da River basin and its parameters is not possible. For sub-basins without observed discharge data, parameters have to be estimated from other sources of information. Regionalisation is the process of transferring parameters from comparable sub-basins, in this case the gauged Vietnamese section, to the sub-basin of interest. In this chapter, the type of regionalisation used in this research project is described. There are different methods to regionalise HBV-RR. In Section 4.2 regionalisation based on spatial proximity and regionalisation based on basin characteristics are discussed and the most suitable method for this project is chosen. Regionalisation of conceptual model parameters is complicated due to the equifinality problem (see Section 2.5). Section 4.3 deals with the approach to minimise the equifinality problem in this project. An aspect of this approach is that the functional forms of the regionalisation relations need to be presumed. Section 4.4 describes these presumed functions. Finally, a discussion and conclusions in Section 4.5 finish the chapter.

4.2 Regionalisation Methods To transfer parameters from one sub-basin to another, these sub-basins must be in some way physically similar. Merz and Blöschl (2004) mention 2 approaches based on different similarities. One common approach is based on spatial proximity. The second approach is based on similar basin characteristics. Of course, a mixture of the 2 methods is possible. For example, several parameters can be regionalised on the basis of spatial proximity, while others can be regionalised on the basis of basin characteristics. Spatial Proximity The approach of spatial proximity is based on the assumption that (sub-) basins that are close to each other have a similar run-off regime, because dominant factors like climate and basin characteristics, if proper scales are used, hardly vary in space. The (sub-) basins are considered highly homogeneous. Model parameters of an ungauged basin are determined from parameters of neighbouring gauged basins. Parameters are extrapolated from the gauged (sub-) basins to the ungauged (sub-) basin on the basis of the proximity and area of the gauged (sub-) basins. The fact that there are almost no data necessary is an important advantage. Vandewiele and Elias (1995) successfully used this type of regionalisation for a monthly water balance model in Belgium. Basin Characteristics Parameters represent the functional behaviour of hydrological response, which in turn is influenced by basin characteristics. Therefore, the assumption is made that basin characteristics such as land use, soil type and topographic characteristics are related to model parameters. If these relations between model parameters and basin characteristics are found (so called ‘regionalisation relations’), model parameter estimation of an ungauged basin is possible. Regionalisation relations obtained in a gauged basin area then are used for the ungauged basin. To estimate model parameters, only regionalisation relations and basin characteristics required in


26

the regionalisation relations are needed. This method requires not only meteorological data, but also other basin characteristics, both from the gauged area and the ungauged area. Regionalisation on the basis of basin characteristics has been used for several purposes. Yokoo et al. (2001) obtained regionalisation relations in Japan on the basis of twelve basins with dammed reservoirs downstream. Application of their regression equations worked successfully at 2 (out of 2) other basins. They suggest that parameters of the conceptual Tank model ‘could be evaluated based solely on the geographical characteristics of the basin’. Obviously, regionalisation on the basis of basin characteristics is used when the effect of land use changes on river run-off is modelled. The possibility of using the basin characteristic ‘land use’ as a variable in a regionalisation relation is a decisive factor. Hundecha and Bárdossy (2004) modelled the effect of land use changes on the river run-off in some of the Rhine sub-basins in Germany using a HBV version of the University of Stuttgart (HBV-IWS). They called the performance of their model for both calibration and validation of sub-basins ‘reasonably good’. Comparison Merz and Blöschl (2004) and Kokkonen et al. (2003) compared the 2 regionalisation methods described above with case studies. Both studies conclude that regionalisation based on spatial proximity is preferable over regionalisation based on basin characteristics. However, regionalisation relations are not universal. One advantage of regionalisation on the basis of characteristics over regionalisation on the basis of proximity, is the larger area in which obtained regionalisation relations are applicable. The most upstream sections in China do not have any neighbouring basins with known parameters. The distance, and therefore heterogeneity, between the most upstream sections in China and the gauged sub-basins in Vietnam is considered too large to use regionalisation based on spatial proximity. So, in this research project, regionalisation on the basis of basin characteristics is used.

4.3 Regionalisation Relations Integrated in the Model Calibration The regionalisation method in this research project links model parameters with basin characteristics. A regionalised conceptual model parameter p is expressed as a regionalisation function Rp of basin characteristic C:

)(CRp p= (4.1)

The traditional way to obtain the regionalisation function Rp first requires the calibration of several well gauged sub-basins (see Section 2.5). After that, every well gauged sub-basin has its typical value for the calibrated model parameter p. Also, every sub-basin has its typical characteristics. Now, relations between the parameter p and characteristics can be obtained. The regionalisation function Rp is usually obtained with regression methods. Yokoo et al. (2001) applied a multiple linear regression model to the relationship between (lumped) model parameters and basin characteristics. Seibert (1999) also used, next to linear regression, exponential regression functions, power regression functions and log regression functions. There are many possible sets of conceptual model parameters that lead to the same model performance. As described in Section 2.5, traditional conceptual model calibration leads to equifinality: different sets of parameters with the same performance (Beven, 2001a,b). This could lead to weak or even false regionalisation relationships. To minimise this equifinality problem, in this research project, the model is regionalised before the calibration processes according to a method used by Hundecha and Bárdossy (2004). Initially, the functional forms of the regionalisation functions are assumed. During a calibration process, the parameters (a and b in Equation (4.2) of the regionalisation relation Rp are optimised for all the gauged sub-basins


27

simultaneously, instead of model parameters for all sub-basins separately. Since different researchers found different relations between basin characteristics and model parameters, a simple linear relationship between parameter p and basin characteristic C is presumed:

baCCRp p +== )( (4.2)

Thus, instead of optimisation of the conceptual model parameter p for every sub-basin separately, the regionalisation parameters a and b of the function Rp are optimised for all gauged sub-basins together. So, a and b have the same values for every cell, but parameter p has a different value per cell, depending on the cell value for C. In this way, before the calibration procedure, the equifinality problem is avoided, which is not the case using regression in the traditional way. Of course, it probably will show up after calibration, different sets of regionalisation parameters will perform equally well (a feature of all models with more than 1 calibrated parameter). When the regionalisation function is optimised, it is used for the estimation of the parameters of the cells in ungauged sub-basins. Known values for the characteristic C of the cells are substituted in the regionalisation function to obtain the model parameter p in the cells of the ungauged sub-basins.

4.4 Presumed Regionalisation Functions Not all conceptual model parameters are suitable for regionalisation. Model sensitivity to the parameters is important. If model outcomes do not change with varying the parameter, relating characteristics with that parameter is superfluous. However, this project lacks a sensitivity analysis. The suitability of parameters for regionalisation is based solely on earlier research in other river basins with other circumstances, and on the HBV-RR model structure. At high river discharges, storm flow contribution is dominating the total river discharge, the groundwater contribution is minimised. Since the focus of this research project is on flood forecasting, only parameters important for storm flow are regionalised, because model outcomes of interest (high discharges) are assumed to be sensitive to these parameters. Hundecha and Bárdossy (2004) and Foppes (2004) confirm this. In Section 2.4, a description of HBV-RR is given. Obviously, the routines of relevance for flood forecasting are the soil moisture routine and the fast flow routine. Except for LP (discussed below), all parameters of these routines are regionalised on the basis of basin characteristics. Parameter LP, and parameters of the slow flow routine are assigned values on the basis of expert opinion and previous research. These values are kept constant for the whole Da River basin. Due to limited data availability of the Chinese section of the basin, parameters of the river routing routine are copied from the most upstream nearby gauged sub-basins in Vietnam (which is in fact a form of regionalisation based on spatial proximity).

4.4.1 Soil Moisture Routine HBV-RR parameter FC, called the maximum soil moisture storage, but in fact also the storage on the vegetation is accounted for. Once this maximum is reached, excess rainfall becomes directly available to the fast flow routine. So the importance of FC to storm flow is obvious. Land use influences the maximum soil moisture storage FC. For example urbanisation, with the construction of pavements and buildings, leads to a decrease of FC. A straightforward available basin characteristic to relate FC to, is land use. For every cell, the class of land use (type of land use) is coupled to a certain class of FC (see Table 4.1). All occurring land use classes in the Da River basin are assigned to one of the three


28

classes of FC: low, medium or high. Cells with much and large vegetation can store large amounts of water (both on their leaves and between their roots in the loose soil), and thus are considered to have a large FC. Contrarily, cells with little and low vegetation, are considered to have a low FC. For example, all forests are believed to have the same large constant value for FC, all croplands are believed to have the same low constant value for FC. Calibration leads to the optimal FC values for the ‘low’ class of FC (FC1), ‘medium’ class of FC (FC2) and ‘high’ class of FC (FC3). So, in the calibrated model, every cell is assigned FC1, FC2 or FC3 for maximum soil moisture storage FC, depending on its class of land use. FC1, FC2 and FC3 are the parameters to be varied in the calibration procedure and of course FC1 < FC2 < FC3.

Table 4.1: FC for classes of land use

Class of land use Class of FC Irrigated cropland and pasture Low (FC1) Cropland/ grassland mosaic Low (FC1) Grassland Low (FC1) Cropland/ woodland mosaic Medium (FC2) Shrub land Medium (FC2) Savannah Medium (FC2) Deciduous broadleaf forest High (FC3) Evergreen broadleaf forest High (FC3) Evergreen needle leaf forest High (FC3) Mixed forest High (FC3)

HBV-RR parameter � describes how the yield to the fast flow reservoir and slow flow reservoir increases when the soil moisture storage SSM increases. Parameter � can be regarded more as an index of heterogeneity than of soil properties (Bergström and Graham, 1998). Therefore, � is regarded as the degree of heterogeneity of the basin (the closer � comes to 1, the more heterogeneous a sub-basin is). As has been formulated by the developer of the HBV model, heterogeneity (and thus �) is related to the area A of a sub-basin (Booij, 2002). Large sub-basins are relatively more heterogeneous, so the regionalisation function for � becomes:

211

* bA

b +=β with 1≥β (4.3)

In the regionalisation function for �, b1 and b2 have the same values for all sub-basins. Since � is dependent on the size of the sub-basin, all cells in one sub-basin have the same value for �. Optimal values of regionalisation parameters are established during the calibration process. HBV-RR parameter LP is the limit for potential evaporation (between 0 and 1). As defined in the HBV-RR (see Section 2.4), LP*FC is a soil moisture storage level above which potential evaporation occurs, so a change in LP can be neutralised by FC and vice versa: LP is highly interdependent with FC. Hundecha and Bárdossy (2004) and Foppes (2004) found low model sensitivity when varying only LP. Because of this low sensitivity and its interdependence with parameter FC, LP is not regionalised, but is kept constant for the whole Da River basin. However, since physically based parameters values are sought for, and because of the interdependency with FC, the constant value of LP has to be varied during the calibration process. So, parameters to vary in the soil moisture routine during the calibration procedure are: FC1, FC2, FC3, b1, b2 and LP.


29

4.4.2 Fast Flow Routine Generally, steep hills, low permeable soils and a dense river network increase the run-off speed and the contribution of storm flow to the total river discharge. However, soil permeability data of China are absent and the river network map of Vietnam only covers the main tributaries of the Da River basin, which makes the maps useless for regionalisation. Booij (2002) found both HBV-RR parameters � and kfast to be positively dependent on the slope S. The regionalisation function of HBV-RR parameter �, a measure of non linearity of the fast flow process, then results in:

21 a*a += Sα (4.4)

With a1 and a2, as regionalisation parameters, and constant for all cells. The regionalisation function of kfast becomes:

21 * kSkk fast += (4.5)

With k1 and k2, as regionalisation parameters, and constant for all cells. Slope S is determined for each cell and is calculated on the basis of the difference between elevation of its eight nearest neighbour cells and the elevation of the cell under consideration. So parameters to vary in the fast flow routine during the calibration procedure are: a1, a2, k1 and k2.

4.5 Conclusions and Discussion Regarding the ungauged upstream section of the Da River basin and the spatial differences between the Vietnamese section and the Chinese section, regionalisation on the basis of basin characteristics is a useful approach to estimate unknown HBV-RR parameters. In this research project, a method proposed by Hundecha and Bárdossy (2004) is adopted. This method integrates regionalisation functions in the model calibration. Aim of this method is to avoid the equifinality problem before the calibration procedure. On the basis of model sensitivity to the parameters and the structure of HBV-RR the parameters FC and � from the soil moisture routine, and kfast and � from the fast flow routine, are considered to be suitable for regionalisation. Parameters from the slow flow routine and parameter LP, a limit for potential evaporation in the soil moisture routine, are not suitable for regionalisation. These parameters (not suitable for regionalisation) are kept constant over the whole basin. However, the value of parameter LP will be varied during the calibration procedure. Unknown Chinese parameters of the river routing routine are copied from the most upstream gauged sub-basins in Vietnam. Parameter FC, maximum soil moisture storage, is related to the class of land use of a certain cell. All classes of land use are assigned to three groups. These groups are assigned a low FC (FC1), a medium FC (FC2), or a high FC (FC3), so the value for FC depends on the land use class of a cell. Parameter � can be regarded more as an index of heterogeneity than of soil properties (Bergström and Graham, 1998). Large sub-basins are relatively heterogeneous, therefore, as has been formulated by the developer of the HBV model, � is linearly related to the inverse of area A of a sub-basin (Booij, 2002). The parameters from the fast flow routine kfast and �, are both presumed to be linearly related on the slope. The slope is determined for each cell and is calculated on the basis of the difference


30

between elevation of its eight nearest neighbour cells and the elevation of the cell under consideration. So parameters to vary during the calibration procedure are: FC1, FC2, FC3, b1, b2, LP, k1, k2, a1 and a2,. A disadvantage of the approach described in this chapter is that the functional forms of unknown relations between parameters and characteristics have to be presumed. Is it possible to presume regionalisation relations on the basis of the physical basis of the conceptual HBV-RR model and on weak relations earlier found in other research projects? Can a simple linear function or classical function based on only one characteristic describe the HBV-RR parameter successfully? Linear relations between a HBV-RR parameter and a characteristic or the classification of a characteristic might be too simple. Also, the limited data availability of basin characteristics is problematic. For example, lithology and soil texture are expected to influence HBV-RR parameter FC, and the river network influences the fast flow processes. However, this approach suits this project better than an approach based on proximity, because some Chinese sub-basin are considered too different, or an approach based on regression, because now, the equifinality problem is avoided on a crucial point (before calibration).


31

5 Calibration and Validation

5.1 Introduction In this chapter, the calibration and validation of the regionalised HBV-RR model (described in Chapter 3 and Chapter 4) is discussed. In Section 5.2 the performance criteria used for the calibration and the criteria used for the validation are described. The calibration is carried out in 2 steps, both in Vietnam during the rainy seasons from 1996 to 2000. The first step consists of the optimisation of the water balance. Since the soil moisture routine is considered to be the main routine influencing the water balance, only parameters of this routine are calibrated in this step. Section 5.3 deals with the calibration of the regionalised HBV-RR regarding the soil moisture routine. The second calibration step includes the fitting of the model output to the observed hydrograph. Most important routines for the shape of the hydrograph are the flow routines. In Section 5.4 parameters of the regionalisation relations regarding the flow routine are estimated. 2 kinds of validation are carried out. First, the model is validated in the same Vietnamese area as used for the calibration, for the rainy seasons from 1992 to 1995. This is described in Section 5.5. The second validation concerns the total Da River basin upstream the inlet of the Hoa Binh Reservoir including the ungauged Chinese sub-basins. Only proper data of the rainy season of 1991 are available for this kind of validation. Section 5.6 discusses the results. HBV-RR requires potential evaporation values as input (see Section 2.4). Unfortunately, these are not available. Instead, potential evaporation values are used (see Section 3.2.2). Penman (as referred to by Shaw, 1994) proposed the potential evapotranspiration to be linearly related to the potential evaporation, with different factors for different seasons. The factor should be lower during the winters with less leaves and vegetation. Since vegetation in Vietnam hardly varies between the seasons (tropical climate), it is assumed that potential evapotranspiration and potential evaporation are correlated with a constant factor throughout the whole year. This unknown constant factor will be neutralised by a change in the parameters FC and LP. Finally, conclusions are drawn in Section 5.7.

5.2 Performance Criteria In a calibration an objective is needed to search for adequate model parameters. A measure of adequacy of the model is reflected by a certain value of a performance criterion. This value should be as close as possible to an optimal value representing the ‘perfect’ model. Because the soil moisture routine and the fast flow routine have different roles in the model (see Section 2.4), the 2 steps in the calibration (results described in Section 5.3 and Section 5.4) both have different performance criteria: the Relative Volume Error and a modified Nash-Sutcliffe coefficient. To be able to give a general judgement of the model, the commonly known Nash-Sutcliffe coefficient (Nash and Sutcliffe, 1970) is added as well.

5.2.1 Relative Volume Error There are several sub-basins with river inflow upstream of these sub-basins (during calibration procedures observed discharges are used as input). However, regionalisation relations incorporate


32

sub-basin characteristics and thus parameters should only represent processes in the sub-basins and not processes of sub-basins upstream. Therefore, the performance criterion to estimate a correct water balance excludes upstream river inflow. For each sub-basin the Relative Volume Error (RVE, in %) becomes:

[ ]

[ ]100*

)()(

)()(

1

1

�

�

=

=

−

−=

N

iino

N

toc

tQtQ

tQtQRVE (5.1)

Here, t is the time step and N is the total number of time steps. Qc(t) is the computed discharge at the downstream end of a sub-basin, Qo(t) is the observed discharge at this point and Qin(t) is the Muskingum modelled contribution of the observed upstream river inflow (see Section 2.4) to the discharge at the downstream end of a sub-basin. In the upper part of Equation (5.1) the contributions of the upstream river inflow to Qc(t) and Qo(t) compensate each other. The influence of the sub-basins’ RVE on the performance criterion RVEtotal (in %) is weighed by the sub-basins’ observed mean generated discharge wsb:

minmosb QQw ,, += (5.2)

Where Qo,m is the observed mean discharge at the downstream end of the sub-basin over the calibrated or validated period, and Qin,m is the mean observed inflow to the sub-basin over the calibrated or validated period. So, again river inflow upstream the sub-basin is excluded from the performance criterion.

sbNsbsb

sbNsbNsbsbsbsbtotal www

wRVEwRVEwRVERVE

++++++

=........

*........**

21

2211 (5.3)

Here, RVEsbi is the Relative Volume Error of sub-basin i, as defined in Equation (5.1), wsbi is the weight factor of sub-basin i and N is the total number of sub-basins selected for the calibration. The objective of this part of the calibration is the optimisation of the water balance of the whole area involved in the calibration procedure, therefore RVEtotal should be as close to 0 as possible.

5.2.2 (Modified) Nash Sutcliffe Coefficient To fit the computed hydrograph to the observed hydrograph, a modified version of the Nash-Sutcliffe coefficient is used. Again, inflow from upstream sub-basins is excluded:

( )[ ]( )[ ]�

�

=

=

−−−

−−=

N

tminmoino

N

toc

m

QQtQtQtw

tQtQtwR

1

2

,,

1

2

2

)()(*)(

)()(*)(1 (5.4)

Where t is the time step and N is the total number of time steps. Qc, Qo and Qin are respectively the computed discharge, the observed discharge and the Muskingum modelled contribution of the observed upstream river inflow. Qo,m and Qin,m are the mean observed discharges over the calibration or validation period at the outlet of the sub-basin and upstream the sub-basin respectively (Muskingum is irrelevant, because these are means). The weight w(t) (notice the time dependency) gives emphasis to a certain part of the hydrograph. Since the objective of the project


33

is improving flood forecasting, emphasis is given to high discharges. So, in this case the sub-basin generated discharge is taken as the weight factor w(t):

)()()( tQtQtw ino −= (5.5)

Like with RVEtotal, the influence of the sub-basins on the performance criterion is weighed by the sub-basins’ observed mean generated discharge wsb (not to be confused with w(t) see Equation (5.2) and Equation (5.5):

sbNsbsb

sbNsbNmsbsbmsbsbmtotalm www

wRwRwRR

++++++

=......

*......**

21

2,2

22,1

21,2

, (5.5)

To be able to give a general judgement of the model, the commonly known Nash-Sutcliffe coefficient is added.

( )[ ]( )[ ]�

�

=

=

−

−−=

N

tmoo

N

toc

QtQ

tQtQR

1

2,

1

2

2

)(

)()(1 (5.7)

The optimal value and maximum value for R2m, R2

m,total and R2 is 1, so the objective for the estimation of the fast flow routine parameters is to reach a R2

m,total as close to 1 as possible.

5.3 Calibration of the Soil Moisture Routine Precipitation and upstream discharges (model input during calibration procedures) eventually lead to discharges downstream (model output). Therefore, it is assumed that a correct water balance is a prerequisite in the calibration of the regionalised HBV-RR model. Since evapotranspiration is the only loss between model input and model output (storage is neglected on this time scale), only parameters influencing evapotranspiration are important for the optimisation of the water balance. Evapotranspiration is determined in the soil moisture routine (see Section 2.4), so the estimation of the parameters in this routine is done through optimisation of the water balance.

5.3.1 Used Soil Moisture Parameter Values Parameters in the soil moisture routine of the HBV-RR model are FC, � and LP (see Section 2.4). Parameter FC and parameter � are regionalised (see Section 4.4), so finally, there are six parameters in the soil moisture routine of the regionalised HBV-RR model (see Table 5.1).

Table 5.1: Description of the parameters in the soil moisture routine

Parameter Description FC1 FC for land use classes with a small value for FC FC2 FC for land use classes with a medium value for FC FC3 FC for land use classes with a large value for FC b1 Coefficient in linear regionalisation relation between HBV parameter � and area

of the sub-basin b2 Constant term in linear regionalisation relation between � and area of the sub-

basin LP Threshold for potential evapotranspiration

The HBV model has been frequently used in many river basin studies. Booij (2002) summarised some parameter values and ranges from different studies (see Appendix I). In this research


34

project, experiences from these studies are used to identify reasonable ranges of parameter values. For example, regionalisation parameter value ranges for the manual calibration are chosen in such a way that substitution of these regionalisation parameters in the regionalisation relations (see Section 4.4), lead to ranges in ‘acceptable’ HBV parameters corresponding to ranges in studies summarised by Booij (2002). Used regionalisation parameter values of FC1, FC2, FC3, b1 and b2 with according values of mean FC (per sub-basin)and �, and used ‘normal’ parameter LP, are given in Appendix I. All parameters of the flow routines are kept constant during this calibration step (see Appendix I).

5.3.2 Calibration Results of the Soil Moisture Routine In Section 5.3.1 several values for all parameters in the soil moisture routine of the regionalised HBV-RR model are chosen. Different combinations of these possible parameter values are used to search for an optimal water balance as defined in Section 5.2.1. In Appendix II values of the RVE for the gauged sub-basin 3, 4, 5, 6 and values of RVEtotal are given for different combinations of parameter values. The exceptional high values of RVEsb3 for all parameter combinations are problematic. A value of around +50% for RVEsb3 means that the modelled sub-basin generated discharge over 5 rainy seasons is 1.5 times larger than the observed sub-basin generated discharge over five rainy seasons. This can only be explained by incorrect input data for precipitation, as already discussed Section 3.3. Having such a large overestimation, sub-basin 3 is considered inadequate to use for calibration, so RVEsb3 is left out of the performance criteria. Absolute values of RVEsb4, RVEsb5 and RVEsb6 are smaller than 0.2 and considered sufficiently small to use in RVEtotal for the calibration. A notable remark from Appendix II is the low dependency of model outcomes on the regionalisation parameters b1 and b2 determining ‘normal’ HBV parameter �. Over the whole range of used values for b1 and b2 in combination with other parameter values, the model outcomes are insensitive to b1 and b2. When keeping other parameters constant, the maximum difference between RVEtotal values for different values of b1 and b2 is only 1%. Possible explanation for the small difference lies in the period of calibration. During the rainy seasons, the soil moisture storage often reaches its maximum FC, resulting in a value close to one for the term SSW/FC (see also Section 2.4). Since � is an exponent of this term its influence is minor, because 1� is always 1, whatever �’s value. Hence, in this project, the presumption that model outcomes are sensitive to �, an assumption that founded the choice to regionalise �, is wrong. The regionalisation of � is superfluous.


35

0

50

100

150

200

250

300

350

0 0.2 0.4 0.6 0.8

LP (-)

Ave

rage

FC

(mm

)

Absolute RVE<1%

1%<Absolute RVE<3%

absolute RVE>3%

Figure 5.1: Different combinations of FC and LP and their corresponding RVEtotal

Figure 5.1 shows the different combinations of FC and LP and their corresponding values for RVEtotal. As can be seen in Table 5.2, an optimal value close to zero for RVEtotal can be reached for LP of 0.3, 0.5 and 0.7. Apparently, values close to the dashed line all give values for RVEtotal close to zero, which implies a proper water balance. Indeed, like Chapter 4 announced, the equifinality problem keeps appearing. R2

m,total, a criterion for the shape of the hydrograph, varies negligibly between different optima of RVEtotal with different parameters. However, parameters need to be fixed to start the second step of the calibration. Parameter values and ranges of other studies (Booij, 2002) agree most with the bottom row of Table 5.2. Therefore, this combination is used throughout the following calibration step.

Table 5.2: Different optimal sets of soil moisture routine parameters

LP(-) FC1(mm) FC2(mm) FC3(mm)

b1(-) b2(-)

RVEtotal (%)

R2m,total (-)

0.3 100 150 200

3 0.5 0.6 0.65

0.5 100 175 250

3 0.5 0.0 0.65

0.7 100 225 350

7 0.9 0.0 0.66


36

5.4 Calibration of the Flow routines The second calibration step includes the fitting of the model output to the observed hydrograph. Most important routines for the shape of the hydrograph are the flow routines. Therefore calibration of these routines is based on the optimisation of performance criterion R2

m,total. Of course, visual inspections of the observed and simulated hydrographs should accompany the optimal fitting of the parameters. Though the soil moisture routine also influences the shape of the hydrograph, parameters of this routine are kept constant during this calibration step. The influence of the soil moisture routine on the hydrograph is presumed to be sufficiently reflected in the first calibration step (the optimisation of RVEtotal also leads to a better fit to the observed hydrograph, RVEtotal and R2

m,total are interdependent). This presumption limits the number of model runs required.

5.4.1 Used Flow Routine Parameter Values Parameters in the flow routines of the HBV-RR model are kslow, PERC, kfast, and � (see Section 2.4). Parameter kf and parameter � are regionalised (see Section 4.4, so there are six parameters in the flow routines of the regionalised HBV-RR model (see Table 5.3).

Table 5.3: Description of the parameters in the flow routines

Parameter Description k1 Coefficient in linear regionalisation relation between kfast and slope k2 Constant term in linear regionalisation relation between kfast and slope a1 Coefficient in linear regionalisation relation between � and slope a2 Constant term in linear regionalisation relation between � and slope PERC Percolation from soil moisture reservoir to slow flow reservoir kslow Recession coefficient of slow flow reservoir outflow

Again, HBV parameter values and ranges from other studies (Booij, 2002) in Appendix I are used to find proper ranges of parameter values in the regionalised HBV-RR model. Appendix I gives an overview of the used parameter values in the calibration steps.

5.4.2 Calibration Results of the Flow Routines In Section 5.4.1, several value ranges for all parameters in the flow routines of the regionalised HBV-RR model are selected for calibration. Different combinations of these possible parameter values are used to search for an optimal fit of the computed hydrograph to the observed hydrograph, as defined in Section 5.2.2. In Appendix III values of the R2

m for sub-basin 3, 4, 5, 6 and values of R2

m,total are given for different combinations of parameter values. Conspicuous are the relatively low values for R2

m in Appendix III, almost all values are beneath the value obtained with the calibration of the soil moisture parameters only. Because both kfast and � are regionalised on basis of the slope, cells with a flat slope are assigned low values for both kfast and �. This results in very low discharges from the fast flow routine in these ‘low slope cells’. The more important the slope in the regionalisation relations is (thus a low constant term k2 or a2, or high k1 or a1), the worse these cells react. Therefore, higher constant terms perform better. Since the first calibration step in Section 5.3 is done without regionalisation of parameters kfast and � (they were kept constant, see Appendix I), high values for R2

m were already achieved then. However, also higher constant terms do not achieve much higher values for the performance criterion R2

m,total than the first calibration step achieved. Obviously, the optimisation of the water


37

balance has been of major importance for the calibration. Apparently, the soil moisture routine plays a more important role in the optimisation of the hydrograph fitting than presumed.

Table 5.4: Optimal parameters for flow routines

k1(day-1) k2(day-1)

kfast,mean (day-1)

a1(-) a2(-)

�mean (-)

PERC (mm/day) ks(day-1) R2

m,total

0.0040 0.0015 0.0020 1.6

1.9 2.1 1.0 0.01 0.68

0.0040 0.0015 0.0020 3.9

1.6 2.1 1.0 0.01 0.64

The results in Appendix III do not show the equifinality problem (see Section 2.5) any more. The parameter set with the highest value for R2

m,total of 0.68 is given in Table 5.4. But for the purpose of this study, an � which is hardly regionalised (a very high constant a2 of 1.9), is not useful in China. The parameter � is considered to be distinctively different in the sub-basins in China. Moreover, it is considered to be dependent on the slope. Therefore, for the validation a set of parameters is used which achieve a reasonably high R2

m,total of 0.64 and regionalises � and kfast to a sufficient degree (the constant factor a2 is 1.6).

0

500

1000

1500

2000

2500

3000

3500

4000

15-May 4-Jun 24-Jun 14-Jul 3-Aug 23-Aug 12-Sep 2-Oct 22-Oct 11-Novdate

disc

harg

e (m

3/s)

computed

observed

Figure 5.2: Computed and observed discharges at Ban Cung 1996, after calibration


38

0

2000

4000

6000

8000

10000

12000

14000

16000

18000


disc

harg

e (m

3/s)

computed

observed

Figure 5.3: Computed and observed discharges at Quynh Nhai 1996, after calibration

0

5000

10000

15000

20000

25000


disc

harg

e (m

3/s)

computed

observed

Figure 5.4: Computed and observed discharges at Ta Bu 1996, after calibration

In 1996 severe floods occurred. Hydrographs of 3 different stations, after calibration, are shown in Figure 5.2, Figure 5.3 and Figure 5.4. Due to the large observed upstream river inflow, compared to the (computed) sub-basin generated discharge (see Table 1.1), the computed and observed hydrographs of Ta Bu (sub-basin 6) and Quinh Nhai (sub-basin 4) correspond well. Without inflow upstream, computed discharges from sub-basin 5 at Ban Cung correspond less with the observed discharges. Apparently, the river routing routine (see Section 2.4) works well.

5.5 Validation in Time For the validation in time, the model is run with data from 1992-1995 using observed inflow from the poorly gauged sub-basin 3. So, the lead time of the model is only 12 hours, because river


39

floods travel from station Lai Chau, to station Ta Bu, in (approximately) 2 time steps of 6 hours. Computed and observed hydrographs of the year 1992 (a ‘normal flood year’) are shown in Figure 5.5, Figure 5.6 and Figure 5.7.

0

500

1000

1500

2000

2500

3000

3500

30-May 19-Jun 9-Jul 29-Jul 18-Aug 7-Sep 27-Sep 17-Octdate

disc

harg

e (m

3/s)

computed

observed

Figure 5.5: Computed and observed discharges at Ban Cung 1992, validation

0

1000

2000

3000

4000

5000

6000

7000

8000


dis

char

ge

(m3/

s)

computed

observed

Figure 5.6: Computed and observed discharges at Quynh Nhai 1992, validation


40

0100020003000400050006000700080009000

10000


disc

harg

e (m

3/s)

computed

observed

Figure 5.7: Computed and observed discharges at Ta Bu 1992, validation

The results of sub-basin 5 (see hydrograph of Ban Cung, Figure 5.5, and Table 5.5) are positive. The results are even better than calibration results. Here, the differences between discharge regimes of 1992-1995 (validation) and 1996-2000 (calibration) are minimal (see Table 3.2). Probably, precipitation data that are more accurate result in better values for the validation. This also The discharge regimes of sub-basin 4 and sub-basin 6 are substantially different (see Table 3.1). Especially large discharges from sub-basin 4 (hydrograph of Quynh Nhai in Figure 5.6) and sub-basin 6 (hydrograph of Ta Bu in Figure 5.7) are overestimated, resulting in decreasing R2 and R2

m (see Table 5.5). Errors in the large discharges origin from the fast flow routine, that is highly dependent on the slope (see Section 4.4). Probably, the fast flow routine is too dependent on the slope.

Table 5.5: Values for R2m and R2 after calibration and validation

R2m after

calibration R2

m after validation

R2 after

calibration R2

after validation

Sub-basin 4 0.71 0.46 0.96 0.95 Sub-basin 5 0.66 0.75 0.61 0.75 Sub-basin 6 0.60 Not relevant 0.98 0.91

5.6 Validation in Space: The Da River Basin in China For the validation in space the model is run without any observed discharges used as input. As feared in Chapter 2, the amount of available meteorological data in China and the upstream section of Vietnam is too little to do proper forecasts. Using all available precipitation stations, R2 for all stations stays below 0, implicating a worse predictor than just the mean annual discharge. Many peaks in the computed hydrographs from the Chinese sub-basins (Muong Te and Nam Giang) are highly overestimated (see Appendix IV). Apparently, the influence of the precipitation stations is too large. The area they cover is too large: there are not enough stations available to reflect the spatial variability of precipitation. Some observed peaks are almost totally neglected


41

by the model outcomes. Probably, these peaks are generated by local precipitation events in areas without precipitation stations in their neighbourhood. In Figure 5.8, Figure 5.9 and Figure 5.10 results of a second run are shown. Now, 2 North Vietnamese precipitation stations with large precipitation values, Sin Ho and Muong Te, are left out. Some exaggerated peaks are gone, others stay (probably caused by other stations). However, values for R2 increase and the hydrograph of Ta Bu even has one of over 0 (0.18). But the correlations (between observed and computed hydrographs) of all stations decrease, indicating a less proper shape of the hydrograph (regardless of the quantity) of the hydrograph. So, leaving out stations results in a better estimate of the discharge at Ta Bu, but worse forecasts of a peak (regardless its size).

0

1000

2000

3000

4000

5000

6000

7000

22-May 11-Jun 1-Jul 21-Jul 10-Aug 30-Aug 19-Sep 9-Oct 29-Oct

date

disc

harg

e (m

3/s)

computed

observed

Figure 5.8: Computed and observed discharges at Muong Te 1991, validation

0

1000

2000

3000

4000

5000

6000


date

disc

harg

e (m

3/s)

computed

observed

Figure 5.9: Computed and observed discharges at Nam Giang 1991, validation


42

0

2000

4000

6000

8000

10000

12000

14000

16000

22-May 11-Jun 1-Jul 21-Jul 10-Aug 30-Aug 19-Sep 9-Oct 29-Octdate

disc

harg

e (m

3/s)

computed

observed

Figure 5.10: Computed and observed discharges at Ta Bu 1991, validation

5.7 Conclusions and Discussion The HBV-RR model is calibrated in 2 steps. The first step includes the optimisation of the water balance. It is assumed that a correct water balance is a prerequisite in the calibration of a rainfall-runoff model. Since only the soil moisture routine influences the water balance, only parameters of this routine are varied, other parameters are kept constant. The second step fits the computed hydrograph to the observed hydrograph. Now, parameters of the soil moisture routine are kept constant. Its influence on the shape of the computed hydrograph is presumed to be sufficiently reflected in the first calibration step. Calibration of the soil moisture routine of the regionalised HBV-RR model, by optimising the water balance, leads to different combinations of parameters with the same model performance. This undermines the assumption that HBV-RR has a firm physical basis. Parameters FC (regionalised), the maximum soil moisture storage, and LP, a limit for potential evaporation, are highly interdependent. The influence of � (or its regionalisation parameters b1 and b2) on the model outcomes, is limited. Therefore, its regionalisation is superfluous. Calibration of the flow routine results only in minor improvements of the model performance compared to the model performance after the first calibration step. Apparently, the soil moisture routine does play an important role in optimising the shape of the hydrograph. 2 types of validation are carried out. The first one, in the same area as the calibration area, but for another time period, shows reasonable results. However, since sub-basin 4, with relatively large slope values, overestimates high discharges, and sub-basin 6, with low slope values, underestimates high discharges, the influence of the slopes in the regionalisation functions of the fast flow routine is overestimated. The second type of validation is carried out in the whole Da River basin. There are not any discharges used as input values. Simulations of the Chinese sub-basins are unreliable. A simple mean value would be better. Some computed discharge peaks are highly overestimated, which indicates that a precipitation value of a station is distributed over an area that is too large. Some


43

observed peaks are neglected by the model outcomes, which indicates that the precipitation event responsible for the discharge peak is not recorded by any station, because there was none in the area of the event. So, in China, there are not enough precipitation stations available.


44


45

6 Conclusions and Recommendations In the previous chapters, preliminary conclusions were drawn. Now, after discussing the results in Chapter 5, final conclusions can be drawn and the objective is discussed in Section 6.1. In Section 6.2, recommendations are given for further research.

6.1 Conclusions and Discussion The performance of a rainfall-runoff model like the HBV-RR model, highly depends on the availability and accuracy of its meteorological input data. Precipitation and evapotranspiration are the 2 decisive components that influence run-off quantity (over a time period larger than the hydrological response time). During floods, discharge generated by precipitation of the last few days, dominates the total discharge. Discharge generated by groundwater flow (from precipitation fallen much longer ago), is only subordinate. Also, during precipitation events causing floods, precipitation highly exceeds evapotranspiration. So, accurate precipitation data are essential for flood forecasting using a rainfall-runoff model. The elevation in the Da River basin varies strongly. Large elevation differences and the tropical climate, result in extreme precipitation events, with a distribution highly variable in space. The available network of precipitation stations in the Da River basin is not dense enough, considering the spatial variation in precipitation. In the relatively well gauged Vietnamese section of the basin, only the 3 most southern sub-basins upstream the Hoa Binh reservoir inlet, are sufficiently gauged to be used for calibration of the HBV-RR. In the more northerly Vietnamese sub-basins, the differences in elevation are higher, so precipitation quantities vary more, and are larger. However, the density of the stations in the more northerly sub-basins in Vietnam is lower. Even more northwards, in the Chinese areas, precipitation is milder, but there are much less stations available. So, a proper validation of the HBV-RR in this project, in a different area than the calibration area, is impossible. Therefore, conclusions of this project are discussed with reservation. The conceptual HBV-RR model is suitable for flood forecasting of the whole Da River basin upstream of the reservoir inlet at Ta Bu. The absence of discharge data in China can be managed by the regionalisation of HBV-RR. A method proposed by Hundecha and Bárdossy (2004) is adopted for this project. It presumes relations between model parameters and basin characteristics before calibration (it integrates regionalisation functions in the model calibration). In this way, it avoids the equifinality problem (Beven 2001a,b) before calibration, which is crucial for the reliability of the regionalisation relations. However, after calibration, different sets of parameter values with the same model performance do exist. The successful calibration and validation in 3 relatively well gauged sub-basins do not prove the physical basis of the regionalised HBV-RR. Because calibration and validation take place in the same area, it might as well be a ‘disguised’ normal calibration, without physically based relations between basin characteristics and model parameters. Perhaps the (calibrated) relations happen to result in the proper mean HBV parameters (for the whole basin) with reasonable model performance. But, since relations are based on the HBV-RR model structure and previous research it is likely that regionalisation relations are physically based. Spatial scaling and temporal scaling are highly interdependent. Considering the spatial and temporal distribution of the input variables precipitation and evapotranspiration, the spatial distribution of the basin characteristics and data availability, the scales are determined. The model time step is 6h, the model lead time is a multiple of 6h, a PCRaster cell measures 5x5km and the formed sub-basins are shown in Figure 3.6.


46

For the relations between basin characteristics and model parameters (question 3), first proper model parameters, suitable for regionalisation, are selected on the basis of previous research, HBV-RR model structure and model sensititvity, and expert opinion. The soil moisture routine and the fast flow routine are considered the most important routines for flood forecasting. So, parameters FC and � from the soil moisture routine, and kfast and � from the fast flow routine, were assumed to be suitable for regionalisation. However, results showed model insensitivity to value variations of �. Therefore, its regionalisation is superfluous in this research project. Parameters from the slow flow routine and parameter LP, a limit for potential evaporation in the soil moisture routine, are kept constant for the whole research area. Parameters of the river routing routine are copied from the most upstream nearby gauged sub-basins in Vietnam. Parameter FC, the maximum soil moisture storage, is related to the class of land use of a certain cell. The parameters from the fast flow routine kfast and �, are both presumed to be linearly related to the slope. Errors in the large discharges of the computed hydrographs of the validation in time, imply a malfunction in the fast flow routine. Probably, the influence of the slopes in the regionalisation functions of the fast flow routine is overestimated. The calibration of HBV-RR is carried out in 2 steps. It is assumed that the calibration of the soil moisture routine, based on the optimisation of the water balance, and the calibration of the flow routines, based on the fit to the observed hydrograph, could be split up. But, after the calibration of the soil moisture routine, hardly any improvement of the model performance is made. This indicates the importance of the soil moisture routine for the fit to the hydrograph. Apparently parameter FC (and its interdependent parameter LP) is of major importance for the model performance. Both calibration and validation of the regionalised HBV-RR in 3 relatively well gauged sub-basins in Vietnam show reasonable results (Nash-Sutcliffe coefficient R2 of 0.61-0.98). However, results of the model validation in the whole Da River basin upstream of Ta Bu are poor (R2 almost always below 0). Some computed discharge peaks are highly overestimated, which indicates that a precipitation value of a station is distributed over an area that is too large. Some observed peaks are neglected by the model outcomes, which indicates that the precipitation event responsible for the discharge peak is not recorded by any station, because there was none in the area of the event. So, in China, there are not enough precipitation stations available. So, finally, referring to the objective again: To improve flood forecasting at the inlet of the Hoa Binh reservoir, by regionalising a HBV rainfall-runoff model of the Da River basin, using the Vietnamese section to estimate the parameters for the ungauged Chinese section. The original meaning was to be able to estimate discharges from China to extend the lead time of the forecast at the inlet of the Hoa Binh Reservoir. Since there are not enough precipitations stations available this is impossible. However, a suitable tool to improve the forecasting of high discharges from China is set up. With more precipitation stations available, the model will succeed in improving flood forecasting. Furthermore, the model is robust to land use changes. Any new land use data can be simply implemented in the model.


47

6.2 Recommendations To successfully use and further improve the model as described in this report, some recommendations are suggested in this section. The most important recommendation is to implement a denser network of meteorological stations in the upstream sections of the Da River basin or to acquire more available meteorological data (both in time and in space). Though the validation time for the Chinese sub-basins is only one rainy season, the results are clear. More downstream, where the required meteorological data are available, reasonable results can be obtained. In this project, the calibration is totally independent of the Chinese area. When much more (more than 5 years) meteorological data from China are available, the Chinese areas can be integrated in the calibration. Because relatively few discharge stations are available for the calibration, every extra station can improve model performance. The discharges of the 2 stations near the Vietnamese/Chinese border, in this report used as input discharges during the calibration procedure, could then be used as discharges to compare model output with. More important, relations between HBV parameters and basin characteristics are then not (only) based on the Vietnamese section, but (also) on the Chinese section, probably resulting in better model performance. If it is not possible to obtain more meteorological data, more effort should be put into the distribution of the available data. Since precipitation is the so called ‘bottle neck’, most improvement can be achieved there. As Section 5.6 revealed, certain efficiency coefficients can be improved by leaving stations, with extreme values, out. Or probably, the Thiessen polygon method (Thiessen, 1911, as referred to by Shaw, 1994) can be replaced by another one. Human judgement based on geographical maps and climate knowledge (the isohyetal method, described in Shaw, 1994), might improve model performance. The determination of the area influenced by a certain station is more accurate when more factors (for example: location mountains and leeside/ weather side) are taken into consideration than only the distance to a station. A disadvantage of the method used in this project to avoid the equifinality problem (Beven 2001a,b) before the calibration, is that relations between HBV parameters and basin characteristics have to be presumed before calibration, while these relations are highly uncertain. It might be better to try more presumed relations, though it would be very time consuming. Since the soil moisture routine is found to be very important for flood forecasting, a more central role should be given to its parameters in the calibration method. Because model sensitivity to � is very low, parameter FC, the maximum soil moisture capacity, should not be kept constant during any calibration step.


48


49

References Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connel, P.E. and Rasmussen, J.L., 1986. An introduction to the European hydrological system – Système Hydrologique Europeen, ‘SHE’. 2. Structure of a physically-based, distributed modelling system. Journal of Hydrology, Vol. 87, pp. 61-77. Bergström, S. and Forsman, A., 1973. Development of a conceptual deterministic rainfall-runoff model. Nord. Hydrol., Vol. 4, pp. 147-170. Bergström, S., 1990. Parameter values for the HBV model in Sweden (in Swedish). SMHI rapporter hydrologi no. 28. Swedish Meteor. Hydrol. Inst., NorrKöping, 35 pp. Bergström, S. and Graham, L.P., 1998. On the scale problem in hydrological modelling. Journal of Hydrology, Vol. 211, pp. 253-265. Beven, K.J., 2001a. Rainfall-Runoff Modelling: The Primer. Wiley, New York, 360 pp. Beven, K. and Freer, J., 2001b. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, Journal of Hydrology, Vol. 249, pp. 11-29. Booij, M.J., 2002. Appropriate modelling of climate change impacts on river flooding. Ph.D. thesis. University of Twente, Enschede, The Netherlands. 206 pp. Booij, M.J., 2003. Decision support system for flood control and ecosystem upgrading in the Red River basin. Water Resources Systems – Hydrological Risk, Management and Development (proceedings of symposium HS02b held during IUGG2003 at Sapporo, July 2003). IAHS Publ. no. 281. Diep, N.V., Can, N.H., Hien, H.N., Trung, M.D., Hanh, N.V., Que, N.V., 2000. Modelling Technology and Red River System Flood Control. In: K.D. Nguyen (editor), Ecosystem & Flood, International European-Asian Workshop Hanoi June 2000. Institute of Mechanics, Hanoi, Vietnam. Diermanse, F.L.M., 2001. Physically based modelling of rainfall-runoff processes. Ph.D. thesis. Delft University Press, Delft, The Netherlands, 234 pp. Foppes, S., 2004. Da River Regionalisation, Rainfall-Runoff modelling of the ungauged Chinese catchment area, Master project literature study, Water Engineering and Management, University of Twente, The Netherlands. Haberlandt, U., Klöcking, B., Krysanova, V. and Becker, A., 2001. Regionalisation of the base flow index from dynamically simulated flow components- a case study in the Elbe River Basin. Journal of Hydrology. Vol. 248, pp. 35-53. Hansson, J. and Ekenberg, L., 2002. Flood Mitigation Strategies for the Red River Delta. Dept. of Computer and System Sciences, Stockholm University and KTH. Available at: http://dlc.dlib.indiana.edu/archive/00000830/00/hanssonk080402.pdf (visited on June 7th, 2004)


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Harlin, J. and Kung, C.-S., 1992. Parameter uncertainty and simulation of design floods in Sweden. Journal of Hydrology, Vol. 137, pp. 209-230. Hundecha, Y. and Bárdossy, A., 2004. Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model. Journal of Hydrology. Vol. 292, pp. 281-295. Institute of Mechanics- Vietnam, 2004. FLOCODS. Available at: http://www.geos.unicaen.fr/mecaflu/Flocodsweb/Index.htm (visited on June 6th, 2004). Killingtveit, A. and Saelthun, N.R., 1995. Hydropower development: hydrology. Norwegian Inst. Technol., Oslo. Kokkonen, T.S., Jakeman, J.J., Young, P.C. and Koivusalo, H.J., 2003. Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina. Hydrological Processes, Vol. 17, pp. 2219-2238. Krysanova, V., Bronstert, A. and Müller-Wohlfeil, D.-I., 1999. Modelling river discharge for large drainage basins: from lumped to distributed approach. Hydrological Sciences Jounal, Vol. 44, pp. 313-331. Lindström, G., Johansson, B., Persson, M., Gardelin, M. and Bergström, S., 1997. Development and test of the HBV-96 hydrological model. Journal of Hydrology. Vol. 201, pp. 272-288. Lonely Planet, 2003. Vietnam. 7th edition. Lonely Planet Publications. McCarthy, G.T., 1938. The unit hydrograph and flood routing. Unpublished m/s conference of US Army Corps of Engineers. Merz, R., Blöschl, G., 2004. Regionalisation of catchment model parameters. Journal of Hydrology, Vol. 287, pp. 95-123. Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models. Part I – A discussion of principles. Journal of Hydrology, Vol. 10, pp. 282-290. Nemec, J., 1993. Comparison and selection of existing hydrological models for of the dynamic water balance processes in basins of different sizes and on different scales. CHK/KHR, report no. II-7, 74 pp. Ngia, T.T., 2000, Flood Control Planning for Red River Basin. In: K.D. Nguyen (editor), Ecosystem & Flood, International European-Asian Workshop Hanoi June 2000. Institute of Mechanics, Hanoi, Vietnam. Seibert, J., 1999. Regionalisation of parameters for a conceptual rainfall-runoff model. Agricultural and Forest Meteorology. Vol. 98-99, pp. 279-293. Shaw, E.M., 1994. Hydrology in practice (third edition). Chapman & Hall, London, 569 pp. Sequoyah Research Center, 2005. Available at: http://www.anpa.ualr.edu/digital_library/narratives/SanViet.html (visited on July 10th, 2004).


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Strategic Environment Framework, 2005. Available at: http://www.rrcap.unep.org/sef/index.cfm (visited on July 8th, 2005). Sivapalan M., Takeuchi K., Franks S.W., Gupta V.K., Karambiri H., Lakshmi V., Liang X., McDonnell J.J., Mendiondo E.M., O'Connell P.E., Oki T., Pomeroy J.W., Schertzer D., Uhlenbrook S. & Zehe E., 2003. IAHS Decade on Predictions in Ungauged Basins (PUB), 2003-2012: Shaping an exciting future for the hydrological sciences. Hydrological Sciences Jounal- Journal des Sciences Hydrologiques. Vol. 48(6), pp. 857-880. SMHI, 2005. Homepage of the Original HBV-Model. Available at: http://www.smhi.se/foretag/m/hbv_demo/html/welcome.html (visited on July 8th, 2005). Uhlenbrook, S., Seibert, J., Leibundgut, C. and Rodhe, A., 1999. Prediction uncertainty of conceptual rainfall-runoff models caused by problems identifying model parameters and structure. Hydrological Sciences Jounal. Vol. 44, pp. 779-797. UNDP (United Nations Development Program) Project, 2002. Background on Natural Disasters, economic consequences. Available at: http://www.undp.org.vn/dmu/background/en/frame.htm (visited on May 22nd, 2005), maintained by the Disaster Management Unit, UNDP Project. USGS, 2005. Homepage of the U.S. Geological Servey. Available at: http://www.usgs.gov (visited on August 1st, 2005). Utrecht University, 2005. PCRaster. Available at: http://pcraster.geog.uu.nl (visited on July 28th, 2005) Vandewiele, G.L. and Elias, A., 1995. Monthly water balance of ungauged catchments obtained by geographical regionalization. Journal of Hydrology, Vol. 170, pp. 277-291. Velner, R.G.J., 200. Rainfall-runoff modelling of the Ourthe catchment with the HBV model. A study for extension of the lead time for flood forecasting in the Meuse (in Dutch). RIZA working document 2000.091X. Wageningen University, Wageningen, 116pp. Yokoo, Y., Kazama, S., Sawamoto, M., and Nishimura, H., 2001. Regionalization of lumped water balance model parameters based on multiple regression. Journal of Hydrology, Vol. 246, pp. 209-222.


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1

Appendix I: Used parameter values Table I.1: Parameter values and ranges from other studies (based on Booij, 2002)

Study FC mm

LP -

� -

� -

kfast day-1

PERC mm/day

kslow day-1

Bergström (1990)

100-300 0.50-1.0 1.0-4.0

Diermanse (2001)

0-580 0.80 3.0 0.6 0.01

Harlin and Kung 1992)

50-274 0.73-1.0 1.0-5.9 0.6-2.1 0.0008-0.05

Killingtveit and

S.(1995)

75-300 0.70-1.0 1.0-4.0 0.5-1.0 0.0005-0.002

Krysanova et al. (1999)

220-391 0.70 2.0 1.0 0.0005

Seibert (1999)

50-500 0.30-1 1.0-6.0 0.0-3.0 0.001-0.15

SMHI (1999)

200 0.9 2.0 1.0 0.010

Uhlenbrook et al. (1999)

100-550 0.30-1 1.0-5.0 0.0-4.0 0.00005-0.1

Velner (2000)

180 0.66 1.8 1.1 0.002 0.4

Booij (2002)

180-660 0.28-0.71

1.0-2.3 0.1-1.9 0.002-0.051

0.4-0.8 0.02

Table I.2: Used parameter values and corresponding mean FC per sub-basin

FC1 (mm) FC2 (mm) FC3 (mm)

FCsb3,mean mm

FCsb4,mean mm

FCsb5,mean mm

FCsb6,mean mm

50 100 150

144 134 99 122

100 150 200

194 184 149 172

100 175 250

241 226 173 208

100 225 350

336 311 222 279

100 300 500

477 436 295 387


2

Table I.3: Used parameter values and corresponding � per sub-basin

b1 -

b2 -

�sb3 -

�sb4 -

�sb5 -

�sb6 -

3.00 0.50 1.05 1.30 1.65 1.18 3.00 0.70 1.25 1.50 1.85 1.38 3.00 0.90 1.45 1.70 2.05 1.58 5.00 0.50 1.41 1.83 2.41 1.63 5.00 0.70 1.61 2.03 2.61 1.83 5.00 0.90 1.81 2.23 2.81 2.03 7.00 0.50 1.77 2.37 3.17 2.08 7.00 0.70 1.97 2.57 3.37 2.28 7.00 0.90 2.17 2.77 3.57 2.48

Table I.4: Used parameter values and corresponding mean kfast (per sub-basin)

k1 day-1

k2 day-1

kfast,mean day-1

kfast,sb4,mean day-1



kfast,min day-1

kfast,max day-1

0.0020 0.0008 0.0010 0.0010 0.0010 0.0010 0.0008 0.0014 0.0060 0.0003 0.0010 0.0011 0.0011 0.0009 0.0003 0.0023 0.0040 0.0015 0.0020 0.0021 0.0021 0.0019 0.0015 0.0029 0.0120 0.0005 0.0020 0.0023 0.0022 0.0018 0.0006 0.0046 0.0060 0.0023 0.0030 0.0031 0.0031 0.0029 0.0023 0.0043 0.0175 0.0008 0.0030 0.0033 0.0032 0.0026 0.0009 0.0067 0.0100 0.0038 0.0050 0.0052 0.0052 0.0048 0.0039 0.0072 0.0295 0.0013 0.0050 0.0056 0.0054 0.0044 0.0015 0.0113 0.0155 0.0060 0.0080 0.0083 0.0082 0.0076 0.0062 0.0113 0.0465 0.0020 0.0080 0.0088 0.0086 0.0069 0.0025 0.0178

Table I.5: Used parameter values and corresponding mean � ( per sub-basin)

a1 -

a2 -

�mean -

� sb4,mean -

� sb5,mean -

� sb6,mean -

�min -

�max -

1.6 1.0 1.2 1.2 1.2 1.2 1.0 1.5 5.4 1.0 1.7 1.8 1.8 1.6 1.1 2.8 2.3 1.4 1.7 1.7 1.7 1.6 1.4 2.2 7.8 1.0 2.0 2.1 2.1 1.8 1.1 3.7 3.1 1.6 2.0 2.1 2.0 1.9 1.6 2.7 3.9 1.6 2.1 2.2 2.2 2.0 1.6 2.9

1.60 1.90 2.1 2.1 2.1 2.1 1.9 2.4 9.3 1.0 2.2 2.4 2.3 2.0 1.1 4.2 3.1 1.8 2.2 2.3 2.2 2.1 1.8 2.9 2.3 2.0 2.3 2.3 2.3 2.2 2.0 2.8


3

Table I.6: Used parameter values for the non regionalised HBV-RR parameters (kfast and � are not regionalised for the calibration of the soil moisture routine)

PERC mm/day

kslow day-1

kfast day-1

� -

1.0 0.01 0.001 2.0


4

Appendix II: Calibration of the soil moisture routine

Table II.1a: Calibration results for LP=0.3

b1 (-) b2 (-)

criterion FC1=50mm FC2=100mm FC3=150mm

FC1=100mm FC2=150mm FC3=200mm

RVEsb3 51 50 RVEsb4 18 17 RVEsb5 -17 -17 RVEsb6 2 -1

3.0 0.5

RVEtotal 2 1

RVEsb3 51 51 RVEsb4 18 17 RVEsb5 -17 -17 RVEsb6 2 0

3.0 0.7

RVEtotal 3 1


3.0 0.9

RVEtotal 3 1


5.0 0.5

RVEtotal 3 1


5.0 0.7

RVEtotal 3 1


5.0 0.9

RVEtotal 3 1


5

Table II.1b: Calibration results for LP=0.3

b1 (-) b2 (-)




7.0 0.5

RVEtotal 3 2


7.0 0.7

RVEtotal 3 2


7.0 0.9

RVEtotal 3 2


7.0 0.7

RVEtotal 3 2


7.0 0.9

RVEtotal 3 2


b1 (-) b2 (-)




RVEsb3 51 49 46 RVEsb4 18 17 14 RVEsb5 -17 -17 -18 RVEsb6 1 -1 -5

3.0 0.5

RVEtotal 1 0 -3


6


b1 (-) b2 (-)





3.0 0.7

RVEtotal 2 0 -3


3.0 0.9

RVEtotal 2 0 -3


5.0 0.5

RVEtotal 2 0 -3

RVEsb3 51 51 48 RVEsb4 18 17 15 RVEsb5 -17 -17 -18 RVEsb6 2 0 -4

5.0 0.7

RVEtotal 2 1 -2


5.0 0.9

RVEtotal 3 -1 -4


7.0 0.5

RVEtotal 2 1 -2


7

Table II.2c: Calibration results for LP=0.5

b1 (-) b2 (-)





7.0 0.7

RVEtotal 2 1 -2


7.0 0.9

RVEtotal 2 1 -2


b1 (-) b2 (-)





RVEsb3 52 51 48 44 RVEsb4 19 18 16 13 RVEsb5 -16 -16 -17 -19 RVEsb6 4 2 -2 -7

3.0 0.5

RVEtotal 4 3 -1 -4


3.0 0.7

RVEtotal 4 3 -1 -5


3.0 0.9

RVEtotal 4 3 -1 -5


8


b1 (-) b2 (-)






5.0 0.5

RVEtotal 4 3 -1 -4


5.0 0.7

RVEtotal 4 3 -1 -4


5.0 0.9

RVEtotal 4 3 -1 -4


7.0 0.5

RVEtotal 4 3 -1 -4


7.0 0.7

RVEtotal 4 3 -1 -4


7.0 0.9

RVEtotal 4 3 0 -4


9

Appendix III: Calibration of the flow routines

Table III.1a: Calibration results with high kfast

a1 (-) a2 (-)

�mean(-)

criterion k1=0.0060 k2=0.0023

kfast,mean=0.0030

k1=0.0175 k2=0.0008

kfast,mean=0.0030

k1=0.0100 k2=0.0038

kfast,mean=0.0050

k1=0.0155 k2=0.0060

kfast,mean=0.0080

R2m,sb3 0.17 0.21

R2m,sb4 0.07 0.15

R2m,sb5 -0.01 0.07

R2m,sb6 -0.59 -0.41

1.6 1.0

1.2

R2m,total -0.31 -0.18

R2

m,sb3 -0.44 -0.87 R2

m,sb4 0.61 0.65 R2

m,sb5 0.52 0.56 R2

m,sb6 0.28 0.38

5.4 1.0

1.7

R2m,total 0.41 0.48

R2

m,sb3 R2

m,sb4 R2

m,sb5 R2

m,sb6

2.3 1.4

1.7

R2m,total

R2

m,sb3 -1.69 -1.61 R2

m,sb4 0.66 0.66 R2

m,sb5 0.56 0.54 R2

m,sb6 0.39 0.34

7.8 1.0

2.0

R2m,total 0.49 0.46

R2

m,sb3 -2.17 -2.03 R2

m,sb4 0.72 0.71 R2

m,sb5 0.66 0.65 R2

m,sb6 0.61 0.58

3.1 1.6

2.0

R2m,total 0.65 0.62

R2

m,sb3 -3.10 -2.96 -3.94 -4.78 R2

m,sb4 0.66 0.65 0.65 0.63 R2

m,sb5 0.57 0.56 0.58 0.59 R2

m,sb6 0.43 0.39 0.44 0.44

9.3 1.0

2.2

R2m,total 0.51 0.49 0.52 0.52


10

Table III.1b: Calibration results with high kfast

a1 (-) a2 (-)

�mean(-)

criterion k1=0.0060 k2=0.0023

kfast,mean=0.0030

k1=0.0175 k2=0.0008

kfast,mean=0.0030

k1=0.0100 k2=0.0038

kfast,mean=0.0050

k1=0.0155 k2=0.0060

kfast,mean=0.0080

R2m,sb3 -4.30 -4.10

R2m,sb4 0.70 0.69

R2m,sb5 0.68 0.67

R2m,sb6 0.58 0.58

3.1 1.8

2.2

R2m,total 0.63 0.63

Table III.2a: Calibration results with low kfast

a1 (-) a2 (-)

�mean(-)

criterion k1=0.0020 k2=0.0008

kfast,mean=0.0010

k1=0.0060 k2=0.0003

kfast,mean=0.0010

k1=0.0040 k2=0.0015

kfast,mean=0.0020

k1=0.0120 k2=0.0005

kfast,mean=0.0020

R2m,sb3 -0.08 -0.05

R2m,sb4 -0.08 -0.06

R2m,sb5 -0.15 -0.14

R2m,sb6 -0.89 -0.89

1.6 1.0

1.2

R2m,total -0.54 -0.53

R2

m,sb3 0.11 R2

m,sb4 0.52 R2

m,sb5 0.42 R2

m,sb6 0.04

5.4 1.0

1.7

R2m,total 0.23

R2

m,sb3 -0.13 R2

m,sb4 0.49 R2

m,sb5 0.40 R2

m,sb6 0.19

2.3 1.4

1.7

R2m,total 0.31

R2

m,sb3 -0.55 -1.20 -1.17

R2m,sb4 0.62 0.65 0.65

R2m,sb5 0.50 0.54 0.52

R2m,sb6 0.21 0.33 0.29

7.8 1.0

2.0

R2m,total 0.37 0.45 0.42


11

Table III.2b: Calibration results with low kfast

a1 (-) a2 (-)

�mean(-)

criterion k1=0.0020 k2=0.0008

kfast,mean=0.0010

k1=0.0060 k2=0.0003

kfast,mean=0.0010

k1=0.0040 k2=0.0015

kfast,mean=0.0020

k1=0.0120 k2=0.0005

kfast,mean=0.0020

R2m,sb3 -0.78 -1.58 -1.50

R2m,sb4 0.65 0.70 0.53

R2m,sb5 0.56 0.64 0.70

R2m,sb6 0.40 0.57 0.62

3.1 1.6

2.0

R2m,total 0.50 0.61 0.62

R2

m,sb3 -2.36 R2

m,sb4 0.71 R2

m,sb5 0.66 R2

m,sb6 0.60

3.9 1.6

2.1

R2m,total 0.64

R2

m,sb3 -2.70 R2

m,sb4 0.74 R2

m,sb5 0.70 R2

m,sb6 0.64

1.6 1.9

2.1

R2m,total 0.68

R2

m,sb3 -1.62 -1.58 -2.50 -2.42

R2m,sb4 0.65 0.52 0.40 0.65

R2m,sb5 0.53 0.52 0.66 0.54

R2m,sb6 0.32 0.28 0.56 0.36

9.3 1.0

2.2

R2m,total 0.45 0.39 0.54 0.47

R2

m,sb3 -2.27 -2.17 -3.46 -3.32

R2m,sb4 0.72 0.71 0.72 0.71

R2m,sb5 0.67 0.65 0.69 0.67

R2m,sb6 0.61 0.58 0.62 0.61

3.1 1.8

2.2

R2m,total 0.65 0.63 0.66 0.64

R2

m,sb3 -4.69 R2

m,sb4 0.69 R2

m,sb5 0.68 R2

m,sb6 0.56

2.3 2.0

2.3

R2m,total 0.62


12

Appendix IV: Validation in Time with all Available Precipitation Stations

0

1000

2000

3000

4000

5000

6000

7000

8000

22-May 11-Jun 1-Jul 21-Jul 10-Aug 30-Aug 19-Sep 9-Oct 29-Octdate

disc

harg

e (m

3/s)

computed

observed

Figure IV.1: Computed and observed discharges at Muong Te with all available precipitation stations

0

1000

2000

3000

4000

5000

6000

7000

8000

9000


date

dis

char

ge

(m3/

s)

computed

observed

Figure IV.2: Computed and observed discharges at Nam Giang with all available precipitation stations


13

0

5000

10000

15000

20000

25000


date

disc

harg

e (m

3/s)

computed

observed

Figure IV.3: Computed and observed discharges at Ta Bu with all available precipitation stations

regionalisation based on basin characteristics applied to ... · regionalisation based on basin...

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