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Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management Edited by Eva Boegh, Harald Kunstmann, Thorsten Wagener, Alan Hall, Luis Bastidas, Stewart Franks, Hoshin Gupta, Dan Rosbjerg & John Schaake IAHS Publ. 313 (2007) ISBN 978-1-90150278-09-1 508 + iv pp. price £87.00

The atmosphere is the primary driving force for all hydrological processes, yet the availability of spatially and temporally reliable hydrometeoro-logical information remains a critical issue in many hydrological studies. The problem is made more urgent by the suggestion that a warmer climate will lead to an intensification of the hydrological cycle, and to an increase in the frequency of extreme events. In order to accurately represent and understand the impact of climate dynamics on the development of freshwater resources, water manage-ment tools that account for the coupled land–atmosphere system are needed. Indeed, the derivation of spatially and temporally representative hydromet-eorological data and their accurate representation in water management tools is important to predict current and future developments in freshwater resources, and the influence of changing climate and land surface patterns due to intensified human activities.

The contributions in this volume consider the uncertainties in the end-to-end prediction of hydrological variables, beginning with the atmospheric driving, and ending with the hydrological calculations for scientifically-sound decisions in sustainable water management. The book is organized in two main parts; the first addresses the Quantification and reduction of predictive uncertainty in hydrometeorological forcing, and the second includes studies aiming at Minimizing risks in water management decisions by improving the understanding and spatial representation of the coupled land–atmosphere system.

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Contents

Preface by Eva Boegh, Harald Kunstmann, Thorsten Wagener, Alan Hall, Luis Bastidas, Stewart Franks, Hoshin Gupta, Dan Rosbjerg & John Schaake

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QUANTIFICATION AND REDUCTION OF PREDICTIVE UNCERTAINTY IN HYDROMETEOROLOGICAL FORCING

Meteorological prediction and uncertaintyReducing uncertainty in selecting climate models for hydrological impact assess-ments A. J. Pitman & S. E. Perkins

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Multi-model climate change scenarios for southwest Western Australia and potential impacts on streamflow Guobin Fu & Stephen P. Charles

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Propagation of convection in Africa: implications for predictability of precipita-tion Arlene Laing, Richard Carbone & Vincenzo Levizzani

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GPS and satellite meteorology for understanding monsoon dynamics over the Indian sub-continent Anup K. Prasad, Ramesh P. Singh, Shatrughan Singh & Dip S. Nanda

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Linking the West African monsoon’s onset with atmospheric circulation patterns Patrick Laux, Harald Kunstmann & András Bárdossy

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A multi-index approach to inflow prediction for water resources management Stewart W. Franks & Adam M. Wyatt

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1.2 Spatial climate data and uncertaintySpace–time representativity of precipitation for rainfall–runoff modelling: exper-ience from some case studies Uwe Haberlandt, Anna-Dorothea Ebner von Es-chenbach, Aslan Belli & Christian Gattke

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Uncertainty characterization in a combined IR/microwave scheme for remote sensing of precipitation Carlo De Marchi, Aris Georgakakos & Christa Peters-Lidard

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Empirically-based generator of synthetic radar-rainfall data Gabriele Villarini, Witold F. Krajewski & Grzegorz J. Ciach

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Uncertainties in water balance estimations due to scarce meteorological informa-tion: a case study for the White Volta catchment in West Africa Sven Wagner, Harald Kunstmann & Andras Bardossy

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Assessment of rainfall and evaporation input data uncertainties on simulated run-off in southern Africa Tendai Sawunyama & Denis Hughes

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1.3 Hydrological predictions using integrated climate–hydrological modelling

GEWEX Hydrology Alan J. Hall, Richard G. Lawford, John O. Roads, John C. Schaake & Eric F. Wood

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Hydrological predictability investigation of global data sets for high-latitude river basins Yeugeniy M. Gusev, Olga N. Nasonova, Larisa Y. Dzhogan & Yeugeniy E. Kovalev

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Integrated atmospheric and hydrologic modelling for short-term and basin-scale forecasts in a tropical semi-arid context Ana Cláudia Braga, Carlos O. Galvão, Enio P. Souza, Enilson P. Cavalcanti, Renato Fernandes & Klécia Oliveira

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Regional climate change in the Middle East and impact on hydrology in the Upper Jordan catchment Harald Kunstmann, Peter Suppan, Andreas Heckl & Alon Rimmer

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Modelling regional climate change and the impact on surface and sub-surface hy-drology in the Volta Basin (West Africa) G. Jung & H. Kunstmann

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Monthly streamflow forecasts for the State of Ceará, Brazil Dirceu S. Reis Jr, Eduardo S. Martins, Luiz Sérgio V. Nascimento, Alexandre A. Costa & Alan M. B. Alexandre

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Coupling meteorological and hydrological models for medium-range streamflow forecasts in the Paraná Basin Walter Collischonn, Daniel Allasia, Carlos E. M. Tucci & Adriano R. Paz

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1.4 Uncertainty in hydrological forecastingExperimental hydrometeorological and hydrological ensemble forecasts and their verification in the US National Weather Service Julie Demargne, Limin Wu, Dong-Jun Seo & John Schaake

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Adjusting ensemble forecast probabilities to reflect several climate forecasts Jery R. Stedinger & Young-Oh Kim

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“Outlier” catchments: what can we learn from them in terms of prediction uncertainty in rainfall–runoff modelling? Nicolas Le Moine, Vazken Andréassian, Charles Perrin & Claude Michel

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Understanding sources of uncertainty in flash-flood forecasting for semi-arid regions Thorsten Wagener, Hoshin Gupta, Soni Yatheendradas, David Goodrich, Carl Unkrich & Mike Schaffner

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Long-term probabilistic forecasting of snowmelt flood characteristics and the forecast uncertainty Lev Kuchment & Alexander Gelfan

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Predictive uncertainty in climate change impacts on floods Martijn J. Booij, Martijn Huisjes & Arjen Y. Hoekstra

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MINIMIZING RISKS IN WATER MANAGEMENT DECISIONS BY IMPROVING THE UNDERSTANDING AND SPATIAL REPRESENTATION OF THE COUPLED LAND–ATMOSPHERE SYSTEM

Hydrological predictions using spatial data and integrated land surface–atmosphere–hydrology modelling

AMMA forcing data for a better understanding of the West African monsoon surface–atmosphere–hydrology interactions A. Boone & P. deRosnay

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Improved scenario prediction by using coupled hydrological and atmospheric models Jesper Overgaard, Michael B. Butts & Dan Rosbjerg

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Representativeness of point soil moisture observations, upscaling and assimilation Gabriëlle J. M. De Lannoy, Valentijn R. N. Pauwels, Paul R. Houser, Timothy Gish & Niko E. C. Verhoest

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Can a land surface model simulate runoff with the same accuracy as a hydrological model? Olga N. Nasonova & Yeugeniy M. Gusev

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Water balance evaluation in Denmark using remote sensing-driven land surface modelling and spatially distributed hydrological modelling Eva Boegh, Britt S. B. Christensen & Lars Troldborg

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Comparing model performance of the HBV and VIC models in the Rhine basin Aline Te Linde, Ruud Hurkmans, Jeroen Aerts & Han Dolman

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Analysis of water resource variability over the irrigated area along the downstream reach of the Yellow River Huimin Lei, Dawen Yang, Xinbing Liu & Shinjino Kanae

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Water consumption of Populus euphratica woodlands in an arid region of China Yonghua Zhu, Liliang Ren & Haishen Lü

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2.2 Geographical transferability of methods and predictions in ungauged basins

Testing similarity indices to reduce predictive uncertainty in ungauged basins Ludovic Oudin, Vazken Andréassian, Claudia Rojas-Serna, Nicolas Le Moine & Claude Michel

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The use of physical basin properties and runoff generation concepts as an aid to parameter quantification in conceptual type rainfall–runoff models Denis Hughes & Evison Kapangaziwiri

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Regionalization of parameters of hydrological models: inclusion of model parameter uncertainty Satish Bastola, Hiroshi Ishidaira & Kuniyoshi Takeuchi

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Regionalization for uncertainty reduction in flows in ungauged basins Martijn J. Booij, Dave L. E. H. Deckers, Tom H. M. Rientjes & Maarten S. Krol

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Assessment of the watershed yield of the Sakarya River basin, Turkey Sabahattin Isik & Vijay P. Singh

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Applicability of the GIUH model to estimate flood peaks from ungauged catchments in arid areas – a case study for the West Bank Ammar Jarrar, Niranjali Jayasuriya, Anan Jayyousi & Maazuza Othman

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Prediction of rainfall–runoff model parameters in ungauged catchments M. Zvolenský, K. Hlavčová, S. Kohnová & J. Szolgay

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Multilevel river classification as the methodological basis for analysis of maximum runoff values in different geographical regions Elena Asabina

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The Xinanjiang model from the perspective of PUB Liliang Ren, Fei Yuan, Zhongbo Yu, Xiaoli Yang, Rulin Ouyang & Xianghu Li

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2.3 Meteorological predictions and data assimilation for flood risk management

Integrating meteorological and uncertainty information in flood forecasting: the FLOODRELIEF project Michael B. Butts, Anne Katrine V. Falk, Yunqing Xuan & Ian D. Cluckie

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Reducing the uncertainty of flood forecasts using multi-objective optimization algorithms for parameter estimation Yan Wang, Christian Gattke & Andreas Schumann

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Effects of soil moisture parameterization on a real-time flood forecasting system based on rainfall thresholds Giovanni Ravazzani, Marco Mancini, Ilaria Giudici & Paolo Amadio

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Estimation of extreme flow quantiles and quantile uncertainty for ungauged catchments Donald H. Burn, Taha B. M. J. Ouarda & Chang Shu

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Peak flow estimation under parameter uncertainty in a real-time flood warning system for ungauged basins Daniela Biondi & Pasquale Versace

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2.4 Integrated water management systems for sustainable water management

Demonstrating Integrated Forecast and Reservoir Management (INFORM) for northern California in an operational environment K. P. Georgakakos, N. E. Graham, A. P. Georgakakos & H. Yao

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Coping with predictive uncertainties in optimization of sustainable water resources Chin Man Mok, Nisai Wanakule, Armen Der Kiureghian, Steven M. Gorelick & Miao Zhang

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RNN-based inflow forecasting applied to reservoir operation via implicit stochastic optimization Camilo Allyson Simões De Farias, Akihiro Kadota, Alcigeimes B. Celeste & Koichi Suzuki

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Effect of uncertainties on the real-time operation of a lowland water system in The Netherlands Steven Weijs, Elgard van Leeuwen, Peter-Jules van Overloop & Nick van de Giesen

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Data assimilation in a large-scale distributed hydrological model for medium-range flow forecasts Adriano Rolim Da Paz, Walter Collischonn, Carlos E. M. Tucci, Robin T. Clarke & Daniel Allasia

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Predictive models of reservoir storage-yield-reliability functions: inter-comparison of regression and multi-layer perceptron artificial neural network paradigms Adebayo Adeloye

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Analyse des périodes sèches pour la gestion d’un barrage au nord de la Tunisie / Analysis of dry periods for dam operation in northern Tunisia Mathlouthi Majid & Lebdi Fethi

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Hydrological simulation and prediction for environmental change Jian Yun Zhang, Zhiyu Liu & Chuanbao Zhu

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Key word index 505

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Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007., 3-15

Reducing uncertainty in selecting climate models for hydrological impact assessments

A. J. PITMAN1 & S. E. PERKINS2

1 Climate Change Centre, University of New South Wales, Sydney, New South Wales, [email protected]

2 Department of Physical Geography, Macquarie University, New South Wales 2109, Australia

Abstract Deciding which climate models to use to assess the impact of climate change on water resources is particularly difficult in environments where precipitation dominates resource vulnerability. We show that assessing climate models based on their simulation of mean precipitation provides little guide to a model’s ability to simulate the more extreme events that affect hydrological systems. In contrast, a probability density function based assessment using daily climate model data provides a good basis for confidence in a model’s ability to simulate the 95th rainfall percentile. We demonstrate that climate models have useful skill in simulating observed probability density functions over two regions of Australia, although the well-known bias of excess rainfall at low rates remains common. We conclude by identifying those climate models that produce the best basis for hydrological impacts assessment over two regions of Australia.Key words climate models; probability density function; skill-score

Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007, 6-23

Multi-model climate change scenarios for southwest Western Australia and potential impacts on streamflow

GUOBIN FU & STEPHEN P. CHARLESCSIRO Land and Water, PB 5, Wembley, Western Australia 6155, [email protected]

Abstract This paper examines future climate change scenarios of annual and monthly temperature, precipitation, and sea-level pressure (SLP) from 19 general circulation models (GCMs) used in the IPCC Fourth Assessment Report, and discusses the potential impacts on streamflow for southwest Western Australia (SWWA). The ranges of annual temperature increase indicated by the 19 GCMs for three emission scenarios (A1B, A2 and B1) are 0.76–0.91 C, 1.17–1.57C and 1.69–2.72C for 2025, 2050 and 2085, respectively. The annual precipitation is projected to decrease by 16.0–19.9 mm (4.7–5.6%), 31.2–38.4 mm (9.1–11.6%), and 38.1–56.2 mm (11.2–16.4%) for these periods. The annual SLP increases by 0.2 hPa, 0.3–0.4 hPa and 0.4–0.6 hPa correspondingly. Changes in the seasonality of precipitation, temperature, and SLP also occur. Together these changes, although with some uncertainties, are likely to lead to major challenges for regional water resource management and planning. These results provide a reference for decision-makers when determining adaptation policies and the methods can be used for other regions in assessing the potential future climatic change for the given emission scenarios. Key words climate change; emission scenarios; GCMs; southwest Western Australia; streamflow; water resources management


Propagation of convection in Africa: implications for predictability of precipitation

ARLENE LAING1, RICHARD CARBONE1 & VINCENZO LEVIZZANI2

1 National Center for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307, USA [email protected]

2 National Research Council, Institute of Atmospheric Sciences and Climate, I-40129 Bologna, Italy

Abstract Knowledge of the spatial and temporal variability of precipitation is needed for African societies to manage agriculture, water resources, public health, renewable energy, and hazard mitigation. This study examines the occurrences of organized convection in Africa using five years (1999–2003) of digital infrared imagery. Domains are: 0 to 20N and 20W to 40E; 15S to 15N and 20W to 45E; 35S to 15S and 10E to 45E. Reduced-dimension techniques are used to document properties of cold clouds, proxies for precipitation. Large-scale environments are diagnosed from global analyses. A sizeable fraction of the rainfall in Africa results from long-lived “episodes” of deep convection. Episodes are coherent sequences of organized convection that propagate and regenerate on regional and continental scales. Most episodes have phase speeds of 10–20 m s-1. A major generating factor for convection is thermal forcing associated with large elevated heat sources. Episodes occur with moderate vertical wind shear. Study results infer the potential for increased predictive skill in sub-seasonal weather prediction, which could enable substantial societal benefits.Key words convection; diurnal cycle; southeast Africa; Central Africa; Sahel; West African monsoon; tropical precipitation


GPS and satellite meteorology for understanding monsoon dynamics over the Indian sub-continent

ANUP K. PRASAD1, RAMESH P. SINGH1,2, SHATRUGHAN SINGH1 & DIP S. NANDA1

1 Department of Civil Engineering, Indian Institute of Technology, Kanpur 208016, India2 Centre for Earth Observing and Space Research, College of Science, George Mason University, Fairfax, USA

[email protected]

Abstract Atmospheric water vapour plays a major role in the radiative balance of the Earth’s atmosphere and influences the hydrological cycle. The total column of atmospheric water vapour, obtained from Global Positioning System (GPS) and Moderate Resolution Imaging Spectroradiometer (MODIS), is found to be very dynamic over the Indian sub-continent. Water vapour is found to be highly variable over the Indo-Gangetic (IG) plains, one of the agriculturally very productive regions in the world, where a strong coupling is found to exist between the land and atmosphere. Monthly and seasonal water vapour over northern India shows a large deficiency for the summer season during 2005, which was one of the warmest years compared to the year 2004. A deficiency in the GPS and MODIS water vapour (monsoon season) is also observed during the drought year of 2004 due to failure of the Indian monsoon in northern India compared to a normal monsoon rainfall year 2005. The water vapour variability and its spatio-temporal

dynamics will be useful in understanding the dynamics of the Indian monsoon. Key words water vapour; GPS; MODIS; TRMM; monsoon; rainfall; surface temperature


Linking the West African monsoon’s onset with atmospheric circulation patterns

PATRICK LAUX1, HARALD KUNSTMANN1 & ANDRÁS BÁRDOSSY2

1 Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlsruhe, D-82467 Garmisch-Partenkirchen, [email protected]

2 Institute for Hydraulic Engineering, University of Stuttgart, D-70569 Stuttgart, Germany

Abstract Particularly in regions where precipitation is limited to only a few months per year, the reliable determination of the rainy season’s onset and thus the start of the sowing time on a daily basis is of crucial importance for sustainable water management and food production. A fuzzy-logic based definition for the major rainy season’s onset on a regional scale has been developed using ground-measured rainfall information. The definition accounts for the most important agricultural aspects. Further, this study presents a methodology, which is conditioning the single event “onset of the rainy season” to daily large-scale atmospheric circulation via automated objective classification of atmospheric circulation patterns based on fuzzy rules. This study has been carried out within a sector from 40°W to 30°E and 10°S to 60°N, covering a large area of the North Atlantic for atmospheric circulation analysis, and, for sea surface temperature, a sector from 30°W to 10°E and 20°S to 0°. These fuzzy rules are obtained using a simulated annealing optimization of the classification performance. A bootstrapping resampling scheme is applied in order to check the significance of the results. Sensitive predictor variables, with regard to the onset of the rainy season in West Africa, as well as their spatial patterns, are presented and discussed. The aim is for an overall integration in a hydro-meteorological Decision Support System (DSS).Key words Volta basin; West Africa; rainy season onset; objective circulation patterns; simulated annealing; occurrence probability


A multi-index approach to inflow prediction for water resources management

STEWART W. FRANKS & ADAM M. WYATTSchool of Engineering, University of Newcastle, Callaghan 2308, New South Wales, [email protected]

Abstract Significant variability in reservoir inflows is experienced across eastern Australia as a result of a number of known, identified climate modes. In particular, the El Nino Southern Oscillation (ENSO) is known to affect primarily summer (October–March) inflows. In this paper, a software suite is presented that enables: (i) the routine prediction of ENSO events, (ii) the assessment of likely inflow on monthly and seasonal timescales, and (iii) the updating of both climate and inflow prediction as new data are received. The scheme is demonstrated by application to a Sydney Catchment Authority (SCA) reservoir that supplies potable water for

metropolitan Sydney. The tool is generalized and can be applied anywhere that significant correlations between ENSO and inflows are found.Key words eastern Australia; ENSO prediction; monthly inflow; multi-index approach; reservoir; seasonal inflow


Space–time representativity of precipitation for rainfall–runoff modelling: experience from some case studies

UWE HABERLANDT1, ANNA-DOROTHEA EBNER VON ESCHENBACH1, ASLAN BELLI1 & CHRISTIAN GATTKE2

1 Institute of Water Resources Management, Leibniz University of Hannover, Appelstr. 9a, D-30167 Hannover, Germany, [email protected]

2 Institute for Hydrology, Water Management and Environmental Engineering, Ruhr-University Bochum, Universitätsstrasse 150, D-44780 Bochum, Germany

Abstract This contribution discusses possibilities to improve the space–time representativity of precipitation for flood simulations based on some case studies. The spatial representativity of precipitation is considered using an example of the interpolation of hourly rainfall using multivariate geostatistics with additional information from weather radar, daily data and topography. A second example discusses the conditional spatial simulation of rainfall for flood simulation focusing on the uncertainty from precipitation input. Regarding the representativeness of precipitation in time, an example of the disaggregation of daily rainfall into hourly rainfall using a multiplicative random cascade model is presented. Another example deals with stochastic synthesis of hourly precipitation using a modern alternating renewal model.Key words precipitation model; geostatistics; radar; disaggregation; random cascade


Uncertainty characterization in a combined IR/Microwave scheme for remote sensing of precipitation

CARLO DE MARCHI1, ARIS GEORGAKAKOS2 & CHRISTA PETERS-LIDARD3

1 School of Natural Resources and Environment, University of Michigan, 2205 Commonwealth Blvd, Ann Arbor, Michigan 48105, USA

2 School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, Georgia 30332-0355, USA

3 Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA

Abstract This paper presents a methodology for estimating precipitation that combines data from the precipitation radar aboard the TRMM satellite with infrared/visible (IR/VIS) images by geostationary satellites. The approach estimates half-hour precipitation based on IR/VIS data, storm stage, and terrain, and quantifies the uncertainty of the precipitation estimates by computing their full probability distribution. The probabilistic characterization is composed of a binomial distribution for the probability of rain and a lognormal distribution for the conditional rain intensity. Temporal and spatial autocorrelations are fully accounted for by using spatially

optimal estimator methods (kriging). The procedure is tested in the Lake Victoria basin over the period 1996–1998 against data from more than one hundred raingauges, showing lower bias and better correlation with ground data than commonly used methods and reproducing precipitation variability over a range of temporal and spatial scales.Key words sequential simulation; kriging; Lake Victoria; Nile River


Empirically-based generator of synthetic radar-rainfall data

GABRIELE VILLARINI, WITOLD F. KRAJEWSKI & GRZEGORZ J. CIACHIIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, Iowa, [email protected]

Abstract To fully characterize the uncertainties associated with radar-rainfall (RR) estimates, Ciach et al. (2007) developed an empirically based model, in which the relationship between true and radar-rainfall can be described by a deterministic distortion function and a random component. This model has the flexibility to account for different spatio-temporal resolutions, distances from the radar, synoptic conditions, and space–time dependence of the errors. Based on this model, two possible scenarios are presented and described: an ensemble generator and a static estimation of probability maps. In the former, given a time series of hourly radar-rainfall fields, a user can generate an ensemble of synthetic RR data congruent with the error model’s characteristics. As far as the second scenario is concerned, given hourly RR maps, it is possible to generate fields with the probability of exceedence of some arbitrary thresholds by the true rainfall. Key words radar-rainfall uncertainties; ensemble forecasting, NEXRAD, radar hydrology


Uncertainties in water balance estimations due to scarce meteorological information: a case study for the White Volta catchment in West Africa

SVEN WAGNER1, HARALD KUNSTMANN1 & ANDRAS BARDOSSY2 1 Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlsruhe, Germany

[email protected] Institute for Hydraulic Engineering, University Stuttgart, Germany

Abstract Scientifically sound decisions in sustainable water management are usually based on hydrological modelling, which can only be accomplished with meteorological information. Especially in regions with a weak infrastructure, where meteorological data are not available at a sufficient spatial and temporal resolution, spatial interpolations of coarse-resolution meteorological point observations are afflicted with uncertainties. This particularly applies to discontinuous variables like precipitation. These input uncertainties are transferred to the hydrological simulations. The uncertainties resulting from precipitation interpolation and their effect on model-based water balance estimations are investigated. First, the results of different spatial interpolation techniques will be compared and analysed. Second, the results of the hydrological simulations driven by these meteorological fields shall be investigated to estimate the propagating effect of the precipitation uncertainties on water balance estimations. The area

under study is the White Volta catchment (94 000 km2) in the semi-arid environment in West Africa, for which basin-wide uncertainty estimates are required for sustainable water management decisions. The three geostatistical interpolation methods: inverse distance weighting, ordinary and external drift kriging, were applied for the calculation of areal precipitation. The results show that the interpolation technique selected influences the areal precipitation field. The results from the impact study of the differences in areal precipitation on hydrological modelling show that the different interpolation techniques produce small differences for aggregated variables and corresponding time series, but affect the spatial distribution of the water balance variables. Key words geostatistical interpolation; impact; precipitation; water balance simulations


Assessment of rainfall and evaporation input data uncertainties on simulated runoff in southern Africa

TENDAI SAWUNYAMA & DENIS HUGHESInstitute for Water Research, Rhodes University, 6140, South [email protected]

Abstract Rainfall–runoff models are used extensively in southern Africa for the purposes of water resource assessment. This study presents an assessment of the uncertainties associated with the spatial and temporal resolution of rainfall and evaporation inputs to a commonly applied hydrological model, as well as the extent to which these uncertainties are propagated into runoff estimations. While the effects of rainfall spatial variability are greater for daily rather than monthly estimates, the use of daily spatial data aggregated to monthly totals can reduce the uncertainties. The resulting runoff prediction uncertainties are greater in relative terms for semi-arid basins than for humid basins. Using time series of potential evapotranspiration instead of fixed monthly averages has an impact on simulated runoff in some parts of the country.Key words model input data; rainfall–runoff model; uncertainty; Southern Africa


GEWEX Hydrology

ALAN J. HALL1, RICHARD G. LAWFORD2, JOHN O. ROADS3, JOHN C. SCHAAKE4 & ERIC F. WOOD5

1 Chair IAHS/WMO Working Group on GEWEX, 17 Crisp Street, Cooma, NSW 2630, Australia [email protected]

2 International GEWEX Project Office, Silver Spring, Maryland, USA 3 Co-Chair CEOP, UCSD, La Jolla, California, USA4 Office of Hydrologic Development, NOAA/National Weather Service, Silver Spring, Maryland, USA5 Dept of Civil and Environmental Engineering, Princeton University, USA

Abstract The Global Energy and Water Cycle Experiment (GEWEX), of the World Climate Research Programme (WCRP) was initiated in 1988 and has coordinated the activities of the Continental Scale Experiments (CSEs), which are now known as Regional Hydroclimate Projects (RHPs) and other land surface research through the GEWEX Hydrometeorology Panel (GHP). The GHP was established in 1995 to contribute to the WCRP objective of “developing the

fundamental scientific understanding of the physical climate system and climate processes needed to determine to what extent climate can be predicted and the extent of man’s influence on climate”. More specifically, the GHP contributed to the GEWEX objectives such as “determining the hydrological cycle and energy fluxes, modelling the global hydrological cycle and its impact, developing a capability to predict variations in global and regional hydrological processes and fostering the development of observing techniques, data management and assimilation systems”. GHP activities included diagnosis, simulation and prediction of regional water balances by various process and modelling studies aimed at understanding and predicting the variability of the global water cycle, with an emphasis on regional coupled land–atmosphere processes. GHP efforts were central to providing a scientific basis for assessing critical science issues such as the consequences of climate change for the intensification of the global hydrological cycle and its potential impacts on regional water resources. This paper discusses the more relevant scientific issues relating to hydrology addressed by the GHP in collaboration with the international science community, in particular the IAHS Predictions in Ungauged Basins (PUB) initiative. GHP activities have now been formally merged with the Coordinated Enhanced Observation Period (CEOP) I and II activities to form a new body, called the Coordinated Energy and water-cycle Observations Project (CEOP), which will continue to foster large-scale hydroclimate research. Within GHP and now within CEOP the Water Resources Applications Project (WRAP) was established in 2000 to facilitate the broader use of GEWEX products in water resource applications and initially promoted dialogue between the GEWEX community and the water resources community. With members from each of the RHPs, IAHS, UNESCO programmes, and the World Meteorological Organization (WMO), this group provided a wide range of expertise related to water management. WRAP relied on the development of physically-based hydrology and “application” or decision support models, and the coupling of these models with regional climate models. Application studies require a capability to downscale large area (model grid square) precipitation forecasts and observed averages, statistical analyses of the relationships between SST anomalies and seasonal streamflow, and analysis of the value and utility of seasonal forecasts in water management decisions. WRAP is now evolving toward a Hydrologic Applications Project (HAP) which was defined in October 2006 together with the “Roadmap” for the remainder of the Second Phase of GEWEX (2006–2012). This paper summarises the achievements to date of GEWEX pertaining to hydrology and gives an indication of the planned hydrological focus of the project over the next five years. It also discusses the more relevant scientific issues relating to IAHS hydrological issues to be addressed by CEOP, including the IAHS Predictions in Ungauged Basins (PUB) initiative.


Hydrological predictability investigation of global data sets for high-latitude river basins

YEUGENIY M. GUSEV, OLGA N. NASONOVA, LARISA Y. DZHOGAN & YEUGENIY E. KOVALEVInstitute of Water Problems, Russian Academy of Sciences, Gubkina St.3, 119991 Moscow, [email protected]

Abstract The aim of the work is to investigate the hydrological predictability of global data sets for the northern river basins using the land surface model SWAP. The input data were taken from the global 1-degree data sets provided within the framework of the Second Global Soil Wetness Project. Application of the global data sets (without calibration of model parameters) for the simulation of the Mezen River runoff, a high-latitude basin, has shown poor results. To improve the results, a stochastic optimization technique was developed for model calibration using the

streamflow observations for the period 1986–1990. The period 1991–1995 was used for model validation. The Nash-Sutcliffe efficiency of daily streamflow simulation was 0.80 and 0.82 for the calibration and validation periods, respectively. Analysis of the results showed that global 1-degree data sets may be applied with appropriate calibration for streamflow simulation and prediction in high latitudes. Key words river runoff; land surface model SWAP; Mezen River; parameter optimization


Integrated atmospheric and hydrologic modelling for short-term and basin-scale forecasts in a tropical semi-arid context

ANA CLÁUDIA BRAGA1, CARLOS O. GALVÃO1, ENIO P. SOUZA2, ENILSON P. CAVALCANTI2, RENATO FERNANDES1 & KLÉCIA OLIVEIRA1

1 Department of Civil Engineering, Federal University of Campina Grande, Caixa Postal 505, 58100-900, Campina Grande, PB, Brazil [email protected]

2 Department of Atmospheric Sciences, Federal University of Campina Grande, Campina Grande, PB, Brazil

Abstract Precipitation in the Brazilian northeastern semi-arid region is characterized by a convective rainfall regime. Recently, numerical weather prediction models have been run with resolutions fine enough to potentially improve the quality of the simulated rainfall patterns within hydrographic basins and therefore contribute to more reliable flow forecasts. This paper presents a unidirectional coupling between BRAMS – Brazilian Regional Atmospheric Modelling System – and a lumped hydrological model in order to analyse the impact of precipitation uncertainties on runoff forecasts. The results show that forecasts produced one day in advance lead to reliable hydrographs, but for two to five days in advance, precipitation uncertainties impact the runoff forecast skill.Key words runoff forecast; hydrological model; atmospheric model; semi-arid; uncertainty


Regional climate change in the Middle East and impact on hydrology in the Upper Jordan catchment

HARALD KUNSTMANN1, PETER SUPPAN1, ANDREAS HECKL1 & ALON RIMMER2

1 Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlsruhe, Kreuzeckbahnstraße 19, D-82467 Garmisch-Partenkirchen, Germany [email protected]

2 Kinneret Limnological Laboratory, Israel Oceanographic & Limnological Research Ltd, PO Box 447, Migdal 14950, Israel

Abstract The impact of climate change on water availability in the Middle East and the Upper Jordan catchment (UJC) is investigated by dynamic downscaling of ECHAM4 time slices and subsequent hydrological modelling. Two time slices (1961–1990 and 2070–2099) of the global climate scenario B2 of ECHAM4 were dynamically downscaled with the meteorological model MM5 in two nesting steps of 54 km and 18 km resolution. The meteorological fields were used to drive a physically-based hydrological model, computing in detail the surface and subsurface

water flow and water balance of the UJC. The results of the joint regional climate-hydrology simulations indicate mean annual temperature increases of up to 4.5°C, and 25% decreases in mean annual precipitation in the mountainous part of the UJC. Total runoff at the outlet of the catchment is predicted to decrease by 23%, and is accompanied by a significant decrease of groundwater recharge. Key words climate change; dynamic downscaling; Middle East; hydrological modelling; water availability


Modelling regional climate change and the impact on surface and sub-surface hydrology in the Volta Basin (West Africa)

G. JUNG1,2 & H. KUNSTMANN1

1 Institute for Meteorology and Climate Research (IMK-IFU), Forschungszentrum Karlsruhe, Germany2 now at: Institute for Atmospheric Pollution (IIA-CNR), Arcavacata di Rende, Italy

[email protected]

Abstract In order to estimate the effect of an anthropogenic influence on the water balance in the Volta Basin, located in West Africa, joint regional climate–hydrology simulations were performed using the mesoscale meteorological model MM5 and the hydrological model WaSiM. The regional climate simulations show a decrease in rainfall at the beginning of the rainy season, an increase at the height of the rainy season and a clear increase in temperature. A mean delay in the onset of the rainy season accompanied with an increase in inter-annual variability of precipitation in the early stage of the rainy season was delineated. Due to the increase in potential evaporation, following the increase in temperature, most of the surplus rainfall evaporates. The highest sensitivity of the hydrological model to changing meteorological input conditions is found for direct runoff. Changes in the components of the hydrological cycle only seldom exceed the simulated present-day inter-annual variability. Key words joint modelling; climate–hydrology modelling; West Africa; Volta Basin


Monthly streamflow forecasts for the State of Ceará, Brazil

DIRCEU S. REIS Jr, EDUARDO S. MARTINS, LUIZ SÉRGIO V. NASCIMENTO, ALEXANDRE A. COSTA & ALAN M. B. ALEXANDREFundação Cearense de Meteorologia e Recursos Hídricos, Av. Rui Barbosa, 1246, Aldeota, 60115-221 Fortaleza, CE, [email protected]

Abstract This paper presents the methodologies employed to generate monthly and seasonal streamflow forecasts for the State of Ceará, Brazil. The procedure uses a variety of linked models. Seasonal climate forecasts provided by the General Circulation Model ECHAM 4.5 are used to feed two regional atmospheric models, the Regional Spectral Model (RSM) and the Regional Atmospheric Modeling System (RAMS), that provide monthly and seasonal regional precipitation forecasts for the State. These precipitation forecasts are then interpolated into a finer

resolution grid to estimate the monthly-averaged basin precipitation. A bias-correction procedure is applied to these forecasts so they can be used by a conceptual lumped hydrological model to generate monthly and seasonal streamflow forecasts for several sites within the State. The paper also discusses the bias-correction procedures applied for the precipitation forecast, the estimation of the hydrological model parameters for ungauged sites, and the use of ensemble forecasts.Key words climate forecast; seasonal forecast; streamflow forecast; ensemble forecast


Coupling meteorological and hydrological models for medium-range streamflow forecasts in the Parana Basin

WALTER COLLISCHONN, DANIEL ALLASIA, CARLOS E. M. TUCCI & ADRIANO R. PAZ Instituto de Pesquisas Hidráulicas – Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil [email protected]

Abstract Forecasts of inflow into major reservoirs of the Brazilian hydroelectric power system are essential to the operation planning of this system. Medium range forecasts of the order of a few days to two weeks were usually obtained by simple ARMA models, which do not include information of observed or forecast precipitation. We present results obtained by using a methodology based on one-way coupling of the ETA regional atmospheric model run by the Brazilian Center for Weather Prediction with a large-scale hydrological model in three sub-basins of the Paraná River basin. Results were compared to the currently used ARMA model, showing that reductions in errors of inflow forecasts could be obtained. Comparison of results in different sub-basins suggests that the quality of rainfall forecasts depends on climate.Key words forecasting; hydrological modelling; real-time forecasting; Parana Basin


Experimental hydrometeorological and hydrological ensemble forecasts and their verification in the US National Weather Service

JULIE DEMARGNE1,2, LIMIN WU1,3, DONG-JUN SEO1,2 & JOHN SCHAAKE1,4 1 Office of Hydrologic Development, National Weather Service, National Oceanic and Atmospheric Administration,

1325 East-West Highway, Silver Spring, Maryland 20910, [email protected]

2 University Corporation for Atmospheric Research, PO Box 3000, Boulder, Colorado 80307, USA3 RS Information Systems, 1651 Old Meadow Road, McLean, Virginia 22102, USA4 Consultant, Annapolis, USA

Abstract An ensemble preprocessor is being developed by the Office of Hydrologic Development, NOAA/National Weather Service (NWS), USA, to produce reliable short-term hydrometeorological ensemble forecasts from single-value forecasts of precipitation and temperature. These hydrometeorological ensemble forecasts are then ingested into the NWS Ensemble Streamflow Prediction system to produce probabilistic hydrological forecasts that

reflect the hydrometeorological uncertainty. The preprocessor methodology attempts to remove biases in single-value forecasts, and capture the skill and uncertainty therein, while preserving the space–time statistical properties of the hydrometeorological variables. The ensemble preprocessor currently operates experimentally at four NOAA/NWS River Forecast Centers in the USA. The verification results presented in this paper show that the precipitation ensembles generated from the ensemble preprocessor produce highly reliable probability estimates and improve the streamflow ensemble forecast performance. Further work is needed to reduce and fully account for hydrological uncertainties in order to improve the quality of streamflow ensemble forecasts.Key words ensemble forecasting; probabilistic verification; uncertainty


Adjusting ensemble forecast probabilities to reflect several climate forecasts

JERY R. STEDINGER1 & YOUNG-OH KIM2

1 School of Civil and Environmental Engineering, Cornell University, Hollister Hall, Ithaca, New York 14853-3501, [email protected]

2 School of Civil, Urban, and Geosystem Engineering, Seoul National University, Seoul 151-742, Korea

Abstract An activity of growing importance is the use of forecast information to update meteorological and hydrological series and their associated probabilities so as to describe the distribution of future events of interest. A simple and flexible pdf-ratio method generates a consistent and smooth set of probabilities for climate series across the entire range of the key variable reflecting the change in the likelihood of each individual climate series. This paper addresses the use of the pdf-ratio method with several forecasts, which could represent different forecast periods, different variables, or different basins. Examples demonstrate that if separate and independent adjustments are adopted to capture the conditional probabilities of different variables, such as temperature, precipitation, or seasonal flow for different forecast periods or different basins, then the resulting joint distribution of such variables can be grossly distorted.Key words ensemble forecasting; forecast information; multivariate forecast; pdf-ratio method; probability adjustment; scenarios


“Outlier” catchments: what can we learn from them in terms of prediction uncertainty in rainfall–runoff modelling?

NICOLAS LE MOINE, VAZKEN ANDRÉASSIAN, CHARLES PERRIN & CLAUDE MICHELCemagref, Hydrology and Water Quality Research Unit, PB 44, F-92163 Antony cedex, France [email protected]

Abstract What exactly are the catchments that we usually exclude from our data sets before submitting a paper to a conference, on the grounds that our models fail to represent their behaviour? What can be the consequences of the commonly-used (but rarely discussed) data set cleansing practices: does it help us improve our models? Does it contribute to making our

hydrological simulations less uncertain? Or does it just give us a false sense of confidence in our capacity to represent catchment hydrological behaviour? This paper focuses specifically on the “outlier” catchments found in a large set of 1045 French catchments. This large catchment set allows statistical quantification of the likely sources of model failure; it shows regional clustering (linked with the geology), the surprising effect of catchment area (the largest basins get the best performances) and last, that noise in input data is in no way sufficient to explain the difficulties of five rainfall–runoff models in representing catchment behaviour.Key words rainfall–runoff modelling; outliers; GR4J


Understanding sources of uncertainty in flash-flood forecasting for semi-arid regions

THORSTEN WAGENER1, HOSHIN GUPTA2, SONI YATHEENDRADAS2, DAVID GOODRICH3, CARL UNKRICH3 & MIKE SCHAFFNER4

1 Department of Civil and Environmental Engineering, The Pennsylvania State University, Sackett Building, Pennsylvania 16802, USA [email protected]

2 Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona, USA3 USDA-ARS-SWRC, 2000 E. Allen Rd, Tucson, Arizona 85719, USA

4 National Weather Service, Tucson Weather Forecast Office, 520 North Park Ave, Suite 304, Tucson, Arizona 85719, USA

Abstract About one-third of the Earth’s land surface is located in arid or semi-arid regions, often in areas suffering severely from the negative impacts of desertification and population pressure. Reliable hydrological forecasts across spatial and temporal scales are crucial in order to achieve water security – protection from excess and lack of water – for people and ecosystems in these areas. At short temporal scales, flash floods are extremely dangerous hazards accounting, for example, for more than 80% of all flood-related deaths in the USA. Forecasting of these floods requires a connected spatially-distributed hydro-meteorological modelling system which accounts for the specific meteorological and hydrological characteristics of semi-arid watersheds, e.g. summertime convective rainfall and channel transmission losses. The spatially highly heterogeneous nature of the precipitation and the non-linear response behaviour of the system demand the explicit accounting and propagation of uncertainties into the model predictions. This short paper presents the results of a multi-year study in which such a system was developed for flash-flood forecasting in the semi-arid southwestern USA. In particular, we discuss our effort to understand and estimate underlying uncertainties in such a modelling system. To achieve this we use the GLUE approach to uncertainty analysis, in combination with a variance-based global sensitivity analysis technique. In general, the level of uncertainty found was very high and largely dominated by uncertainty in the radar rainfall estimates. Regarding the model parameters, uncertainties in the hillslope model parameter values had a greater impact on the predictions than the uncertainties in the channel parameters, at least for relatively small basins.Key words flash floods; semi-arid; uncertainty; sensitivity; GLUE; Sobol’s method


Long-term probabilistic forecasting of snowmelt flood characteristics and the forecast uncertainty

LEV KUCHMENT & ALEXANDER GELFANWater Problems Institute of Russian Academy of Sciences, 119991 Gubkin 3, Moscow, [email protected]

Abstract A methodology for long-term (i.e. with a lead time of 2–3 months) probabilistic forecasting of snowmelt flood volumes and peak discharges has been developed. The methodology is based on the use of a physically-based model of runoff generation combined with a weather generator. The distributed physically-based model includes description of snow accumulation and melt, soil freezing and redistribution of soil moisture during autumn and winter, and processes of runoff generation after the beginning of the spring snowmelt period. The weather generator consists of stochastic models of daily temperature and precipitation. The physically-based model has been applied to estimate missing initial river basin conditions before forecasting (usually, the soil moisture and depth of frozen soil; sometimes, the snow water equivalent), and the runoff hydrographs during the lead-time period using meteorological data for the autumn–winter period. The weather generator with Monte Carlo simulations, or selected weather scenarios, have been used to provide opportunities of assessing the meteorological inputs for lead-time periods and to estimate the probability distributions of the forecast runoff volumes and peak discharges. The results of forecasting of flood volumes have been compared with the results obtained on the basis of using the averaged meteorological conditions for lead-time periods and regression relationships between spring runoff volume and the initial indexes of river basin conditions before forecasting (the present day procedure for long-term flood forecasting). The case study was carried out for the Sosna River basin (catchment area 16 400 km2) and for the Seim River basin (7500 km2). Key words long-term forecast; physically-based model; snowmelt flood; weather generator


Predictive uncertainty in climate change impacts on floods

MARTIJN J. BOOIJ, MARTIJN HUISJES & ARJEN Y. HOEKSTRAWater Engineering and Management, Faculty of Engineering Technology, University of Twente, PO Box 217, 7500 AE Enschede, The [email protected]

Abstract It is crucial for flood management that information about the impacts of climate change on floods and the predictive uncertainties therein becomes available. This has been achieved by using information from different Regional Climate Models for different emission scenarios to assess the uncertainty in climate change for the Meuse River in northwestern Europe. A hydrological model has been used to simulate flows for current and changed climate conditions. The uncertainty in the hydrological model is assumed to be represented by the difference between observed and simulated discharge and incorporated in the uncertainty analysis through the model parameters. Climate change results in an increase of the 100-year flood of about 30%. This increase is primarily caused by an increase of precipitation in winter. The predictive uncertainty in this impact is about 20% resulting from uncertainties in climate change (about 50%) and uncertainties in hydrological model parameters (about 50%). Key words climate change; floods; fuzzy objective function; HBV model; Meuse basin; Monte Carlo analysis; Regional Climate Model; uncertainty


AMMA forcing data for a better understanding of the West African monsoon surface–atmosphere–hydrology interactions

A. BOONE1 & P. DEROSNAY2

1 GAME/CNRM, Météo-France, CNRS, 42 avenue G. Coriolis, F-31057 Toulouse, [email protected]

2 CESBIO, CNRS; 18 avenue Edouard Belin, F-31401 Toulouse cedex 9, France

Abstract West Africa has been subject to extreme climatic variability over the last half century which has lead to dramatic socio-economic consequences for the people and the relatively agrarian-dominated economies of this region. Seasonal to inter-annual prediction of the West African monsoon (WAM) has proven difficult due to both the paucity of observations at sufficient space–time resolutions, and because of the complex interactions of processes between the biosphere, atmosphere and hydrosphere. One of the main goals of the AMMA (African Monsoon Multidisciplinary Analysis) project is to improve the understanding and prediction of the WAM in order to ameliorate sustainable water management and related activities. Land–atmosphere coupling is theorized to be significant in this region, thus improvement of the modelling of the related processes is critical. To this end, a multi-scale land-surface model atmospheric and land-surface parameter forcing database is being constructed. One of the main uses of this database is to drive a host of land surface, vegetation and hydrological models over a range of spatial scales in order to gain better insights into the attendant processes.Key words AMMA; hydrology; land surface models; forcing database; West Africa; monsoon

Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007.242-248

Improved scenario prediction by using coupled hydrological and atmospheric models

JESPER OVERGAARD1, MICHAEL B. BUTTS1 & DAN ROSBJERG2

1 DHI Water – Environment – Health, Agern Allé 5, DK-2970 Hørsholm, [email protected]

2 Institute of Environment & Resources, Technical University of Denmark, Building 115, Bygningstorvet, DK-2800 Kongens Lyngby, Denmark

Abstract The hydrological model MIKE SHE, provided with a new energy-based land-surface module, has been dynamically coupled to the non-hydrostatic model ARPS. Using data from the FIFE experiment, both the land-surface module and a 1-D coupling of the models have been validated. Subsequently, a hypothetical scenario, where grassland is turned into agriculture, was investigated by running the model system in both uncoupled and coupled mode. It was found that the uncoupled system significantly over-predicts the change in evapotranspiration caused by the land-use change in comparison to the coupled model results, which emphasizes the importance of taking feedback effects at the land surface into account.Key words land-surface modelling; coupled models; land–atmosphere feedback


Representativeness of point soil moisture observations, upscaling and assimilation

GABRIËLLE J. M. DE LANNOY1, VALENTIJN R. N. PAUWELS1, PAUL R. HOUSER2, TIMOTHY GISH3 & NIKO E. C. VERHOEST1

1 Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, B-9000 Ghent, Belgium [email protected]

2 George Mason University & Center for Research on Environment and Water, 4041 Powder Mill Road, Suite 302, Calverton, Maryland 20705-3106, USA

3 Hydrology and Remote Sensing Laboratory, USDA-ARS, BARC-West Beltsville, Maryland 20705-2350, USA

Abstract To estimate the temporal evolution of the spatial mean soil moisture in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) field, the relationship between point measurements and the average behaviour of field-scale soil moisture has been investigated. In a simple variational assimilation experiment with the Community Land Model (CLM2.0), it has been shown that the soil moisture information from a representative site was much more appropriate for estimating the spatial mean soil moisture profile than the information from other sites. The best results for the re-analysis as well as for the prediction of the spatial mean soil moisture were obtained through the assimilation of observations from probes with time-mean differences between their recorded point values and the spatial mean values close to zero. Further improved results can be obtained by upscaling the point data, e.g. after matching the point observations cumulative density function to that of the spatial mean soil moisture.Key words soil moisture; stability; assimilation; scaling


Can a land surface model simulate runoff with the same accuracy as a hydrological model?

OLGA N. NASONOVA & YEUGENIY M. GUSEVInsitute of Water Problems, Russian Academy of Sciences, Gubkina St 3, 119991 Moscow, [email protected]

Abstract The ability of the land surface model SWAP (Soil Water–Atmosphere–Plants) to reproduce runoff from the 12 MOPEX (Model Parameter Estimation Experiment) experimental river basins compared to the hydrological Sacramento model (SAC-SMA) was investigated. In previous investigations, the SAC-SMA model (calibrated with 16 model parameters using both manual and automatic calibration techniques) demonstrated much better performance than the SWAP model. In the present study, the behaviour of SWAP was, however, substantially improved by means of advanced model calibration. For the 12 MOPEX basins, the median values of model efficiency and bias for simulated daily streamflow during the calibration period (1960–1979) were found to be 70% and 2.6% respectively for the SAC-SMA model, and 68% and 0.9% respectively for the SWAP model, During the validation period (1980–1998), model efficiency

and bias were 65% and 6.3% for SAC-SMA model, and 64% and 3.9% for the SWAP model. It was found that model performance depends greatly on the skill of calibration, and that the LSM SWAP under appropriate calibration can simulate runoff with accuracy comparable to that of the hydrological model.Key words hydrological Sacramento model; land surface model SWAP; MOPEX river basins; parameter calibration; river runoff


Water balance evaluation in Denmark using remote sensing-driven land surface modelling and spatially distributed hydrological modelling

EVA BOEGH1, BRITT S. B. CHRISTENSEN2 & LARS TROLDBORG2 1 Department of Environmental, Social and Spatial Change, Roskilde University, PO Box 260, D-4000 Roskilde,

[email protected]

2 Hydrology Department, Geological Survey of Denmark and Greenland, Oester voldgade 10, D-1350 Copenhagen K, Denmark

Abstract This study evaluates the water balance of the island of Sjælland (7330 km2) in Denmark using two different types of physically-based model approaches. The DaisyGIS model is an advanced 1-D land surface model which is parameterized at the plot-scale and upscaled using remote sensing and GIS (Geographical Information System) data to represent land cover and soil characteristics. In comparison, the DK-model constitutes a 3-D integrated hydrological model setup (based on the MIKE SHE code) which uses a comprehensive geological data set and is calibrated to obtain global model parameters representing all catchments at Sjælland. A good agreement was found between annual net precipitation (precipitation minus evapotranspiration) estimated by the two model systems, but seasonal differences in evapotranspiration occurred which could be related to the use of the remote sensing for evapotranspiration calculation in Daisy or caused by dissimilar soil properties. Despite these differences, the DK-model simulated streamflows efficiently for both agricultural and forest catchments. In contrast, Daisy simulations suggest that a more advanced distributed land surface parameterization can contribute to improving water balance simulations of urbanized surface-water dominated catchments. The integrated annual water balance of Sjælland was found to be nearly in balance but large spatial variations occurred among the 30 studied catchments. Overall, the results indicate a need for further studies on model sensitivity and uncertainties related to net precipitation quantification in spatially distributed hydrological modelling.Key words water balance modelling; large-scale; land surface model; 3-D hydrological model; Denmark


Comparing model performance of the HBV and VIC models in the Rhine basin

ALINE TE LINDE1,2, RUUD HURKMANS3, JEROEN AERTS1 & HAN DOLMAN1

1 Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1087, 1081 HV Amsterdam, The [email protected]

2 WL | Delft Hydraulics, Rotterdamseweg 185, 2629 HD Delft, The Netherlands3 Hydrology and Quantitative Water Management, Wageningen University, PO Box 47, 6700 AA Wageningen, The

Netherlands

Abstract The general idea exists among hydrologists that physically-based distributed modelling better represents observed discharges as compared to lumped model approaches. In this paper, the hydrological models HBV and VIC were compared for the Rhine basin by testing their performance for simulating discharge. Overall, the semi-distributed lumped conceptual HBV model performed much better than the distributed physically-based VIC model. It is argued here that, even for a well documented river basin, such as the Rhine, the available approaches are still far from providing a satisfactory representation of the rainfall–runoff transformation and that more complex modelling does not always lead to better results. Moreover, it is concluded that deviations between observed and simulated discharge in many cases seem not to result from a structural problem in model definition, but from errors or deviations in forcing data.Key words hydrological modelling; HBV model; VIC model; model performance; River Rhine


Analysis of water resource variability over the irrigated area along the downstream reach of the Yellow River

HUIMIN LEI1, DAWEN YANG1, XINBING LIU2 & SHINJINO KANAE3

1 State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, Chinalhm @ mails.tsinghua.edu.cn

2 Management Office of the Weishan Irrigation District, Liaocheng 252000, China3 Institute of Industrial Science, University of Tokyo, Tokyo, Japan

Abstract Long-term changes in climate, river discharge, irrigation water and water-use efficiency were examined during a case study in the Weishan Irrigation District located to the northern side of the lower Yellow River. The relationship between actual evaporation and potential evaporation was examined on annual–regional scales. It was found that the annual precipitation fluctuated dramatically especially during the 1990s–2000s. High interannual water resource variability aggravates the water shortage, and irrigation from the Yellow River is required in order to secure agricultural production. Even though the water-use efficiency increased during the recent decade, there is a high potential for further water-saving by optimizing the irrigation schedule and irrigation methodology. In additional, a significant negative correlation between the actual and potential evaporation in the natural catchment, was found to be much weaker in the irrigated area which is located in the same region. This implies the local climate changed to some extent due to irrigation.Key words irrigation; water-use efficiency; trend analysis; evapotranspiration; Yellow River


Water consumption of Populus euphratica woodlands in an

mailto:[email protected]

arid region of China

YONGHUA ZHU, LILIANG REN & HAISHEN LÜ State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, College of Water Resources and Environment, Hohai University, Nanjing 210098, China [email protected]

Abstract The Ejina basin is a sub-watershed in the lower reaches of the Heihe River basin, which is situated in an exceedingly arid region of China. In recent years, the reduction of flow into the Ejina basin has endangered its ecological and environmental quality and the social development. P. euphratica is the predominant tree species forming natural woodlands in the Ejina basin, and changes of the P. euphratica woodlands symbolize changes in the Ejina basin’s ecology. Understanding the quantity and pattern of water consumption of P. euphratica woodlands in the Ejina basin will provide a scientific basis for maintaining its present ecological environment and can help prevent deterioration. In this paper, ecological water consumption of P. euphratica is determined using an improved Penman-Montieth method, soil moisture content and effective root distribution density, and the pattern of water consumption of P. euphratica is analysed. The minimum ecological water requirement of P. euphratica woodland is found to be 1.45 × 108 m3

year-1.Key words arid desert region; Ejina basin; P. euphratica woodland; water consumption


Testing similarity indices to reduce predictive uncertainty in ungauged basins

LUDOVIC OUDIN1, VAZKEN ANDRÉASSIAN2, CLAUDIA ROJAS-SERNA3, NICOLAS LE MOINE2 & CLAUDE MICHEL2

1 Université Pierre-et-Marie Curie, UMR SISYPHE, Case 105, 4 place Jussieu, F-75252 Paris cedex 05, [email protected]

2 Cemagref, Hydrology and Water Quality Research Unit, PB 44, F-92163 Antony cedex, France3 Instituto Mexicano de Tecnología del Agua, Paseo Cuauhnáhuac 8532, Jiutepec, Morelos, Mexico

Abstract In ungauged watersheds, the common approach for estimating the parameter values of lumped rainfall–runoff models consists of a two-step approach. First, relationships between watershed characteristics and parameter values are established on gauged sites, and second, these relationships are used to estimate parameter values on ungauged sites. However, several studies suggested that there are strong limitations to this approach and that consideration for similarity and/or proximity offered a better outlook. We propose here an original approach based both on similarity considerations and multi-model methodology. First, gauged watersheds are clustered into 27 classes depending on the values of three characteristics (either physical or hydro-climatic). Then, for each ungauged watershed, we used the calibrated parameters of similar gauged watersheds, i.e. from the same class. Then, a combination of the simulations obtained with the different sets of parameters was performed. The methodology is based on the GR4J rainfall–runoff applied on more than 1000 basins located in France. Results show that the physical similarity approach performs slightly better than the regression-based approach. Refinements of these two approaches, such as regional calibration of regressions or multi-model


considerations for regionalization based on physical similarity, do not yield significant improvements.Key words rainfall–runoff modelling; regionalization; ungauged catchments


The use of physical basin properties and runoff generation concepts as an aid to parameter quantification in conceptual type rainfall–runoff models

DENIS HUGHES & EVISON KAPANGAZIWIRIInstitute for Water Research, Rhodes University, Grahamstown 6140, South [email protected]

Abstract A physically-based approach to estimating the soil moisture accounting and runoff parameters of a conceptual, monthly time-step rainfall–runoff model is proposed. The approach is based on a conceptual interpretation of the model parameters taking into account the spatial and temporal scales used in typical model applications. The results of applying the approach to five test basins in South Africa suggest that while very different parameter sets are obtained compared to existing parameter regionalization methods, the simulated flows are similar. While current estimates of the physical basin properties are somewhat subjective, the estimation approaches have the potential to make use of remotely sensed soil and soil moisture information that is likely to become more readily accessible in the future. Key words hydrological models; parameter estimation


Regionalization of parameters of hydrological models: inclusion of model parameter uncertainty

SATISH BASTOLA, HIROSHI ISHIDAIRA & KUNIYOSHI TAKEUCHI Department of Civil & Environmental Engineering, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi, 400-8511, Japan [email protected] Abstract Regionalization of hydrological model parameters is a simple approach to model ungauged basins, but the uncertainties in model parameters and regionalization schemes hinder such an approach. To address the effect of model parameter uncertainties on regional ization, the vectors of model parameters generated from the posterior distribution of the parameters were regionalized and combined with the model parameters estimated from a non-parametric bootstrap method. In this study, 26 catchments from different regions in the world have been used. The study reveals that the effect of uncertainities in model parameters are significant, and the effect of uncertainities in regionalization propagated through generalized regression schemes were higher compared to univariate and correlated regression based schemes. Finally, the proposed methodology was validated by comparing the ensemble of simulated flow resulting from regionalized vectors of model parameters with the one produced by the model parameters

generated from the posterior distribution of parameters.Key words optimization; rainfall–runoff models; regional models; regionalization; uncertainty


Regionalization for uncertainty reduction in flows in ungauged basins

MARTIJN J. BOOIJ1, DAVE L. E. H. DECKERS1, TOM H. M. RIENTJES2 & MAARTEN S. KROL1

1 Water Engineering and Management, Faculty of Engineering Technology, University of Twente, PO Box 217, 7500 AE Enschede, The [email protected]

2 International Institute for Geo-Information Science and Earth Observation (ITC), PO Box 6, 7500 AA Enschede, The Netherlands

Abstract The objective of this study is to contribute to the reduction of predictive uncertainty in flows in ungauged basins through application of a regionalization method to 56 well-gauged basins in the United Kingdom. The classical approach of regionalization is adopted, where regression relationships between calibrated hydrological model (HBV) parameters and physical characteristics of the basin are established and used to estimate parameters for ungauged basins. The calibration resulted in optimum parameter sets for 48 basins of which 17 were used for the regression analysis. The results of the regression analysis showed statistically significant relationships for six out of seven parameters, and hydrologically sensible relationships for only three parameters. The validation for eight basins revealed that the regionalization model does not perform satisfactorily. The use of default parameter values seems to favour the use of regionalized parameter values. Therefore, the applicability of the classical approach of regionalization for HBV aiming at simulating all aspects of the hydrograph is questioned.Key words calibration; HBV model; Monte Carlo analysis; multiple objective function; regionalization; uncertainty; United Kingdom; validation


Assessment of the watershed yield of the Sakarya River basin, Turkey

SABAHATTIN ISIK1,2 & VIJAY P. SINGH2

1 Department of Civil Engineering, Sakarya University, 54187 Sakarya, [email protected]

2 Biological and Agriculture Engineering, Texas A&M University, College Station, Texas 77843, USA

Abstract The purpose of this study is to classify watershed yields into homogeneous regions and identify the regional membership of watersheds. Monthly river yields of 118 gauging stations in the Sakarya River basin, which is located in northwestern Turkey, were classified by cluster analysis on the basis of hydrological homogeneity. An agglomerative hierarchical clustering

algorithm is used so that stations from different geographical locations are considered in the same cluster independently of their geographical location. The study uses Ward’s minimum variance linkage method together with the Euclidean distance similarity measures in order to determine the number of homogeneous regions. The Sakarya River basin is clustered into three homogeneous regions and the yield distribution map of the basin is obtained. Out of 118 stations used in this study, 74 gauging stations were grouped into cluster 1, 32 into cluster 2, and 12 into cluster 3. The average watershed yields for each cluster are 0.001966, 0.007336 and 0.01826, respectively, and the average watershed yield of all the basins is 0.005066. Correlation between modelled specific yield and observed yield from gauging stations within each cluster varied from 0.77 to 0.99.Key words watershed yields; classification; cluster analysis; Sakarya River

Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007, 346-356.

AMMAR JARRAR1, NIRANJALI JAYASURIYA1, ANAN JAYYOUSI2 & MAAZUZA OTHMAN1

1 School of Civil and Chemical Engineering, RMIT University, GPO Box 2476, Melbourne,3001 Victoria, Australia [email protected]

2 Civil Engineering Department, An-Najah National University, PO Box 7 Nablus, Palestine

Abstract Water scarcity and low per capita water allocation are the major characteristics of arid and semi-arid regions. This paper provides a methodology to determine runoff from ungauged catchments. The basic approach involves the utilization of a simple event-based Geomorphological Instantaneous Unit Hydrograph model (GIUH) that is capable of determining the hydrograph based on rainfall data and on catchment geomorphological characteristics obtained from GIS tools. A GIUH model has been developed for the Badan and Faria subcatchments within the Faria catchment in Palestine, and successfully applied and validated against observed flow data. The model estimated that peak discharge increased as the overland flow roughness coefficient decreased, which reflects the surface roughness conditions in the catchment. However, when compared with the overland flow roughness coefficient, the channel flow roughness coefficient had a smaller effect on simulated peak flow. Results of the sensitivity test indicated that changing the value of each of the geomorphological parameters resulted in a change in the peak discharge, with changes to subcatchment area having the largest impact. However, with the GIS maps, the geomorphic properties of catchments could be measured accurately minimizing the error in estimating runoff.Key words Geomorphological Instantaneous Unit Hydrograph (GIUH) rainfall–runoff model; ungauged


Prediction of rainfall–runoff model parameters in ungauged catchments

M. ZVOLENSKÝ1, K. HLAVČOVÁ2, S. KOHNOVÁ2 & J. SZOLGAY2 1 Slovak Hydrometeorological Institute, Jeséniová 17, 833 15 Bratislava, Slovakia2 Department of Land and Water Resources Management, Slovak University of Technology, Radlinského 11, 813 68

Bratislava, [email protected]

Abstract The paper presents a modification of a rainfall–runoff parameter regionalization procedure, which can be used for model parameter estimation in ungauged basins. The upper Hron River basin in Slovakia was selected as a study area. Data from 19 sub-catchments were collected and a lumped conceptual rainfall–runoff model was calibrated in each of them. The model accounts for snow accumulation, soil moisture and groundwater balance, and runoff generation in a daily time step and has 15 parameters to calibrate. The catchments were pooled into homogeneous groups with respect to selected physiographic catchment characteristics using non-hierarchic clustering. Multiple regression relationships between rainfall–runoff model parameters and physiographic catchment characteristics were sought separately within the pooled catchments groups. The performance of these was compared with the performance of such relationships derived for the entire Hron catchment. The performance of model parameter predictions improved through considering groups of similar catchments as a basis for predictions.Key words model parameter regionalization; clustering; homogeneous regions


Multilevel river classification as the methodological basis for analysis of maximum runoff values in different geographical regions

ELENA ASABINAUgra State University, 16, Chehov Str., 628012 Khanty-Mansiysk, [email protected]

Abstract This paper presents a methodology for analysis of river runoff hydrometric and hydrographic data obtained in different geographical regions. The purpose of the analysis is the investigation and classification of the effect of different climates on maximum river runoff. The difficulties of analysis of river runoff at the global scale include the diversity in climatic conditions (continental, semi-arid, tropical, equatorial climates) and geographic environmental conditions (tundra, forests, steppes, savanna), as well as heterogeneity of regional environmental conditions (permafrost, bogs, karst, etc.). The methodology presented proposes river classification in terms of runoff types such as transitional, local, zonal and intrazonal runoff types. It is shown that only the rivers with zonal type of runoff can be used to estimate climatic effects on maximum runoff values.Key words multilevel river classification; climate; maximum river runoff; transitional runoff; local runoff; intrazonal runoff; zonal runoff


The Xinanjiang model from the perspective of PUB

LILIANG REN, FEI YUAN, ZHONGBO YU, XIAOLI YANG, RULIN OUYANG & XIANGHU LI

State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, 1 Xikang Road, Nanjing 210098, [email protected]

Abstract The progress of the Xinanjiang model, a semi-distributed conceptual hydrological model for use in humid or semi-humid regions, including model inputs, model structure and its parameters, is reported from the perspective of the PUB science and implementation plan. Based upon the knowledge that the use of an inadequate model structure may be more problematic than the use of sub-optimal parameter values, the focus of this paper is on the modification of the structure of the Xinanjiang model by considering the relations between sensitive model parameters and catchment characteristics. In particular, the focus is on the biological (mainly referring to vegetation) aspects in order to improve the estimation of evapotranspiration using the energy conservation principle. The upstream catchment of Hanzhong hydrological station in the Hanjiang River, the source region of the middle route of the greater South-to-North Water Transfer Project in China, was selected as the study area. The results shows that the modified version, called the Xinanjiang vegetation-hydrology model, provided similar streamflow simulation results in the calibration period (1980–1983) in terms of the Nash-Sutcliffe model efficiency coefficient, and gave slightly better streamflow simulation results in the validation period (1984–1986).Key words Xinanjiang model; model structure; leaf area index; digital elevation model; runoff generation; discharge hydrograph; predictions in ungauged basins


Integrating meteorological and uncertainty information in flood forecasting: the FLOODRELIEF project

MICHAEL B. BUTTS1, ANNE KATRINE V. FALK1, YUNQING XUAN2 & IAN D. CLUCKIE2

1 Department of Water Resources, DHI Water & Environment, Agern Alle 5, DK 2970 Hørsholm, Denmark [email protected]

2 Water & Environmental Management Research Centre, University of Bristol, Bristol BS8 1UP, UK

Abstract Flood forecasting specialists and operational water managers require ready access to a wide range of information, such as catchment status and meteorological forecasts, to make decisions during a flood. To have real value, however, decision-makers are now recognising that real-time flood management decisions must be based on an understanding of the uncertainties and associated risks. It is therefore critical for effective flood management to provide reliable estimates of the flood forecast uncertainty. To address these requirements the EU project FLOODRELIEF has focused on developing new ensemble-based methodologies for estimating and reducing forecast uncertainty and on the development of the FLOODRELIEF DSS, able to use ensemble methodologies to provide uncertainty information. This paper presents firstly, the development of a high-resolution mesoscale ensemble Quantitative Precipitation Forecast (QPF) system. An ensemble weather forecast is not only able to provide improvements in the forecast lead time and useful information about the nature of the weather system, but also estimates of the levels of confidence for the forecast. Investigations of the rainfall forecast uncertainty using this system and their impact on flood forecasts are presented for case studies in the UK. Secondly, to improve flood forecast accuracy using real-time data, as well as determining the propagation of uncertainty through the hydrological system, an ensemble Kalman filter methodology for data assimilation has been developed. An evaluation of the benefit of data assimilation in terms of forecasting accuracy is carried out and the performance of two different assimilation strategies is compared for case studies in the USA and New Zealand. Finally, a decision support system is

presented that provides flood management and forecast information in a flexible, efficient and easily understood manner to operational users and decision-makers. A general framework for ensemble forecasting has been developed to provide a flexible approach for estimating uncertainty within this system. In this manner a direct and intuitive estimate of forecast uncertainties, that can be communicated to flood managers and decision-makers, is achieved. Key words flood forecasting; precipitation forecasting; risk; uncertainty; data assimilation; downscaling; ensemble modelling; decision making


Reducing the uncertainty of flood forecasts using multi-objective optimization algorithms for parameter estimation

YAN WANG, CHRISTIAN GATTKE & ANDREAS SCHUMANNInstitute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr-University Bochum, D-44801 Bochum, [email protected]

Abstract The focus of this study was to characterize the extrapolation uncertainty resulting from different calibration strategies. A single-objective parameter estimation based on Monte Carlo simulations as well as a multi-objective optimization, are employed for the calibration. The extrapolation uncertainties that were obtained with these methods are evaluated with an extreme flood event. The results demonstrate that a unique parameter set, suitable for the entire hydrograph, does not exist. Utilization of a multi-objective optimization approach proved that the considerable uncertainty regarding model extrapolation originates from structural model inadequacies, which cause an inability of the model to reproduce all aspects of the hydrograph equally well with a single parameter set. It is suggested to use a multi-objective optimization strategy, which utilizes a problem-oriented definition of the performance measures to reduce the prediction uncertainty of peak flows. This method has been applied for flood modelling in Germany.Key words flood forecasts; multi-objective optimization; extrapolation uncertainty

Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007, ,407-416

Effects of soil moisture parameterization on a real-time flood forecasting system based on rainfall thresholds

GIOVANNI RAVAZZANI, MARCO MANCINI, ILARIA GIUDICI & PAOLO AMADIOPolitecnico di Milano, P. Leonardo da Vinci, 32, I-20133 Milan, [email protected]

Abstract The rainfall threshold is the cumulated rainfall depth required to cause flooding flow at the basin outlet. Thresholds are used in operational flood forecasting systems as a means to provide flood warnings based on the comparison with rainfall amounts (either observed or


forecast). This approach results in a simple system that can also be used by non expert technicians; it is a complementary tool to “classical” rainfall–runoff modelling systems. Despite the simple usage, a flood forecasting system based on thresholds requires great accuracy in definition of the critical rainfall. Special attention is required in modelling the basin moisture condition. The aim of this paper is to assess a reliability analysis of a framework for the definition of rainfall thresholds using the distributed hydrological model FEST. The AMC value (antecedent moisture condition) of the conventional SCS-CN method is employed to describe the soil moisture initial condition. The case study is the Arno River basin located in Italy. A detailed investigation of the most recent flood events shows that precise accounting of the watershed wetness based on analysis of actual soil moisture can improve the prediction accuracy of flood forecasting systems.Key words flood forecasting; rainfall threshold; antecedent moisture condition; reliability analysis

Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (Proceedings of Symposium HS2004 at IUGG2007, Perugia, July 2007). IAHS Publ. 313, 2007,417-424

Estimation of extreme flow quantiles and quantile uncertainty for ungauged catchments

DONALD H. BURN1, TAHA B. M. J. OUARDA2 & CHANG SHU2

1 Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, [email protected]

2 INRS-ETE, University of Québec, 490 rue de la Couronne, Québec City, Quebec G1K 9A9, Canada

Abstract Pooled frequency analysis is used to estimate extreme event quantiles at catchments where the data record is either short or not available. This can be accomplished by combining (pooling) information from hydrologically similar sites to increase the available information for estimating the required extreme event quantiles. This paper compares methods for estimating extreme event quantiles at ungauged catchments and determines the uncertainty associated with the estimated extreme event quantiles. The techniques are demonstrated and evaluated using data from a collection of catchments in the Canadian province of Ontario. A geographic nearest neighbour index flood-based approach resulted in the lowest mean squared error value.Key words frequency analysis; quantile estimation; site-focused pooling; uncertainty; ungauged sites


Peak flow estimation under parameter uncertainty in a real-time flood warning system for ungauged basins

DANIELA BIONDI & PASQUALE VERSACE Department of Soil Conservation “V. Marone”, University of Calabria, Ponte P. Bucci, I-87036 Arcavacata di Rende (CS), [email protected]

Abstract An operational flood forecasting system designed to produce peak flow predictions at multiple ungauged sites is described. The system is based on the ensemble approach and uses a

simple conceptual rainfall–runoff model. This paper accounts only for parameter uncertainty; to this end, parameter sampling distributions with Monte Carlo generated simulations are related to basin geomorphic characteristics and event antecedent moisture conditions. Application to four gauged sites exhibited satisfactory system performance in terms of reproducing observed peak flows. Further assessment evaluated the system’s forecasting skill with a back-analysis performed on 40 ungauged basins over the period 2002–2005. Results showed that the system yielded a reasonable number of warnings and that all the selected floods events, characterized by high documented social impacts, were successfully detected.Key words flood forecasting system; rainfall–runoff model; ungauged basin; parameter uncertainty; Monte Carlo simulation; southern Italy


Demonstrating Integrated Forecast and Reservoir Management (INFORM) for northern California in an operational environment

K. P. GEORGAKAKOS1, N. E. GRAHAM1, A. P. GEORGAKAKOS2 & H. YAO2 1 Hydrologic Research Center, 12780 High Bluff Drive, Suite 250, San Diego, California 92130, USA

[email protected] 2 Georgia Water Research Institute, Civil and Environmental Engineering, Georgia Tech, Atlanta, Georgia 30332, USA

Abstract We describe the principle components of a prototype integrated climate–weather–hydrology forecast and reservoir management system suitable for operational implementation, and the initial demonstration of such a system for improving operational forecasting and water resources management in northern California.Key words climate and weather forecasting; reservoir management; decision models; uncertainty; operational forecasting; operational management; INFORM


Coping with predictive uncertainties in optimization of sustainable water resources

CHIN MAN MOK1, NISAI WANAKULE2, ARMEN DER KIUREGHIAN3, STEVEN M. GORELICK4 & MIAO ZHANG1

1 Geomatrix Consultants, Inc., 2101 Webster St, 12th Floor, Oakland, California 94612, [email protected]

2 Tampa Bay Water, 2575 Enterprise Road, Clearwater, Florida 33763, USA3 University of California at Berkeley, 723 Davis Hall, Berkeley, California 94720, USA4 Stanford University, School of Earth Sciences Building 320, Room 118, Stanford,

California 94305, USA

Abstract This paper presents a reliability-based water resources management framework that utilizes stochastic optimization techniques to account for uncertainties associated with the prediction of water demand, surface water availability, baseline groundwater levels, a non-

anthropogenic reservoir water budget, and hydrological/hydrogeological properties. Except for the hydrogeological properties, these uncertainties are partially caused by uncertainties in prediction of future climate conditions. The framework was developed to manage a water supply system that serves about two million people in the Northern Tampa Bay region in Florida, USA, while protecting wetland ecology and preventing seawater intrusion. The supply sources include about 180 groundwater production wells, three streamflow withdrawals, a regional reservoir, and a desalination plant. The developed method maximizes the reliability of achieving the goals that all protected wetlands in the area are healthy by maintaining high groundwater levels. The framework involves: (1) a distribution system simulation model to represent the water supply operation under an Optimal Regional Operation Plan (OROP), (2) a Monte Carlo simulation model to generate realizations of climatic events, water demand, available surface water quantity, and (3) a unit response matrix (URM) that relates groundwater level response to groundwater extraction. An operator response model simulates how water supply operators adjust the optimized rates of groundwater extraction, surface water withdrawal, and reservoir inflow/outflow according to meeting the water demand in all circumstances. The reliability optimization problem is solved using a differential evolutionary algorithm.Key words water resources management; climate; uncertainty; water demand; groundwater; optimization; stochastic; socio-economic; reliability; wetland


RNN-based inflow forecasting applied to reservoir operation via implicit stochastic optimization

CAMILO ALLYSON SIMÕES DE FARIAS1, AKIHIRO KADOTA1, ALCIGEIMES B. CELESTE2 & KOICHI SUZUKI1

1 Dept of Civil and Environmental Engineering, Ehime University, 3 Bunkyo-Cho, Matsuyama, Ehime 790-8577, [email protected]

2 Dept of Civil Engineering, Federal Univ. of Campina Grande, Aprígio Veloso 882, Campina Grande 58109-970, PB, Brazil

Abstract A Recurrent Neural Network (RNN) is proposed for monthly reservoir inflow forecasting. In order to verify its performance and applicability, the network is used to assist reservoir operations carried out by Implicit Stochastic Optimization (ISO). The ISO approach defines the release at each month conditioned on the month’s initial storage and the forecasted inflow for the month. This inflow is determined by the RNN. For comparison, optimal ISO-based releases assuming the inflows as perfect forecasts are also conducted. The RNN estimates the current-period inflow as a function of the previous inflow and current forecasted rainfall. The excellent accuracy obtained by the RNN suggests that it is very effective for one-month-ahead forecasting of reservoir inflows. Furthermore, the optimal reservoir releases obtained by the ISO using the RNN-based forecasts were shown to be highly correlated with those using perfect forecasts and superior to those obtained by standard rules of operation.Key words inflow forecasting; recurrent neural networks; reservoir operation


Effect of uncertainties on the real-time operation of a lowland water system in The Netherlands

STEVEN WEIJS1, ELGARD VAN LEEUWEN1,2, PETER-JULES VAN OVERLOOP1 & NICK VAN DE GIESEN1

1 Water Management, Civil Engineering & Geosciences, Delft University of Technology, Stevinweg 1, 2628CN Delft, The [email protected]

2 WL|Delft Hydraulics, PO Box 177, 2600 MH Delft, The Netherlands

Abstract Due to the limited pumping capacity in lowland water systems, reduction of system failure requires anticipation of extreme precipitation events. This can be done by Model Predictive Control that optimizes an objective function over a certain time horizon, for which the system behaviour is calculated by a model and a prediction of the inputs to the system. The forecast inputs usually contain large uncertainties. Because the pump constraints make the optimization problem non-certainty equivalent, uncertainties need to be considered to adequately control the water system. In this paper, the way uncertainties influence the control decision is investigated. An information-control horizon and an information-prediction horizon are introduced as time-limits for the sensitivity to future input information and the value of predictions. These horizons need to be considered in the design of a controller. Multiple Model Predictive Control is suggested to deal with the uncertainties in a risk based way.Key words Boezem canals; decision support system; model predictive control; multiple model optimization; Netherlands; polder; prediction horizon; real time operation; risk; uncertainty


Data assimilation in a large-scale distributed hydrological model for medium-range flow forecasts

ADRIANO ROLIM DA PAZ, WALTER COLLISCHONN, CARLOS E. M. TUCCI, ROBIN T. CLARKE & DANIEL ALLASIAInstituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul (IPH/UFRGS), Av. Bento Gonçalves, 9500, CEP 91501-970, Porto Alegre (RS), Brazil [email protected]

Abstract As part of a research project aimed to improve medium-range streamflow forecasts used in the operational planning of Brazilian hydroelectric power systems, a large-scale distributed hydrological model has been used to obtain streamflow forecasts for up to 12 days in advance using observed and predicted precipitation data. Observed streamflow data up to the time of forecast start were used to update state variables calculated by the model. This data assimilation was performed by applying an empirical procedure. Several configurations of this empirical procedure have been tested and this paper presents results obtained in applying it to the Rio Grande basin, one of the test beds of the Hydrologic Ensemble Prediction Experiment (HEPEX), focusing on forecasts at Furnas sub-basin (51 900 km2). Results show that flow forecasts were not improved by updating state variables related to river flow, while significant improvements were obtained by updating state variables related to groundwater storage. Key words flow forecasting; data assimilation; distributed hydrological model


Predictive models of reservoir storage-yield-reliability functions: inter-comparison of regression and multi-layer perceptron artificial neural network paradigms

ADEBAYO ADELOYE School of The Built Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK [email protected]

Abstract In this study, functions for predicting the total (within-year plus over-year) reservoir capacity have been developed using: first classical multiple regression, and secondly artificial neural networks (ANNs). The basis of the models is the storage-yield-reliability (S-Y-R) analysis of 18 international rivers using the sequent-peak algorithm (SPA). The results showed that the regression model performed better than the ANN model. The relative superiority of the regression model was attributed to its use of the over-year capacity as an independent variable. In contrast, the ANNs use basic variables as inputs and thus offer more flexibility than the regression model, particularly at ungauged sites. Key words artificial neural networks; storage-yield-reliability; sequent peak algorithm; over-year capacity; within-year capacity; multiple regression


Analyse des périodes sèches pour la gestion d’un barrage au nord de la Tunisie

MATHLOUTHI MAJID & LEBDI FETHILaboratoire de Recherche en Sciences et Techniques de l’Eau, Institut National Agronomique de Tunisie (INAT), 43 av. Charles Nicolle, 1002 Tunis, [email protected]

Résumé Cette contribution porte sur l’emploi de l’analyse des périodes sèches pour la gestion des barrages réservoirs sur une base différente de celle des observations faites à intervalle de temps régulier. Le cas d’étude est le barrage Ghézala localisé au nord de la Tunisie à climat méditerranéen. Les évènements secs sont constitués d’une série de jours secs encadrés par des évènements pluvieux. Un événement pluvieux est une série ininterrompue de jours pluvieux comprenant au moins un jour ayant reçu une précipitation supérieure ou égale à un seuil de 4 mm. Les événements pluvieux sont définis par leurs durées et hauteurs qui ont été trouvées corrélées. Une analyse de la hauteur de pluie par événement conditionnée par la durée de l’événement a été effectuée. La loi binomiale négative apparaît la meilleure loi pour l’ajustement de la hauteur de pluie par événement de durée un jour. La durée de l’événement de pluie suit la loi géométrique alors que celle de l’événement sec suit la loi binomiale négative. La loi Gamma s’ajuste à la longueur de l’année hydrologique. Une procédure de simulation de lois de probabilité a été exécutée pour générer des séquences synthétiques d’événements pluvieux et secs avec les longueurs correspondantes de l’année hydrologique. Ces séquences permettent de définir et de calibrer des modèles de simulation pour la planification réaliste des réservoirs, l’estimation de la demande en eau d’irrigation et l’étude des effets d’un changement climatologique.Mots clefs événement pluvieux; gestion de barrages; période sèche

Analysis of dry periods for dam operation in northern TunisiaAbstract This contribution concentrates on a statistical analysis of dry and wet spells with the purpose of deriving operational rules for small dams for agricultural purposes in a Mediterranean climate with its annual wet and dry seasons. Central to this study is the Ghézala Dam in northern Tunisia. Thirty-four years of daily rainfall data are available. Dry events are characterized by a sequence of dry days separated by threshold-rainfall events. A threshold-rainfall event is defined as a sequence of days with rainfall containing at least one day with an amount of precipitation equal to or exceeding 4 mm. Rainfall events are defined by their total depth in mm, and by their duration in days, which were found to be correlated. A statistical analysis of the rainfall depth for given classes of rainfall event duration was performed. A negative binomial distribution appears to yield the best overall fit for the depth per event for one-day long events. The durations of rainfall events follow a geometric distribution. The durations of dry events also follow a negative binomial distribution. A Gamma distribution appears to fit the length of the hydrological year (wet plus dry seasons). A simulation procedure has been used to generate synthetic sequences of wet and dry events for Gamma distributed hydrological year lengths. These sequences enable definition and calibration strategies for realistic planning of small reservoirs, the estimation of irrigation water demand and the study of the effects of climatological change.Key words rainfall event; dam operation; dry spell


Hydrological simulation and prediction for environmental change

JIAN YUN ZHANG1, ZHIYU LIU2 & CHUANBAO ZHU2

1 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineer, Nanjing Hydraulic Research Institute, 223,Guangzhou Road, 210029 Nanjing, [email protected]

2 Bureau of Hydrology, Ministry of Water Resources, Lane 2, Baiguang Road, 100053 Beijing, China

Abstract A strong environmental change has been recognized in China with the impacts of climate change and intensive human activities on land use. In this paper, first environmental changes in China, such as those caused by human activities, land use, climate change, and social and economic development, are presented. Secondly, the hydrological prediction models and systems and their application in China are briefly introduced. Finally, it is pointed out that new hydrological prediction models, such as distributed hydrological models based on geographical information, should be developed because long-time data series will be invalid for traditional model calibration due to changing current and future environmental conditions.Key words environmental change; hydrological prediction; PUB

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