Advanced Hydrological Forecast Modeling Systems for Reliable and Sustainable Decision-Support

Water is crucial to life on earth, a fundamental biological building block required throughout organisms’ lifespans.

While globally there is plenty of water, it is unevenly distributed. Some areas of the world receive at least some rainfall

most every day, while others receive little on an annual basis. Furthermore, changing climates, land use patterns, and/or

large-scale geophysical oscillations accentuate either over-abundances or scarcities.

According to a recent 30-year survey issued by the United Nations Office for Disaster Risk Reduction, floods were the

most frequent kind of natural disaster, exceeding storms of all kinds, droughts, and extreme temperature impacts.

Furthermore, there has been a dramatic increase in the number of flood-related disasters over the last fifteen years,

partially resulting from increased human exposure to flood-prone areas. Complicating matters is that – due to our need

to manage water (i.e. reservoirs, diversions, and urban drainage and water treatment systems) – flood disasters tend to

have a man made component.

01

A core competency of Baron’s global mission is the development and deployment of tailored decision support systems

designed to mitigate threats to personal safety, and reduce property losses. In this article, we focus on the advanced

hydrological forecast modeling systems (HFMS) we deploy and their implementation for reliable and sustainable

decision support. To meet that goal, we strive to ensure that the chosen HFMS is able to perform at a “best possible”

level – yielding the most accurate (peak flows and flow forecasts) – and reliable (least number of false alarms) information

to guide the issuance of alerts and warnings by decision-making authorities.

All of our DSS’s are built around an automated weather infrastructure’s --- typically including observational data ingest

and data archiving, operational modeling, integrated output displays and information management, data dissemination,

public alerting, and the ability to re-run cases enabling overall system improvements – i.e., improvements in model

calibration or science. Because streamflow is a net small residual of the difference between input precipitation and

output evapotransipiration, Baron HFMSs focus significant effort on ensuring the best possible input precipitation and

observational streamflow data are available for ingest into the hydrological models themselves.

02

QPE With our recent deployment of 171 Dual-Pol radars for the U.S. National Weather Service, Baron is a global leader in

DP technology. We are continuing to develop improvements in DP calibration, reduction in anomalous propagation,

and other DP technologies. These are all designed to provide the best-in-market solution for real-time weather detection

and alerting as well as scientific data assimilation for HFMSs. By using dual-polarimetric moments along with standard

reflectivities (dBZ), hydrometeor classification and density and can be more accurately sensed. This promises

dramatic improvements to the quantitative precipitation estimates (QPE) that can be derived from WSRs. Recent case

study analysis using a Baron DP QPE algorithm showed a remarkable agreement with surface rain-gauge observations for

a heavy precipitation event in Florida. Figure 1 shows the Baron DP QPE-estimate for a 24-hour accumulation during that

event, while Figure 2 shows the same estimate using “standard” single-pole Z/R based estimates. It is obvious that the

DP algorithm suggested a much heavier precipitation event. Figure 3 shows that “ground truth,” based on a network of

precipitation gauges, verifies that the DP estimate is far superior.

Figure 1. Baron DP QPE estimates for the 24-hour period ending at 7:00am local time near Pensacola, Florida on April 30, 2014.

Figure 2. Single-pol Z/R based QPE estimates for the 24-hour period end-ing at 7:00am local time near Pensacola, Florida on April 30, 2014.

Figure 3. Single-pol Z/R based QPE estimates for the 24-hour period ending at 7:00am local time near Pensacola, Florida on April 30, 2014.

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For some National Meteorological and Hydrological Services (NMHSs), acquisition and implementation of the latest

state-of-the-art equipment, such as DP radars, is limited by budgetary and other resources. Yet, upgrades to modeling

technology can still proceed. In this circumstance, Baron has significant experience in building and deploying

“correction systems” that adjust the radar-based QPE. One of these was fielded on behalf of the Romanian Institute for

Hydrology and Water Management (INHGA) as part of the DESWAT (Destructive Waters Abatement) project. The Baron

radar gauge correction (RGC) system produces an optimal blend – using a mathematical/statistical algorithm similar to

what is deployed in modern NWP data assimilation systems – of the radar and gauges.

After first removing the biases from the single-pol radar Z/R-based QPE estimate, the algorithm produces a QPE field

that preserves the overall reflectivity structure adjusted to be a best-fit to the currently available gauge observations.

This allows far more spatial and temporal detail to be retained in the QPE field than would be there if gauge-based

QPE maps or lumped-basin estimates were instead provided. In turn, this detail is crucial to the kind of very high

resolution explicit streamflow models that Baron deploys, because these models estimate overland in-flow to streams at

the 100-meter or finer scale. Figure 4 shows an example of QPE system performance before and after application of the

RGC algorithm.

Figure 4.

Performance of single-pol radar accumulated QPE with gauge blending at a single gauge location before and after application of the RGC assimilation algorithm to the radar field as part of the Romanian Hydrological Model Forecast System (HFMS).

04

Streamflow Observations Observations of stream and river flows represent a second class of measurements extremely important to providing

accurate and reliable HFMSs. All hydrological discharge models require some form of historical calibration, and a

trustworthy long-term record of stream / river flows is vital to producing the best possible calibration. As the use of

explicit high-resolution “distributed” models becomes more widespread (with the Baron “LN2” model running over the

entire country of Romania at 100-meter resolution being the largest single operational deployment to date), it will be-

come less and less feasible to calibrate the model “everywhere.” Thus, representative catchments must be chosen and

then some sort of statistical “regionalization” must be applied. With good data and a modern calibration

methodology, the result is an initial set of calibrated parameters that significantly outperforms the set of “a-priori”

uncalibrated parameters. Figure 5 shows the result of such a calibration for a single representative catchment in which

LN2 was calibrated for the DESWAT project.

Figure 5. Uncalibrated (left) and calibrated (right) Baron “LN2” explicit distributed model results against stream-gauge observations for the Pastveni Catchment within the Siret major basin in Romania. The achievement of very significant model performance improvements hinges on both validated historical stream-gauge data as well as high quality historical QPE.

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Because calibrations cannot be done “everywhere”,

large-scale, high resolution distributed/explicitly routed

models like LN2 benefit when real-time stream gauge

observations can be “assimilated” into the model. This

approach enables the model to achieve better estimates of

the overall current state of the stream and river flows than it

would without the stream gauge data. For example, Figure

6 shows the fully-connected stream/lake/reservoir network

modeled by the Baron LN2 for the northern half of the Arges

Basin in Romania. To start up a model forecast, the model

itself must first be used to estimate what the current flow

rates are at every 100-meter stream/lake/reservoir pixel in

the network. If high-quality stream gauge data are available

in real-time, the model estimate will improve if that data

is assimilated.

Figure 6. Fully-integrated stream-lake-reservoir network within the Baron “LN2” explicit distributed model for the northern half of the Arges Basin in Romania, at 100-meter resolution.

Fortunately, several Baron HFMSs, including the LN2 and the Baron version of the Distributed Hydrology Soil Vegetation

Model (DHSVM, published by the University of Washington) contain stream gauge data assimilation capabilities. In the

LN2 model, these are often deployed where gauges are located just downstream of managed reservoirs. This is a very

effective way to introduce the impact of water release decisions on the potential for downstream flooding. In the Baron

version of the DHSVM model, real-time gauge observations (or calculated flow estimates based on mass balance

considerations) are assimilated to prevent model drift that might otherwise compromise the usability of the forecast by

decision makers. Figure 7 compares results from a calibrated DHSVM model for a nearly six month run without (left) and

with (right) stream-gauge data assimilation.

Figure 7. Six-month retrospective simulation using the Baron version of the DHSVM model, for total inflow into the Howard Hanson dam in NW Washington State, US (Left: without stream-gauge data-assimilation; Right: with stream-gauge data assimilation).

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Sustainable Decision Support Modernization of the National Meteorological Administration (NMA) and National Institute for Hydrology and Water

Management (INHGA) in Romania took place in two phases. Baron’s role was to achieve improved weather radar

capabilities through integration of radar data and creation of composite displays using radar systems built by several

different manufacturers (Phase 1), and follow-that by implementing a state-of-science HFMS (Phase 2, Figure 8).

Certification of the upgraded HFMS was achieved in 2011, and Baron is extremely proud that both our Phase 1 and

Phase 2 upgrades remain operational. Unfortunately, this is not always the case. A recent panel hosted by the World

Bank’s Global Facility for Disaster Reduction and Recovery (GFDRR) reported that a majority of similar modernization

efforts have “failed” over the last 20 years because they have not been sustainable. The lack of sustainability results

from too little funds being allocated to infrastructure, training, operations, and maintenance. In light of this disturbing

news, the GFDRR and WMO are looking toward revising the overall donor business model to mitigate these reported

problems. The good news is that with our commitment to redundant system implementation, substantial personnel

training, fully integrated engineering design, and follow-on support, Baron’s approach already demonstrates world

leadership in successful sustainability.

Figure 8.

Baron HFMS Decision-Support display in operational use in Romania during a recent rainfall event in the northern part of the country (Courtesy Marius Matreata, INHGA).System (HFMS).

©2016 Baron Services, Inc. All rights reserved. The information in this publication is accurate as of its publication date; such information is subject to change withouth notice.