Koray K. Yilmaz

A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

Received 29 November 2007; revised 12 May 2008; accepted 23 May 2008; published 11 September 2008. [1] Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant ‘‘signature measures’’ to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL-DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models. Citation: Yilmaz, K. K., H. V. Gupta, and T. Wagener (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, doi:10.1029/2007WR006716.

Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but ov...

Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

The coupled routing and excess storage (CREST) distributed hydrological model

Abstract The Coupled Routing and Excess STorage model (CREST, jointly developed by the University of Oklahoma and NASA SERVIR) is a distributed hydrological model developed to simulate the spatial and temporal variation of land surface, and subsurface water fluxes and storages by cell-to-cell simulation. CRESTs distinguishing characteristics include: (1) distributed rainfall–runoff generation and cell-to-cell routing; (2) coupled runoff generation and routing via three feedback mechanisms; and (3) representation of sub-grid cell variability of soil moisture storage capacity and sub-grid cell routing (via linear reservoirs). The coupling between the runoff generation and routing mechanisms allows detailed and realistic treatment of hydrological variables such as soil moisture. Furthermore, the representation of soil moisture variability and routing processes at the sub-grid scale enables the CREST model to be readily scalable to multi-scale modelling research. This paper presents the model development and demonstrates its applicability for a case study in the Nzoia basin located in Lake Victoria, Africa. Citation Wang, J., Yang, H., Li, L., Gourley, J. J., Sadiq, I. K., Yilmaz, K. K., Adler, R. F., Policelli, F. S., Habib, S., Irwn, D., Limaye, A. S., Korme, T. & Okello, L. (2011) The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J. 56(1), 84–98.

Evaluation of a satellite-based global flood monitoring system

This study provides an initial evaluation of a global flood monitoring system (GFMS) using satellite-based precipitation and readily available geospatial datasets. The GFMS developed by our group uses a relatively simple hydrologic model, based on the run-off curve number method, to transform precipitation into run-off. A grid-to-grid routing scheme moves run-off downstream. Precipitation estimates are from the TRMM Multi-satellite Precipitation Analysis (TMPA). We first evaluated the TMPA algorithm using a radar/gauge merged precipitation product (Stage IV) over south-east USA. This analysis indicated that the spatial scale (and hence the basin size) as well as regional and seasonal considerations are important in using the TMPA to drive hydrologic models. GFMS-based run-off simulations were evaluated using observed streamflow data at the outlet of two US basins and also using a global flood archive. Basin-scale analysis showed that the GFMS was able to simulate the onset of flood events produced by heavy precipitation; however, the simulation performance deteriorated in the later stages. This result points out the need for an improved routing component. Global-scale analysis indicated that the GFMS is able to detect 38% of the observed floods; however, it suffers from region-dependent bias.

Evaluation of Multiple Satellite-Based Precipitation Products over Complex Topography

This study evaluates the performance of four satellite-based precipitation (SBP) products over the western Black Sea region of Turkey, a region characterized by complex topography that exerts strong controls on the precipitation regime. The four SBP products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis version 7 experimental near-real-time product (TMPA-7RT) and postreal-time research-quality product (TMPA-7A), the Climate Prediction Center morphing technique (CMORPH), and the Multisensor Precipitation Estimate (MPE) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Evaluation is performed at various spatial (point and grid) and temporal (daily, monthly, seasonal, and annual) scales over the period 2007‐11. For the grid-scale evaluation,araingauge‐basedgriddedprecipitationdatasetwasconstructedusingaknowledge-basedsystem in which ‘‘physiographic descriptors’’ are incorporated in the precipitation estimation through an optimizationframework. TheresultsindicatedthatevaluatedSBPproductsgenerallyhaddifficultyin representing the precipitation gradient normal to the orography. TMPA-7RT, TMPA-7A, and MPE products underestimated precipitation along the windward region and overestimated the precipitation on the leeward region, more significantly during the cold season. The CMORPH product underestimated the precipitation on both windward and leeward regions regardless of the season. Further investigation of the datasets used in the development ofthese SBPproductsrevealedthat,althoughboth infrared(IR) andmicrowave (MW)datasets contain potential problems, the inability of MW sensors to detect precipitation especially in the cold season was the main challenge over this region with complex topography.

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

AbstractAn extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cevennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimatio...

Validation of a TRMM-based global Flood Detection System in Bangladesh

Although the TRMM-based Flood Detection System (FDS) has been in operation in near real-time since 2006, the flood ‘detection’ capability has been validated mostly against qualitative reports in news papers and other types of media. In this study, a more quantitative validation of the FDS over Bangladesh against in situ measurements is presented. Using measured stream flow and rainfall data, the study analyzed the flood detection capability from space for three very distinct river systems in Bangladesh: (1) Ganges– a snowmelt-fed river regulated by upstream India, (2) Brahmaputra – a snow-fed river that is braided, and (3) Meghna – a rain-fed and relatively flashier river. The quantitative assessment showed that the effectiveness of the TRMM-based FDS can vary as a function of season and drainage basin characteristics. Overall, the study showed that the TRMM-based FDS has great potential for flood prone countries like Bangladesh that are faced with tremendous hurdles in transboundary flood management. The system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations. For this reason, FDS users are cautioned to verify FDS estimates during the monsoon period and for regulated rivers before implementing flood management practices. Planned improvements by FDS developers involving physically-based hydrologic modeling should transform the system into a more accurate tool for near real-time decision making on flood management for ungauged river basins of the world.

Potential Impacts of Climate Change on Turkish Water Resources: A Review

Water resources are mainly controlled by the climate conditions. Global warming will therefore have evolving impacts on water resources and poses important challenges for sustainable development. Studies are rapidly emerging with focus on potential implications of climate change on Turkish water resources. These studies can be grouped into two major fields: (1) Studies investigating the degree of climate change reflected in the past observed hydro-meteorological records, and (2) studies investigating potential future impacts of climate change on water resources. In this paper, we present a summary of the current knowledge in the area of climate change impacts on Turkish water resources with emphasis on the two major fields listed above. Overall conclusion of the review is that climate change will put additional pressure on already stressed water resources in Turkey. The credibility of water management scenarios – whether focused on maintaining ecosystems or on food and energy security – largely depends on improved consideration of plausible climate change scenarios, and their potential uncertainties, in decision making.

Model calibration in watershed hydrology

Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must, therefore, be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. This Chapter reviews the current state-of-the-art of model calibration in watershed hydrology with special emphasis on our own contributions in the last few decades. We discuss the historical background that has led to current perspectives, and review different approaches for manual and automatic single- and multi-objective parameter estimation. In particular, we highlight the recent developments in the calibration of distributed hydrologic models using parameter dimensionality reduction sampling, parameter regularization and parallel computing.