scholarly journals Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

2017 ◽  
Vol 21 (2) ◽  
pp. 879-896 ◽  
Author(s):  
Tirthankar Roy ◽  
Hoshin V. Gupta ◽  
Aleix Serrat-Capdevila ◽  
Juan B. Valdes
Keyword(s):  
Model Performance ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Hydrologic Models ◽  
Catchment Scale ◽  
Model Driven ◽  
Alternative Approach ◽  
Precipitation Estimates ◽  
Daily Water ◽  
Rainfall Runoff Model

Abstract. Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

scholarly journals Using Satellite-Based Evapotranspiration Estimates to Improve the Structure of a Simple Conceptual Rainfall-Runoff Model

2016 ◽  
Author(s):  
Tirthankar Roy ◽  
Hoshin V. Gupta ◽  
Aleix Serrat-Capdevila ◽  
Juan B. Valdes
Keyword(s):  
Model Performance ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Catchment Scale ◽  
Modified Model ◽  
Model Driven ◽  
Alternative Approach ◽  
Precipitation Estimates ◽  
Daily Water ◽  
Rainfall Runoff Model

Abstract. Daily, quasi-global (50° N-S and 180° W-E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate use of these data to improve the performance of a simple lumped catchment scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying the daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. The results suggest that the modified model can provide improved simulations of both streamflow and evapotranspiration, even if GLEAM-satellite-based evapotranspiration data are not available.

Predictably Predictable -  The Role of Catchment Characteristics and Complexity.

2021 ◽  
Author(s):  
Sophia Eugeni ◽  
Eric Vaags ◽  
Steven V. Weijs
Keyword(s):  
Water Resource Management ◽  
Performance Metrics ◽  
Complexity Analysis ◽  
Model Performance ◽  
Principal Component ◽  
The United States ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Hydrologic Modelling

<p>Accurate hydrologic modelling is critical to effective water resource management. As catchment attributes strongly influence the hydrologic behaviors in an area, they can be used to inform hydrologic models to better predict the discharge in a basin. Some basins may be more difficult to accurately predict than others. The difficulty in predicting discharge may also be related to the complexity of the discharge signal. The study establishes the relationship between a catchment’s static attributes and hydrologic model performance in those catchments, and also investigates the link to complexity, which we quantify with measures of compressibility based in information theory. </p><p>The project analyzes a large national dataset, comprised of catchment attributes for basins across the United States, paired with established performance metrics for corresponding hydrologic models. Principal Component Analysis (PCA) was completed on the catchment attributes data to determine the strongest modes in the input. The basins were clustered according to their catchment attributes and the performance within the clusters was compared. </p><p>Significant differences in model performance emerged between the clusters of basins. For the complexity analysis, details of the implementation and technical challenges will be discussed, as well as preliminary results.</p>

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

2005 ◽  
Vol 6 (4) ◽  
pp. 497-517 ◽  
Author(s):  
Koray K. Yilmaz ◽  
Terri S. Hogue ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
Hoshin V. Gupta ◽  
...  
Keyword(s):  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Soil Moisture Accounting ◽  
Time Period ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
System Maps ◽  
Accounting Model ◽  
Rain Gauge Network

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 over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

scholarly journals Parameter-dependent convergence bounds and complexity measure for a class of conceptual hydrological models

2011 ◽  
Vol 14 (2) ◽  
pp. 443-463 ◽  
Author(s):  
Saket Pande ◽  
Luis A. Bastidas ◽  
Sandjai Bhulai ◽  
Mac McKee
Keyword(s):  
Model Selection ◽  
Linear Models ◽  
Convergence Rates ◽  
Model Performance ◽  
Hydrologic Model ◽  
Complexity Measure ◽  
Model Complexity ◽  
Finite Sample ◽  
Hydrologic Models ◽  
Complexity Measures

We provide analytical bounds on convergence rates for a class of hydrologic models and consequently derive a complexity measure based on the Vapnik–Chervonenkis (VC) generalization theory. The class of hydrologic models is a spatially explicit interconnected set of linear reservoirs with the aim of representing globally nonlinear hydrologic behavior by locally linear models. Here, by convergence rate, we mean convergence of the empirical risk to the expected risk. The derived measure of complexity measures a model's propensity to overfit data. We explore how data finiteness can affect model selection for this class of hydrologic model and provide theoretical results on how model performance on a finite sample converges to its expected performance as data size approaches infinity. These bounds can then be used for model selection, as the bounds provide a tradeoff between model complexity and model performance on finite data. The convergence bounds for the considered hydrologic models depend on the magnitude of their parameters, which are the recession parameters of constituting linear reservoirs. Further, the complexity of hydrologic models not only varies with the magnitude of their parameters but also depends on the network structure of the models (in terms of the spatial heterogeneity of parameters and the nature of hydrologic connectivity).

scholarly journals Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2312
Author(s):  
Joseph A. Daraio
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Stream Flow ◽  
General Circulation ◽  
Model Performance ◽  
General Circulation Models ◽  
Hydrologic Model ◽  
Historical Period ◽  
Hydrologic Models ◽  
Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

scholarly journals EVALUATING HYDROLOGIC MODEL PERFORMANCE OF GLOBAL AND LOCAL WEATHER DATA INPUTS

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 265-271
Author(s):  
J. Serrano ◽  
J. M. Jamilla ◽  
B. C. Hernandez ◽  
E. Herrera
Keyword(s):  
Assessment Tool ◽  
Statistical Significance ◽  
Model Performance ◽  
Daily Rainfall ◽  
Hydrologic Model ◽  
Weather Data ◽  
Annual Rainfall ◽  
Hydrologic Models ◽  
Event Based ◽  
Local Weather

Abstract. Runoffs from hydrologic models are often used in flood models, among other applications. These runoffs are converted from rainfall, signifying the importance of weather data accuracy. A common challenge for modelers is local weather data sparsity in most watersheds. Global weather datasets are often used as alternative. This study investigates the statistical significance and accuracy between using local weather data for hydrologic models and using the Climate Forecast System Reanalysis (CFSR), a global weather dataset. The Soil and Water Assessment Tool (SWAT) was used to compare the two weather data inputs in terms of generated discharges. Both long-term and event-based results were investigated to compare the models against absolute discharge values. The basin’s average total annual rainfall from the CFSR-based model (4062 mm) was around 1.5 times the local weather-based model (2683 mm). These basin precipitations yielded annual average flows of 53.4 cms and 26.7 cms for CFSR-based and local weather-based models, respectively. For the event-based scenario, the dates Typhoon Ketsana passed through the Philippine Area of Responsibility were checked. CFSR only read a spatially averaged maximum daily rainfall of 18.8 mm while the local gauges recorded 157.2 mm. Calibration and validation of the models were done using the observed discharges in Sto. Niño Station. The calibration of local weather-based model yielded satisfactory results for the Nash-Sutcliffe Efficiency (NSE), percent of bias (PBIAS), and ratio of the RMSE to the standard deviation of measured data (RSR). Meanwhile, the calibration of CFSR model yielded unsatisfactory values for all three parameters.

Distributed conceptual modelling in a Swedish lowland catchment: a multi-criteria model assessment

2012 ◽  
Vol 44 (2) ◽  
pp. 318-333 ◽  
Author(s):  
Sebastian Wrede ◽  
Jan Seibert ◽  
Stefan Uhlenbrook
Keyword(s):  
Spatial Representation ◽  
Environmental Flows ◽  
Model Performance ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Model Assessment ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Model Concept ◽  
Flow Measurements

Operational management and prediction of water quantity and quality often requires a spatially meaningful simulation of environmental flows and storages at the catchment scale. In this study, the performance of a fully distributed conceptual hydrologic model was evaluated based on the HBV (Hydrologiska Byråns Vattenbalansavdelning) and TACD (Tracer Aided Catchment model – Distributed) model concept in the meso-scale Fyrisån catchment in the Central Swedish lowlands. For a more spatially explicit representation of runoff generation processes of small landscape elements such as wetlands, a new sub-grid parameterization scheme was implemented in the model. In addition, a simple flow distribution and lake retention routine was introduced to better conceptualize the flow routing. During intensive model evaluation and comparison the model underwent conventional split-sample and proxy-basin tests. In this process, shortcomings of the model in the transferability of parameter sets and in the spatial representation of runoff generating processes were found. It was also demonstrated how a detailed comparison with a lumped benchmark model and the additional use of synoptic stream flow measurements allowed further insights into the model performance. It could be concluded that such a thorough model assessment can help to detect shortcomings in the spatial representation of the model and help facilitate model development.

scholarly journals Flood hazard and change impact assessments may profit from rethinking model calibration strategies

2020 ◽  
Author(s):  
Manuela I. Brunner ◽  
Lieke A. Melsen ◽  
Andrew W. Wood ◽  
Oldrich Rakovec ◽  
Naoki Mizukami ◽  
...  
Keyword(s):  
Model Calibration ◽  
Flood Hazard ◽  
Model Performance ◽  
Hydrologic Model ◽  
Flood Hazards ◽  
Hydrologic Models ◽  
Change Impact ◽  
Hazard Assessments ◽  
Flood Characteristics ◽  
Flood Flow

Abstract. Floods cause large damages, especially if they affect large regions. Assessments of current, local and regional flood hazards and their future changes often involve the use of hydrologic models. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding, which is, however, not guaranteed especially when using standard model calibration metrics. In this paper we investigate how flood timing, magnitude and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance. We compare how four well-known models (SAC, HBV, VIC, and mHM) represent (1) flood characteristics and their spatial patterns; and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics is challenging. They further show that changes in precipitation and temperature are not necessarily well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. We conclude that models calibrated on integrated metrics such as the Kling–Gupta efficiency alone have limited reliability in flood hazard assessments, in particular in regional and future assessments, and suggest the development of alternative process-based and spatial evaluation metrics.

scholarly journals Snow Model Verification Using Ensemble Prediction and Operational Benchmarks

2008 ◽  
Vol 9 (6) ◽  
pp. 1402-1415 ◽  
Author(s):  
Kristie J. Franz ◽  
Terri S. Hogue ◽  
Soroosh Sorooshian
Keyword(s):  
Snow Water Equivalent ◽  
Initial Conditions ◽  
Model Performance ◽  
Hydrologic Model ◽  
Snow Accumulation ◽  
Forecast Skill ◽  
Model Verification ◽  
Ensemble Prediction ◽  
Snow Model ◽  
Rainfall Runoff Model

Abstract Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow–Atmosphere–Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service’s (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2–4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3–4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating.

scholarly journals Assessing the impact of PET estimation methods on hydrologic model performance

2020 ◽  
Author(s):  
Dilhani Ishanka Jayathilake ◽  
Tyler Smith
Keyword(s):  
Potential Evapotranspiration ◽  
Model Performance ◽  
Monte Carlo Sampling ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Rainfall Runoff ◽  
Data Set ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

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