Case Study: Watershed Modeling with Distributed Weather Model Data

Kouwen, N.; Danard, Maurice B.; Bingeman, Allyson K.; Luo, Wuben; Seglenieks, Frank; Soulis, E. D.

doi:10.1061/(asce)1084-0699(2005)10:1(23)

Cited by 32 publications

(21 citation statements)

References 21 publications

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“…These include orographic precipitation models (Xu, 1999) or simple boundary layer models (Kouwen et al, 2005). This "hybrid" approach may be particularly useful in complex terrain and may warrant closer attention for climate change impact studies in mountainous regions.…”

Section: Uses Of Hydrological Modelsmentioning

confidence: 99%

“…Bias correction fields can be obtained through a comparison of forecasted and observed precipitation (Westrick et al, 2002), or of forecasted and observed streamflows (Kouwen et al, 2005). The use of time-dependent bias correction factors can offer additional improvements, because precipitation biases can be highly dependent on synoptic-scale forcing, especially in complex terrain (Westrick et al, 2002;Yoshitani et al, 2009).…”

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

“…Imperfect process representation may be caused by the necessary use of simplified functional relations between hydrological elements or, alternatively, by our insufficient knowledge of the physics that govern these processes (Kitanidis and Bras, 1980;Niehoff et al, 2002). As data availability and computational speed have increased, the model representation of hydrological processes has become more accurate (Kouwen et al, 2005). In some cases, however, process understanding is still so limited that we are forced into black-box approaches (Sivapalan et al, 2003).…”

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

“…Unfortunately, a common impediment to the successful calibration of model parameters is the availability of observations with which to compare the various model outputs (e.g., Brun and Band, 2000;Eckhardt and Ulbrich, 2003). As hydrological models have increased in complexity, the additional parameter requirements have led to an increase in associated uncertainty despite more complete simulation of hydrological processes (Kouwen et al, 2005). Jakeman and Hornberger (1993) cite overparameterization as a major challenge in rainfall-runoff modelling, and Beven (1989) has suggested that three to five parameters should be sufficient to reproduce the hydrological record.…”

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

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Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Uses Of Hydrological Modelsmentioning

confidence: 99%

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

Section: Weather Forecast Uncertaintymentioning

confidence: 99%

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Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…One of the challenges in modeling watershed hydrology is obtaining accurate weather input data [1,2], which are generally one of the most important drivers of watershed models [3,4]. Spatial and temporal variability are key characteristics of hydrological processes [5].…”

Section: Introductionmentioning

confidence: 99%

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Sidike

Chen

et al. 2016

Water

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Abstract:The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton's Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 • C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 • C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.

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Using the Climate Forecast System Reanalysis as weather input data for watershed models

Fuka

Walter

MacAlister

et al. 2013

Hydrological Processes

381

238

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Obtaining representative meteorological data for watershed-scale hydrological modelling can be difficult and time consuming. Land-based weather stations do not always adequately represent the weather occurring over a watershed, because they can be far from the watershed of interest and can have gaps in their data series, or recent data are not available. This study presents a method for using the Climate Forecast System Reanalysis (CFSR) global meteorological dataset to obtain historical weather data and demonstrates the application to modelling five watersheds representing different hydroclimate regimes. CFSR data are available globally for each hour since 1979 at a 38-km resolution. Results show that utilizing the CFSR precipitation and temperature data to force a watershed model provides stream discharge simulations that are as good as or better than models forced using traditional weather gauging stations, especially when stations are more than 10 km from the watershed. These results further demonstrate that adding CFSR data to the suite of watershed modelling tools provides new opportunities for meeting the challenges of modelling un-gauged watersheds and advancing real-time hydrological modelling. a 'Replace' indicates that values were replaced within an initial range published in the literature, and 'percent' indicates that values were determined by adjusting the base initialization default variables by a certain percentage.

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Case Study: Watershed Modeling with Distributed Weather Model Data

Cited by 32 publications

References 21 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Using the Climate Forecast System Reanalysis as weather input data for watershed models

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