Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?

Balin, Daniela; Lee, Hyosang; Rode, Michael

doi:10.1029/2009wr007848

Cited by 32 publications

(28 citation statements)

References 29 publications

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“…Most of this work has used relatively simple lumped and semidistributed hydrological models that represent watersheds with area ranging between 100 and 10,000 km 2 (Sorooshian and Dracup, 1980;Gupta et al, 1998;Andréassian et al, 2001;Vrugt et al, 2003;Muleta and Nicklow, 2005;Balin et al, 2010;McMillan et al, 2010;Vaze et al, 2010, amongst many others Troy et al (2008), Gosling and Arnell (2011), Nasonova et al (2011) and Pappenberger et al (2011). Not only do GHMs pose significant computational challenges, they also require a wealth of input data to accurately characterize global scale variations in land-use, soil type, elevation, climate conditions, and groundwater table depths (amongst others).…”

Section: Introductionmentioning

confidence: 99%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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Keywords:Global hydrological modeling Forcing uncertainty Model calibration Parameter uncertainty s u m m a r yIn the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on largescale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5°and smaller.

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Section: Introductionmentioning

confidence: 99%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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“…Examples for the application of rainfall multipliers in combination with distributed models are given by Datta and Bolisetti (), who use a Bayesian approach, or Salamon and Feyen (), who use a particle filter. Balin et al () also use a Bayesian approach, but contrary to the other examples, they separate the assessment of rainfall uncertainty from that for the parameters, albeit that the treatment of rainfall uncertainty is very simplistic (point measurement uncertainty only). Although usage of the simple multiplier concept may be adequate and useful for catchment rainfall to force lumped hydrological models, they are inadequate for simulation of spatially varying uncertainty in spatially distributed models.…”

Section: Discussionmentioning

confidence: 99%

“…Second, we assessed uncertainty separately for four different model outputs ( Q , h , ET , and SM ). In the hydrological community, it is common practice to evaluate hydrological models and assess their uncertainty based on the lumped catchment response discharge (e.g., Balin et al, ; Engeland, Steinsland, Johansen, Petersen‐Overleir, & Kolberg, ; Gotzinger & Bardossy, ). With regard to simple rainfall run‐off models, where the only model output is river discharge, this is a straightforward exercise.…”

Section: Discussionmentioning

confidence: 99%

Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

Ehlers

Sonnenborg

Refsgaard

2019

Hydrological Processes

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In this study, uncertainty in model input data (precipitation) and parameters is propagated through a physically based, spatially distributed hydrological model based on the MIKE SHE code. Precipitation uncertainty is accounted for using an ensemble of daily rainfall fields that incorporate four different sources of uncertainty, whereas parameter uncertainty is considered using Latin hypercube sampling. Model predictive uncertainty is assessed for multiple simulated hydrological variables (discharge, groundwater head, evapotranspiration, and soil moisture). Utilizing an extensive set of observational data, effective observational uncertainties for each hydrological variable are assessed. Considering not only model predictive uncertainty but also effective observational uncertainty leads to a notable increase in the number of instances, for which model simulation and observations are in good agreement (e.g., 47% vs. 91% for discharge and 0% vs. 98% for soil moisture). Effective observational uncertainty is in several cases larger than model predictive uncertainty. We conclude that the use of precipitation uncertainty with a realistic spatio‐temporal correlation structure, analyses of multiple variables with different spatial support, and the consideration of observational uncertainty are crucial for adequately evaluating the performance of physically based, spatially distributed hydrological models.

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“…A major concern of traditional calibration processes is that the model uncertainty is either neglected (Balin et al ., ) or assumed to be attributed only by model parameters (Ajami et al ., ). Fortunately, there is increasing research work that explores contributions of and interactions among other sources of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the forcing input data (e.g., data used to be model inputs such as precipitation and temperature), measured calibration/validation data (MCVD, data used for model calibration and validation such as flow rate and sediment loads), and model structure should be considered in calibration and validation. Conclusions are made to support the idea that uncertainty sources exert considerable impact on model predictions (Ajami et al ., ; Salamon and Feyen, ; Balin et al ., ; Harmel et al ., ; Yen et al ., ). Model predictions will be altered if varying sources of uncertainty are incorporated in watershed modeling.…”

Section: Introductionmentioning

confidence: 99%

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

Yen

Hoque

Wang

et al. 2016

J American Water Resour Assoc

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In the field of watershed modeling, the impact of measurement uncertainty (MU) on calibration results indicates the potential issue of inaccurate model predictions. It is important to note that MU refers to the uncertainty in measured data such as flow and nutrient values that are used to evaluate model outputs. The calculation of error statistics assuming measured data are deterministic may not be appropriate as has been frequently stated in literature. Although MU can affect model calibration results, it is rarely incorporated in modeling practice. MU can be incorporated in two schemes: explicitly incorporated (MU-EI) during model calibration and post-processed (MU-PP) after calibration is completed. In this study, both schemes are implemented in a case study of the Arroyo Colorado Watershed, Texas. Unexpectedly, no substantial differences were observed between each scheme for flow predictions. Although MU did not cause dramatic differences in most sediment and NH 4 -N predictions, error statistics were affected in cases with MU greater than 50%, especially for sediment and NH 4 -N. Therefore, it is concluded that MU may not exert a significant impact on model predictions until certain threshold is reached. This study demonstrates that high levels of uncertainty in measured calibration/validation data significantly affect parameter estimation, especially in the auto-calibration process. (KEY TERMS: measurement uncertainty; model calibration; SWAT; uncertainty analysis; IPEAT.) Yen, Haw, Yamen M. Hoque, Xiuying Wang, and Robert Daren Harmel, 2016. Applications of Explicitly Incorporated/Post-Processing Measurement Uncertainty in Watershed Modeling.

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Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?

Cited by 32 publications

References 29 publications

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

Applications of Explicitly Incorporated/Post‐Processing Measurement Uncertainty in Watershed Modeling

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