Probabilistic Assessment of the Satellite Rainfall Retrieval Error Translation to Hydrologic Response

Moradkhani, Hamid; Meskele, T.

doi:10.1007/978-90-481-2915-7_14

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…The newest techniques were developed due to a recent shift in focus of model calibration from simple optimization to probabilistic characterization of model parameters [ Beven and Freer , 2001]. With the recognition of multiple different uncertainty sources (i.e., forcing data, observation, model structure, and parameters), much of the community has tried to account for these uncertainties at varying levels [e.g., Bulygina and Gupta , 2009; Kavetzki et al , 2006; Moradkhani et al , 2006; Moradkhani and Meskele , 2009; Vrugt et al , 2008]. This has led to an array of different probabilistic techniques to estimate the uncertainty in a given modeling framework.…”

Section: Introductionmentioning

confidence: 99%

Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

DeChant¹,

Moradkhani²

2012

Water Resources Research

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166

108

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[1] In hydrologic modeling, state-parameter estimation using data assimilation techniques is increasing in popularity. Several studies, using both the ensemble Kalman filter (EnKF) and the particle filter (PF) to estimate both model states and parameters have been published in recent years. Though there is increasing interest and a growing literature in this area, relatively little research has been presented to examine the effectiveness and robustness of these methods to estimate uncertainty. This study suggests that state-parameter estimation studies need to provide a more rigorous testing of these techniques than has previously been presented. With this in mind, this paper presents a study with multiple calibration replicates and a range of performance measures to test the ability of each technique to calibrate two separate hydrologic models. The results show that the EnKF is consistently overconfident in predicting streamflow, which relates to the assumption of a Gaussian error structure. In addition, the EnKF and PF were found to perform similarly in terms of tracking the observations with an expected value, but the potential for filter divergence in the EnKF is highlighted.Citation: DeChant, C. M., and H. Moradkhani (2012), Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting, Water Resour. Res., 48, W04518,

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Section: Introductionmentioning

confidence: 99%

Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

DeChant¹,

Moradkhani²

2012

Water Resources Research

Self Cite

166

108

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“…The precipitation was selected as true precipitation. For data assimilation and open loop simulation, the precipitation uncertainty was defined as a multiplicative log normally distributed error (defined in Equations (8)- (11)) [43]; In the DA and NODA cases, GSWAT was driven by the perturbed precipitation (P perturbed ) to simulate basin states and fluxes with and without ET assimilated throughout year 2000. Finally, we analyzed streamflow results derived from this experiment.…”

Section: Synthetic Experiments Designmentioning

confidence: 99%

Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region

Zhang

Hou

et al. 2017

Sustainability

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Streamflow estimates are substantially important as fresh water shortages increase in arid and semi-arid regions where evapotranspiration (ET) is a significant contribution to the water balance. In this regard, evapotranspiration data can be assimilated into a distributed hydrological model (SWAT, Soil and Water Assessment Tool) for improving streamflow estimates. The SWAT model has been widely used for streamflow estimations, but the applications combining SWAT and ET products were rare. Thus, this study aims to develop a SWAT-based evapotranspiration data assimilation system. In particular, SWAT is gridded at Hydrologic Response Unit (HRU) level to incorporate gridded ET products acquired from the remote sensing-based ETMonitor model. In the modeling case, Gridded SWAT (GSWAT) shows a good agreement of streamflow modeling with the original SWAT. Such a scant margin between them is due to the modeling domain mismatch caused by different HRU delineations. In the ET assimilation case, we carry out a synthetic data experiment to illustrate the state augmentation Direct Insertion (DI) method and a real data experiment for the upper Heihe River Basin. The results demonstrate the benefits of the ET assimilation for improving hydrologic processes representations. In the future, more remotely sensed data can be assimilated into the data assimilation system to provide more reliable hydrological predictions.

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“…Additional probabilistic forecast measures can be used including normalized root‐mean‐square error ratio (NRR) [ Moradkhani et al , 2005b; Moradkhani and Meskele , 2009], Q‐Q plots [ Thyer et al , 2009], reliability diagrams, and different decompositions of the Brier score [ Clark and Slater , 2006]. However, because of the quantity of methods being compared, the analysis is limited to the three probabilistic measures described above, to concisely demonstrate the forecast skill across all of the BMA strategies.…”

Section: Forecast Verificationmentioning

confidence: 99%

Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation

Parrish

Moradkhani

DeChant

2012

Water Resources Research

Self Cite

140

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[1] Multimodeling in hydrologic forecasting has proved to improve upon the systematic bias and general limitations of a single model. This is typically done by establishing a new model as a linear combination or a weighted average of several models with weights on the basis of individual model performance in previous time steps. The most commonly used multimodeling method, Bayesian model averaging (BMA), assumes a fixed probability distribution around individual models' forecast in establishing the prior and uses a calibration period to determine static weights for each individual model. More recent work has focused on a sequential Bayesian model selection technique with weights that are adjusted at each time step in an attempt to accentuate the dynamics of an individual model's performance with respect to the system's response. However, these approaches still assume a fixed distribution around the individual models' forecast. A new sequential Bayesian model-averaging technique is developed incorporating a sliding window of individual model performance around the forecast. Additionally, this new technique relaxes the fixed distribution assumption in establishing the prior utilizing a particle filter data assimilation method that reflects both the performance dynamics of the models' forecasts along with their uncertainty. A comparative analysis of the different BMA strategies is performed across different rates of change in the hydrograph. Results show that methods employing the particle filter show higher probabilistic skill in high ranges of volatility but are overconfident in medium and low ranges of volatility.

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Probabilistic Assessment of the Satellite Rainfall Retrieval Error Translation to Hydrologic Response

Cited by 15 publications

References 23 publications

Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting

Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region

Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation

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