A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting. Issue 1 (17th January 2017)

• Record Type: Journal Article
• Authors: Roy, Tirthankar; Serrat‐Capdevila, Aleix; Gupta, Hoshin; Valdes, Juan
• Is Part Of: Water resources research. Volume 53:Issue 1(2017)
• Page Start: 376
• Page End: 399
• Publication Date: 2017-01-17
• Subjects: streamflow forecasting -- satellite precipitation products -- bias correction -- model averaging -- uncertainty analysis -- real‐time monitoring -- MMSF; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR019752
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: We develop and test a probabilistic real‐time streamflow‐forecasting platform, Multimodel and Multiproduct Streamflow Forecasting (MMSF), that uses information provided by a suite of hydrologic models and satellite precipitation products (SPPs). The SPPs are bias‐corrected before being used as inputs to the hydrologic models, and model calibration is carried out independently for each of the model‐product combinations (MPCs). Forecasts generated from the calibrated models are further bias‐corrected to compensate for the deficiencies within the models, and then probabilistically merged using a variety of model averaging techniques. Use of bias‐corrected SPPs in streamflow forecasting applications can overcome several issues associated with sparsely gauged basins and enable robust forecasting capabilities. Bias correction of streamflow significantly improves the forecasts in terms of accuracy and precision for all different cases considered. Results show that the merging of individual forecasts from different MPCs provides additional improvements. All the merging techniques applied in this study produce similar results, however, the Inverse Weighted Averaging (IVA) proves to be slightly superior in most cases. We demonstrate the implementation of the MMSF platform for real‐time streamflow monitoring and forecasting in the Mara River basin of Africa (Kenya & Tanzania) in order to provide improved monitoring and forecasting tools to inform water management decisions. Key Points: Bias‐corrected satellite precipitation and recalibrated hydrologic models enable improved streamflow forecasting in sparsely gauged basins Bias correction of simulated streamflows improves forecast accuracy and precision Forecast accuracy and precision are improved by merging simulations from multiple hydrologic models and satellite precipitation inputs