Utilizing Probabilistic Downscaling Methods to Develop Streamflow Forecasts from Climate Forecasts

Abstract

Statistical information from ensembles of climate forecasts can be utilized in improving the streamflow predictions by using different downscaling methods. This study investigates the use of multinomial logistic regression (MLR) in downscaling large-scale ensemble climate forecasts into basin-scale probabilistic streamflow forecasts of categorical events over major river basins across the U.S. Sun Belt. The performance of MLR is then compared with the categorical forecasts estimated from the traditional approach, principal component regression (PCR). Results from both cross validation and split sampling reveal that in general, the probabilistic categorical forecasts from the MLR model have more accuracy and exhibit higher rank probability skill score (RPSS) compared to the PCR probabilistic forecasts. MLR forecasts are also more skillful than PCR forecasts during the winter season as well as for basins that exhibit high interannual variability in streamflows. The role of ensemble size of precipitation forecasts in developing MLR-based streamflow forecasts was also investigated. Because of its simplicity, MLR offers an alternate, reliable approach to developing categorical streamflow forecasts.

Assessing Retrospective National Water Model Streamflow with Respect to Droughts and Low Flows in the Colorado River Basin

“…These limitations can inhibit future planning and management (Stagnitta et al 2018). Therefore, mathematical modeling is often employed as an alternative to the direct measurement of river flows (Mazrooei and Sankarasubramanian 2017).…”

Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

“…Integration of the ECHAM4.5 precipitation forecasts with the NLDAS-2 non-precipitation forcing variables supports the idea of evaluating the LSM in realtime SM forecasting. Our previous studies have also showed the robust performance of ECHAM4.5 forecasts for improving streamflow forecasting (Sinha et al, 2014;Mazrooei and Sankarasubramanian, 2017). Both forecast verification metrics, correlation coefficient and RMSE, show that the forecasted SM captures the variability in SMAP observations with decent accuracy.…”

Technical Note: Evaluation of the Skill in Monthly-to-Seasonal Soil Moisture Forecasting Based on SMAP Satellite Observations over the Southeast US

“…Integration of the ECHAM4.5 precipitation forecasts with the NLDAS-2 non-precipitation forcing variables supports the idea to evaluate the LSM in real-time SM forecasting. Our previous studies have also showed the robust performance of ECHAM4.5 forecasts for improving streamflow forecasting (Sinha et al, 2014;Mazrooei and Sankarasubramanian, 2017). Both forecast verification metrics, correlation coefficient and RMSE, show that the forecasted SM captures the variability in SMAP observations with decent accuracy.…”