Abstract
The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.
Highlights
Precipitation bias correction methods (HPR strategies) can dramatically perturb the volume of water entering to the system, in the Hydrological Processing (HP) strategies the most sensitive model parameters can significantly modify water routing though the model structure, and Hydrological Postprocessing (HPP) strategies can have a large impact by directly perturbing the forecasts to adjust scaling and fitting issues derived from a poor model representation
To avoid unbounded uncertainty bands, the Variational Ensemble Forecasting (VEF) approach allows improving the accuracy of streamflow forecasts through a ranking evaluation and posterior identification of the best hydrologic ensembles for the Upper Zambezi River Basin (UZRB) (Figure 10b1 –b7 for forecasts ranked using skill analysis of R2 and Figure 10d1 –d7 for forecasts ranked using total uncertainty defined as Root Mean Squared Error (RMSE))
This paper described the main stages and processes required to implement and improve an operational hydrologic forecasting system (HFS) in the UZRB and its sub-basins
Summary
Critical decisions about the design, functionality, and operability of a Hydrologic Forecasting System (HFS) need to be made to reduce the total hydrologic uncertainty (THU) propagated from different components of a hydrologic modelling paradigm (HMP). Reducing the total hydrological uncertainty is key to developing reliable Integrated Water Resources Management (IWRM) strategies across spatial and temporal scales. For river basins across the world, the allocation of water resources largely relies on accurate streamflow forecasts. In Africa, for instance, the waters of the Upper Zambezi River Basin (UZRB) are shared by eight countries: Angola, Namibia, Zambia, Botswana, Malawi, Tanzania, Zimbabwe, and Mozambique. The administrative complexities created by the transnational nature of the Zambezi Basin (Figure 1)
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