Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data

Abstract

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. For the management of ecological releases even discharge forecasts with moderate accuracy can be beneficial. Sequential data assimilation using the Ensemble Kalman Filter provides a tool that is both efficient and robust for a real-time modelling framework. One key parameter in a hydrological system is the soil moisture, which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by a radar scatterometer. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modelling framework yields good results in watersheds where soil storage is an important factor. The lead time of the forecast is dependent on the size and the retention capacity of the watershed. For the largest watershed a forecast over 40 days can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In a watershed with little soil storage and a quick response to rainfall events, the performance is relatively poor and the lead time is as short as 10 days only.

Hydraulic–hydrologic model for water resources management of the Zambezi basin

“…Compared with past attempts to model discharges in key locations of the Zambezi basin (Vorosmarty et al 1991;Harrison & Whittington 2002;Winsemius et al 2006a;Meier et al 2011), the results can be considered as acceptable for applying the model to simulate development scenarios, taking into account the fact that the scenarios comparison will be based on relative values. The present work constitutes a significant contribution in terms of reliability and error assessment as it implemented a thorough validation procedure and used a hydrological model tailored to meet some of the specificities of the Zambezi River basin.…”

“…More recently, a forecasting framework for the discharge prediction at three different sub-basins of the Zambezi River (Upper Zambezi, Luangwa and Kafue) was developed by Meier et al (2011) for the period from July 1995 to January 2002 at a 10-daily time step. The NS efficiency coefficients were around 0.8 but, again, no validation was implemented as the six years of data were used for calibration.…”

“…Reasons leading to this state of things go beyond data scarcity and include the uneven distribution of the existing gauging stations and the hydrological particularities of the wetlands. Several studies partly addressed the problem of lack of data by using novel satellite-derived data sources in addition to rainfall measurements, such as terrestrial water storage change (Winsemius et al 2006b), radar altimetry (Michailovsky et al 2012) and soil moisture (Meier et al 2011).…”

Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment

“…Studies using SSM data have also been published on the neighbouring Zambezi catchment. A high correlation was found in the Zambezi between soil moisture and runoff by Scipal et al (2005) and was recently exploited for real-time hydrological modelling by Meier et al (2011). The study by Parajka et al (2006) revealed no improvement of runoff simulations by assimilating SSM data into a model for ungauged catchments in Austria.…”

On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments

“…DA incorporates observations into the numerical model to issue an analysis, which is an estimation of the best current state of the system. This has not only been largely applied to remote sensing for snow (Kuchment et al, 2010), soil moisture estimates (Forman et al, 2012;Meier et al, 2011;Renzullo et al, 2014;Alvarez-Garreton et al, 2014) or hydraulic information (Bailey and Bau, 2012), but also to update radar forcing (Harader et al, 2012;Kim and Yoo, 2014). Many applications also use in situ observations such as catchment discharge, snowpack measurements, or soil moisture to update models (e.g., Seo et al, 2009;Clark et al, 2008;Thirel et al, 2010;DeChant and Moradkhani, 2011;Franz et al, 2014).…”