Evaluation of Global Water Resources Reanalysis Products in the Upper Blue Nile River Basin

Abstract

Water resources reanalysis (WRR) can be used as a numerical tool to advance our understanding of hydrological processes where in situ observations are limited. However, WRR products are associated with uncertainty that needs to be quantified to improve usability of such products in water resources applications. In this study, we evaluate estimates of water cycle components from 18 state-of-the-art WRR datasets derived from different land surface/hydrological models, meteorological forcing, and precipitation datasets. The evaluation was conducted at three spatial scales in the upper Blue Nile basin in Ethiopia. Precipitation, streamflow, evapotranspiration (ET), and terrestrial water storage (TWS) were evaluated against in situ daily precipitation and streamflow measurements, remote sensing–derived ET, and the NASA Gravity Recovery and Climate Experiment (GRACE) product, respectively. Our results highlight the current strengths and limitations of the available WRR datasets in analyzing the hydrological cycle and dynamics of the study basins, showing an overall underestimation of ET and TWS and overestimation of streamflow. While calibration improves streamflow simulation, it results in a relatively poorer performance in terms of ET. In addition, we show that the differences in the schemes used in the various land surface models resulted in significant differences in the estimation of the water cycle components from the respective WRR products, while we noted small differences among the products related to precipitation forcing. We did not identify a single product that consistently outperformed others; however, we found that there are specific WRR products that provided accurate representation of a single component of the water cycle (e.g., only runoff) in the area.

Extraction Method of Baseflow Recession Segments Based on Second-Order Derivative of Streamflow and Comparison with Four Conventional Methods

“…The normalized Taylor diagram can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their standard deviation. For a detailed introduction to the Taylor diagram, please refer to [34][35][36]. It can be seen from Figure S5 that the correlation coefficients between the daily evapotranspiration calculated by the four conventional methods and that calculated by the Sec-D method are between 0.7 and 0.95, the root-mean-square differences are between 0.4 and 0.8, and the ratios of standard deviation are between 0.75 and 1.25.…”

Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data

“…Due to the complexity of the model, it is difficult to estimate the uncertainty of ET and runoff caused by the uncertainty of the input precipitation forcing dataset. To quantify the uncertainty of the hydrological model output data, Koukoula et al [64] evaluated water cycle components from 18 state-of-the-art water resources reanalysis (WRR) datasets derived from different hydrological models, meteorological forcing, and precipitation datasets. They found that the uncertainty in the estimation of the water cycle components from different products is mainly attributable to the differences in the schemes used by the different models, while different precipitation forcing datasets have less impacts on the precision of the WRR output data.…”

Reply on RC1

“…Several studies have demonstrated that model-based gridded WRR products can be used as an alternative to observe river discharge in ungauged basins to: (1) understand hydrological processes (Koukoula et al, 2020), (2) support transboundary water management (Sikder et al, 2019), (3) identify flood events (Gründemann et al, 2018;López et al, 2020), and (4) support national water policies (Rodríguez et al, 2020). These examples demonstrate that WRR products have great potential for addressing water security challenges in ungauged basins.…”

“…These examples demonstrate that WRR products have great potential for addressing water security challenges in ungauged basins. Despite their numerous advantages, model outputs from WRR are also fraught with uncertainties resulting from errors in the forcing data, model structure, and the parameterisation of the physical processes in the model scheme (Koukoula et al, 2020). Therefore, it is necessary to evaluate the performance of these products against observed river discharge where available.…”