Reply on RC4

TL;DR: In this paper , the authors analyzed the water balance derived runoff from open access Remote Sensing (RS) products for 591 catchments across the globe, and compared time-series of runoff estimated through a simplified water balance equation using 3 precipitation (CHIRPS, GPM and TRMM), 5 evapotranspiration (MODIS, SSEBop, GLEAM, CMRSET and SEBS) and 3 water storage change (GRACE-CSR, GRACE-JPL and GRACE -GFZ) datasets with monthly in situ discharge data for the period 2003–2016.

Abstract: <strong class="journal-contentHeaderColor">Abstract.</strong> Remote sensing (RS) data is becoming an increasingly important source of information for water resources management as it provides spatially distributed data on water availability and use. However, in order to guide appropriate use of the data, it is important to understand the impact of the uncertainties of RS data on water resources studies. Previous studies have shown that the degree of closure of the water balance from remote sensing data is highly variable across basins and that different RS products vary in their levels of accuracy depending on climatological and geographical conditions. In this paper we analyzed the water balance derived runoff from open access RS products for 591 catchments across the globe. We compared time-series of runoff estimated through a simplified water balance equation using 3 precipitation (CHIRPS, GPM and TRMM), 5 evapotranspiration (MODIS, SSEBop, GLEAM, CMRSET and SEBS) and 3 water storage change (GRACE-CSR, GRACE-JPL and GRACE-GFZ) RS datasets with monthly in situ discharge data for the period 2003&ndash;2016. Results were analyzed through the lens of 11 quantifiable catchment characteristics in order to investigate correlations between catchment characteristics and the quality of RS based water balance estimates of runoff, and whether specific products performed better than others in certain conditions. The median Nash Sutcliffe Efficiency (NSE) for all gauges and all product combinations was &minus;0.03, and only 43.3 % of the time-series reached positive NSE. A positive NSE could be obtained for 72.5 % of stations with at least one product combination, while the overall best performing product combination was positive for 53.8 % of stations. This confirms previous findings that the best performing products cannot be globally established. When investigating the results by catchment characteristic, all combinations tended to show similar correlations between catchment characteristics and quality of estimated runoff, with the exception of combinations using MODIS ET for which the correlation was frequently reversed. The combinations with the GPM precipitation product performed generally worse than the CHIRPS and TRMM data. However, this can be attributed to the fact that the GPM data is available at higher latitudes compared to the other products, where performance is generally poorer. When removing high latitude stations, this difference was eliminated and GPM and TRMM showed similar performance. The results show the highest positive correlation between highly seasonal rainfall and runoff NSE. On the other hand, increasing snow cover, altitude and latitude all decreased the ability of the RS products to close the water balance. The catchment&rsquo;s dominant climate zone was also found to be correlated with time series performance with the tropical areas providing the highest (median NSE = .11) and arid areas the lowest (median NSE = &minus;0.09) NSE values. No correlation was found between catchment area and runoff NSE. The results point to the importance of further detailed studies on the uncertainties of the different data products and how these interact, as well as new approaches to using the data rather than simple water balance type approaches. Efforts to improve specific satellite products can also be better targeted using the results of this study.