Upper Blue Nile basin water budget from a multi-model perspective

Abstract

a b s t r a c tImproved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, insitu streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Can we trust remote sensing evapotranspiration products over Africa?

“…This is true if long-term trends in global ET are not visibly present. However, Jung et al (2010) claim that there have been declining trends in global ET estimates in the recent past in association with the last major El Niño event in 1998, with the largest regional contributions to the declining trend in Australia and southern Africa. The exact opposite effect is reported by Zhang et al (2016), who describe significant increases in global land ET trends, especially in Australia and southern Africa.…”

“…The exact opposite effect is reported by Zhang et al (2016), who describe significant increases in global land ET trends, especially in Australia and southern Africa. Other studies also focus on investigating trends in long-term ET and do not come to a consensus as to the cause or direction of the trend (Miralles et al, 2014;Douville et al, 2013;Jung et al, 2010;Zhang et al, 2016). With this in mind, it is difficult to assume that there is long-term global trend in one direction or another.…”

Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus

“…In general, these products are best used for bi-monthly monitoring and situational awareness, examples of which are in FEWS NET special reports (FEWS NET, 2015 to illustrate the severity and extent of recent droughts in sub-Saharan Africa. FLDAS outputs are well-correlated with remotely sensed ET and soil moisture (R > 0.7) (McNally et al, 2016(McNally et al, , 2017 and accurately represented the water balance in the Blue Nile Basin, Ethiopia (Jung et al, 2017) in terms of remotely sensed ET (R = 0.9), total water storage (R = 0.86), and streamflow (R = 0.9). Given that these data are publicly available a growing body of literature is utilizing and evaluating the data (e.g., Philip et al, 2017).…”

Acute Water-Scarcity Monitoring for Africa

“…The FLDAS model is uncalibrated and relies on global soil and vegetation parameters and parameters that are a source of considerable uncertainty [50]. However, select comparisons with data from the Global Runoff Data Center show that FLDAS performs well (R > 0.70) in naturalized flow regimes and larger basins (e.g., R > 0.7 in the Orange Basin [51] and R > 0.8 in the Upper Blue Nile Basin [52]). These data are also publicly available for independent verification by interested parties, which we encourage before applying FLDAS data to local scale studies and other applications.…”