The Gravity Recovery And Climate Experiment (GRACE) satellite mission pro- vides a unique opportunity to monitor changes in total waterstorage (TWS) of large river basins such as the Nile. Use of GRACE-TWS changes for moni- toring the Nile is, however, difficult since stronger TWS signals over the Lake Victoria Basin (LVB) and the Red Sea obscure those from smaller sub-basins making their analysis difficult to undertake. To mitigate this problem, this study employed Independent Component Analysis (ICA) to extract statisti- cally independent TWS patterns over the sub-basins from GRACE products. Such extraction enables an in-depth analysis of waterstoragechangeswithin each sub-basin and provides a tool for assessing the influence of anthropogenic as well as climatevariability caused by large scale ocean-atmosphere interac- tions such as the El Ni˜ no Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Our results indicate that LVB experienced effects of both anthro- pogenic and climatevariability during 2002-2011 coinciding with the drought that affected the Greater Horn of Africa. Ethiopian Highlands (EH) gener- ally exhibited a declining trend in the annual rainfall over the study period. A correlation of 0.56 was found between ENSO and TWS changes over EH indicat- ing ENSO’s dominant influence. TWS changes over Bar-el-Ghazal experienced mixed increase-decrease, with ENSO being the dominant climatevariability in the region during the study period. A remarkable signal is noticed over the Lake Nasser region indicating the possibility of the region losing water not only through evaporation, but also possibly through over extraction from wells in the
The twin Gravity Recovery and Climate Experiment (GRACE) satellite mission, a project jointly sponsored by the National Aeronautics and Space Administration (NASA) and the German Aerospace Center (DLR), was launched in March 17, 2002. The spatial-temporal change of the Earth gravity fields mapped by the GRACE satellite provides information directly related to the mass redistribution at or near the sur- face of the Earth, e.g., changes in terrestrial waterstorage (TWS), ocean, polar ice sheets and mountain glaciers . Particularly, hydrological applications would be the largest contribution of GRACE among many related studies  since TWS changes associated to climatevariability and change play a crucial role in regional and global hydrological cycles and water management. GRACE-derived TWS changes, including changes in surface water, soil moisture, groundwater, snow and ice, have been widely used to investigate water balance in many river basins, especially for drought and flood assessment. Examples include the Amazon Basin [14,15], the Yangtze River Basin [16,17], the Mississippi River Basin , the Murray Darling Basin  and the NileBasin .
Figure 1: Location of the 62 virtual altimetry stations and the three study regions; Upper Nile (Kenya, Uganda, Tanzania, Rwanda, Burundi, Ethiopia and South Sudan), Central Nile (Sudan, Ethiopia and Eritirea), and Lower Nile (Egypt) in the NileBasin. This figure also contains the positions of the gauge stations and Hydroweb ( Cretaux et al. , 2011 ) for measuring water levels, as well as major infrastructures (e.g., dams) and irrigation areas (following The Food and Agriculture Organization (FAO) and Multsch et al. , 2017 ).
Macho and Fan, 2012a; Yamazaki et al., 2011; Zulkafli et al., 2016). The spatiotemporal patterns of precipitation are, how- ever, changing due to climate change and variability (Brando et al., 2014; Cook et al., 2012; Lima et al., 2014; Malhi et al., 2008, 2009; Nepstad et al., 2008), large-scale alterations in land use (e.g., deforestation) (Chen et al., 2015; Coe et al., 2009; Davidson et al., 2012; Kalamandeen et al., 2018; Lima et al., 2014; Panday et al., 2015; Tollefson, 2016), and more recently the construction of mega-dams (Finer and Jenkins, 2012; Latrubesse et al., 2017; Moran et al., 2018; Soito and Freitas, 2011; Timpe and Kaplan, 2017; Wine- miller et al., 2016), among others. Such changes in precipita- tion patterns typically manifest themselves in terms of altered magnitude, duration, and timing of streamflow (Marengo, 2005). A prominent streamflow alteration pattern that has been widely observed across the Amazon is the extended dry- season length (Espinoza et al., 2016; Marengo et al., 2011) and an increase in the number of dry events (i.e., droughts) over the longer term (Malhi et al., 2009; Marengo and Es- pinoza, 2016), which has been suggested to be a result of ongoing climatic and human-induced changes (Cook et al., 2012; Cook and Vizy, 2008; Lee et al., 2011; Malhi et al., 2008; Shukla et al., 1990). However, the cross-scale interac- tions and feedbacks in the human–water relationship make it difficult to explicitly quantify the causes. These changes have resulted in decreases in runoff (Espinoza et al., 2009; Haddeland et al., 2014; Lima et al., 2014) and loss of ter- restrial biodiversity (Barletta et al., 2010; Newbold et al., 2016; Tófoli et al., 2017; Toomey et al., 2011; Winemiller et al., 2016). Increased variability in streamflow has also re- sulted in the disruption of the food pulse and fishery yields, which the Amazon region thrives upon (Castello et al., 2013, 2015; Forsberg et al., 2017). Moreover, persistent dry events create
withdrawals of the world, supplying an estimated 36%, 42% and 27% of the water for domestic, agricultural and industrial purposes, respectively (Taylor et al. 2013). Although GW is vulnerable to depletion, it is being consumed faster than it is being naturally replenished (Rodell et al. 2009; Sutanudjaja et al. 2011). The rapid population growth, expansion of irrigation agriculture, and economic development globally increased the water demand, and leads to water stress in several parts of the world (Wada et al. 2010). Groundwater levels and fluxes are controlled by a dynamic interplay between recharge and discharge, with a variety of controls and feedback loops from climate, soils, geology, land cover and human abstraction (Cuthbert et al. 2019). According to Abbaspour et al. (2015), the quality and quantity of GW in Europe are under heavy pressure and water levels have decreased. Compared to surface water, groundwater responds more slowly to changes in meteorological conditions (Bovolo et al. 2009). As a result, the laws governing groundwater rights are still static even in developed countries (Rodell et al. 2009). This aggravates overexploitation of GW worldwide and very pronouncedly in arid regions (Hashemi et al. 2015). Higher standards of living, demographic changes, land and water use policies, and other external forces are increasing the pressure on groundwater resources. In Ethiopia, 80% the national water demand is covered from groundwater source (Kebede 2012). The total annual aquifer recharge is estimated to be more than 30 billion cubic meter (Berhanu et al. 2014; Kebede 2010). Due to the infancy institutes and research capabilities in the country, our knowledge about the groundwater recharge, groundwater-surface water connection, and aquifer properties is limited (Berhanu et al. 2014).
Climate change is a change in climate that usually takes place over a long period of time at least 150 years with clear and permanent impact on the ecosystem (Ayoade, 2002 cited in Ayoade, 2016). Like most of developing countries, Ethiopia is one of the countries that are exstremely affected by climate change. Menberu and Aberra (2014) argue that water shortage and soil erosion are the major problems in Amhara Regional State of Ethiopia. The solution is partly depending on how rural communities perceive climate change and variability. According to Aberra (2012), scientific evidence now abounds about the inevitability of climate change and what measures should be taken to combat it. In spite of this, people of the world, from policy-makers down to the individual, are bogged down in an awkward of conflicting mind-sets of urgency, disbelief, and indecision about the extent of the climate change impact and the responses to it. To combat the problem, understanding the perception of all that have a stake in it provides the stronger ground for the decision-making process. Therefore, for this reason, perception research on climate change becomes extremely crucial to increase the adaptive capacity of rural communities to climate change impacts.
From 2002 to 2017, changes in terrestrial waterstorage (TWS) have been measured by the GRACE satellites with an unprecedented accuracy. Because these observations inte- grate both natural and anthropogenic effects across all wa- ter reservoirs (i.e., soil moisture, groundwater, snow, lakes, wetlands, rivers, and land ice), isolating the contribution of specific reservoirs or the relative importance of natural versus anthropogenic effects is still relatively uncertain and has been the focus of several recent publications (Reager et al., 2016; Eicker et al., 2016; Wada et al., 2016; Fasullo et al., 2016; Felfelani et al., 2017; Getirana et al., 2017; Pan et al., 2017; Andrew et al., 2017; Rodell et al., 2018; Hanasaki et al., 2018; Khaki et al., 2018; WCRP Global Sea Level Budget Group, 2018). In this context, one critical aspect is to model the effect of climatevariability on TWS changes. At this time, only global hydrological models and land sur- face models can provide long-term estimates of natural TWS variability; however, they are usually not calibrated against GRACE measurements and sometimes exhibit large biases in TWS amplitude (Schellekens et al., 2017; Zhang et al., 2017; Scanlon et al., 2018). Typically, only a small number of such model runs is available and exploring the uncertainty related to the use of different meteorological forcing datasets is not possible. With this paper, we aim to address these shortcom- ings with a computationally cheap alternative. Unlike hy- drological models which represent physical processes and model water reservoirs individually (e.g., snow, soil mois- ture, lakes), we train a statistical model to directly reconstruct the total TWS changes from precipitation and temperature information.
One consequence of climate change in sub Saharan Africa is that farmers will be more exposed to environmental risk. More erratic and scarce rainfall and higher temperature imply that farmers will be facing a larger extent of uncertainty. Ethiopia is a prime example. Rainfall variability and associated drought have been major causes of food shortage and famine in Ethiopia. During the last forty years, Ethiopia has experienced many severe droughts leading to production levels that fell short of basic subsistence levels for many farm households (Relief Society of Tigray (REST) and NORAGRIC at the Agricultural University of Norway 1995, p. 137). Harvest failure due to weather events is the most important cause of risk-related hardship of Ethiopian rural households, with adverse effects on farm household consumption and welfare (Dercon 2004, 2005). Climate change is projected to further exacerbate these issues (Parry et al. 2005; Lobell et al. 2008; Schlenker and Lobell 2010; World Bank 2010). Thus, the implementation of adaptation strategies can be very important (Mendelsohn and Dinar 2003; Deressa et al. 2009; Di Falco and Veronesi 2013). For instance, farmers may face drier soil, and therefore they implement investments in soil conservation so that soil moisture may be retained. They can plant trees to procure some shading on the soil or resort to irrigation and water harvesting technologies (Kurukulasuriya et al. 2011). They can also simply switch to different crops or activities that are more suited to drier or wetter environmental conditions (Seo and Mendelsohn 2008a). 1
Ecosystems differ in their responses to climatevariability (Knapp and Smith, 2001). Different plant species respond differently to drought conditions based on their physiological and structural characteristics, to prevent loss of water (Van Der Molen et. al., 2011). Understanding how vegetation types respond to drought and climatevariability can lead to more efficient management of these land covers and can, in turn, assist significantly in securing water and food in the future. Many studies have been conducted on this matter, yet, most of them have focused on the climate driver of the NPP variation on a global scale (e.g. Huang et al., 2016; Liu et. al., 2015). Recent analyses have found that semi-arid areas are the major controllers of the global NPP variation (Huang et al., 2016; Ahlström et. al., 2015). However, it is important to examine whether the same pattern of NPP found by Zhao and Running (2010) on a global scale also applies to regional and local scales (Chen et. al., 2013). Different patterns may reveal when zooming into a local scale. Drought is expected to be more severe in the future (Ault et. al., 2014). Therefore, it is important to investigate the effect of drought on primary productivity and efficiency of the land cover types in terms of water and carbon use. Analysis of these interactions at regional and national levels is useful and provides essential information for land cover management and climate policy-making (Peng et. al., 2017; Liu et. al., 2015). Recently, a country scale analysis of the relationship between NPP and drought was published by Peng et al. (2017). The data used in their work was that of Moderate Resolution Imaging Spectroradiometer (MODIS) NPP and the Standardized Precipitation Evapotranspiration Index (SPEI) for drought. They found that countries show different trends in NPP for the period 2000 to 2014, and only 35 countries accounted for more than 90% of the global NPP.
- SDT (strong decreasing trend): tau < 0 and α ≤ 0.05 The methodology proposed by Beguer´ıa et al. (2003) was used to infer the potential impact of land use changes on runoff generation in the various sub-basins of the Ebro basin. The method is based on removing the influence of the two most influential climatic parameters (temperature and precip- itation) from the annual river discharge series. Obviously, the selection of temperature and precipitation to synthesize cli- matic variability is a simplification of the reality since other variables like air humidity, wind speed or solar radiation may also affect the evaporation rates. However, the lack of long- term records of the aforementioned variables, and the as- sumed capability of combined precipitation and temperature to summarize the water balance at the basin scale at seasonal and annual basis (Droogers and Allen, 2002), prevented us to use more complex approaches in this study. The removal of climatic influence from runoff series was done using step- wise linear regression models for each sub-basin. The inde- pendent variables were the sum of annual precipitation and mean temperature of the area drained at each gauging sta- tion. The annual runoff in each sub-basin was the dependent variable. Series of residuals were evaluated to assess whether they exhibited significant trends. The occurrence of a signif- icant trend suggested that water yield had changed in time independently of the evolution of climatic conditions. Given that most of the factors that explain hydrological evolution (except climate) remain stationary in time (e.g. lithology) or change slowly (e.g. soil depth or soil characteristics), only the intense changes in land cover and vegetation occurred in the region in the last decades can explain the observed trends in water yield. This premise is only valid in basins where human activities are moderate and do not affect interannual hydrological behavior (pluriannual reservoirs, or diversions to agricultural areas or large urban settlements). Hence, this premise will apply in headwater catchments, whereas in the lower reaches of the Ebro River the trends in residuals may also be due to water consumption by cities and for agricul- tural activities.
The upper Nilebasin was selected as the case study with specific focus on the Lake Victoria basin and its two medium-size river catchments (River Ruizi and Katonga). The research was based on a combination of statistical techniques and hydrological modeling using rainfall-runoff. Statistical techniques were used to understand the patterns of trends and changes in the past and recent climate of the study area. The presence of linear trends in the long-term hydrometeorological variables (e.g. rainfall, temperature and streamflow) was tested using Man-Kendall methods. Meanwhile, the inter- annual variability and changes in the past and recent climate was analysed based on empirical statistical approach. The emphasis was put on the identification of significant linear trends and significant inter-annual changes in the observed variables in order to provide possible evidence of the change in the observed climate. The impact modeling was entirely based on the outputs from several GCMs and a lumped conceptual model based on the VHM approach. For the outputs from climate models, the special report of emission scenarios, identified as A2, A1B and B1, were considered. The selection of the GCM runs completely employed in the study was based on a validation technique, which utilizes key statistical metrics. A non-parametric statistical downscaling technique was employed to derived local-scale future variables (rainfall and temperature) from the selected GCM runs. Because of the complex nature of rainfall, compared to temperature, emphasis was put on analysing the projected changes in rainfall extremes using special statistical technique, the extreme value analysis. The lumped conceptual (VHM) model was developed for each selected river catchment. The calibration and validation of the models were carried out in a manner that ensure that each model has a good proficiency in communicating with peaks during streamflow peaks simulation such that the projected peaks are gleamingly captured. Based on the flood frequency analysis, it was possible to provide information on how future peak streamflows for the Ruizi and Katonga catchments were likely to response to anthropogenic induced climate change based on the emissions scenarios A2, A1B and B1.
the potential water resource management problems associ- ated with water supply, power generation, and agricultural practices as well as for future water resource planning, reser- voir design and management, and protection of the natural environment, it is necessary to provide quantitative estimates of the hydrological effects of climate change. In this regard as Taye et al. (2011) stated several studies have been con- ducted on the sensitivity of streamflow to climatechanges for many parts of the Nile. Among these studies, Elsahmay et al. (2009) run an ensemble of climate change scenarios using the Nile Forecasting Model with bias corrected pre- cipitation and temperatures from 17 coupled general circu- lation models (AOGCMs) for the 2081–2098 period to as- sess the effects on the streamflow of the Blue Nile at Diem which belongs to Eastern Nilebasin. One of the conclu- sions in Elshamy et al. (2009) was that the uncertainty in future precipitation change due to increased greenhouse gas emissions are large, making the future changes in streamflow very uncertain. Recently Taye et al. (2011) simulated the cli- mate change impact on hydrological extremes in two regions (Nyando basin found in white Nile and Lake Tana catchment located in upper Blue Nile subbasin) and noted that for Lake Tana catchment the GCM uncertainty was more important than the hydrological models uncertainty.
and not on changes in runoff variability (for an overview see Kundzewicz and Robson, 2004). The assessment of historic high and low flows as demonstrated by Burn and Hag Elnur (2002), or statistical analyses applied to climate change sce- narios as demonstrated for low flows by Arnell (2003), have shown the impact of climatevariability on the variability of river runoff. Studies of runoff effects caused by both climatevariability and basin developments should consider long and discrete periods, preferably more than 50 years in order to capture multi-decadal variability of climate and river runoff. The main goal of the present research was to develop and test a method to separate the relative impact of observed cli- mate variability (which in this study is defined to include both natural variability and anthropogenic climate change) versus human water use on river runoff variability at the river basin scale. We have limited ourselves to studying the impacts of increasing water consumption for irrigation and evaporation losses from waterstorage for hydropower pro- duction on the annual and seasonal river runoff over a period of 100 years. These factors were studied in the arid region of the Krishna river basin, which is located in central India. The objectives of this study were to:
Abstract. GRACE (Gravity Recovery and Climate Experi- ment) satellite data monitor large-scale changes in total ter- restrial waterstorage (1TWS), providing an invaluable tool where in situ observations are limited. Substantial uncer- tainty remains, however, in the amplitude of GRACE grav- ity signals and the disaggregation of TWS into individual terrestrial water stores (e.g. groundwater storage). Here, we test the phase and amplitude of three GRACE 1TWS sig- nals from five commonly used gridded products (i.e. NASA’s GRCTellus: CSR, JPL, GFZ; JPL-Mascons; GRGS GRACE) using in situ data and modelled soil moisture from the Global Land Data Assimilation System (GLDAS) in two sub-basins (LVB: Lake Victoria Basin; LKB: Lake Kyoga Basin) of the Upper NileBasin. The analysis extends from January 2003 to December 2012, but focuses on a large and accurately ob- served reduction in 1TWS of 83 km 3 from 2003 to 2006 in the Lake Victoria Basin. We reveal substantial variability in current GRACE products to quantify the reduction of 1TWS in Lake Victoria that ranges from 80 km 3 (JPL-Mascons) to 69 and 31 km 3 for GRGS and GRCTellus respectively. Rep- resentation of the phase in TWS in the Upper NileBasin by GRACE products varies but is generally robust with GRGS, JPL-Mascons, and GRCTellus (ensemble mean of CSR, JPL, and GFZ time-series data), explaining 90, 84, and 75 % of the variance respectively in “in situ” or “bottom-up” 1TWS in the LVB. Resolution of changes in groundwater storage (1GWS) from GRACE 1TWS is greatly constrained by
Abstract: GRACE-derived Terrestrial WaterStorage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial waterstorage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total waterstorage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.
Amsalu and De Graaff 2007). Based on the data from a comprehensive survey of agricultural households across three agro-ecologies in Muger River sub-basin of the Blue-NileBasin, Amare and Simane (2017b) identified soil and water conservation practices are most widely used adaptation option in response to climate change. The use of these adaptation options was found to reduce soil erosion associated with short but heavy rains. Farmers are adapting SWC practices to retain soil-water content and maintain humidity during dry spells through an improved soil structure (McCarthy et al. 2011). Simi- larly, the use of different agronomic practices is consid- ered as the potential adaptation option to the adverse effects of climate change on agriculture. The analysis by Amare and Simane (2017b) in the Muger River sub-basin showed that using agronomic practices such as drought- tolerant crop varieties, crop diversification, and improved crop varieties is an another dominant strategy that is found to be used by smallholder farmers in adapting to the negative effects of climatevariability and change as well as resultant changes in crop pest and disease pres- sures. Improved varieties (drought-tolerant varieties and/ or short cycle) allow for increased productivity even dur- ing dry seasons (Lobell et al. 2008). Furthermore, Ellis and Freeman (2004) found that crop diversity is used as a strategy for risk avoidance due to sharp fluctuations in crop yield or prices.
Nonfarm income is negatively related to depending on food aid and liquidating other assets as coping strategies. This shows that farmers who have off-farm incomes depend less on food aid and the need to dispose of assets at times of climate extreme events. This result implies the need for creating off- farm job opportunities for farming communities to better enable them to cope. In addition, farm size is negatively related to selling livestock and borrowing from relatives. This result could be because farmers with larger land sizes are also wealthier farmers who can depend on other sources, such as savings, than on selling livestock and borrowing from relatives.
The analysis shows that upstream and downstream units of the watershed signifiantly differ in all of the indicators except for labor exchange, climate related shocks experienced and generations interacting.. The mean number of crop varieties planted in the upstream (4.60) is significantly higher than the downstream (3.06) (P < 0.01). The new system of irrigated farming in the downstream is creating a different land use system characterized by reduced diversity and increased specialization of on-farm crops. In recent years, land area used for maize and wheat production has been increasing; and the traditional crops like ‘teff’, have been on the decline in the lower watershed partly due to farmers’ perception that maize and wheat provide higher yields and respond high to fertilizers compared to others. Statistically significant difference has also been observed in the verities of land use cultivated (P < 0.01). The average land use varieties cultivated in the downstream (1.87) is lower than the upstream (2.06). The traditional use of grasses for thatched roofs has drastically decreased in the downstream area due to the absence of grasslands. Natural forest cover has drastically decreased in the last thirty years. For its quick eco‐ nomic return, eucalyptus is dominating in the watershed. Due to market availability, downstream communities allocate considerable size of their croplands to eucalyptus.
Improved understanding of the water balance in the Blue Nile is of critical importance because of increas- ingly 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 hydro- logical 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 data- sets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial waterstorage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in- situ 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.