The fertile Anatolian lands in Turkey, supporting about 80 million people, rely on abundant water resources. The Kızılırmak River basin in Anatolia is vulnerable to global warming, mainly due to snowmelt in its headwaters. Quantifying the upper watershed’s climate sensitivity is crucial for assessing water availability. Instead of using Global Climate Model (GCM)-driven projections, a sensitivity-based approach was employed with the Variable Infiltration Capacity (VIC) hydrologic model to assess the region’s hydrological vulnerability to potential future climatic changes. Considering the consistent projections of increasing temperature (T) over this region in GCMs, the system was perturbed to examine gradients of a more challenging climate, characterized by warming and drying conditions. The sensitivity of streamflow, snowpack water equivalence, and evapotranspiration to T and Precipitation (P) variations under each perturbation or “reference” climates was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. Streamflow responding to P increases nonlinearly as P decreases in the reference climates. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: 1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, 2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and 3) streamflow sensitivity to hydroclimatic variability will increase, meaning that water managers will likely need to plan for a system that is more sensitive to weather variations.

The African Flood and Drought Monitor (AFDM) was developed by Princeton University in collaboration with UNESCO-IHP, regional hydroclimate centers in Africa and ICIWaRM. Based on advanced land surface modeling driven by satellite data, the system provides 25-km resolution near-real-time evaluations of the terrestrial water cycle, plus forecasting by merging seasonal climate forecasts with hydrological modeling. Originally targeted toward hydrometeorological services, drought managers and researchers, the AFDM has found many applications in health and epidemiology, e.g.: Malaria: In northern Zambia, populations of the two malaria-carrying mosquito species had strong, time-lagged correlations to rainfall events. Relations to temperature were more complex. The AFDM helped show the effectiveness of targeted indoor residual spraying (Hast et al., 2019a, b). Trypanosomiasis: In the Maasai Steppe of Tanzania, negative relationships were found between abundance of two tsetse fly species and AFDM temperature. Trypanosome prevalence in the species increased with rising maximum temperatures from 26-31° C, and then declined (Nnko et al., 2017). Cholera: In Cameroon, associations were found between AFDM average daily maximum temperature and risk of cholera transmission that varied across four climate subzones (Ngwa et al., 2016). Related studies using AFDM data with implications for human health and well-being in Africa, include: Stunted growth: In four African countries, a rainfall deficit of 1 standard deviation from the mean was associated with an increase of stunting in children age 0-60 months of 2.2 to 3.2% (Hill et al., 2019). Human migration: Rainfall shortages and excess temperature, and less so soil moisture, are strong drivers of out-migration from South Africa, especially for black and low-income migrants (Mastrorillo et al., 2015). Herbivore parasites: In Botswana, temperature had a small negative effect on abundance of nematode parasites of herbivores (Walker et al., 2016), and modeled helminth larval development affecting goats was primarily driven by daily rainfall (Walker et al., 2018). Overall, these examples provide evidence that the provision of climate and hydrological information in consistent and accessible ways can help enable health-related research and applications.

Estimation of aquifer recharge is a prerequisite for understanding groundwater systems and sustainable water resources management. In arid and semi-arid areas, where access to hydrologic data for groundwater studies is often limited with spatial and temporal inconsistencies, data collection is difficult. Therefore, in these regions satellite products of meteorological data series have recently turned into the most reliable alternative. This study presents a daily groundwater recharge estimation in the NW part of the Lake Chad Basin using VisualBALAN, a water-soil-plant distributed model, from two data sources (ground and satellite-based meteorological data) under non-irrigated and irrigated land. Precipitation and temperature data from ground-based gauge stations and the Multi-Source Weighted-Ensemble Precipitation product (MSWEP), was gathered for the years 2005-2014; average annual values were 283 and 417 mm and 30 and 29 ℃ for gauges and for MSWEP, respectively. The estimated mean annual aquifer recharge from precipitation equals 10.9 and 14.7 mm/yr, while in irrigated areas was 33 and 41 mm/yr, for ground and satellite-based data, respectively, being always slightly higher for the satellite estimates. Sensitivity analysis was performed to assess the dependency of input data in final recharge. Recharge estimates were mainly sensitive to soil type, soil parameters such as field capacity and wilting point, and to surface hydrology-related parameters such as curve number.