Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

With the implementation of the “Grain-for-Green” program, artificial vegetation was introduced on the Loess Plateau, which resulted in high soil water content (SWC) depletion. Currently, lack of soil water recharge is one of the most serious challenges on the Loess Plateau. Soil drying and wetting processes are critical for the sustainability of soil water recycling, but this has not been well studied. There is also a lack of physical definition of the upper bound SWC of dried soil layers (DSL). In this study, soil water dynamics – the change of SWC affected by precipitation and vegetation transpiration – were studied under converted vegetation. In-situ SWC measurements from the 0–5 m or 0–8 m deep profile over consecutive wet years (from 2016 to 2018 with an average precipitation of 660.9 mm) were analyzed to understand soil water depletion and restoration processes. Results showed distinct differences in soil water dynamics in the soil profiles and soil water balances under different vegetation types. SWC under continuous perennial alfalfa (Medicago sativa) had greater fluctuations between 0 and 300 cm than below 300 cm, and a DSL was observed below 300 cm. After converting from alfalfa to soybean (Glycine max), SWC increased greatly during the three wet years. Soil water storage (S) increased at an average rate of 35.8 mm year⁻¹ m⁻¹ within the top 500 cm of the soil profile, average evapotranspiration (ET) was 482.0 mm year⁻¹, and maximum restoration depth of soil water extended to 660 cm. However, SWC gradually decreased over time after replacing food crop with alfalfa. S declined at an average rate of 21.4 mm year⁻¹ m⁻¹ within the top 500 cm of the soil profile, average ET was 680.4 mm year⁻¹ and the maximum depth of soil water depletion extended to 360 cm. These results suggest that SWC in deep layers can be depleted and replenished quickly, and the processes were dominated by vegetation types and precipitation. Taking vegetation types and soil texture into consideration, the calculation of upper bound SWC of DSL was redefined. Given the long-term effects of high water demand from vegetation such as alfalfa on the soil water balance, ET of vegetation should be reduced through conversion to less water-intensive vegetation types or biomass control (i.e. reduced planting density appropriately) in arid areas of the Loess Plateau.

The urban food garden is an interesting natural solution to the need to develop sponge cities structured and designed to absorb and capture rain water for reducing flooding, worldwide. This study applied a storm water management model and field experiments to investigate properties of the garden substrates. Taipei City was taken as a case study as the Taiwan government has promoted urban food garden projects since 2015. The urban food garden in Taipei has established a cultivable area of 197,168 m², 64,026 m² (32.5%) of which is designated as green-roof gardens and the rest as domestic gardens. Four substrate mixtures were found to have infiltration rates positively related to their soil water content. Substrate 1 had the highest infiltration rate (6.47 × 10⁻⁵ m/s) and soil water content (281%) when vegetation grows in limited containers. In 2019, the total water retention capacity of the urban food garden in Taipei City was 50,550.7 m³. This means that 1 m² of the urban food garden in Taipei retained 256.4 kg of water. Considering climatic conditions, the water retention capacity of the green-roof gardens in Taipei ranges from 28.2% to 41.0%. During short-term high-density rainfall events, the green-roof gardens were found to be more efficient in reducing the runoff volume, whereas during long-term high-density rainfall events, they were found to be more efficient in reducing the runoff peak flow duration (~20 mins) compared with concrete surfaces. This study proved that establishing the urban food garden contributes to increasing the water retention capacity and reducing the volume of surface runoff and the duration of runoff peak flow in prevention of flood disasters.