NITI Aayog estimated that India’s GDP by 2050 could be lost by 6% due to water scarcity. Given the growing climate-induced uncertainties and the centrality of water in India’s largely agrarian and rural livelihoods, building the resilience of agriculture to climate change is critical. Towards this goal, GIZ India has been implementing the Indo-German project on ‘Water Security and Climate Adaptation in Rural India’ (WASCA) with the Ministry of Rural Development (MoRD) and the Ministry of Jal Shakti (MoJS) since 2019. Commissioned by the German Federal Ministry for Economic Cooperation and Development (BMZ), the project aims to improve water security and rural climate adaptation through better management of rural water resources. The project is operational at national level and in select 10 districts of five states namely Madhya Pradesh, Rajasthan, Tamil Nadu, Uttar Pradesh, and Karnataka.

Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region.

The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.

Ethiopia is exposed to a wide range of disasters associated with the country’s extensive dependence on rainfed subsistence agriculture, climate change, resource degradation, diverse geoclimatic and socio-economic conditions and conflicts. Drought and floods are the major challenges, but a number of other threats affect communities and livelihoods. These include conflict, desert locust, fall armyworm, frost and hail, crop pests and diseases, livestock diseases, human diseases, landslides, earthquakes, and urban and forest fires.

Every source of evidence suggests that Ethiopia would feel the human and economic impacts of climate change intensely, and the impacts will only continue to grow if the country continues a business-as-usual approach to crisis response, and will not be able to manage the increasing scale of the challenges. Thus, there is call by all stakeholders for a paradigm shift in the way the country deals with communities at risk, in order to take preventive actions to reduce exposure, vulnerability and impact at local level. This requires moving away from a reactive system that solely focuses on drought and supply of life-saving humanitarian relief and emergency responses during disasters to a comprehensive proactive disaster and climate risk management approach, including climate change adaptation, among which are interventions to enhance livelihood diversification, social protection programmes and risk transfer mechanisms.

Furthermore, resilient agrifood systems support should include a range of proven interventions that are context-relevant and cover the whole agrifood system, such as increase in fertilizer use where appropriate and high-yielding and drought-tolerant seeds, strengthened extension and advisory systems at the kebele (local) level through the use of farmer field schools and pastoral field schools, expansion of access to credit, livelihood diversification, risk transfer mechanism and institutional development that link short-term emergency relief to long-term development pathways. This approach is essential for building resilience to natural hazard and human-induced disasters resulting in food insecurity challenges.