The water footprint (WF), based on irrigation water quality, is important as a decision-making tool for crop selection based on the comparative advantage of water consumption and yield to maximize agricultural water productivity and sustainably improve water use efficiency. This paper presents a generic link between climate variability and water footprint. To support this link, a case study is presented for wheat and maize in different agro-climate zones in Egypt. In this study, the three agro-ecological zones, Nile Delta, Middle Egypt, and Upper Egypt, were selected to represent three different microclimates. The climate data were analyzed to estimate reference evapotranspiration (ETo) and calculate crop water use (CWU) for wheat and maize from 2015 through 2019. Cultivated area and yield data were analyzed during the study period. Water footprint (WF) was calculated for old land (clay soils) and new lands (sandy soils) in three climate regions based on blue and grey water. Green water was excluded due to negligible rainfall depths in Egypt. The results showed that the mean values of WF for maize were 1067, 1395, 1655 m³/ton in old land and 1395, 1634, 2232 m³/ton in new land under the three climate regions, respectively, while it was 923, 982, 1117 m³/ton in old land and 1180, 1258, 1452 m³/ton for wheat in new land for the three regions, respectively. The results show that the crop water use fluctuated over regions due to climate variability where the CWU values were 6211, 7335, 8007 m³/ha for maize and 4348, 4825, 5774 m³/ha for wheat in the three regions, respectively. The results show an 11% and 33% increase in maize and an 18% and 29% increase in wheat CWU in Middle and Upper Egypt regions comparing to what was observed in Nile Delta due to an increase in solar radiation, temperature, and wind speed. The Egypt mean value of wheat water footprint was 1152 m³/ton and mean value of maize water footprint was 1563 m³/ton. The data clearly show the effect of microclimate variability on WF and irrigation requirements between regions. The methodology and results from this study provide a pathway to help the policy makers to mitigate climate change impacts on crop yield and to enhance water resources management in major crop production regions by redistribution of the cropping patterns based on the comparative advantages of each crop within each region. The crop choices relative to the soil water retention characteristics could also contribute to the moderation of microclimate, which affects ETo and ETc and the water footprint.

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.