Irrigation enhances resilience to the negative impacts of climate change through sustainable food production and environmental health. However, water is a scarce resource that needs efficient utilization. This study explored (1) farmers’ perceptions about the roles of irrigation in climate change adaptation and (2) determinants of the choices to selected WUE-improving soil and water management practices in southern Ethiopia. A multistage sampling technique was used to survey 373 households. The results indicated that the majority of surveyed households were male-headed: 90.6%, above 40 years old: 56.8%, and uneducated: 73.5%. They perceived that irrigation improved their net income (INCOM): 88%, acted as insurance against decreased rainfall (IADR): 44.8%, and insurance against increased temperature (IAIT): 70%; though the water was not available in all dry seasons: 55%. The choice to tightly close water-diversion points after use is significantly positively ( p < .05) affected by education level (EDUC) and perceptions about irrigation water as IADR and IAIT. However, the farmers’ perceptions about INCOM significantly negatively affected their choice to not irrigate at peak sunshine hours. The choice of mulching is significantly positively affected by the perception of INCOM and IAIT. Similarly, the choice of using compost is significantly positively affected by EDUC and their perceptions of IADR and IAIT, and significantly negatively affected by INCOM. The choice of not practicing conventional tillage is strongly negatively affected by the farmers’ perceptions about equitable water distribution (EWD) and INCOM. Therefore, it can be concluded that the farmers’ understanding of the roles of irrigation in climate change adaptation is good but their understanding of WUE-improving practices is poor due to poor water distribution systems and low education levels. So, improving water distribution systems and farmers’ awareness about WUE-improving practices are suggested to the study area and other countries under related conditions.

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Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.

Crop canopy water content and crop root zone soil water content have been predicted and observed for estimation of crop water balance for the rainy season nitrate runoff and leaching index of the Bilate watershed cropland. For the calibration of earth data observation, the watershed rain gauge station estimated the pattern of rainfall for the main cropping season of crop growth. Cropping season (Apr, May and Jun) monthly mean rainfall between (125mm/month to 165 mm/month) of agro metrological station has predicted time series crop canopy water in the analysis for crop land nitrate-nitrogen leaching/runoff index for the quantification of groundwater and surface runoff nitrate from the Bilate watershed farming zone. May and June were the months in which crops grew in the cropland of the study area, as indicated by the crop biomass statistics from MODIS 006 MOD13Q1-EVI of (Mine 0.19 and Max, 0.57) which is the average crop coefficient Kc for crop growth curve analyzed for model crop parameter. Hyper-spectral indices derived from enhanced vegetation indices (EVI) have been calculated for the analysis of crop zonal biomass statistics (kc) and integrated into the prediction model. The objective of the current research was to predict crop canopy water content in order to determine crop water balance for farmland Nitrogen Nitrate (NO− 3-N) runoff\leaching index for surface and groundwater pollution in the Bilate downstream. The overall predicted result of crop canopy water content has been validated with a regression coefficient (R2) with observed crop root zone soil water content. And the crop land nitrogen balance has been used to confirm the nitrate-nitrogen leaching and runoff index for the study area by comparing the current result with the crop land nitrogen balance.

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