Drought is a frequent occurrence in semidesert areas of southern Ethiopia that significantly affect regional, social, economic, and environmental conditions. Lack of rainfall monitoring network, instrument measurement, and failure are major bottlenecks for agro-and hydroclimate research in developing countries. The objectives of this study were to evaluate the performance of CHIRPS rainfall product and to assess meteorological drought using SPI for the period 2000 to 2020 over Gamo Zone, southern Ethiopia. The performance of CHIRPS v2 was assessed and compared to station observations (2000–2020) in the study domain to derive SPI on a three-month timescale. The Pearson correlation coefficient (R), bias, probability of bias (PBias), mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Nash simulation efficiency (NSE) values across the zone for CHIRPS v2 were found to be 0.88, 1.02, 2.56, 0.25, 22.41, 33.14, and 0.77, respectively. The results indicate that CHIRPS performed good ability to analyze the drought characteristics in the Gamo Zone. The spatial and temporal distribution method of meteorological drought has been evaluated using the Climate Data Tool (CDT). The Standardized Precipitation Index (SPI) was computed using the gamma distribution method. The magnitude of (SPI-3) of monthly and seasonal (MAM) meteorological drought in the zone from 2000 to 2020. The result shows that the known historic drought years (2014, 2015, 2010, 2009, and 2008) were indicated very well. Furthermore, sever and extreme droughts were observed in 2008 and 2009 with drought duration of 6.7 and 6.3, respectively, in most areas of the zone. Hence, this study revealed that CHIRPS can be a useful supplement for measuring rainfall data to estimate rainfall and drought monitoring in this region.

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In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach.

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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