Before planning the use of existing land resources for irrigation, it is necessary to determine their availability. The primary goal of this research was to examine the Guder watershed's land resource potential for irrigation development and to create a geo-referenced map of these resources using a geographic information system coupled with fuzzy logic. Irrigation suitability criteria such as slope, land use, proximity to water body, rainfall deficit, soil texture class, soil depth, soil drainage classes, and proximity to road were considered when evaluating prospective irrigable properties. The criteria maps were divided into four suitability classes using a natural break interval range technique. According to the study's findings, 39.8% of the watershed area is Highly suitable, 34.5% is moderately acceptable, 24.5% is marginally suitable, and 1.2% is not suitable for the aforementioned reasons. According to the irrigation suitability study of these characteristics, 70.42% of the slope, 15.57% of the slope, 10.6% of the slope, and 3.95% of the slope are Highly, moderately, and marginally suitable for surface irrigation, respectively. In addition, 15% of the soil in the study Area is suitable for a surface irrigation system. In terms of land cover and use, 75% is highly favorable, whereas 0.3% is not suitable for irrigation development. GIS and remote sensing offer a straightforward and powerful framework for combining spatially complicated field variables for land suitability research. This study demonstrates the effectiveness of the fuzzy logic technique combined with GIS as an effective model for finding prospective irrigable land on a continental scale.

The univariate analysis of hydrological extremes is a well-established practice in developing countries such as Ethiopia. However, for the design of hydrological and hydraulic systems, it is essential to have a thorough understanding of flood event characteristics, including volumes, peaks, time of occurrence, and duration. This study utilizes copula functions for bivariate modeling of flood peak and volume characteristics, examining the performance of four Archimedean copulas in the Guder basin located in Ethiopia from 1987 to 2017. Flood peak and volume were extracted using the theory of runs for analysis of their joint characteristics with the truncation level chosen as equal to the lowest annual maximum event. Univariate distributions with the best fitness on both variables were determined, and results showed that gamma and GEV-fitted flood peaks and lognormal-fitted flood volumes are the most suitable. Four Archimedean copulas were evaluated, and the Gumbel-Hougaard copula was found to be the best fit for the data based on graphical and measurable tests. Bivariate probability and return period were computed in AND and OR states. The joint return period for flood peak (97.49m³/s) and volume (77.35m³/s) was found to be 15years in the AND state and approximately 4years in the OR state. The study also evaluates univariate and conditional return periods, comparing them with the primary one. The copula method was an effective method for distributing marginal variables, highlighting its potential as a valuable tool in flood management.

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions.