Kafta Sheraro National Park is endowed with richest biodiversity and physical resources. These resources have a great potential contribution to the development of ecotourism. However, so far there have been no well-identified and mapped potential ecotourism sites. This study is aimed to identify potential ecotourism sites using a combination of Geographic Information System and Analysis Hierarchy Process method. In this paper, an integrated geospatial baseline data, such as biophysical features, wildlife, topography, climate, culture, and accessibility, were considered as main independent factors. Eleven criteria were designed based on 22 discussions with experts, as well as literatures, intensive field surveys, satellite imageries, and exiting thematic maps. The overall analysis result indicates that 27.63% of the area was highly suitable, 35.49% moderately suitable, 26.22% marginally suitable, and 10.66% not suitable. Most of the highly suitable areas lie in the western, north western, and some parts of the south eastern of the park. But, the moderately suitable areas are found in the central and southern parts of the park. Model area under the curve in the training data is 86.15%. Therefore, the output of this study can use as a basis for evaluating the suitability ecotourism development by governmental policy makers.

Water quality degradation is one of today's key environmental challenges. Once contaminated, its treatment becomes difficult and prohibitively costly. Therefore, efficient and sustainable groundwater management is essential to conserve this vital resource. This is particularly true for the Elalla-Aynalem catchment, which serves as the primary water source for Mekelle town. This study aimed to produce optimized maps of groundwater table depths and nitrate concentrations using geospatial models, based on 123 data points for groundwater levels and 71 data points for nitrate concentrations. Empirical Bayesian Kriging (EBK) proved to be the most effective interpolation technique for groundwater table depth, while Simple Kriging (SK) was the best choice for predicting nitrate concentration, as determined by the lowest root mean square error (RMSE). The ranking of interpolation models for groundwater table depths was EBK > SK > OK > UK > IDW > RBF > LPI, whereas for nitrate concentrations, the models ranked as SK > EBK > IDW > OK > UK > RBF > LPI. These results demonstrate that geostatistical methods outperformed deterministic interpolation techniques for both parameters. The northwest and central regions of the study area were found to have shallow groundwater table depths, areas that also exhibited high nitrate concentrations, likely due to their proximity to pollutant sources. This research provides valuable insights for promoting sustainable groundwater exploration and implementing effective measures to protect groundwater from contamination.