Abstract:
This study provides a new methodology for simulating environmental water stress index (EWSI) that addresses environmental droughts' stochastic nature at regional and local scales. The current research used a case study of the Upper Ewaso Ngiro river basin in Kenya that possesses regional disparities attributed to climatic, biophysical, and anthropogenic variables. A stochastic modelling approach that ensembled 4D Euclidean feature space algorithm, least-squares adjustment, and iterations integrated the four environmental droughts indicators (meteorological, agricultural, socio-economic, and hydrological) into a single multivariate index called EWSI. The correlation between the simulated EWSI and initial reconnaissance drought index ) produced a correlation coefficient (r) of −0.93 and a p-value < 0.02. The correlation between EWSI and river discharge had a correlation coefficient of −0.89 and a p-value < 0.02. The assessment of severity revealed that 67–100% of the basin exhibited moderate to extreme environmental water stresses conditions between 1986 and 2018.
