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Purpose Understanding the spatial dynamics of soil loss (SL) and sediment yield (SY) from ungauged watersheds would have a profound importance to devise informed land resource and flood management strategies. Such information particularly substantial for rivers like Koshi, that typically causing severe land degradation and frequent devastating flood risks to large number of inhabitants in the basin. This study aimed to estimate the spatial heterogeneity of mean annual SL and SY in Triyuga river watershed (TRW) and delivers into Sapta-Koshi. Materials and methodsThe Revised Universal Soil Loss Equation (RUSLE) and Slope-based Sediment Delivery Ratio (SDR) models along with Geographic Information System (GIS) and remote sensing techniques were applied. Results and discussionThe mean SL and SY were found to be 31 and 3.04 t/ha/year, respectively. Accordingly, an estimated 2.2 × 10 5 tons of sediment was delivered into the Sapta-Koshi river annually. In this watershed, nearly 62% of the area is under the category of high to very severe soil loss rate (SLR), which together accounted for 96% of the total SL in the TRW. The critical SLR was mostly distributed along the northern escarpment of the study area. Most of the sloping areas in the northwestern and some patches of the northeastern tip regions that accompany with slope class > 26.8% are particularly vulnerable to severe SL and SY in the TRW. In this regard, the RUSLE LS factor has played the significant (r = 0.88, p < 0.001) impact over other RUSLE factors. Conclusion High elevation and steep slope class in the northwestern and northeastern parts of the watershed are the major SL and SY hotspot sites that require special attention and priority for conservation interventions in the TRW.

With climate change, hydro-climatic hazards, i.e., floods in the Himalayas regions, are expected to worsen, thus, likely to affect humans and socio-economic growth. Precisely, the Koshi River basin (KRB) is often impacted by flooding over the year. However, studies on estimating and predicting floods still lack in this basin. This study aims at developing flood probability map using machine learning algorithms (MLAs): gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations with available topography, hydrogeology, and environmental datasets were further considered to build flood model. Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: training dataset (location of floods between 2010 and 2019) and testing dataset (flood locations of 2020) with thirteen flood influencing factors. The validation of the MLAs was evaluated using a validation dataset and statistical indices such as the coefficient of determination (r2: 0.546~0.995), mean absolute error (MAE: 0.009~0.373), root mean square error (RMSE: 0.051~0.466), relative absolute error (RAE: 1.81~88.55%), and root-relative square error (RRSE: 10.19~91.00%). Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The final flood probability map could add a greatt value to the effort of flood risk mitigation and planning processes in KRB.

This study was conducted to fit and evaluate ten existing nonlinear height diameter functions for Cupressus lusitanica in Gergeda forest Ethiopia. A total of 260 trees were measured for their diameter at breast height (D) and height using destructive sampling methods. This data were randomly split in to two datasets for model development (50%) and for model validation (50%). Considering hetroscadasticity of variance, all functions were fitted using weighted nonlinear least square regression. To evaluate the performance of each model, five fit statistics-such as Coefficient of determination (R 2), root mean square error (RMSE), bias (E), absolute mean deviation (AMD), and coefficient of variation (CV%) were used. Among all the models tested, the Weibull type function of the form H ¼ 1.3 þ a (1-exp (-bD c)) þ E was observed to give the best fit based on the model's goodness of fit and predictive ability. Therefore, this model with three parameters has been conformed to provide reliable estimate of total tree height for Cupressus lusitanica in Gergeda forest, Ethiopia.

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