Water quality is a big concern for the humankind as it is the most important natural resource. However, the non-controlled growth of rural and urban areas in rapidly developed countries such as India is affecting water quality. In this research, we pretend to assess and demonstrate the heavy-metal status of three different water bodies of Indus, Beas and Sutlej Rivers and Harike Wetland. Data on water quality from 2013 to 2017 were analyzed through multivariate statistic techniques (cluster analysis – CA – and principal component analysis – PCA) and compared among them using public water pollution indexes. The results of CA and PCA showed that Sutlej River and Harike Wetland are included in the same group, whereas Beas River formed a separate one. This may be due to the high content of Pb, Cu, and Zn in these water bodies as compared with Beas River. The Pb content of all the water bodies exceeded the limit for EPR, BIS and WHO guidelines for drinking water. The heavy-metal pollution index was also applied to the contents of different heavy metals to find which water body is more polluted, and from their results, it was found that Harike Wetland and Sutlej River are critically polluted with heavy metals.

Natural hazards are diverse and uneven in time and space, therefore, understanding its complexity is key to save human lives and conserve natural ecosystems. Reducing the outputs obtained after each modelling analysis is key to present the results for stakeholders, land managers and policymakers. So, the main goal of this survey was to present a method to synthesize three natural hazards in one multi-hazard map and its evaluation for hazard management and land use planning. To test this methodology, we took as study area the Gorganrood Watershed, located in the Golestan Province (Iran). First, an inventory map of three different types of hazards including flood, landslides, and gullies was prepared using field surveys and different official reports. To generate the susceptibility maps, a total of 17 geo-environmental factors were selected as predictors using the MaxEnt (Maximum Entropy) machine learning technique. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic-ROC curves and calculating the area under the ROC curve-AUCROC. The MaxEnt model not only implemented superbly in the degree of fitting, but also obtained significant results in predictive performance. Variables importance of the three studied types of hazards showed that river density, distance from streams, and elevation were the most important factors for flood, respectively. Lithological units, elevation, and annual mean rainfall were relevant for detecting landslides. On the other hand, annual mean rainfall, elevation, and lithological units were used for gully erosion mapping in this study area. Finally, by combining the flood, landslides, and gully erosion susceptibility maps, an integrated multi-hazard map was created. The results demonstrated that 60% of the area is subjected to hazards, reaching a proportion of landslides up to 21.2% in the whole territory. We conclude that using this type of multi-hazard map may be a useful tool for local administrators to identify areas susceptible to hazards at large scales as we demonstrated in this research.