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.

Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.

Predicting the effects of land-use (LU) changes and hydrological processes on a rapidly urbanized catchment using the Markov chain and WetSpa models is the main objective of this research. Hourly hydrometeorological data for 2001-2016, land use maps, a digital elevation model (DEM) and soil texture were used as inputs into the models. The simulation results verified some negative impacts of LU changes, such as increases in peak discharge and flow velocity from 2001 to 2032 by 57.1% and 39.4%, respectively. Additionally, the time of concentration decreased from 6 h in 2001 to 5 h in 2016 and to 4 h in 2032. Surface runoff recorded the highest increases by 48.4% and 83.9%, respectively, in 2016 and 2032, compared to 2001. We concluded that the combination of both models is an appropriate tool for predicting the possible effects of LU changes on different hydrological features, which provide vital information for land managers.

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The GIS-based distributed hydrological model, WetSpa, whose flow routing method is described in this paper is suitable for flood prediction and watershed management on catchment scale. The model predicts outflow hydrographs at the basin outlet or at any converging point in the watershed, and it does so at a user-specified time step. The model is physically based, spatially distributed and timecontinuous. This paper focuses on the GISbased diffusive transport approach for the determination of rainfall runoff response and flood routing through a catchment. The watershed is represented as a grid cell mesh, and routing of runoff from each cell to the basin outlet is accomplished using the first passage time response function based on the mean and variance of the flow time distribution, which is derived from the advection-dispersion transport equation.The flow velocity is location dependent and calculated in each cell by the Manning equation based on the local slope, roughness coefficient and hydraulic radius. The hydraulic radius is determined according to the geophysical properties of the catchment and the flood frequency. The total direct runoff at the basin outlet is obtained by superimposing all contributions from every grid cell. The model is tested on the Ziarat _Gorgan watershed with 4 years of observed hourly rainfall and discharge data, and the results are in excellent agreement with the measured hydrograph at the basin outlet