Abstract. Climate change has been identified as a leading human and environmental crisis of the twenty-first century. Drylands throughout the world have always undergone periods of degradation due to naturally occurring fluctuation in climate. Persistence of widespread degradation in arid and semiarid regions of Iran necessitates monitoring and evaluation. This paper aims to monitor the desertification trend in three types of land use, including range, forest and desert, affected by climate change in Tehran province for the 2000s and 2030s. For assessing climate change at Mehrabad synoptic station, the data of two emission scenarios, including A2 and B2, were used, utilizing statistical downscaling techniques and data generated by the Statistical DownScaling Model (SDSM). The index of net primary production (NPP) resulting from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images was employed as an indicator of destruction from 2001 to 2010. The results showed that temperature is the most significant driving force which alters the net primary production in rangeland, forest and desert land use in Tehran province. On the basis of monitoring findings under real conditions, in the 2000s, over 60 % of rangelands and 80 % of the forest were below the average production in the province. On the other hand, the longterm average changes of NPP in the rangeland and forests indicated the presence of relatively large areas of these land uses with a production rate lower than the desert. The results also showed that, assuming the existence of circumstances of each emission scenarios, the desertification status will not improve significantly in the rangelands and forests of Tehran province.

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

The study was carried out to assess meteorological drought on the basis of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) evaluated in future climate scenarios. Yazd province, located in an arid region in the centre of Iran, was chosen for analysis. The study area has just one synoptic station with a long‐term record (56 years). The impact of climate change on future drought was examined by using the CanESM2 of the CMIP5 model under three scenarios, that is, representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. Given that a drought is defined by several dependent variables, the evaluation of this phenomenon should be based on a multivariate analysis. For this purpose, two main characteristics of drought (severity and duration) were extracted by run theory in a past (1961–2016) and future (2017–2100) period based on the SPI and SPEI, and studied using copula theory. Three functions, that is, Frank, Gaussian and Gumbel copula, were selected to fit with drought severity and duration. The results of the bivariate analysis using copula showed that, according to both indicators, the study area will experience droughts with greater severity and duration in future as compared with the historical period, and the drought represented by the SPEI is more severe than that associated with the SPI. Also, drought simulated using the RCP8.5 scenario was more severe than when using the other two scenarios. Finally, droughts with a longer return period will become more frequent in future.

The accurate modelling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms – Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production – and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability 𝑝 of landslide occurrence decreases nearly exponentially with the distance 𝑥 to the next road, fault or river. Specifically, the results indicated that 𝑝≈exp(−𝜆𝑥), where the length-scale 𝜆 is about 0.0797 km−1 for road, 0.108 km−1 for fault and 0.734 km−1 for river. Furthermore, according to the results, 𝑝 follows, approximately, a lognormal function of elevation, while the equation 𝑝=𝑝0−𝐾∙(𝜃−𝜃0)2 fits well the dependence of landslide modeling on the slope-angle 𝜃, with 𝑝0≈0.64, 𝜃0≈25.6° and |𝐾|≈6.6×10−4. However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modelling of landslide risk, as well as for priority planning in landslide risk management.

The accurate modelling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socioeconomic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms -Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Productionand combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability of landslide occurrence decreases nearly exponentially with the distance to the next road, fault or river. Specifically, the results indicated that ≈ exp(− ), where the length-scale is about 0.0797 km − for road, 0.108 km − for fault and 0.734 km − for river. Furthermore, according to the results, follows, approximately, a lognormal function of elevation, while the equation = 0 − • ( − 0 ) 2 fits well the dependence of landslide modeling on the 2 slope-angle , with 0 ≈ 0.64, ≈ 25.6° and | | ≈ 6.6 × 10 −4 . However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions.Obtained results provide insights for quantitative modelling of landslide risk, as well as for priority planning in landslide risk management.