mohammad taghi avand

In recent years, our country has experienced an increase in the frequency and severity of natural hazards such as floods, droughts, and pests. These changes can be attributed to shifts in climate variables. Due to its influential role in other natural hazards such as drought, climate can significantly impact the economy and people's lives. This is particularly evident in the agricultural, animal husbandry, and industrial sectors, where it can cause damage and destruction in various regions. Drought is one of the biggest climatic challenges that our country has been facing in recent years. When a drought occurs, it initially manifests as a meteorological drought. If the drought persists, it can lead to other types of droughts, including hydrogeological, agricultural, and economic droughts. Each of these droughts, as well as their cumulative impact, affects various aspects of the ecosystem within a watershed. Climatic changes cause many problems due to their impact on the temporal and spatial distribution of precipitation in various regions. Due to its gradual process and slow speed, this phenomenon has been operating for a relatively longer period. Its effects may be revealed after a few years and with a longer delay than other risks. This phenomenon has more tangible effects in rural areas because rural communities are more vulnerable. Therefore, understanding this phenomenon and analyzing and evaluating its impact on ecosystems within a watershed is crucial for effective planning and decision-making. This knowledge is necessary for the implementation of appropriate adaptation strategies.

To carry out this research, the required climatic data were first collected from the Meteorology and Regional Water Department of Mazandaran Province. The SPI (Standardized Precipitation Index) index was also used to evaluate meteorological drought. Additionally, to examine the trend of changes in flow rate, the data from existing hydrometric stations in the region, which have more comprehensive data, were utilized. Lars-WG software was used to project future climate (2023-2050) conditions under two climate scenarios: an optimistic scenario (RCP2.6) and a pessimistic scenario (RCP8.5). After preparing the current and future data, the DrinC software was used to determine drought thresholds based on the standard table for the SPI. In this research, the IHACRES hydrological model was also utilized to forecast future discharge. To achieve this objective, the future precipitation and temperature data obtained from the previous stage were utilized, along with the observed discharge data (1990-2020), to project the trend of discharge in the future (2022-2040). Subsequently, the hydrological drought of the Tajan Watershed was calculated at the outlet station (Kordkhil) using the SDI (Streamflow Drought Index) in the DrinC software. After analyzing the stream flow data and using the standard table associated with the SDI, the thresholds for hydrological drought were determined.

The results of this study showed that, based on the SPI standard table, the degree of drought in the area under investigation has fluctuated between -3.3 (indicating very severe drought) and 2.4 (indicating extremely humid conditions) in recent years. Also, the future climate was projected for a period of approximately 27 years (2023-2050) under the influence of two optimistic scenarios (RCP2.6) and one pessimistic scenario (RCP8.5) at three selected stations (Kordkhil, Soleiman Tange, Rig Cheshme). The results of the climate drought conditions and the index values for the projected period (2023-2050) at Soleiman Tange station, in 12-month steps showed that there is a possibility of a severe climate drought occurring in this region during the water year 2049-2050. Also, the results show that there is no significant difference in the future drought index between the two scenarios. The results of the climate drought situation at Rig Cheshme station indicate a high likelihood of a severe climate drought in the region during the water year 2029-2030. Additionally, the results of the climate drought condition at Kordakhil station show that the condition at Kordakhil station differs somewhat from the other two stations, which are located at higher altitudes. This difference is likely due to the proximity of this station to the Caspian Sea, as the climate in that region is influenced by the coastal humidity. It can also be expected that there will be a severe drought in the water year 2032-2033 in this region. The results of the IHACRES model showed that the comparison chart of the simulated discharge and the observed discharge is in good agreement. Additionally, the error coefficient values obtained during the two stages of evaluation and recalibration were 0.48 and 0.53, respectively. These values indicate the model's acceptable ability to simulate future discharge. The results indicate that the discharge rate of the Watershed is unlikely to undergo significant changes in the future as a result of climate change. The probable cause for this is the occurrence of intense rainfall events, which lead to flooding and subsequent increases in river discharge. Considering that these results only show the effect of climate change on discharge, there is a possibility that other factors, such as land use changes, may also contribute to changes in the amount of discharge.

Conclusion :

The results of this analysis indicate that in both climate scenarios, there is a possibility of experiencing severe droughts in some years in the future. However, it is also possible for the climate to be very wet in certain years. These changes in the state of drought over approximately 30 years indicate climate fluctuations, which are one of the signs of climate change in a region. Therefore, based on the obtained results, it cannot be concluded that we will experience either a completely dry or completely wet state in the next 30 years. Thus, it is necessary to employ adaptation strategies to mitigate the impacts of drought during certain years. The hydrological drought situation of the Tajan River in the next 20 years (2020-2040) was evaluated using both optimistic and pessimistic scenarios. The drought situation in this river has also exhibited fluctuations, with a decreasing trend of flow in some years and an increasing trend in others. These fluctuations also indicate another effect of climate change in a region, which causes heavy rains and floods, leading to increased river flow during certain times of the year. However, relying solely on the study of the flow rate of a region cannot accurately indicate the drought situation. This is because certain measures implemented in watersheds can significantly impact the flow rate of a river. Therefore, considering all the conditions and climatic factors that govern our country, it is not surprising to see such changes occurring in different periods.

Floods are one of the most important natural disasters that cause the most damage to infrastructure sectors and greatly affect people's economic and social activities. The occurrence of this phenomenon cannot be prevented, but by taking measures, its effects can be reduced to a great extent. One of the management methods to reduce damage caused by floods is to identify vulnerable areas. The purpose of this research is to determine the vulnerable areas of Tajen watershed using a new approach.

Therefore, in this study, with the aim of identifying flood vulnerable areas in four economic, socio-cultural, structural-physical and policy-institutional dimensions, the "Best of the Worst Method (BWM)" which is based on linear planning, for Index weighting was used. In order to rank the criteria, Friedman test was used. The statistical population of the study is the villages at risk of flooding in Tajan watershed in Mazandaran province. To do this, 208 questionnaires were randomly completed by residents of 40 villages. In this study, in order to analyze the questionnaires and implement the BWM method, SPSS16 software and Lingo18 optimization model were used, respectively.

The results of Friedman statistical test showed that there was a significant difference (Sig = 0.00) between the criteria studied in this study. The economic criterion with a rank of 2.85 has the highest importance compared to the other three criteria. Also, the results of weighting the indicators using the BWM method, which are based on the preference of the best and the worst, also showed that in the economic dimension of the agricultural income dependence index (0.284), in the structural-physical dimension of the index, there are appropriate communication channels (road and bright) (0.243), in the policy dimension, the index of destruction of natural resources (0.379) and in the socio-cultural dimension, the index of access to health centers (0.391) have the highest weight and have a great impact on vulnerability in terms of respondents. Residents are flooded. Also, in this study, the value of incompatibility index was less than 0.1, which indicates the good accuracy of the BWM model in weighting vulnerability indices.

In order to reduce costs and reduce the complexity caused by the large number of criteria and sub-criteria affecting the vulnerability of an area to floods, the use of new multi-criteria decision-making methods based on linear programming is emphasized.

Drought and water scarcity in Iran is a climatic reality and due to the increasing need for water in various sectors, the phenomenon of drought will become more severe in the coming years. Therefore, in such conditions, one of the effective and practical solutions is the optimal use and saving of various water uses, especially in the agricultural sector. In this study, by examining the meteorological and soil science information of Imamzadeh Jafar area, the water requirement of crops and orchards was calculated. Then, based on the yield and net income of the dominant crops per hectare, the amount of physical and economic productivity of water in each crop was calculated. Finally, by evaluating the current cropping pattern, the future cropping pattern of the aquifer was proposed. The results showed that the average productivity for the dominant aquifer products was 0.98 kg / m3 and the economic productivity of the products per m3 of water consumption was calculated at an average of 8904 Rials. The results of economic productivity assessment showed that the economic value of the proposed cropping pattern is 7 times higher than the current cropping pattern.

The purpose of this research was to model and predict land use/cover changes using the LCM model based on two management scenarios, namely the sustainability scenario and the agricultural increase scenario in the Tajan Watershed, the Province of Mazandaran. Landsat satellite images and the data collected by the TM and OLI sensors were analyzed for 1991, 2010 and 2019. Images from the three-time periods were categorized into five categories: residential, farmland agricultural, forest, orchard, and rangeland. Land use forecasting for 2019 was performed using the 1991 and 2010 land use maps in the LCM model. The LCM model uses the perceptron multilayer neural network to construct transmission sub-models, map the probability of transmission, and ultimately predict the land use changes. Spatial variables such as distance from a road, distance from a river, slope, altitude, distance from a village, and distance from farmland were used as the influencing factors on the neural network changes. The land use map of 2019 was used to validate the model, which resulted in the Kappa and ROC coefficients of 0.84 and 0.89, respectively. The modelling results based on the sustainability scenario show a 5.74 percent decrease in the rangeland area and a 9.17 percent growth in the orchard area over the next 20 years, while the second scenario showed an increase of 8.61% in farmlands and a decrease of 5.59% in rangeland areas. Therefore, both rangeland and forest areas are declining and farmland and residential areas are increasing. A better land use management will require conservation scenarios in different parts of the watershed.

Over the past decade, Maxent (maximum entropy) algorithm has been used extensively in natural resource studies, especially on topics related to animal habitat suitability, plant species distribution, and so in prediction of species presence potential areas. Since the presence of springs as a point feature at the watershed indicates the ability of the occurrence of a spring at that point, the Maxent model can be used to assess water resources potential of the watershed. Therefore, this study tries to evaluate the capability of Maxent algorithm to determine the potential areas for predicting and mapping presence of springs on the Kabgian karstic watershed.

The data used in this study are divided to two categories of variables including dependent variable as points of presence of the spring, and independent variable as environmental information layers, including: Geographical Aspect, Lineament Density, Vertical Distance to the Channel Network (VDCN), Topographic Wetness ( TWI), Topographic Roughness (TRI), Topographic Position (TPI), Stream Density, Stream Power, Slope, Real Surface Area(RSA), Digital Elevation Model (DEM), Slope Curvature, Fault Density, Vegetation (NDVI), Vector Ruggedness Measure (VRM), precipitation and lithology. All modifications, layer preparation, classification, analysis, and map extraction was done using ArcGIS® 10.5, PCI Geomatica® 2018, SAGA GIS, MaxEnt® 3.3.3, Google Earth Pro 9, and Excel 2016 software.

The results showed that the accuracy of the model was very good with AUC 82.7 % on the ROC curve. According to Jackknife test, among 17 environmental variables, DEM, VDCN, rainfall, TPI and NDVI indices had the most impact on modeling, respectively. The least impact on modeling is VRM, geographical aspect, curvature, fault density, and lineament, respectively. The impact of the lithology index for predicting has been moderate; this can be attributed to the fact that more than 73% of the watershed is covered with limestone and gypsum which has a relatively uniform effect in modeling; This condition can also be seen in the lithology response curve which has given a significant response for Asmari and Gachsaran Formations. In general, the response curves of the most influential variables in the modeling were interpreted as follows: DEM response, first, increases in the range of 2200-2000 meters, then decreases to 2700 meters and again increases to 3700 meters. By moving away from the valley, the effect of the VDCN index is reversed; In other words, most springs have appeared near the valley. Areas with 1000 mm (and more) rainfall have been the most prone part of the basin. TPI index shows that with increasing basin surface bulge and slope, the areas prone to the presence of the spring decreases. The response of the NDVI index is positive and relatively uniform. The stream density response diagram has two significant breaks; it first decreases sharply at a density of 0.5 (km / km2) and increases sharply at a density of 1 to 1.3 (km / km2), then decreases again. The impact of watershed lithology in the area of Asmari and Gachsaran formations has been positive due to karstification and significant outcrop, and has had a negative response for other formations, especially Gurpi and Pabdeh formation due to being fine-grained. In general, the breaks in response curves are likely to be due to the interaction of other variables. The turning points of these curves and the range between these points, however, are the most effective part of the index that indicates whether the area is prone or not. According to the water resources suitability map, the areas with high, medium and low potential of the spring presence are 1894, 21795 and 63637 hectares, respectively. Areas with high potential are mainly located on the heights of Asmari and Gachsaran formations.

Between the 17 environmental indices used, none had acceptable predictive ability alone; this can be attributed to the complexity of karst systems and the difficulty of modeling it. The accuracy of the model was 82.7%, indicating the very good ability of the model in achieving the research goal. The final map of the areas prone to the presence of the spring is divided into three classes: high, medium and low, which include 2.2, 25 and 72.8 percent of the basin, respectively. Maxnet software, based on Shannon's entropy maximum, only needs the presence information of dependent variable and the information layers of the independent environmental variables to predict the most suitable regions for the presence of a dependent variable. Hence, the main advantage of this model is the accurate classification of potentials using layers of the presence of dependent variables; therefore, the possible error resulting from the absence or absence of points does not occur using this model. In overall, Shannon maximum entropy algorithm has a significant ability to determine the potential of groundwater resources in different areas and can be used for predicting and mapping karst water resources potential in watersheds.

Flood is one of the most devastating natural disasters that annually causes financial and life losses. Therefore, developing a susceptibility map for flood management and reducing its harmful effects is essential. The present study was conducted to prepare a flood susceptibility map using data mining models including Random Forest (RF) and Gradient Boosting Machine (GBM). At first, 275 flooding locations flood and 275 non-flood locations were identified in the Komijan watershed of Markazi province. Spatial locations were randomly divided to 70% (190 location) and 30% (82 location) for modeling and validation, respectively. Then, 12 factors affecting the occurrence of flood including slope, aspect, altitude, rainfall, land use, distance from river, drainage density, plan curvature, profile curvature, lithology, soil and stream power index were determined. The ROC curve was used to evaluate the models used. The results showed that in the validation stage, the under curve for RF and GBM models was 0.83 and 0.75%, respectively, which indicates that the RF model is more accurate in producing a flood susceptibility map. The most important factors affecting the flood are rainfall, distance from river and altitude.

Soil erosion is a problem for agriculture in arid and semi-arid regions and is of great importance due to its long-term effects on soil fertility and sustainable agriculture. Among the types of water erosion, gully erosion is one of the most important events in soil erosion and land reclamation. Given that the Markazi province is located in a region with arid and semi-arid climate, the intensity of rainfall is high in some months of the year. Also because of the abandoned agricultural land in the study area, there is much vegetation exposed to severe erosion which is conducive to erosion such as gutter erosion, so serious attention is needed for this area. The data mining method extracts useful information from a large volume of data and has shown good performance based on the literature review. Therefore, the aim of the present study was to prioritize environmental factors affecting the occurrence of gully erosion with data mining and statistical methods.

In order to conduct the present study and to map the distribution of gully erosion zones in the Robat Turk watershed, 242 gully data were identified in the study area and used. A total of 242 points were identified as non-flooded areas. In order to model the data, it was divided into two categories of training and validation, with 70% of data used as training and 30% of data used as validation. Based on the research background, hydrological, geological and physiographic factors including elevation, slope, aspect, curvature, slope shape, distance from river, distance from road, lithology, land use, annual precipitation and NDVI, variables were selected for modeling. In order to model the gully erosion, artificial neural network (ANN) and generalize linear model (GLM) models were used, and the ROC and Kappa, TSS coefficient were used to determine the accuracy of the gully erosion susceptibility map.

The results of gully erosion susceptibility showed that the central areas of the watershed are highly sensitive to erosion. Considering that most of the lands in the central part of the watershed are bare land and agricultural, the study of the gully erosion susceptibility map showed that the most sensitive and highly sensitive erosion susceptibility area was formed in the bare land. In relation to the influence of different elevation and slope classes in the study area on susceptibility to erosion, it should be stated that altitude class of 1800-2000 meters and slope class of 0-12% had the highest contribution to erosion susceptibility in the study area. This may be due to the higher soil compactness of these classes than other classes, which increases the likelihood of water infiltration into the soil and the possibility of material dissolution and piping. Validation results showed that GLM and ANN with ROC of 0.89 and 0.88 have very good performance regarding gully erosion susceptibility in the study area.

Gully erosion is one of the erosion processes that widely affects the appearance of the earth. In this study, GLM and ANN were used to evaluate the impact of environmental variables on gully erosion as well as to identify potential areas for gully erosion. For this purpose, 12 variables and 242 gully erosion points were used. ROC, TSS and KAPPA statistics were used to evaluate the models. The results of evaluation and validation of the models used showed that both models have good performance in zoning susceptibility to gully erosion. Identification and prediction of gully erosion susceptible areas can reduce the damaging effects of this type of erosion and prevent its further development and can be of considerable help to the people of the study area. Given that most of the gutters were created in the central part of the study area near the village of Robat Turk, protective measures should be increased in these areas to prevent the spread of agriculture and residential areas to erosive areas.

Today, the phenomenon of flooding is one of the most complex hazardous events that, more than any other natural disaster, causes deaths and finances every year in different parts of the world. Therefore, flood susceptibility mapping is the first step in a flood management program. The purpose of this study was to identify flood susceptible areas using two methods of random forest (RF) and Bayesian generalized linear model (GLMbayesian) machine learning in the Tajan watershed in Mazandaran province, Sari. Past flood distribution maps were prepared to predict future floods. Of the 263 flood locations, 80% (210 flood locations) was used for modeling and 20% (53 flood locations) was used for validation. Based on previous studies and surveying of the study area, 13 conditional factors were selected for flood zoning. The results showed that three factors of elevation (21.55), distance from the river (15.28) and slope (11.18) had the highest impact on flood occurrence in the study area, respectively. The results also showed that the AUC values for RF and GLMbayesian models were 0.91 and 0.847, respectively, indicating the superiority of the RF model and the accuracy of this model in flood susceptibility mapping in the study area. The highest flood susceptibility area in the RF model is in the very low class and the high class in the GLMbayesian model.

Groundwater has a fundamental role and is a key factor in economic development and environmental protection in our country. In recent years, due to drought-induced rainfall, the extraction of groundwater has increased at a widespread level that could have environmental and geological hazards. Imamzadeh Jafar plain with an area of more than 60 km2 located in the northeast of Gachsaran city has a very good and prone position for agriculture. Abundant use and abandonment of the aquifer by more than 40 wells has led to the criticality of water resources and the loss of static levels, especially during the droughts in this plain. Boolean logic has been used to investigate the impact of watershed geological formations on the quantity and quality of wells. In this study, in order to investigate the spatial relationship between these wells and existing geological formations in the studied watershed, 4 buffers with distances of 1, 3, 5 and 10 km were created around each well. The results of these buffers showed that at 1, 3, and 5 km distances, Quaternary formations (Qft) with an area of 80.82, 47.13, and 35.19 percent had the greatest impact on the water quality of the wells, respectively, while at a distance of 10 km. The impact of the Fars Group formations, especially the Gachsaran Formation (Mgs), had the greatest impact on the water quality with an area of 26.92%.

Increasing population and agricultural development need dramatically water resources groundwater resources, therefore, are increasingly being considered, especially in arid and semi-arid regions. Aim of this research is mapping potential of groundwater resources on Yasouj-Sisakht region using data mining method Random Forest (RF) and Generalized Linear Statistical Model (GLM). For this purpose. For this purpose, information layers including slope, slope direction, slope length, aspect, topographic wetness index (TWI), distance from fault, distance from the stream, rainfall, land use, lithology, topographic position index (TPI) and stream power index (SPI) as the main factors influencing groundwater potential were identified and developed in ArcGIS and SAGAGIS software. From the distribution of 263 springs in the area, 70% (253 springs) were used as educational springs and 30% (109 springs) were used as experimental springs. The results showed that the level of underground water with low, medium, high and very high potential in the map of the random forest was 37.78, 22.22, 18.89 and 21.11%, respectively, and in the generalization linear model were 14.49, 32.04, 31.11 and 22.36%, respectively. Moreover, Sensitivity Analysis show that the factors affecting both methods are rainfall, altitude and distance from the fault factors. The accuracy of the data mining models used in this research was also evaluated using a relative performance curve (ROC). The area under curve (AUC) for both RF and GLM models is 92% and 65%, respectively. The accuracy of RF model, therefore, mapping groundwater potential in the study area is more than GLM model.

Rivers are considered as one of the main sources of water and energy supply for humans, due to their special effects on human life and the formation of different civilizations. Therefore, their behavior should be considered. Flood flow is very complicated in natural rivers, especially in Meanderi Rivers. Therefore, the present research uses a numerical model to evaluate river floods characterized as different return periods of 10 and 50 years. The study area is located on the Kor River. In this study, the geometry model and a numerical mesh system were calculated by taking advantage of topographic surveys and the required parameters for running CCHE2D were collected through filed works.
The CCHE2D model, a two-dimensional hydrodynamic model of flow and sediment transportation for unsteady flows, is able to simulate and analyze sediment transportation flows and morphological processes. It contains two parts: 1- CCHE-MESH generation for meshing the studied area
2- CCHE-GUI for applying the resulted mesh to simulate flow and sediment.
Finally, the model outputs including flow depth were obtained for the considered river reach in different return periods. In this study, using the numerical model CCHE2D, changes in the depth of the water in two return periods of 10 and 50 years in different sections of the river route were obtained and the diagram of these changes was one of the outputs of the model. Discussion and Conclusion: Study shows discharges with different return periods. In the Meander range of the study area, centrifugal force gradient flow of flow on the center and cross slope caused at the level of the water was so high, but the water level in the upper arch cross external and internal were decreasing. This phenomenon caused peripheral gradient pressure within the cross section which resulted in an imbalance of the local effect between the centrifugal force and gradient pressure flow, the secondary flow forms in the transverse section.
Once flooding occurs in the river (50-year return period), water level exceeds the main river channel and enters the surrounding floodplains. Under such circumstances, due to the differences between flood plains roughness and the main channel, flow rate (velocity) on flood plain is much slower than the main channel. Consequently, such difference leads to some shear layers in crossing points of the main channel and floodplain in the entrance, resulting in greater water turbulence. The comparison of the water flow velocity in different reach sections indicated that the highest water velocity was related to the first meander so that in return periods of 10 and 50 years it respectively reached 2.7 m/s and 3 m/s. The results of the study confirmed the applicability of the numerical model to predict river changes using flow parameters. Therefore, it can be said that the present numerical model is capable of analyzing the river changes in the wind tunnel channels in a desirable manner.