پژوهشنامه مدیریت حوزه آبخیز
پیاپی 27 (بهار و تابستان 1402)

The severity and frequency of floods, depends on watershed characteristics, including shape, slope, drainage density and terrain parameters. Also, hydrological characteristics such as rainfall, storage, interception, infiltration, and anthropogenic activities can intensify or reduce the flood losses. Determining flood-prone and flood-prone areas is a prerequisite for flood management planning and damage.

This study was conducted to assess the flood susceptibility of Khiavachai sub-watersheds located in the west of Ardabil province with an area of 12,680 hectares using frequency ratio method. Parameters of elevation, slope, land curvature, topographic wetness index, distance from rivers, direction, drainage density, hydrological soil group, geology and land use were used to assess flood sensitivity and prioritize sub-watersheds. The spatial map of selected parameters was prepared in raster format. Then, according to the historical flood events, the spatial location of 107 flood points in the area was determined. The observed flood event points were randomly divided into groups consisting of 74 points (70%) for calibration and 33 points (30%) for validation. Then, by comparative analysis between the occurrence of previous floods and environmental parameters affecting the occurrence of floods, the impact weight of each class of parameters was obtained. Finally, a flood probability map of the study area was obtained.

According to the flood potential map, flood susceptibility of Khiavachai watershed was shown as five classes with very high, high, medium, low and very low levels. The results showed that the sub-watersheds (1 and 2) adjacent to the outlet of the basin have the highest flood sensitivity. In other words, flooding of sub-watersheds located downstream of Khiavachai watershed is due to runoff accumulation and high flood prone due to the impact of other variables of slope and rainfed agricultural land use.

Protecting the river and carrying out various watershed management operations to convey the flow in these areas can reduce the potential damage caused by floods. The proposed flood sensitivity map can be the basis for prioritizing flood areas as well as control measures for flood management. Therefore, watershed management measures to control floods in this area are more important.

The present study was conducted to conceptualize and evaluate the health of the Asiabrood Watershed in Chalus Township, Mazandaran Province, Iran.

The existing regional data, including climatic, hydrologic, erosion, sedimentation, and economic and social data, were collected and analyzed. In addition, several field visits were also made to make a general assessment of the ecological, economic, and social situation of the region and to complete the information on the study watershed. Then, the watershed health was assessed using the conceptual model of pressure, state, and response at the sub-watershed scale. Accordingly, the pressure index was first investigated by analyzing the driving forces of human activities and climate change over the study watershed. Then, the current state of the natural environment and the watershed performance to the pressures was analyzed as the status index. Further, the response index of the Asiabrood Watershed was calculated as a criterion for expressing the degree of community response or different outcomes of the watershed to the changes and concerns imposed on the watershed system.

 The results showed that the total health index of the Asiabrood Watershed with respective and relative contributions of pressure, state, and response indices of 18, 39, and 43% and an overall health index of 0.50 has a moderate health condition.

Although the health status of the Asiabrood Watershed is in moderate condition, the health status of the study watershed may decline, and, of course, adverse responses are possible if appropriate management policies are not adopted.

Land use changes are one of the important factors in changing the hydrological status of the watershed and soil erosion, so the type of land use is one of the important and determining factors in the production of surface runoff. By knowing the process of land use changes, it is possible to manage the effects of land use changes in the hydrology of the affected areas and led the natural ecosystems towards balance. The following research was carried out with the aim of revealing land use changes using satellite data and analyzing its role in changing the hydrological situation.

The study area is the upstream part of the Siahroud river basin located in the south of Qaemshahr city. In this research, to extract land use maps of different time periods, integrated pixel-based and object-oriented methods, unsupervised and supervised classification of TM sensor images of Landsat 5 satellite related to 1998 and 2009 and OLI sensor images of Landsat 8 satellite related to 2015 and 2019 were used. After preparing the land use maps for each time period, the CN values for each land use were determined based on the standard instructions of the SCS method and the weighted average CN was estimated for the entire study area. At the end, the amounts of runoff production and depth of runoff resulting from rainfall are calculated.

The land use changes of the studied area in the period of 22 years showed that 281%, equivalent to 657 hectares, was added to the area of citrus orchards. About 5% of the area of deciduous forest has decreased and the area of residential areas has increased by 57%. Due to the changes in land use during this 22-year period, the weighted average value of CN has decreased from 63.01 to 62.73. Also, the estimation of the amount of runoff for rainfall equal to 59.8 mm (average maximum rainfall of 24 hours during the period of 22 years) showed that the depth of runoff has decreased from 4.83 mm in the 1988 situation to 4.73 mm in the condition of 2019.

Considering the changes in land use and its effect on watershed runoff production, it seems that these effects have been moderated and land use changes have played a role in reducing runoff production over a period of 22 years. Therefore, it is expected that with the continuation of this process of land use changes, the production of runoff in the upstream of the Siahroud River watershed will decrease.

Silt is one the most important constituents of soil texture that directly influence the soil erosion process and should take into account in many projects of soil erosion management and conservation. The study of this fraction using the traditional and prevalent lab methods, especially on large scales, is time-consuming, laborious and costly. Today, this can be done in a quick and cost-effective method applying new high-techs such as the spectroscopy technology. The present work intends to investigate the spectral behaviours of the soil silt fraction using the reflectance spectroscopy technology in Mazandaran province.  

Accordingly, 128 soil samples were collected from 20 cm of soil surface using the SRS method and auxiliary info-layers like as geology, pedology, landuse and road map of Mazandaran province. First, the sample set was sub-divided into two subsets: calibration and validation. Spectral signatures and domains specific to the silt components were detected and specified utilizing the PLSR and Cross-Validation techniques, as well, the hyperspectral pre-processing methods such as averaging, smoothing and 1st derivative algorithms based on the Savitzky-Golay Algorithm were done.

Modeling process was done based on the PLS technique to investigate the spectral signatures and behaviours of silt constituents. The final model with 4 latent factors (LFs) was calibrated with these specs: Rc: 0.55, RMSEc: 8.31 %, RPDc: 1.20 and RPIQc: 1.71 and was eventually selected as the best model for studying the soil silt of Mazandaran province. Results showed the model potentiality in prediction of soil silt of the study area, as well, the most influential spectral domains and ranges were detected and recognized. The correlation coefficients of silt contents with the influential spectral ranges and wavebands were also defined as follows, UV-390 nm: 0.27, Vis-680 nm: 0.31, NIR-970 to 990 nm: 0.32, SWIR- 1400 to 1410 nm wavebands: 0.34, 1910-1930 nm: 0.38, 2200-2210 nm: 0.39, 2340-2350 nm: 0.41 and finally, for 2430-2460 wavebands calculated as 0.43. The obtained spectral wavebands with the highest correlation coefficients (R(CCmax)) indicate the high impact as the independent predictor variables in the processes of soil silt modeling of Mazandaran province. Finally, the capability of the proximal sensing of diffuse reflectance spectroscopy technology (VNIR-PS) was demonstrated in the study of silt contents of Mazandaran province.

In this approach, the spectral ranges and bands affected by the silt components were defined, in addition to the predictive modeling processes. That can be used as a basis for studying silt contents at large scales applying the upscaling operation via airborne/satellite hyperspectral data. Also, it indicates the importance of soil reflectance spectroscopy technology as a fundament for detecting and recognizing the useful and effective spectral wavelengths as well as creating the optimized model for the utilization by remotely sensed satellite data. Moreover, the use of data with higher coefficient of variation and greater amplitude is highly recommended to improve and boost the model preciseness so that, the PLS algorithm can process better.

One of the main factors of the reducing agricultural production is the irrigation water salinity. Mazandaran province is one of the most important citrus-producing area in Iran, where the groundwater used for supplying the irrigation water in citrus orchards of the province. The main objective of this paper was to determine map spatial and temporal variations of groundwater salinity (EC) in the coastal strip of Mazandaran province with an area of 8252 km2. The collected data of 300 wells of the study area were measured by Mazandaran Regional Water Authority for 9 consecutive years and defined for ArcGIS 10.7.1 software as the average ECs for each 6 months.

EC maps were obtained by ordinary kriging (OK) for each 6-month periods from and the beginning of 2012 to the end of 2020, by using ArcGIS 10.7.1 software. According to the derived empirical function of the effect of EC on the citrus yield (Y=100-13(EC-1.4)). According to this function, ECs of 1.40, 2.17, 2.94 and 3.71 dS/m, corresponding to relative yields of 100, 90, 80 and 70% were selected, respectively. Thereby, areas belonging to the classes of 100%, 90-100%, 80-90% and <70% were outlined. In addition, the Mann–Kendall test and Sen’s slope were used to project future changes.

The result of EC maps indicated that the salinity of groundwater increases from west to east of the province. The Mann-Kendall test result showed a significant temporal trend of the size of the area below the <70% yield (EC >3.71 dS/m) and 100% (EC <1.40 dS/m), presenting that the salinity of groundwater can be a concern for citrus orchards in the coastal strip of Mazandaran province. The equation given by Sen’s slope estimator denoted that if this trend is continuing, the area under 100% yield will reach to zero in 2021 and the yield of citrus in the coastal strip will reach below 70% at the latest by 2047.

This study has shown that the salinity of groundwater was increasing in the eastern part over the time and it covered a wider area around the Miankaleh lagoon, which may be due to the leakage of the lagoon to the groundwater aquifer in that area. The reduction of groundwater quality over the time can be attributed to two factors: reduced rainfall in recent years and digging illegal wells and subsequently consumed extraction of wells in the region. Taken together, it is suggested that the wells in the area will be exploited completely controlled to prevent the reduction trend of its quality.

Soil plays an important role in sustainable development. Therefore, protecting and controlling of soil erosion greatly contributes to the well-being, security and sustainability of societies. In order to solve the growing problems of the country in the field of natural resource destruction and soil erosion, assessment of farmers' awareness about soil erosion and their perception and attitudes toward soil erosion phenomena would be essential. Because perception is a kind of mental inclination and the main cause of behavior changes, and hence changing the perceptions alters human behavior. Hence, the study of people's perceptions in different fields would be important to help managers and executives to be aware of people's ways of thinking about specific issues, and if necessary, to develop some plans to change the people's perceptions. Therefore, the main goal of this research is to understand the perception of the farmers of the Chehlchay watershed in Golestan province towards soil erosion. Also, appropriate training methods, the effect of some human factors on soil erosion and the reasons for not implementing conservation works at the level of agricultural fields from the farmers' point of view are surveyed and is introduced as sub-objectives of this research.

In this study, to understand farmers' perception on soil erosion, a conceptual model with seven components including familiarity and understanding of the concept of soil erosion, the effect of natural factors on soil erosion, the effect of human factors on soil erosion, responsibility for causing soil erosion, the consequences of soil erosion, how to deal with soil erosion and control and prevention of soil erosion was designed and following this model, 45 items were defined to cover all aspects of this model. Then, 200 questionnaires were filled out with face-to-face interview with farmers in four villages and the results were analyzed. Statistical analysis of the questionnaires was performed using SPSS software.

In this research, Cronbach's alpha coefficient was estimated 0.804. The results of this research showed that about 31% of farmers have more than average knowledge of soil erosion, 22% have average knowledge and 37% have less than average knowledge. Also, the results of evaluating the scores of the seven components of the conceptual model of farmers' perception towards soil erosion showed that the farmers with an average score of 2.4, 2.3, and 2. 2 had the lowest level of awareness, respectively.

According to the presented data on the awareness of farmers in the region, in general, the majority of farmers (about 68%) have intermediate and low knowledge about the phenomenon of soil erosion, so it can be concluded that the farmers' low knowledge and inappropriate perception about soil erosion causes insufficient attention to be paid in this regard and therefore no serious works are carried out to improve the soil erosion in the region. Therefore, according to the significance of the relationship between the level of education and the amount of education and promotion about soil erosion, it is suggested that by improving the provision of educational and promotional services, a background is provided for improving the perception of farmers in the region. Additionally, by increasing the awareness of farmers, an effective step will be taken to improve the rate of soil erosion in the upstream watersheds.

Applying the raw data of regional climate models in assessing the impact of climate change is not advisable due to possible biases. Therefore, correcting the bias of these data is necessary before using them for climate scenarios of the future. The aim of this study is to evaluate the performance and introduce the best combination of bias correction methods for precipitation and minimum and maximum temperature simulated by three CMIP6 climate models.

Five bias correction methods including linear scaling, variance scaling, local intensity scaling, power transformation and distribution mapping were investigated using root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (r) and Student's t test for the historical period (1990-2014). Afterward, the combination of the best bias correction methods was used to project precipitation and temperature under SSP2-4.5 and SSP5-8.5 scenarios in the future period (2051-2075).

Based on the results, three methods of variance scaling, local intensity scaling and power transformation for correcting the bias of the investigated data had weaker performances compared to the other methods. Two methods of linear scaling and distribution mapping had the lowest RMSE and highest r and NSE. Projecting the future climate using the combination of these two selected methods showed that the average annual precipitation in the Hamedan-Bahar region will decrease by 28 and 37 percent under scenarios of SSP2-4.5 and SSP5-8.5, respectively. Furthermore, the annual average of the maximum and minimum temperature will increase by 0.7, 0.9 under scenarios of SSP2-4.5 and 1.4 and 1.5 °C, under scenarios of SSP5-8.5, respectively. In addition, the highest seasonal decrease in precipitation (19.8 mm) compared to the baseline period will occur in the spring under the SSP5-8.5 scenario. Moreover, the highest seasonal increase of maximum and minimum temperature compared to the baseline period was projected in winter (1.6°C) and spring (1.7°C), respectively, under the SSP5-8.5 scenario.

Two methods of linear scaling and distribution mapping are suitable for reducing the bias of the CMIP6 models in the Hamedan-Bahar plain. Also, considering the projected increase in temperature and decrease in precipitation in this region, this study can provide useful information for policy-makers of water resources and agriculture to decide about the rainwater harvesting, recharging of aquifers, crop selection, cultivating period, crop rotation and management methods to reduce the impact of future climate change.

The problem of drought and water crisis in the in the central Iran watershed has become serious in recent years due to consecutive droughts and increased water consumption in drinking, agriculture and industry sectors. Since rain is the only source of water supply in this catchment area, a decrease in the amount of rainfall with this intensity can cause a widespread drought and eventually large migrations from these areas. Therefore, the aim of this research is to analyze the changes in the aridity index in the vast area of the central Iran watershed in future.

In order to achieve the study's objective, the minimum and maximum air temperature, sunshine hours, and precipitation of 40 synoptic stations in the Central Iran watershed for a period of 20 years (1991-2010) were received from the Meteorological Organization.Then the values of potential evapotranspiration, aridity index, and Angot precipitation index were calculated for each station in the current conditions. In order to project changes in the aridity index in the future, the output of three MIROC5, HadGEM2-ES, and GFDL-CM3 models for the historical period (1991–2010) and the medium future (2041–2060) under the RCP4.5 scenario and based on the LARS- WG6 scaled down. After evaluating the accuracy of the models against the observational data, the index values were calculated in future conditions, and zoning maps were drawn for the study area.

Projection of changes in evapotranspiration values under the RCP4.5 scenario (2041–2060) showed that the most changes were in January, March, December, and November. Shahrekord showed the highest percentage of incremental changes, with 36.8% change in January compared to the base period. In terms of rainfall regime, the Central Iran watershed has a winter rainfall regime based on the Angot precipitation index in the current conditions. The HadGEM2-ES model is consistent with the base period, but based on the MIROC5 and GFDL-CM3 models, the winter precipitation in many stations will change to the all-season precipitation regime in the future. Climatic zoning of the UNEP aridity index in the studied area indicated that in current conditions, more than half of the stations have a dry climate. Only Kohrang has a humid climate with an aridity index value of 0.78. Shemiranat and Oligoders have semi-dry conditions with values ​​of 0.21 and 0.20, respectively. The evaluation of the future climate conditions according to the GFDL-CM3 model shows that the humid climate in central Iran will gradually disappear, while the semi-arid climate will expand in this region. The output of the MIROC5 model also shows the expansion of the ultra-arid climate zone in different parts of the catchment area of Central Iran.

The results showed that the aridity index values based on selected GCM models have decreased in the studied area compared to the observation period. Therefore, a large number of stations that have dry conditions in the base period will experience ultra-dry conditions in the future climate.The evapotranspiration potential in the Central Iran watershed is increasing during all months of the year, especially in wet stations in the west. This issue shows the dominance of the aridity index over this region in future conditions.

Water erosion causes as loss of quality and environmental issues problems. The best methods of soil management are prevention of soil erosion, control of runoff and sediment, and maintenance of soil quality.

This research was conducted in order to investigate the effect of compost and vermicompost on physical and chemical properties of soil (based on a completely randomized blocks) and runoff and sediment (factorial based on a completely randomized blocks). For the experiment 9 plots were used, (3 plots include of compost, 3 plots include of vermicompost and 3 plots control) in an average 5 percent slope area. 18 ton/ha compost and vermicompost were added to each plot and mixed it with soil 30 cm deep. Soil erosion was estimated by runoff and sediment by 2 natural rain and 1 rainfall simulator with 3 Repetition in each rain. Some physico chemical and biological properties of soil, as pH, EC, MWD, BD, NPK, OM, Sp, CaCO3 were measured.

The results showed that Vermicompost and Compost fertilizers significantly increase the total N, available k, available P, organic matter, Mean weight diameter (MWD) and saturation percentage more than the control plot. Soil bulk density decreased in plots of Vermicompost and Compost compare to the control. The compost and vermicompost in the level of 1 percent reduce significantly the runoff volume in the first natural rainfall 68.5 and 82.6 and the second natural rainfall 62.56 and 74.2 and the simulator 30.3 and 45.45. The compost and vermicompost in the level of 1 percent reduced significantly sedimentation the compare to the control. Vermicompost treatments had the least and control treatments had the most of runoff volume and sedimentation. The compost and vermicompost in the level of 1 perecent reduce significantly sedimentation the compare of control. Vermicompost treatments had the least and control treatments had the most of runoff volume and sedimentation.

Thus, it seems Using the fertilizers such as compost and vermicompost can significantly effect on the soil nutrition preservation and improve soil properties and can be found useful in the development of erosion control programs.

In recent years, river flow forecasting is one of the most important issues for water resources management in Iran. This prediction requires statistics and information, unfortunately, most of the basins of the country lack data of the desired quantity and quality.

Therefore, hydrological modelling and the use of artificial intelligence are examples of solutions that are used to solve this challenge in hydrology. The criteria for selecting the appropriate model for this process are to evaluate the performance of the models according to the hydrological conditions of each region. In this research, IHACRES model and Artificial Neural Network (ANN) were used to predict the streamflow in Bakhtiary basin. The data from 1984 to 1994 were used as calibration period and the data from 1995 to 2006 were used for validation.

The evaluation results of the hydrological model and the artificial neural network were evaluated using Kling-Gupta, Nash-Sutcliffe indices, coefficient of determination, mean squared error and absolute mean error. Results showed that the artificial neural network had better results in the simulation in all the evaluated evaluation criteria.

According to the results of the methods used in the research, the artificial neural network method has a more accurate prediction of the Bakhtiary river flow than the hydrological model.

In recent years, the increases in population and expansion of settlements in hazardous areas have greatly increased the impact of natural disasters in industrialized and developing countries. Landslide risk zoning helps to identify strategic points and geographically prone critical areas. Therefore, measures for rapid, safe mitigation and strategic planning for the future are important. In fact, landslide risk assessment may be a suitable and cost-effective help for land use planning, so in this regard, the aim of the current research is to model the risk of landslides using non-parametric data mining in watersheds of Hyrcanian forests.

For this purpose, the map of landslide risk according to the Mora and vahrson method with the effect of factors affecting the occurrence of landslides including topographical factors, hydrological and climatic factors, geological factors, land cover factors, human factors, hydrographic network from digital elevation model, geological map It was prepared using the map of the Geological Organization of the country, and to obtain the elevation index and the relative height, first, using the curve of height levels taken from the 1:25000 topographic maps of the region, the map of the elevation classes was prepared. After that, the study area was divided into one-square-kilometer grids, and maps with the lowest and highest elevations in one-square-kilometer grids were obtained; in the last step, by subtracting these two maps, a map was obtained whose information shows the value of the postal index and relative height. Monthly rainfall was also used to obtain the soil moisture index. Finally, algorithms of three random forest models, an artificial neural network, and a decision tree algorithm were used to model the risk of landslides in the STATISTICA 12.0 software environment.

According to the results, the highest distribution of landslide areas belongs to the low-risk class (76%). TP, SL, SR, and TS variables were considered the essential factors of landslide occurrence based on their importance. The results of validation with three algorithms of RF, CART, and ANN showed; According to the coefficient of explanation obtained in modeling the risk of landslides, the artificial neural network model with (R2=0.99) is more accurate than other methods.

The results of the present study showed that data mining methods have a high capability in predicting the risk of landslides. Therefore, the use of the mentioned methods can be considered in reducing the risks associated with landslides and planning for land use.

mankind has witnessed various hydrological events such as water shortage, drought, flood, avalanche, etc., and the scope of these events depends on the behavior of hydrological variables and characteristics of watersheds. The lack of statistics and information or the lack of correct and appropriate statistics has caused the investigation and recognition of the behavior of hydrological variables and the characteristics of watersheds with acceptable accuracy. Based on this, hydrological models are a suitable tool for evaluating the current state of water resources and predicting future conditions with knowledge and understanding of the prevailing interactions at the level of watersheds.

In this research, using the SWAT model, the precipitation-runoff simulation of the Lar watershed was carried out, and for this purpose, the daily data of precipitation, temperature and discharge during the common statistical period (1988-2017) were used. The initial simulation of the hydrological model was done in the SWAT plugin, and the SWAT-CUP software and the SUFI-2 method were used as an optimal algorithm for sensitivity analysis, calibration and validation of the model.

Based on the SWAT model, the study area was divided into 37 sub-basins and 308 hydrological response units. In addition, by analyzing the sensitivity of 24 parameters affecting the simulation of the monthly runoff of the Lar watershed, it was determined that 7 parameters based on the values of the t-state and p-value indicators were selected as the parameters with the greatest effect, and out of these 7 parameters, Three parameters curve number for medium humidity conditions, base flow return factor to the main waterway and average usable water were identified as the most sensitive parameters in runoff simulation. Then 18 years of the statistical period (1988-2005) were selected for recalibration and 12 years (2006-2017) for validation. Also, the effectiveness and efficiency of the model was evaluated using NS, R2, p-factor and r-factor coefficients. Model efficiency was evaluated using NS, R2, p-factor and r-factor coefficients. The values of these coefficients were estimated as 0.80, 0.82, 0.80 and 0.33 during the calibration period and 0.90, 0.90, 0.39 and 0.43 during the validation period. In addition, the temporal compatibility of the high and low points of the simulated and measured discharge hydrographs also confirmed the high accuracy of the model in the simulation.

The obtained results show that despite the seasonal flow and no base flow, as well as the frequency of days with zero flow, the SWAT model has a high ability to simulate the monthly discharge of watersheds in dry areas.