Watershed Management Research
Year:2024 | Volume:37 | Issue:1( پیاپی 142)|Papers Count:8

Introduction and GoalAmong natural disasters, flood is undoubtedly the most catastrophic hazard in the world. One of the basic strategies for reducing the damage caused by floods is to prepare a flood sensitivity map. Spatial prediction of the flooding probability using models created from spatial and historical data, which ultimately leads to the preparation of flood sensitivity maps is an appropriate solution for land management planners in different areas to prevent the occurrence of this phenomenon. In this research, in order to determine flood-prone areas, the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of imperial competition (ANFIS-ICA) and the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of particle swarm (ANFIS-PSO) are used.Materials and MethodsThe Zarine River watershed has an area of 4485 km2 and is located in the northwest of Kurdistan province between the longitude of 45°48ʹ30ʺand 46°48ʹ20ʺ east and the latitude of 35°42ʹ20ʺ and 36°23ʹ15ʺ north. The climate of the region is humid and the average annual rainfall is 480 mm. Locations of flood events were randomly divided into two groups: training (70%) and validation (30%). Various environmental factors (height, direction, slope, surface curvature, land use, lithology, rainfall, flow power index, distance from river and topographic wetness index) were selected as independent variables in the modeling and their digital layers were prepared. The ANFIS-ICA and ANFIS-PSO models were used in this research and their prediction results were evaluated based on the criterion (AUC) and the true skill statistic (TSS).Results and DiscussionOn the basis of these findings, in the validation stage, the model (ANFIS-PSO) with an AUC of 0.98 and a true skill statistic (TSS) of 0.89 had the highest accuracy. The results also showed that the factor of distance from the stream was identified as the most important environmental factor. In addition, ground slope and TWI were ranked second and third in importance, respectively.Conclusion and SuggestionsBased on the results, the hybridization approach, which combines machine learning models and meta-exploratory optimization algorithms, improves the learning power as well as the predictive power of the model. The results of this research showed that the distance from the stream and the slope of the land are the most important factors affecting flooding. Based on the results and analysis, it can be concluded that machine learning models have a high capability for predicting flood potential. The flood potential maps prepared in this research can be very useful for managers and experts and can be used in planning flood prevention measures. Directing flood control facilities and measures in situations with a high flood potential will improve flood management from an economic and technical point of view.

Introduction and GoalDue to the large volume of human and commercial activities, one of the most important goals of infrastructure is to collect and transfer urban runoff, control floods and prevent flooding in cities. To correctly estimate the runoff and characteristics of hydrological units and channels, it is important to use appropriate hydrological and water models. Considering the importance of this topic, this research was conducted with the aim of estimating the urban runoff of the Malayer to determine the potential flooding points using a storm water management model (SWMM).Material and MethodsOn the basis of available technical reports, the area of the sub-watersheds, nodes and main channels of the city were first determined. Flow direction and slope of the area was determined using a digital elevation model and 11 sub-watersheds were identified. In addition, 11 nodes were determined, regardless of the initial water depth and water level. Using the daily rainfall data of the Malayer synoptic station during the 1992–2020 statistical period, a nine-hour rainfall with a return period of 2 years was calculated and entered into the model. The geometrical shape of the channels was an open rectangle and the flow trend in the channels was determined using the kinetic wave method. The size of the maximum flow of water through the channels was calculated using the relationship between the cross-section of the flow and the speed of the flow a 9-hour the rainfall with a return period of 2 years. To calibrate the model, the variables of percentage of impervious areas, pond storage and roughness coefficient of impervious areas were used in the change range of the allowed modification range. In order to evaluate the model, Nash-Sutcliffe efficiency coefficient and the root mean square error were used.Results and DiscussionThe results showed that after optimizing the sizes of the variables, the Nash-Sutcliffe efficiency coefficient and the root mean square error were 0.73 and 0.02, respectively. Of the total rainfall of 14.54 mm, 5.53 mm was related to infiltration losses, 7.55 mm was related to surface runoff, and 1.45 mm was related to pond storage. The results showed that the sub-watersheds that were in the north of the city and overlooking the heights and leading to node number 10 had more volume and drainage and it is necessary to revise the design and expansion of the channels in this area. In addition, the sub-watersheds of the western part of the city (sub-watersheds no. 4) and the southwestern part (sub-watersheds no. 8) had the highest and lowest flood potential of 0.651 and 0.547, respectively.Conclusion and SuggestionIn this research, the results showed that almost half of the city would be affected by flood risks in the event of rains. Therefore, the current drainage network does not have the necessary efficiency to discharge urban runoff in the northern part of the city, and it is necessary to determine the optimal dimensions of the channels. Because the eastern elevations of the studied area are snow-covered and considering the geology of the area, in future research, models capable of calculating the runoff caused by snow melting should be used.

Introduction and GoalAwareness of spatial variations in groundwater quality parameters is an important tool for identifying the capacity of the region and land management. Jiroft is located on an arsenic belt in the country and considering the effect that arsenic has on underground water and the carcinogenic nature of this element, analysis of this element is very important. Moreover, most rural regions of Jiroft use the well water for drinking. Therefore, the analysis of groundwater in this region appears to be very important for drinking purpose.Materials and MethodsUsing geostatistical methods, the present study attempted to analyze the spatial variation of nitrate, arsenic, manganese, and all dissolved solid parameters in the groundwater of the Jiroft watershed during 2019. For this purpose, water from 36 agricultural wells and wells used for drinking in villages, as well as urban drinking water samples, were sampled in three replicates. The geostatistical methods used for zoning the above parameters include ordinary kriging (OK), simple kriging (SK), radial basis function (RBF), and inverse distance weighting (IDW) with different powers. Geostatistical methods were evaluated using the mutual evaluation technique applying the root mean square error (RMSE) and mean bias error (MBE) criteria between the actual and estimated data.Results and DiscussionIn this research, the spatial changes of nitrate, arsenic, manganese and total solids dissolved in groundwater in the Jiroft watershed were analyzed using the evaluation criteria ranking results showed that the estimation of arsenic and TDS with the spherical model of the simple kriging method had the least error and the estimation of nitrate and manganese with the radial basis function (RBF) method had the lowest error. The study showed that the spatial correlation of the quality metric of the Jiroft watershed is very high. Therefore, the error of the semivariable model of the measured data of arsenic with a partial effect of 0.00025 and a range of influence of 36.7 km was 38.4%, whereas the error of the semivariable model of nitrate with a partial effect of 0.3 and a range of influence of 28.5% was 4.9%. In the Jiroft watershed, the concentration of arsenic in the water of other areas was much higher than the permissible amount for drinking based on standard 1051 of the Iranian Institute of Standards and Industrial Research except for urban water. In addition, the concentration of nitrate in all the samples was lower than the permissible amount for drinking water, so there is no obstacle to its use.

Introduction and GoalUsers prepare accurate data of the amount of rainfall by using rain gauge stations. However, an interpolation of rainfall data is difficult due to temporal and spatial variability. Therefore, rain gauge stations are not well distributed in many areas, especially in mountainous areas. In a mountainous area, understanding the interaction between the resolution of the Digital Elevation Model (DEM) and climate variables is necessary for accurate spatial interpolation of average rainfall in many areas, and on the other hand, the need for accurate information in hydrological modeling and many environmental studies and it is climatic. One of the problems that exists in many hydrological studies is that rainfall maps are always prepared using interpolation or available DEM, regardless of rainfall, which have an estimated rainfall error.Materials and MethodsIn this study, four DEMs with spatial resolutions of 30, 90, 1000, and 10000 m, which are the most common DEMs in studies, were used to introduce the best elevation digital model for extracting the rainfall gradient map from the data of 11 meteorological stations in Kermanshah province. A rainfall map for Kermanshah province was prepared using a linear regression model fitted between the height of each station and the 20-year average rainfall. The best DEM for rainfall estimation was then determined on the basis of error evaluation criteria.ResultsThe results of this research showed that in estimating rainfall, DEMs with cell sizes of 1000 and 10000 m (R2 = 0.76, 0.81) were more accurate than DEMs with spatial accuracy of 30 and 90 m (R2 = 0.75). In the examination of the Nash–Sutcliffe coefficient (NS), compared to other digital height models of accuracy, DEM with a spatial resolution of 1000 m (one km) with a Nash–Sutcliffe coefficient of 0.76, a significance level of 0.01, and a correlation coefficient of 0.81 was found to have greater accuracy.Conclusion and SuggestionsThe results of the present study can be used to estimate and generalize rainfall in areas that do not have stations and to prepare rainfall maps in areas where the number of stations is limited. In addition, it should be used in univariate interpolation methods that do not have proper accuracy because spatial distances are not considered. In addition, due to the complex topography of the earth and the non-uniformity of meteorological stations on the earth’s surface, high-resolution models with higher spatial resolution are required for the estimation of rainfall, which increases the accuracy of digital models in the evaluation of rainfall studies by removing topographical levels that cause errors.

Introduction and GoalThe rangeland ecosystems of arid and semi-arid regions play the most important role in the distribution and density of vegetation in these areas due to the specific environmental conditions governing the soil, which is the bridge between living and non-living parts of ecosystems. Soil enzymes play important biochemical roles in the decomposition of organic matter at all stages in the soil system. These enzymes play an important role in the stability of the soil structure, the decomposition and formation of organic matter, the cycle of elements and the activity of soil microorganisms. Environmental stresses, such as drought, have negative impact on plant growth and soil microorganism's activity. Therefore, evaluating the effect of flood spreading on soil quality indicators can be a useful step in improving knowledge in this field. Therefore, this research was conducted during 2018 and 2019 to investigate the effect of flood spreading, plant types and characteristics on biological soil in the Kowsar floodplains located in Gareh-Bygone, Fasa.Materials and MethodsSampling of the soil around the roots of Artemisia sieberi Besser, Dendrostellera lessertii (Wikstr) Van Tiegh. and Heliantemum lippii (L) Pers, in two situations: with flood spreading and without flood spreading (control area) in spring and autumn from a depth of 0-20 cm and in three repetitions. After ensuring the normality and homogeneity of the variance of the data by the Shapiro–Wilk and Levene tests, the data were analyzed using the two-way variance method and mean comparison were made using Duncan's test at the 1% and 5% level and using the R software.Results and DiscussionThe results of soil fertility characteristics showed that there was no significant difference in the amount of phosphorus and potassium in the three pasture species studied under flood spreading and the control areas. However, the amount of carbon and nitrogen in all studied species was significantly higher in the flood spreading area (containing 0.3% organic matter) than the control area (with 0.15% organic matter). In addition, the results of this research indicated that the highest activity of acid phosphatase enzyme was observed in the rhizosphere of D. lessertii species and in the spring season, and on the contrary the lowest activity was observed in the rhizosphere of H. lippii species under the flood spreading condition. Alkaline phosphatase activity in all species was significantly higher in spring than in autumn season. The highest activity of this enzyme was observed for A. sieberi in the spring season with an average of 186 (µg p-nitrophenol g-1 soil h-1) in the flood spreading area and the lowest level was observed for H. lippii in the control area with an average of 57 (µg p-nitrophenol g-1 soil h-1) in the autumn season. In general, the level of phosphatase enzyme activity for the A. sieberi species in the spring season was approximately 89% higher in the flood spreading area than in the control area. The dehydrogenase enzyme activity was higher in spring than in autumn. Also, the highest activity of this enzyme was observed in the spring season for the D. lessertii  species with an average of 8.5 (µg TPFg-1 soil 24 h-1 ) in the control area, and the lowest level was observed in the autumn season for the A. sieberi species in the flood spreading area with an average amount of triphenol formazan was 1 (µg TPFg-1 soil 24 h-1 ) .In other words, the amount of dehydrogenase enzyme activity in the spring season was approximately 66% higher than that in the fall season in both areas (control and flood spreading). The results of this research showed that the activity of urease enzyme under flood spreading conditions in both seasons (autumn and spring), with an average of 270 and 300 (µg N/g.dm.2h) is more than the control area with an average of 173 and 250 (µg N/g.dm.2h). Also, the soil quality indices in all three studied species in the two (control and flood spreading) areas have a significant difference, so that the flood spreading operation has upgraded the value of these indices for the H. lippii species and the A. sieberi, while which has reduced the soil quality index of D. lessertii.Conclusions and SuggestionsThe results of this research showed that the flood spreading system is a desirable method for improving soil organic carbon, and its efficiency increases with the plant planting. The increase in moisture and the amount of organic matter in the flood spreading area can be a reason for increasing the activity of enzymes in the flood spreading area, so that in both autumn and spring, the activity of soil enzymes is more in the region. Considering the important role of organic matter in improving biological indicators and soil quality and health, the restoration of floodplains with native species can play an important role in providing these conditions. In addition, it is recommended that these tests be repeated with more samples and in different areas and the nitrogen, carbon, sulfur and phosphorus cycles and its relationship with the activity of other soil enzymes related to this cycle should be investigated.

Introduction and GoalFloods cause loss of life and financial losses every year, and their management is one of the essential elements of watershed management. In this research, an attempt is made to determine the flood susceptibility of the Sirwan watershed and finally the importance of various environmental factors in flood susceptibility based on historical flood events.Materials and MethodsIn this research, the maximum entropy model along with 13 topographical, hydrological, morpho-hydrological, geological, and environmental flood-affecting factors were used to model the flood susceptibility of the Sirwan watershed and determine the importance and percentage of participation of various factors in the state of flooding potential. A cellular computing unit (pixel) was chosen as the criterion for preparing the predictive factors and flood susceptibility maps. A total of 123 historical flood inundation events detected in the last decade were used as target variables in the model, of which 70% were considered for learning and the remaining 30% for validating the model results. To evaluate the performance of the model, the criterion of the area under the receiver operating characteristic curve was also used.Results The results indicate that the accuracy of learning and validation were 98.2% and 97.3%, respectively, indicating the excellent performance of the model. Based on the visual interpretation of the flood susceptibility map, streams with a higher order near the watershed outlet, which are the conduits for the passage of the flow with a larger volume and are located in lower areas, often have a higher proneness to flood inundation. Based on the results of the relative importance test, the four factors of distance from the stream, topographic wetness index, drainage density, and land use were introduced as the most important factors in the modeling flood susceptibility, with of 17, 13, 12 and 10% participation, respectively. These results show that natural hydrological, morpho-hydrological and environmental factors (both natural and man-made) have a mutual effect in increasing flooding susceptibility. Based on the quantitative analysis of modeling, about 0.76% (5600 hectares) of the studied area is in the high and very high flood susceptibility class, which requires planning and flood management.Conclusion and SuggestionsThe high classification of flood susceptibility classes in the Sirwan watershed of Kurdistan province and the determination of the importance of environmental factors in the event of flooding make it possible for managers to take an effective preventive approach by planning relief facilities and infrastructure. To reduce the risk of flooding, flood crisis management in the Sirwan watershed should be defined based on the four main factors identified in this study. Application of the maximum entropy model in flood susceptibility analysis is suggested for flood management of watersheds.

Introduction and GoalIn recent years, problems caused by human interference in the Salmas Plain, such as an increase in the irrational use of surface water resources, an increase in the process of groundwater discharge, and a change in land use have caused pressure on the groundwater resources of the region; hence, land subsidence is at least a natural response to the decrease in groundwater level. Therefore, it is important to study and identify subsidence areas in the region.Materials and MethodsIn this research, groundwater information and Sentinel 1 radar images were used. Groundwater information was obtained from the regional water company of West Azarbaijan province. In the present research, the DInSAR algorithm was used to measure the ground surface displacement, and the phase difference of the SAR signals was determined using the repeated-pass interference methods. Finally, by using the manual of differential radar interferometry with synthetic aperture and 52 even numbers of Sentinel 1 image, the subsidence in the Salmas Plain was determined between 2014 and 2015 and 2018 and 2019. Validation of the radar interferometry method using Qareqeshlaq Geodynamic station data, as well as groundwater level changes, underground water drop and field observations was conducted.Results and DiscussionThe results of the subsidence study using the DInSAR method showed that the average annual displacement rate is about 5 cm. Investigation of groundwater changes also indicated a decreasing trend in most of the wells. The wells located in the eastern and southeastern regions (e.g., Kangarlu, Qareqeshlaq, Yushanlu, etc.) have the highest amount of groundwater drop. In the study period (2014 to 2019), where the wells with an increase in groundwater level were affected by their proximity to the Zola dam reservoir and the water supply channels. The wells of Qezeljeh, East Qareqeshlaq, and Maidan Doab have the highest water level reductions, but they do not have sufficient water supply from the dam. Mahlam, West Tazeshahr, and Ian wells, which are in the western part of the plain and close to the dam lake, respectively, have a suitable increase. Checking the subsidence maps of the Salmas plain showed that most of the subsidence is concentrated in the southern areas, especially in the southeast of the plain, in comparison to other parts of the plain the depth of alluvium is also greater in these areas. This subsidence trend continued from 2014 to 2019 and this trend is also prevalent in the final map. During this period, the average water level changes in the Salmas plain aquifer had a downward trend, which was in harmony with land subsidence. By reviewing the groundwater drop map, it was found that the areas with the highest drop coincided with the subsidence areas. More drop in underground water indicated more water extraction. Therefore, the discharge of underground water in the region has caused subsidence.Conclusion and SuggestionsBefore adopting any operational plan and policy for implementation, it is the most important task to identify the exact location of land subsidence in any area, in particular residential areas. Sentinel1 radar images are sufficiently capable of solving this problem at all stages; however, a longer observation time would be more useful for decision making and implementation. Land subsidence in the Salmas plain is fully confirmed by the change of the underground water level and field visits. According to the DInSAR approach, the average displacement rate due to subsidence in the region was 5 cm, which can be fully verified in terms of observations of changes in the groundwater level. The piezometric data show a reduction of in the water level in most wells compared to the past, which depicts the occurrence of excessive extraction of groundwater. In addition, field surveys have been conducted in different areas of the Salmas Plain and the subsidence areas have been recorded. The process of subsidence in the studied area is ongoing, and the attention and care of the relevant authorities is needed to reduce the damage caused by this phenomenon and adjust the possible damages. It is recommended that people should be aware of the consequences of excessive harvesting and recommend alternative crops with a low annual demand for water and early yields.

Introduction and GoalThe aquifer recharge rate is one of the most difficult items to measure in GW resource evaluation. The techniques used in quantifying recharge are categorized in three main groups: unsaturated zone, saturated zone and surface water techniques. The saturated zone method is devided into physical methods (including Darcy's law and tracers), numerical modeling (the purpose of this research), groundwater level fluctuations, and water balance.Materials and methods Hydres and LitchW models were used to simulate soil moisture movement to simulate aquifer  recharge in this study. The models were then calibrated by optimizing the water flow factors of the aquifer layers in the Gareh Bygon with the inverse solution method. The water characteristics of the unsaturated soil layer in three wells with an approximate depth of 30 m in one of the floodwater spreading  basins were measured by field and laboratory methods. The soil moisture measuring device, TDR, was calibrated for the stony soils of the research area The sensors are then placed in holes with insulated walls from the surface to the depth.walls of oneof the wells Amount of soil-water contents were continuously measured from Sep. 2010 to Sep. 2020. The height of floodwater inundation and rainfall were also recorded. Recharge through unsaturated layers was assessed based on the soil water balance method as the observed data set.Results and discusionValidation flow simulation results from the of Hydrus and LEACHW models with observed measurements. The results showed that the Hydrus and LEACHW model very accurately estimated the water flow after the flood event (R2 and RMSE equal to 0.994 and 45.3 cm respectively in Hydrus model and 0.993 and 37.11 cm in LEACHW, respectively). In addition, the results of the Hydrus model were closer to measurements. The discharge ratio (the amount of infiltration divided by the amount of flood + precipitation) in the three methods of soil water balance, the Hydrus model and the LEACHW model were 47, 44 and 52 respectively, with an average of 48 for all flood events and 75, 71 and 92 with an average of 80% for large flood events. In the event of precipitation without flooding, practically all precipitation was spent on evaporation-transpiration, and for this reason, the size of the average infiltration ratio decreased in all events.Conclusion and recommendationValidation simulated flow results with Hydrus and LEACHW were compared with the observed measurements, demonstrating that these models accurately estimated the water flow after the flood event. In addition, , the results of the Hydrus model were closer to measurements. The mean data of the LEACHW and Hydrus models were overestimated by approximately 5 units less than 1 unit respectively. In simulations of water movement in soil using models such as Hydrus, a certain amount of water conductivity for the entire vertical column of the soil profile commonly considered. The results of this research showed how unrealistic such an assumption is. Therefore, in multi-layer soils, special attention should be given to the difference between layers and the use of water conductivity estimation equations. For the researched area, one-dimensional Hydrus model was recalibrated and its validation results were completely acceptable. This model can be used in three-dimensional mode for larger areas as a tool for applying different scenarios to manage the floodwater spreading system and increase their efficiency.