مجله علوم و مهندسی آبخیزداری ایران
پیاپی 63 (زمستان 1402)

In previous sediment fingerprinting studies, optimization of tracer signatures are not considered. Therefore, with different composite fingerprints, the amount of contribution from different sediment sources can be different from each other. Accordingly, the optimal composite fingerprint was determined using an approach of multi-stage refinement of tracers and reduction of uncertainties in the Idelo watershed in Zanjan province. In this approach, the ability and conservative behavior of tracers to separate sources are evaluated using variability ratios, conservatism test, range test, and finally, the conservative composite fingerprint as optimal composite fingerprint was determined. The detected composite fingerprint based on the introduced approach was different from a composite fingerprint obtained from conventional methods. Also, based on the aforesaid approach the related contribution of the geologic units in sediment yield were estimated 49, 32 and 19 percent for young alluvial deposits, red gypsiferous marl and old alluvial clastic, respectively. These results were different from those obtained using conventional and old fingerprinting methods with 11, 85 and 4 percent contribution in sediment yield for the aforementioned geologic units respectively. Generally, these results highlight the importance of applying the approach of multi-stage refinement of tracers and uncertainty reduction in determining the optimal composite fingerprinting of sediment.

One of the important and effective factors in the runoff production and sediment in hillslopes is their geometry. Vegetation also plays an important role in controlling and reducing runoff and sediment. In this regard, the present study was conducted with the aim of creating a better understanding of the effect of Vetiver vegetation on the processes of runoff and soil loss in convex-parallel and concave-parallel hillslopes in laboratory conditions. Accordingly, by using a rainfall simulator with an intensity of 45 mm/h, in 15 min on a plot with dimensions of 2 m2 and a slope of 20 percent, and by creating the shape of convex-parallel and concave-parallel hillslopes and filled with the surface soil of the sample Tahooneh watershed in Yazd province, rainfall simulation was done. Then, the values of runoff volume and soil loss in the said plots were compared and data analysis was carried out in plots with Vetiver vegetation and in two square and rhombic planting patterns with plots without vegetation. The results showed a decrease in the runoff volume in the concave-parallel hillslope in the treatment with square and rhombic vegetation, with values of 58.61 percent and 69.43 percent, respectively. Also, the square and rhombic planting pattern of Vetiver plant in the convex-parallel hillslope caused a decrease of 81.17 percent and 85.96 percent in the amount of soil loss, respectively.

In recent years, extensive practices have been done on flood control, erosion and sediment in the fields of research and implementation of watershed management. The present study was carried out for the modeling of Suspended Sediment Load (SSL) by artificial intelligent models in two subwatersheds in Ghaleh Gol watershed in Lorestan province, Iran. In this research, the flow velocity was measured and the SSL was sampled directly from the beginning of the rainfall events until the end of them. In this study, four soft computing techniques, GP-PUK, GP-RBF, MLP and RF were used to predict the SSL in study area. Total data set consists of temperature, rain, discharge and suspended sediment load that 70 percent of the entire dataset was used in a training stage of the soft computing techniques and 30 percent was used for testing the models. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE), Maximum Absolute Error (MAE) and Nash-Sutcliffe model efficiency (NSE). The obtained results suggest that the GP-RBF model (with C.C= 0.9509, RMSE= 0.067, MAE= 0.041 and NSE=0.924 in North watershed and with C.C= 0.966, RMSE= 0.048, MAE= 0.037 and NSE=0.932 in South watershed) is more accurate to estimate the SSL as compare to the GP-PUK, RF and MLP for the given study area. Thus, GP-RBF was found to be the most suitable model for modelling Suspended Sediment Load (SSL) in the study area. Also, sensitivity analysis concludes that the discharge is the most effective parameter for the estimation of Suspended Sediment Load (SSL).

Flood is one of the most destructive natural phenomenon that occur worldwide, resulting in damage to properties, infrastructure, and even loss of life. This research aims to create a zoning map of flood-prone areas in the watershed of Khosuf County, using machine learning algorithms. In this study, 8 influential parameters and random forest (RF), boosted regression tree (BRT), and support vector machine (SVM) were used to identify areas at susceptibility of flooding in the study area. A total of 81 flood-prone zones were identified. Randomly, 70% of the data were allocated for the modeling process, and the remaining 30% were used for evaluating the generated models. The results of parameter importance analysis indicated that the drainage density parameter had the most significant impact on flood susceptibility. Furthermore, in determining flood-prone areas, the random forest model, boosted regression tree model, and support vector machine showed higher accuracy with receiver operating characteristics (ROC) curves of 0.99, 0.98, and 0.95, respectively. The study area was divided into four categories of flood susceptibility: very high, high, moderate, and low. Overall, the watershed is considered to have a moderate to high flood susceptibility level.

Spillway is among the most economical energy dissipating structures used in dams to dissipate flow at the end of the structure. Scouring is a phenomenon that occurs next to the hydraulic structures, leading to their deterioration and even collapse. One of the methods to control scouring downstream of a dam is using riprap. In this study, the stability of riprap based on relative particle diameter (RPD) downstream of spillways of two different categories of energy dissipators: simple and dentated flip bucket was investigated. The experiments were performed in a rectangular test flume. Ripraps of the same density and of four different diameters were investigated at five discharges at the movement threshold. Results suggested that the RPD was an important factor in stability of the riprap. Besides, in both of the examined spillways, the stability number at the movement threshold was found to decrease with increase in the RPD. For all of the RPDs studied, the stability number downstream of the dentated flip bucket was found to be 11 percent greater than that of the simple flip bucket, which may be ascribed to the presence of dentate in the former. Likewise, dentated flip bucket offered a higher level of energy dissipation.

Automatic flood control approach can prevent dam overtopping and downstream flooding. Doing so, in addition to controlling the optimal storage capacity, flood threats will turn into opportunities for water resources systems. Model-based Predictive Control (MPC), can take the future predicted rainfall runoff, physical and operational constraints, and safety limitations of the water system into account. In a suitable timeframe before destructive floods, MPC creates essential and optimal capacity in the dam’s reservoir to contain floods. In this research, water system modeling method is studied by a control-oriented approach, and the flood automatic control operation using a model predictive control approach. To this end, modeling of the Karun and Dez river system containing six reservoir dams is considered as the case study to model and simulate under the designed MPC. The state space model will extract to design and apply the predictive controller for a prediction horizon of 10 days and eight hours in MATLAB software. The results will show long prediction horizon gives the controller enough time to create the necessary capacity for flood control according to the system constraints, while a short prediction horizon, similar to other controllers without predicting, does not have an effective performance.

The aim of this study was to model the landslide susceptibility using the Random Forest Machine learning technique and prioritization of effective factors on landslide occurrence in Bar watershed in Khorasan Razavi province. The random forest algorithm is based on a bunch of decision trees and is currently one of the best machine learning algorithms. For this purpose, a landslide inventory map was created with 73 historical landslides, which was randomly divided into two datasets for model training (70%) and model testing (30%). A total of 16 landslide-conditioning factors were considered for the susceptibility landslide mapping. The random forest algorithm was run and a landslide susceptibility map was prepared. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. The results of evaluation indicated that the success and prediction rates of the model were 90% in training and 89% in validation, respectively. These results confirm the ability of random forest method for prediction of landslide susceptibility models. Also, prioritization of the effective factors showed that the slope length and slope had the highest effect on landslide occurrence. Based on the results of the random forest model, 23.7% of the study area is located in a very high and high sensitivity zone.

Semnan plain is very susceptible to land subsidence and drought, in recent years has greatly increased the occurrence of subsidence. The aim of current research is land subsidence susceptibility assessment using frequency ratio model. At first locations of the land subsidence were specified using Landsat satellite images and field surveys. After classifying conditioning factors maps, the weight of each map was calculated using the frequency ration model. The highest weight was related to water level less than 41 meters, slope less than 2 degrees, topographic wetness index more than 12, agriculture land use type, plan curvature -0.01 to 0.01, profile curvature -0.001 to 0.001, distance less than 1260 meters of road, less than 7000 meters of faults, Quaternary formations and less than 200 meters of drainage network. Finally, by combining the weights, the final subsidence susceptibility map was prepared. The AUC for the frequency ratio model was 0.76 that showed this model is acceptable for land subsidence susceptibility mapping in the Semnan Plain. Land subsidence susceptibility maps produced in this research can be useful and functional tools for regional planners and policy makers for land use planning, resource management, and natural hazard mitigation.

Evaporation is one of the main reasons for water loss and pressure on water resources. Efforts to reduce its amount under different conditions, along with providing practical solutions and investigating the impact of these methods on water quality, are important. In this regard, the research area was selected as a part of the Cham  Angir watershed in Khorramabad city, Lorestan province, Iran. The present study was conducted with the aim of evaluating the chemical effects of evaporation reduction on water quality using fatty alcohols (a mixture of hexadecanol and octadecanol) in class A evaporation pans. The study was conducted as a time series for 10 hours per day for 2 months, from 2021/8/23 to 2021/10/22. In addition, changes in water quality parameters including pH, electrical conductivity (EC), and dissolved oxygen concentration were monitored daily at a depth of 10 cm during the aforementioned statistical period. The measured evaporation values were analyzed using the SPSS software. The variance ratio of this test, at the 5% level, showed a significant difference in terms of changes in evaporation rate and temperature. The results showed that the cumulative evaporation in the two-month period was 537 mm in the control pan and 398 mm in the pan with the monolayer composition, which reduces the evaporation rate by about 27%. One-way ANOVA with Tukey-Duncan test was used to analyze the data obtained from water quality parameters sampling at a 5% probability level. The mean results of the selected quality parameters including pH, electrical conductivity, and dissolved oxygen were analyzed in two tanks, the control tank and the tank with a monolayer composition of hexadecanol and octadecanol. The results showed a 4.6% decrease in pH, a 4% decrease in electrical conductivity, and a 5% decrease in dissolved oxygen in the tank with the aforementioned composition monolayer compared to the control tank.

The increasing trend of destruction of natural resources, soil erosion and the occurrence of destructive floods in the country, despite extensive watershed management measures during the past four decades, has increased the need to evaluate the effects of these plans and access statistics and information during the exploitation period. The present research has evaluated the effects of watershed management projects on eight global development indicators in the Natanz Urban, Hunejan Shahreza, Qale Qadam Semirom and Sibak Fereidounshahr watersheds. In this regard, after examining previous sources and researches, regarding the collection of information on watershed management plans and natural resources at the level of the mentioned watersheds in the period before and after implementation during the 20-year period from 1375 to 1395 in the form of questionnaires, face-to-face interviews, gathering information from relevant organizations and using satellite images, aerial photos and detailed reports of each watershed have been done; The results of the survey have shown that the implementation of watershed management projects in general has a positive effect on global development indicators and has increased the level of these indicators in such a way that these mechanisms have improved.