مجله علوم و مهندسی آبخیزداری ایران
پیاپی 58 (پاییز 1401)

The issue of water transfer from the high seas (Oman and the Persian Gulf) to Yazd province has been very controversial in recent years. Therefore, it is necessary to examine the infrastructures and capacities needed to implement such projects in the country in order to identify the deficiencies and shortcomings and to take effective measures to address these issues. In this study, using SWOT matrix analysis, we tried to investigate the strengths, weaknesses, opportunities and threats in the field of water transfer at three systemic, organizational and individual levels. To this end, a questionnaire was prepared at three levels and was completed by 30 experts in the executive departments related to water transfer. Analysis of the questionnaires showed that the designed strategies were part of the defensive and conservative strategies. The results showed that at the macro level and policy making of the country, a strategy with a score of 9/835 recalls the importance of preventing aquatic activities and preventing effective measures for desertification has the highest priority. At the individual level, a strategy that addresses the prevention and avoidance of personal and non-professional attitudes with a score of 8.997 has the highest priority, which can be addressed by conducting specialized trainings, workshops and expert meetings.

Monitoring the quality parameters of the river is very important in the management of surface water resources. One of the effective methods in this field is entropy theory which has a very good capability in designing quality monitoring network. In this research, discrete entropy theory has been studied and monitored in order to qualitatively evaluate the temporal and spatial frequencies of the network of hydrometric stations in Kermanshah province with 16 stations and a 29 years common statistical period. The amount of data transfer between each station and all other stations is calculated in two to determine the information coverage between the network stations. The results showed that Bavleh station on The Gavehrud River in Kermanshah province is the most efficient network station in terms of potential of data giving by getting the highest amount in border entropy ranking. Pirsalman, Ghorbaghestan, Doab-mereg and Khersabad stations were also in good condition in terms of potential for information capture, but Totshami station is in poor condition. Polchehr, Biar, Sarpol-e Zahab and Kamish Road Bridge stations have poor status in terms of potential of giving information, bridge stations, Quantified road bridges and biyar in terms of quality indicators are not suitable and the information coverage of qualitative variables in almost all stations in the field is weak. Information transfer index is deficient throughout the study area and there is a need to revise the network. . In terms of optimal sampling distances, EC variable can maintain the effective quality of this index, considering that it is in a state of severe lack of information transmission (current network situation) up to 50 km distance.  Esp index is less sensitive to distance than Ec index and according to data transfer index and distance, the variability of this index is less at distances and distance of 55 km is suitable and for Cl the distance of less than 50 km was obtained for sampling. According to the data transfer index for mg quality variable, there is appropriate information coverage in the network at a distance of 55 km.

Land use change is an essential determinant factor in hydrological processes and it affects many of them. Simulation of land use changes, rise in residential and agricultural area and its effects are among the most important issues related to the watershed management. The objective of this research is land use change detection in the past and simulation of future land use using CLUE-s Model. At first, land use map of years 1991 and 2019 was prepared using satellite images and synthetic method. Then, land use change was calculated, and then land use map of year 2040 was simulated using CLUE-s model. The results indicated that the main land use changes in Doroodzan watershed between 1991 and 2019 was the conversion of natural resources to residential area, agriculture and orchards. Accuracy assessment of regression model to investigate condition factors of each land use types using ROC corves demonstrated acceptable accuracy of this method. Using the CLUE-s model, managers and planners can be informed about the future conditions of the region to take into account the problem of land use change in their planning.

The most important problem in estimating floods is the lack of sufficient statistics and consequently the lack of evaluation of the appropriate amount of flow. One of the flood estimation methods in these basins is the use of hydrological modeling. In this study, Shahbahram river flood modeling was performed with Nazmkan hydrometric station located in Kohkiluyeh Boyer-Ahmad province with HEC-HMS and its comparsion with different flood estimation methods (regional flood analysis and Dicken, Fuller, Creager and Francou- Rodier methods). The HEC-HMS hydrological model presented better results with a lower error rate (maximum value of 10.51 with a return period of 1000 years and minimum value of 2.72 with a return period of 5 years) compared to experimental and analytical methods. The highest error is related to Dicken method with return period of 1000 and the value of 86.85 percent and the lowest error among experimental methods is related to Fuller method with 3.44 percent and return period of 500 years and regional analysis method with 3 percent error with return period of 5 years. Therefore, due to the use of HEC-HMS semi-distributed model of precipitation and area parameters with appropriate calibration, more accurate results can be obtained from this model.

The aim of this study was to evaluate the impact of forest on erosion and runoff variables and compare it with rangeland and agricultural in part of Mereg watershed located in Kermanshah province. At first, three forest mass were identified and divided into three density categories (low, medium and Many), then Soil and hydrological parameters were measured in each forest density area and also in both agricultural and rangeland using a rainfall simulator. Rainfall was run at a rate of 83 mm / h for 24 minutes with 18 repetitions. The hydrological results in forest mass showed that the mass 2 (with medium density) produced 32.6 percent more runoff than the first stand and 33.5 percent more than the third Mass, which could be due to the soil repellency in forest, but no statistically significant difference was observed in other variables. Also, the comparison of the hydrological variables in three land uses (forest, rangeland and agriculture) showed that although the runoff from forest was 61.5 percent more than rangeland and 54.2 percent more than agricultural, however, the amount of sediment output from these plots was 62 percent less than rangeland and 91percent less than agricultural. This decrease in rangeland compared to agriculture was 76.26 percent.

The amount of Total Dissolved Solids (TDS) is an important factor in determining the water quality of rivers. The purposes of this paper was to investigate the efficiency of four intelligent models including, SVM-IWO, SVM-PSO, SVM-ABC, LS-SVM and Bayesian Neural Network, in predicting water quality of Babolrood and Sefidrood rivers, north  of Iran. To achieve this aim, unpublished measured monthly data including; Ca, Mg, HCO3, Na, SO4, EC, pH, and TDS from Quran-Talar station of Babolrood river and Prorijabad station of Sefidrood river were analyzed in the periods of 1966-2015 and 2002-2015, respectively. Evaluation of the four above-mentioned models, based on the correlation coefficient (R2), Root Mean Square Error (RMSE) and the Nash-Sutcliff coefficient (NSE) showed that the SVM-ABC model in both Babolrood and Sefidrood rivers with the highest R2s equal to 0.985 and 0.989 and the least amounts of RMSE equal to 10.493mg/l and 5.289 mg/l and the highest Nash-Sutcliffe equal to 0.983 and 0.992, respectively, has better and faster performance compared to others models in assessing water quality.