JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION
Year:2020 | Volume:9 | Issue:3|Papers Count:9

Many existing hydraulic models, most of which are classified as depth-averaged models, use regular computational networks to discrete the solution field, which in turn reduces their efficiency, especially in larger cell sizes. Unlike the aforementioned models, the 2D HEC-RAS model uses a new concept called subgrid-variability to include elevation changes within a computational cell and thus has very little dependence on computational cell dimensions. The purpose of this study was to evaluate the effect of the concept of subgrid-variability on the simulated flood area in the Sarbaz River. The other purposes of this study is to investigate the effect of map scale on the performance of 1D and 2D HEC-RAS model. The results showed that with increasing computational cell dimensions, the model error in simulating flood zones was relatively low compared to small cell dimensions. For example, if using a computing grid with dimensions of 500 meters instead of a 20 meters grid, the simulated flood area in different parts of the Sarbaz River would be less than 15 percent. The run time of the 2D model in the cell dimension of 500 meters is approximately 45 times less than the run time of the model in case of using cell dimensions of 20 meters. The findings clearly indicate to what extent the use of the concept of subgrid variability in modeling can be effective when implementing numerical models, especially in large and complex rivers. The calculations also show that the difference between 1D and 2D models in the simulation of hydraulic parameters in small scales is relatively small and by increasing the map scale, the difference between the two models increases. The main reason is to consider all the topographic changes of the river bed and floodplain at high scales.

On-time information and real time decision-making are effective factors in the process of water management in the farm. Therefore, in the present study, a real-time decision support system has been developed for the irrigation scheme of Mian-Ab irrigation network in Shoushtar plain with 24 sub-sets of cropping pattern and results are compared with existing pattern. To increase the accuracy of modeling, including soil water balance, crop production and root growth, a one-day time step is considered by replacing real-time data. For this purpose, the Particle Swarm Optimization Algorithm (PSOA ) is used to maximize the net benefit of a growing season. The results showed that the optimal economic efficiency of water was obtained for tomato by 92500 IRR/m 3. Its yield production also has increased more than 3000 kg/ha in an optimal strategy with a 35 percent reduction in water consumption. Furthermore, critical periods of crop water requirement was between the maximum canopy cover and the start of senescence. Crops like wheat, barley, and rapeseed which their canopy coverage decrease after senescence stage, are more compatible with deficit irrigation.

This study aims at evaluating and modifying the Texas method for allocating environmental flow. This hydrologic method, while using the minimum amount of data, is a rival for the Tenant method and has the capability of introducing an amount of discharge to maintain the ecological regime of rivers. In this paper, after distinguishing the worthiness of the Texas method and observing some differences between the results of the method and the historical regime in the case study, the method was modified in such a way that while preserving the basic principal of the original method, could propose a flow regime which was laid between the observed long-term minimum and mean monthly discharges. The new method is called the Modified Texas method. For evaluating the Texas and Modified Texas methods concerning the allocated annual volume, the equivalent scenario from the Tennant method was employed. The allocated volumes by the Modified Texas, Texas, and Tenant methods were 50%, 47%, and 40-60%, respectively. This paper, concerning the achieved results for the case study, shows that employing the Texas and Modified Texas methods instead of the Tenant method and enhancing the method to the level of a native method provides reasonable results comparable with those of the habitat simulation or holistic methods.

The aim of this study was to estimate the ET0 in a moderately cold semi-humid climate in a 22year statistical period by applying a wavelet-neuro-fuzzy model with a minimum number of input parameters. The results were compared with the ANN and ANFIS models to evaluate the performance of the wavelet-neuro-fuzzy model. The sensitivity analysis of the input parameters was done in three ways: the Hill method, coefficient of determination, and StatSoft methods. Sensitivity analysis showed that temperature (T) (minimum, maximum and average of daily air temperature), Rs, Ra, mean daily wind speed at 2 meters (U2) and Rn (net radiation) were an effective parameter, and different combinations of these input parameters can lead to more accurate estimate of ET0. Based on the results of the sensitivity analysis, six combinations with these parameters were selected, and temperature in all of these compounds was used as an input variable. Using three input parameters; Tmin, Tmax, and Rs and sym8 wavelet, the wavelet-neural-fuzzy model had a better performance than the neural network model. The results also showed that the estimated ET0 value with three inputs parameters of maximum and minimum temperature and solar radiation using the fuzzy-neural-wavelet model was more accurate than the neural network. Based on the coefficient of determination and the amount of calculated error for the artificial neural network and the Anfis, use of the combination of 7 input parameters (Ra, Rn, Rs, U2, Tmean, Tmin, and Tmax) (MBEANN=0. 003, MBEAnfis=0. 007, R2ANN=0. 99, R2Anfis=0. 99) and four meteorological input parameters (Ra, U2, Tmean, and Tmax) (MBEANN=0. 07, MBEAnfis=0. 05, R2ANN=0. 93, R2Anfis=0. 98) led to more accurate estimates of ET0 in comparison to the FAO Penman-Monteith method. The results also showed that the highest amount of coefficient of determination and the lowest error value among the different wavelets used in the fuzzy-neuro-wavelet model was for the seven (R2=0. 87, MBE=0. 02) and three input parameters (Tmax, Tmin, Rs) (R2=0. 71, MBE=0. 03), respectively. The results also indicated that, concerning the error criteria (EF, RMSE, NRMSE, MBE and MAE) and the coefficient of determination, the best model for estimation of ET0 was Hargriverz-M4, Tylor-Priestly, Meyer, and IRMAK. Based on the EF and NRMSE, four methods including Hargriverz-M4 (EF=0. 92, NRMSE=0. 11), Tylor-Priestly (EF=0. 88, NRMSE=0. 12), Meyer (EF=0. 73, NRMSE=0. 03) and IRMAK1 (EF=0. 67, NRMSE=0. 25) in long-time period, in according to EF, Hargriverz-M4 (0. 92) and in relation to NRMSE, Meyer had the better performance than the other methods.

In order to evaluate effect of deficit irrigation and application of water superabsorbent and determining principal components in water use efficiency of common bean, sesame and maize a split plots experiment based on RCBD design with three replications was conducted during 2015-2016 growing season, in Ferdowsi University of Mashhad, Iran. Irrigation levels (50 and 100% of water requirement) and application and non-application of water superabsorbent assigned to main and sub plots, respectively. The result showed that application of water superabsorbent increased WUE in bean, sesame and corn 46, 29 and 43%, respectively. In three studied plants, the highest WUE obtained in 50% of water requirement+water superabsorbent treatment. Principal components analysis for three plant species resulted in two principle components with the most considerable variances. The first component explained 73, 60 and 80% of variables variance in bean, sesame and corn, respectively. The correlation of Seed yield and first component was 0. 33, 0. 34 and 0. 33 in bean, sesame and corn, respectively. In bean, variables of seed yield, biological yield, seed weight, plant height, crop growth rate and soil nitrogen, phosphorous content and pH were assigned to the first component and leaf area index, EC and water use efficiency were assigned to the second component. In sesame, variables of seed yield, biological yield, seed weight, plant height, crop growth rate and soil phosphorous and pH had the highest load on the first component and variables of leaf area index, soil nitrogen content, EC and water use efficiency had the highest load on the second component. In corn, variables of seed yield, biological yield, seed weight, plant height, leaf area index, crop growth rate and soil nitrogen and pH were in the first component and variables of soil phosphorous and EC and water use efficiency in the second component had the highest load. The results revealed the amount of saved water resulted from superabsorbent applying would be high considerable from economic and ecological points of view.

Since synoptic metrological stations have non-uniformed scattering pattern in Iran and on the other hand precipitation determination and forecasting is essential for irrigation planning, a method precisely determine precipitation of agricultural lands in farm level has great importance. This study was carried out in Google Earth Engine Code programming environment using GPM, TRMM and CHIRPS satellite data which is daily, decade and monthly, respectively in Ahwaz and Izeh metrological stations for calibration and 9 meteorological stations for validation during 2015-2016 and 2017-2018 Growing season. Results showed that monthly interval could obtain better accuracy with R2 of 0. 99 and NRMSE = 0. 36, respectively. The validation results of the rest 9 meteoroidal station indicated that precipitation prediction had 51% and 3. 1 mm error and under estimation on average, respectively. The efficiency was reasonable and F-Test showed no significant difference between observed and prediction samples. The standard error value was 14. 2 mm which is a significant error and need to work on updated better functions. It can be concluded that this method can be a useful tool for monthly precipitation prediction of areas with no climatic data if integrated with Kriging, co-Kriging and Inverse Distance Weighted (IDW) geostatistical models for interpolation.

The porpose of this study is the ground water potential mapping. using four methods, stochastic forest, GLM, Domain, and GAM. in addition, four methods for combining these methods for potential mapping were also evaluated. for this purpose, eleven criteria include the slope, profile curvature, (Topographic Curvature), total curvature, index (spi), index (spi), index (twi), land use, soil and demographic elevation model were used according to the experience of experts and researchers. in order to validate the 76 of wells with high discharge were, it has been used for simulation (70 %) and validation (30%) before modeling the linear test on the criteria, there is no linear relationship between variables. according to, the results of the evaluation using the ROC curve showed that all four used methods have excellent accuracy and AUC over 90%. then, the results of four methods were combined with mean averaging method. The final potential showed that 32. 89% of the lands have good potential for exploiting groundwater resources. The results of the importance factors also showed that the slope, height, and power index were the most important factors. The results of this research can serve as information bases for planners and local authorities to evaluate, plan, manage, sustainably use and synthesize groundwater resources in the future.

The present research was conducted to evaluate the health of the KoozehTopraghi Watershed in Ardabil Province using 27 criteria analysis related to different hydrological, anthropogenic and climatic aspects. For this purpose, base regional data including hydrology, erosion and sediment, economic, social and climatic data were collected and analyzed from previous reports. Then, according to the nature of the data used, several appropriate databases were prepared in Fragstats 4. 2. 1, Excel 2016, IBM SPSS Statistics 25 and ArcGIS 10. 6 softwares. Then, using the principles governing the conceptual model of pressure-state-response (PSR), the status and spatial variations of the watershed health were evaluated at 36 sub-watersheds level. Different degrees of watershed health were classified into five categories: healthy, relatively healthy, moderately healthy, relatively unhealthy and unhealthy. Based on the results analysis, it was found that this watershed is in average health condition. Also, the values of pressure, state and response indicators were 0. 46, 0. 55 and 0. 31, respectively. The mean score of comprehensive watershed health assessment index is 0. 41. The maximum value of comprehensive watershed health index was 0. 66 for sub-watersheds 8 and 12 and the minimum value for this index was 0. 14 for sub-watershed 26. The eastern and southeastern part of the watershed is relatively healthy and moderately healthy and the watershed health status was better in the western and northern parts. The results of the present study are applicable to identifying and restoration priority areas and guiding management strategies in terms of water and soil resources at national and regional levels.

Investigating the quality and contamination of groundwater resources in the planning and development of water resources is very important and necessary. The purpose of this study is to map the quality of water, pollution, and study the trend of changes in quality parameters in the long run in Najafabad aquifer. For this purpose, the parameters EC, TDS, SAR, pH, TH, Cl, CO3, Ca, Mg, Na, K, HCO3, NO3 and heavy elements including zinc, copper, lead, cadmium and arsenic were investigated from laboratory samples. . The agricultural water quality classification according to the Wilcox diagram is mainly in the range of C3-S1 and in the drinking water according to the Schuler diagram it is mainly acceptable and intermediate and in some cases inappropriate. The amount of heavy elements is allowed. The trend of changes in qualitative parameters was evaluated using MK and TFPWMK methods and the results showed that water quality decreased over time. Some qualitative parameters were mapped in ArcGIS environment. The average nitrate concentration was calculated to be 63. 13 mg / L and the maximum nitrate concentration in the northern and central parts of the aquifer was 193 mg / L. Regarding the results of this study, continuous monitoring and quantitative and qualitative investigations of groundwater resources and necessary measures to control pollution in the Najafabad aquifer area is very necessary.