مجله اکو هیدرولوژی
سال هفتم شماره 3 (پاییز 1399)

The use of satellite data for rapid estimation of soil moisture (SM) and determination of environmental factors affecting it has been developed in recent years. The aim of this study was to investigate the effect of biophysical and topographic characteristics on spatial distribution of SM. For this purpose, SM was measured at 148 points in Balikhli-Chay watershed in Ardabil province and triangular method based on ASTER digital elevation model, land cover map and climatic data was applied for SM modeling. Surface biophysical properties including wetness, greenness, brightness, and land surface temperature and topographic variables (solar local incidence angle, elevation, slope, and aspect) were calculated. Model error in different months was determined using error statistics. According to the results, the average SM content in the region in July, August and September were 4.67, 6.22 and 4.66%, respectively. The lowest coefficient of determination (R2) and root mean square error (RMSE) of estimated and measured SM were related to September (0.78 and 1.44, respectively). The strongest linear relationship between SM and biophysical variables (topography) was related to July (with R2 and RMSE equal to 0.53 and 0.29, respectively). SM decreased with increasing land surface temperature and brightness, however increasing greenness, wetness, elevation and solar local incidence angle increased SM content. This study showed that the triangular model can be used to investigate the spatial distribution of SM using biophysical and surface topographic properties.

Unsteady flow analysis is performed using hydraulic and hydrological routing methods. Hydrological routing methods, while accurate, are much simpler and less expensive than hydraulic routing methods, and for flood routing calculations, only data related to hydrographs (flow changes over time) recorded at upstream and downstream hydrometric stations of the study area are needed. In the present study, the effect of the basic flood on the accuracy of flood routing in the Karun River was investigated using hydrological routing methods including linear Muskingum method, Working Values, Convex, and modified Att-Kin method. In other words, using the Particle Swarm Optimization (PSO) Algorithm and the 4 observational floods data recorded at the Mollasani (upstream) and Ahwaz (downstream) hydrometric stations of the Karun River, for each flood, separately, the linear Muskingum method parameters (X, K, ∆t) Optimized and used to calculate the outflow hydrograph of all floods. The results show that due to the effect of rivers flooding extent on the mentioned parameters if the range of inflow of basic flood changes is closer to the inflow of calculated flood, the accuracy of hydrological routing methods in outflow hydrograph estimation increases.

In order to properly manage water in the agricultural sector, it is necessary to improve the management of agricultural water distribution systems as well as their evaluation. In this research, to achieve this goal, the models of Fuzzy Inference System (FIS), Artificial Neural Network (ANN) and Adaptive Fuzzy Neural Inference System (ANFIS), to develop a smart model for analyzing the adequacy of water delivery in an irrigation canal, given uncertainty. In order to develop and evaluate the performance of the developed models, the main canal of Rudasht Irrigation Network in Isfahan province, which is facing the problem of severe fluctuations in the inflow, was selected as the case study. The HEC-RAS hydrodynamic simulator model was used to generate the information needed to train and validate these models. The results showed that according to the MAPE index, the selected structures in ANN and ANFIS models in estimating the adequacy index of agricultural water delivery compared to FIS model have improved by 57.07% and 56.68%, respectively. Evaluation of the results showed that the developed models compared to conventional evaluation methods (hydrodynamic models and evaluation indicators) not only did not take time but also provided more accurate results, considering the uncertainty and also, ANN and ANFIS models performed better than FIS, so they can be used for other agricultural water distribution systems.

Temperature and rainfall affect the quantity and quality of agricultural products. Therefore, it is important to estimate its spatial-temporal changes. In many region of the country, due to the low density of meteorological stations or the small statistical period of new stations, limited time and space information is available. Therefore, this study aims to use the data of CRU, AgMERRA, AgCFSR and GPCC gridded weather datasets in estimation of yield and water requirement of wheat and comparing with the estimated values with the information of Qazvin Synoptic Station. For this purpose, monthly weather time series of Qazvin synoptic station were extracted from 1980 to 2010 along with the data from the selected gridded datasets extracted from the closest grid cell to the synoptic station (K1), the average of four closest grid cells to the synoptic station (K4), and the average of eight closest grid cells to the synoptic station (K8). The quality of the gridded datasets was assessed with four statistical indices (R2, RMSE, NRMSE, ME) in indirect way (the latter using the outputs of the AquaCrop model). In estimating wheat water requirement, GPCC database with four points (K4) and one point (K1) showed the best performance. Wheat yield simulated with AgMERRA data with K1 and K4 closest grid cells had the highest correlation with the simulated values with data of synoptic station. Results showed that selected gridded datasets can be used to simulated grain yield, but only data from GPCC-CUR would result in reliable estimation of water requirement.

Assessing the drought situation and formation trend has always been one of the important factors to deal with the devastating effects of this phenomenon. Different index are used to determine the drought, depending on location in specific periods. The main purpose of this study is to assess the drought situation of Givi Dam Catchment with the multiple index approach. Accordingly, this phenomenon was analyzed using the precipitation data of the two selected stations in the Givi Dam Catchment during a 31-year statistical period, using SPI, RDI, CZI, SZI, PNI, and DI. The results showed that severe drought occurred in 2005, 2008, and 2013 in Khalkhal station and 2013 and 2017 in Firoozabad station. Moreover, using the Effective Drought Index (EDI), the number of dry days was obtained in the fall and winter seasons of the years with severe drought. By examining the indicators monthly, it became clear that despite the relatively high similarity of SPI, CZI, SZI, and RDI in showing the main drought and wet periods, SPI is more effective in detecting the onset of drought periods than the other indices.