فهرست مطالب محمدرضا عینی

The various semi-distributed and distributed hydrological models have a very high potential for comprehensive water catchment simulation, but these models still need to be developed in order to get closer to the actual conditions of the catchment areas. To simulate the karstic basins using conventional hydrologic models, changes in model codes are required. The SWAT model, due to openness and freedom, has made it possible to implement and configure various conditions in the catchment area. In this research, a new model called SWAT-ML has been developed in which karst regions have different hydrological characteristics. The studied area is a karstic basin of Maharlu Lake and statistical simulation period from 1980 to 2013 for calibration and validation of surface runoff, base flow, agricultural production and actual evapotranspiration. The model was calibrated with an average of determination factor and Nash Sutcliff of 0.65, and it was found that the SWAT model has a great potential for estimating the yields of agricultural products. The developed model showed that in karstic watershed simulation, performance is better than SWAT model, and the calculated balance values by SWAT and SWAT-ML model, has a significant difference compared to hydro climatology budget.

  Rainfall is the most important meteorological factor driving the hydrology of river basins. For the development and management of water resources, it is required to have a reliable coverage of rain gauges, rainfall satellite data and weather radars. Well-maintained ground-based rainfall stations give the best rainfall estimation with high accuracy over time for a small area. The spatial sampling error becomes higher in estimating rainfall when using interpolation techniques. This issue becomes particularly critical in data scarce regions with unevenly distributed rain gauge stations. This is recognized as one of the principal sources of uncertainty in hydrological modeling. Currently, with more and more global precipitation datasets developed, a decline is seen in the application of reanalysis products in hydrological modelling. As a hot research field, many studies focus on the application of directly measured precipitation data on flood risk evaluation at basin scales and discuss their potential for hydrological prediction of ungauged/poorly gauged basins. In this study, we used PERSIANN-CDR gridded database precipitation for modeling runoff in SWAT model in Maharlu Lake basin. The PERSIANN-CDR was used as a gridded database of precipitation for modeling runoff in SWAT model in Maharlu Lake basin. The PERSIANN-CDR is initially compared with rain gauged data and after that it was used as input to SWAT model. SWAT model was calibrated by rain gauge data during 1983 to 2013. The Warmup period set to 3 years. Three discharge stations were used for calibration. Correlation coefficient of, Nash-Sutcliff, POD, CSI, FAR, RMSE, ME and BIAS had been assessed to determine the accuracy of PERSIANN-CDR. SWAT model uncertainty and sensitivity were calculated in SWAT-CUP by SUFI2 method. Comparing the PERSIANN-CDR in monthly scales, we found that this satellite wheatear database less estimates the variables in all months. The results showed average of correlation coefficient is 0.6 and RMSE showed a high error in rainy seasons. In SWAT model, calibration period was set to 1983 to 2010 with validation from 2011 to 2013. Calibration with gauged data showed satisfactory Nash-Sutcliff and R2 statistical indices about 0.6 for the area. The best result was occurred in Chenar-Sokhte-khosh discharge station, R2 was about 72% in calibration and 81% in validation. Calibration with PERSIANN-CDR database showed that this database is not good enough to be used in this semi-distributed model. In Chenar-Sokhte-khosh discharge station, R2 is calculated about 0.59 and Nash-Sutcliff about 0.21. R-factor and P-factor was presented about 0.5 in all discharge stations. These factors show that uncertainty calculation was occurred in good form. The simulation of annual runoff showed that the average runoff simulated using observation database was 1.68 m3/s, the mean runoff simulated by PERSIANN-CDR is 0.84 m3/s, and mean runoff of discharge stations were 1.77 cubic meters per second. On monthly scale, PERSIANN-CDR estimated less runoff like rainfall over all months. Both databases simulate runoff values relative to those recorded in the autumn months less than actual values. The results of this study, which were conducted using the PERSIANN-CDR satellite product, unlike the other studies with global exploratory bases, displayed that in the simulation with the SWAT model, this base cannot be accurately high in simulation. The error of estimating precipitation has been entered directly into the model and caused an error. In this study, with the accuracy of precipitation data, PERSIANN-CDR satellite data on rainfall estimation revealed that this database estimated precipitation values less than real values in all months of the year. Runoff simulation using this satellite product expresses the explanatory factor and the efficiency of Nash-Sutcliff about 0.59 and 0.21.
Despites the time series of precipitation values, this satellite database has a high correlation with the actual values observed on rain-fed stations, but as the findings show the estimated rainfall values are always lower than actual recorded values. Based on the findings from this study, the PERSIANN-CDR satellite is not very accurate on the area of the Maharlou Lake Basin, located in eastern Zagros. In the semi-distributed SWAT model, it cannot simulate runoff. Therefore, it is suggested that before applying estimated rainfall data, this satellite database will have its error and bias values compared with the observed data on rain gauge stations and, then, the estimated precipitation values are corrected based on the bias

Hydrological modeling needs climate data with high spatially accuracy, especially in low-rainfall areas, which usually there is a no-good observational network throughout the country. Currently, the problems of lack in climate observational data can be solved somewhat with using gridded global databases. CRU and NCEP CFSR databases, including the most prestigious gridded databases, which in this study were evaluated by using SWAT model for Maharlu Lake basin. After statistical index comparisons in 33 years (1980 -2013), which Nash Sutcliffe efficiency showed the accuracy approximately 0.91 in monthly scale, modeling was conducted by SWAT for different three datasets. The modeling results showed that both of two databases beside the observation values could have high accuracy in rainfall runoff modeling which CRU database indicated better runoff simulation toward NCEP CFSR database. Coefficients of determination and Nash Sutcliffe efficiency in each calibration and validation periods represented average values above 0.7 for observed datasets, 0.65 for CRU and 0.6 for NCEP CFSR.

Due to high spatiotemporal resolution of gridded data, reanalysis precipitation databases, have many applications in climate prediction, climate change modeling, water resources management and hydrological modeling, especially in areas without observational data. Therefore, in the present study, hydrological simulation was evaluated by SWAT model and spatial data mining for drought monitoring with SPI and SDI indexes over Maharlu Lake and assessing the temporal sense of Asfazari national database by observational stations as a reference on the spatial extent. The results showed high accuracy of Asfazari database in simulating of runoff in comparison with simulated runoff with observation database. The coefficient of determination and Nash efficiency presented the average accuracy of 0.6 in the simulation. During the cold and rainy seasons, the performance of this database is higher than is the warm season. During rainy months of year, the correlation coefficient between observation and Asfazari is about 0.85, and the POD index is more than 0.9. Furthermore, subsequently the accuracy of monitoring drought by Asfazari is too high, so it can be said that the Asfazari database can be used as a reliable database in runoff simulating and drought monitoring, especially in areas with poor or no sufficient precipitation data.