javad omidvar

IDF (Intensity-Duration-Frequency) curves play a crucial role in hydrological modeling, infrastructure design, and flood risk management. Traditional methods, relying on ground-based observations, face challenges such as limited spatial coverage, short temporal records, and the stationary assumption, particularly under climate change. This study addresses these issues by utilizing ERA5 reanalysis data to develop basin-scale IDF curves for the Karkheh River Basin (KRB) in Iran. Annual Maximum Precipitation (AMP) series for 6-, 12-, 18-, and 24-hour durations were extracted from ERA5 data and corrected for bias using observations from seven synoptic stations. Bias correction significantly improved ERA5 estimates, particularly in high-altitude regions prone to systematic errors. An elevation-bias relationship was established to extend corrections basin-wide. The corrected AMP data were modeled with the Generalized Extreme Value (GEV) distribution under stationary and non-stationary conditions to construct spatially distributed IDF curves. Based on 82 grid points, these curves provide detailed rainfall intensity estimates, overcoming limitations of station-based methods. The findings underscore ERA5 data's potential, combined with bias correction, to enhance hydrological analyses in data-scarce regions by better capturing spatial variability and extreme precipitation. This work supports improved flood management and infrastructure planning. However, future research must address uncertainties in bias correction and parameter estimation while extending data records. High-resolution reanalysis datasets are pivotal for adapting to evolving climatic conditions, extreme weather, and prolonged droughts.

Accurate measurement of the rainfall, as one of the essential meteorological variables in hydrological and agricultural studies, has a significant impact on the optimal management of water resources, cultivation of agricultural products, urban management, and identification and classification of areas in terms of the occurrence of floods risk or the ability to extract water. The inappropriate distribution of rainfall measurement stations in different regions has caused researchers to seek to create models that can estimate the amount of rainfall in areas that lack rain gauge stations or have Insufficient of them.

In the current research, the data from Era5, the newest product of the ECMWF, has been used. Era5 data is the fifth generation of ECMWF reanalysis data for climate over the past several decades. These data provide hourly estimates of important atmospheric variables at the surface of the earth and other pressure levels. Era5 data is updated with a delay of 5 days and is available in a gridded form with an accuracy of 0.25 degrees (equivalent to 31 km). In the present study, hourly data in NetCDF format were used during the period from 1997 to 2018. The accuracy of Era5 in the Middle East and Iran has yet to be investigated much. In order to evaluate the accuracy of Era5 model data, the data of 51 rain gauge stations of Iran Water Resources Management Company in Razavi Khorasan province from 1997 to 2017 were used were used. The mentioned stations have a suitable distribution and length of statistical period compared to other types of stations. To evaluate the data of Era5 model, first using Python programming and the nearest neighbor method, NetCDF data was processed and rainfall values for each station were extracted. Then the hourly data were converted to daily. The modeled values compared to the observed values were evaluated using two groups of quantitative and qualitative indicators. The first group of indicators that were used for quantitative evaluation of observed and modeled rainfall values include root mean square error (RMSE), coefficient of explanation (R2) and Nash‐Sutcliffe Efficiency (NSE). The second group of indicators was used to evaluate the quality of Era5 model outputs. Using these indicators, the occurrence or non-occurrence of precipitation (regardless of the amount of recorded precipitation) was investigated in both data groups. The indicators of the second group include probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI). In this group of indicators, the occurrence or non-occurrence of recorded precipitation is recorded as yes or no by observational data and modeling. Using these indicators, it is possible to determine the occurrence or non-occurrence of precipitation by the model.

According to the quality indicators of Era5 rainfall data on a daily scale, the results showed that the said data have a high ability to identify the days that rainfall occurred at the station. It is worth noting that qualitative indicators can only be calculated and evaluated for the daily scale. The results of the investigations show that the Era5 model recorded the maximum rainfall values on a daily scale with a relatively large difference lower than the observed values. The results of the statistical evaluation of the monthly rainfall of the stations in comparison with the estimated values of the model show that unlike the low coefficient of explanation of the observed and modeled data on a daily scale, the coefficient of explanation has increased significantly on a monthly scale so that the range Its fluctuation ranges from 0.45 in Ferizi station to 0.86 in Sarakhs station. As the time scale increases, the accuracy of Era5 model estimates also increases. The results show that the dynamic and numerical methods used in the Era5 model have the ability to provide estimates that are close to reality with the least error and have the ability to improve in providing more optimal results in other areas. These results show that Era5 model estimates have performed best on monthly and seasonal scales in regions with a warmer climate and less rainfall in Razavi Khorasan province. These results show that Era5 model estimates performed best in monthly and seasonal scales in areas with warmer climate and less rainfall in Razavi Khorasan Province. Therefore, if the skew error is fixed, the data of this model can be used as input data to agricultural and hydrological models.

According to Limited water resources in the country, Management of water resources as a strategy, it is essential for the crisis. For correct and scientific management of water resources that are needed A better understanding and knowledge of the complex collection of interactions associated with water in a water balance catchment. Evapotranspiration is one of the most important components of the water balance that it is difficult to measure the actual rate And have limited methods. In this study tried to achieve accurate estimates of actual evapotranspiration in catchment-year scale by using remote sensing method (SEBAL). To accomplish this, firstly using meteorological data and the SPI Index Years 84-83, 85-84 and 87-86 Were determined As the wet, normal and dry years Respectively. Then it was calculated actual evapotranspiration for Neishabour plain in catchment-year scale by using MODIS satellite images and SEBAL method. The results were compared with the results of the SWAT model that is showed good accuracy. Due to the purpose of this study is to provide an accurate, simple and inexpensive estimate for actual evapotranspiration in catchment-year scale, from the calibration relationship, Young's equation for the entire basin with RMSE= 28.3 mm and R2 = 0.90 and abcd equation for the plain with RMSE= 16.24 mm and R2 = 0.90, and Young's equation for the mountain with RMSE= 19.37 mm and R2 = 0.90 estimate The best response. Of course The results of Zhang’s and Fu’s equations were similar to Young's equation and there are a few differences