فهرست مطالب سکینه کوهی

Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.

This research aims to investigate the impact of climate change based on the sixth report (CMIP) under two scenarios SSP3_7.0 and SSP5_8.5 on forecasting temperature, wind speed, evaporation-transpiration (ET), and the amount of extractable water (Q) by two CNRM models and ESM was at 16 meteorological stations during the future period of 2025-2044 and 2045-2064. The statistical analysis results showed that the impact of climate change under two scenarios on temperature, ET and Q was significant. CNRM model performed better than ESM in temperature estimation (CC=0.96-0.98). From examining the results of the CNRM model, the maximum and minimum RMSE of temperature in Khormadreh and Zanjan stations were 8.30 and -0.5 , respectively; Also, the RMSE value of wind speed fluctuated between 0.82-0.5 m.s-1. The examination of ESM model showed the fluctuation of RMSE between 2.55-8.45  in temperature parameter and 0.62-0.68 meters per second in wind speed. The maximum and minimum values of Q and ET in the seasonal survey occurred in summer and winter, respectively. Both models had poor performance in predicting wind speed. The maximum ET under the SSP5_8.5 scenario by the CNRM model (first period) at Khorramdare station is equal to 104.29 mm.month-1 and the minimum value by the ESM model (first period) under the SSP3_7.0 scenario at Firuzkoh station is equal to 25.60 mm.month-1 was estimated. The maximum Q under the SSP3_7.0 scenario by the ESM model (first period) at Malair station is equal to 20.70 Lit.day.m-2 and the minimum value by the CNRM model (second period) under the SSP3_7.0 scenario at the Astara station is equal to 0.3 Lit.day.m-2 were estimated.

Recognizing the effects of climate change in different sectors, as well as the integration of GCM models and the development of Ensemble Climate Models (ECM) are vital. In this study, the efficacy of the climate models from the CMIP5 in simulating atmospheric variables impacting the potential for water harvesting was assessed. These variables encompass mean air temperature, wind speed, relative humidity, and the feasible quantity of water harvested from air moisture. Also, assessing the efficiency of the optimization algorithm (Genetic Algorithm) in the development of an ensemble climate model was another goal of this research. It is noteworthy that the present investigation employed data from 16 synoptic stations situated in the northern and northwestern regions of Iran during the statistical period of 1991-2005. Results indicated that the performance of individual climate models in simulating variations in wind speed and relative air humidity is deemed poorly. Conversely, GA has yielded a reduction in both error magnitude and biases in climatic outputs in estimating wind speed and relative air humidity. Furthermore, the evaluation of the efficacy of climate models in estimating the water harvesting potential from air humidity indicates the acceptable performance of ECM in simulating changes in the amount of extractable water from air humidity. In general, the results showed that Manjil and Bandar-Anzali stations are the most suitable areas for the implementation of water harvesting projects from air humidity. Conversely, Arak and Hamedan stations exhibit the least potential for water harvesting. Based on the results, the average water that can be extracted from air humidity in the summer season for Manjil and Bandar-Anzali stations is estimated to be 1.56 and 1.78 (l/day.m2). Also, the seasonal changes of water harvesting potential from air humidity showed that the potential of extracting water in summer is more than the other seasons. This accentuates the urgency of water resource management and agricultural planning, prompting the implementation of substantial measures to use this water source. The potential applications of using this source encompass agricultural sectors, green space irrigation, and potentially catering to a portion of drinking water demands, contingent upon quantity and quality parameters.

Extracting water from air humidity as one of the new and sustainable methods for providing water resources in areas with a lack of natural water resources and also in the conditions of drought is of great importance. Considering the influence of the amount of water that can be extracted from air humidity on changes in meteorological variables such as air temperature, wind speed and air humidity, therefore the potential of water extraction from air humidity can be strongly affected by climate changes. Therefore, the current research has been carried out with the aim of investigating the potential changes of water extraction from air humidity under RCP4.5 and RCP8.5 scenarios from the fifth climate change report. The results in the base period showed that the climate models have a suitable efficiency for investigating the potential of water extraction from air humidity, so that the average correlation index at the level of study stations is more than 0.67 and the value of the RMSE error index is 1.83 liters per square meter in The day is limited to In addition, the monitoring of water extraction conditions from air humidity under climate scenarios in the coming periods indicates the existence of an increasing trend at the 95% confidence level in the amount of water that can be extracted from air humidity, such as the value of Menkendall's non-parametric test under RCP4.5 and RCP8.5 scenarios. In the periods of 2026-2050 and 2076- 2100, it is estimated to be more than 1.64. In general, based on the results, it was found that the northwest region of the study area has the greatest potential for the implementation of water extraction projects from air humidity. Therefore, due to the occurrence of global warming in recent years, the increase in water needs in different sectors and the consequent increase in pressure on the available water resources, the need to pay attention to this water source, planning and taking serious action for its use is felt more than ever. The results of the present research can be useful in planning for the sustainable management of water resources under the influence of global warming.

Investigation of future drought trends and variations under climate change scenarios plays a key role in developing management strategies for minimizing drought's negative societal and economic impacts. Therefore, this study aimed to assess changes in drought characteristics such as the frequency of dry and wet periods, and the trend of drought index based on fuzzy drought index (at time scales of 3, 6, and 12 months) under SSP3 and SSP5 scenarios of CMIP6 during the 21st century. The data of 6 synoptic stations in Karoon River Basin during 1991-2014 have been used in this study. Assessing the reliability of climate models for drought monitoring with fuzzy drought index in the base period showed that the highest correlation coefficient (CC>0.90) and the lowest root mean square error (RMSE<0.14) are found at 3 and 6 month time-scales. In addition, monitoring the drought conditions of the basin under climatic scenarios in future periods revealed an increasing trend (at the 95% confidence level) and the wetness frequency in the northern, northwestern, and western of the basin is more likely to decrease. Over the periods 2046–2072 and 2073-2099 the result of the non-parametric Mann-Kendall test for the scenarios of SSP3_7.0 and SSP5_8.5 was 1.64. Therefore, due to the increasing trend of fuzzy drought index changes to dry conditions in Boroujerd, Safiabad, and Kuhrang stations, the risk of drought during the periods 2073-2099 and 2046-2072 are higher. Accordingly, water managers and farmers should adopt strategies in order to reduce the damages. The results of this research can be valuable in adopting policies and planning for sustainable management of water resources affected by climate change.

The declining trend of groundwater reserves due to the high importance of these resources for water supply has become a major challenge, especially in arid and semi-arid regions such as many plains of Iran. On the other hand, establishing an urban wastewater project will directly impact the aquifer of the plain and intensify the process of reducing these reserves. This study investigates the effect of revival strategies in the framework of sustainable development principles on improving the aquifer reserves under five different scenarios. The Mashhad aquifer was picked as a case study. Simulation of the aquifer was performed using the system dynamics approach and Vensim PLE model during 1362-1420. Sensitivity analysis and calibration of the model were done during the period of 1362 to 1392, and the model was simulated until 1420 for evaluatation of different scenarios. Sensitivity analysis results demonstrated that aquifer volume has the highest sensitivity to the return coefficient of drinking and agricultural sectors. Increasing the pervious surfaces in urban architecture has improved about 16 and 19 percent in the aquifer recharging compared to scenarios 2 and 3. Generally, using methods such as changing agricultural methods, increasing water productivity, and improving urban architecture with a focus on increasing permeable levels, it can reduce the aquifer shortage between 22.0 % to 58.5%, compared to scenario 2. While not paying attention to the proposed methods led to a negative balance, and the aquifer will be in a critical and irreparable situation.

Due to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). 

ASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. 

Findings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. 

Results indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.

Estimating precipitation plays an important role in different studies, including meteorological, hydrological, flood simulation, and drought monitoring. SM2RAIN-ASCAT is the newest attempt to estimate precipitation using surface soil moisture and inverse solving the soil-water balance equation. This research addressed the performance of the SM2RAIN-ASCAT dataset over diverse climate regions of Iran at daily and monthly time scales in 54 synoptic stations located in Iran (2007-2018). In addition, improving the efficiency of this product using Quantile Mapping bias correction method is another objective of this study. Results showed that the performance of SM2RAIN-ASCAT at the monthly time scale was better than the daily time scale, and in most parts of Iran, the correlation coefficient between observed and estimated datasets was relatively high. At the monthly time step, 67% of the studied stations had a CC value higher than 0.65. Moreover, findings indicated that this product tended to overestimate in Iran's north and west parts. Based on the RMSE value at the monthly time scale, SM2RAIN-ASCAT performed well at extra-arid and arid regions compared to the Mediterranean and humid climate zones. Also, removing bias from the raw dataset increased the efficiency of SM2RAN-ASCAT in most parts of the studied areas. Based on contingency table metrics, the bias-corrected dataset's skills in detecting rainy days and false alarm ratio (FAR) improved significantly. For instance, the FAR metric averagely improved 26 and 45 % at daily and monthly time scales, respectively. Finally, results indicated that SM2RAIN-ASCAT is a valuable dataset for estimating monthly precipitation, especially in arid-desert to extra-arid climates. The accuracy of this dataset increases using bias correction in different climates.

The lack of required datasets over the catchments impose basic problems for applying hydrologic models. By increasing the satellite-based technologies over the past decades, several water balance components have been developed by using remote sensing and data assimilation techniques. This research addresses the efficiency of evapotranspiration values which are obtained from the reanalysis models (GLEAM, W3RA and HBV) for calibration of VIC-3L hydrologic model over the SefidRood basin (SRB). Results showed that using the evapotranspiration dataset, which is estimated by GLEAM, is the best for calibration of VIC-3L (NS=0.56 and CC=0.80) and simulating the streamflow at the outlet of SRB. Also, using the HBV reanalysis model’s results, in both daily and monthly time scales, with the KGE=0.64 leads to the better performance in simulating streamflow when comparing to the base scenario (calibration of VIC-3L model with the observed streamflow data). Finally, results indicated that using W3RA and GLEAM datasets improved the VIC-3L performance in estimation of runoff volume and the maximum relative error restricted to only 4.0 %.

This study addresses the efficiency of ASCAT surface soil moisture (SSM) for calibration of VIC-3L hydrologic model over the Sefidrood river basin (SRB). Findings on daily time scale indicate that the correlation coefficient (CC) between observed and simulated streamflow, using ASCAT dataset, is equal to 0.75, while in the case of calibration of the VIC-3L model using only with hydrometric station datasets the CC value is equal to 0.80. Moreover, at monthly time scale the performance of model in simulating streamflow is better than daily time scale. The relative error (RE) in simulating runoff volume at daily and monthly time scale is 17.2% and 38.5%, respectively and this shows that using ASCAT dataset at monthly time scale leads to reliable results. In addition, one the advantages of model calibration using satellite-based SSm is the extraction of the spatial pattern of model parameters over the catchment, while in the case of using observed discharge dataset for calibration of the model only leads to the constant values of model’s parameter through the catchment. Based on the results of this research it highly recommended to use satellite-based SSMs for calibration of hydrological model, especially in data limited areas or regions in which ground observations are sparsely-distributed or insufficient.

The main objective of this study is to assess the performance of Earth2Observe's GHMs and LSMs reanalysis models in estimating runoff at the outlet of Sefidrood river basin (SRB). In addition, for better evaluating the efficiency of Earth2Observe uncalibrated models, the VIC-3L land surface model is implemented over the SRB and calibrated using observed discharges. Results showed that, based on CC and NS statistics, the performance of SURFEX-TRIP model in both daily and monthly time scales is the best one and it led to the same results as well as VIC-3L calibrated model. The values of CC and NS statistics, at daily time scale, in the case of SURFEX-TRIP model are 0.75 and 0.55, respectively, while at the monthly time scale these values are 0.86 and 0.73, respectively. As an overall, findings indicate that LSMs performs better than GHMs in simulating runoff and this may be due to the ability of LSMs in considering both water and energy budgets and they can exchange energy and mass between land surface and atmosphere. Therefore it is highly recommended to use the results of reanalysis models as an appropriate guidance, particularly in the case of ungauged catchments.

One of the most widely used method for estimation of erosion over the catchment and the sediment delivery from the catchment is revised universal soil loss equation (RUSLE). Several studies have been carried on the importance of topographic factor (LS) which includes slope steepness and slope length factors. Over the past decades, different methods have been developed for calculation of topographic factor and hence, choosing the best one is a confusing and challenging issue. Moreover, by increasing of geographic information system (GIS) application in hydrological modeling, mathematical calculations on grid-based datasets can easily be done in GIS environ. Due to depending on grid-based datasets, RUSLE model is influenced by several factors which among them, the effect of DEM resolution and the method of LS estimation are the most important ones. This research addresses the effects of all important factors on sediment delivery load at the outlet of the Barajin catchment by coupling RUSLE model and GIS environ. Also, in order to evaluate the efficiency of this model, mean annual sediment delivery load is calculated by analyzing discharge-sediment curve and observed data at Barajin hydrometric station between 1987 and 2015. Findings show that using different methods for estimation of LS factor without considering their limitations leads to a significant relative error (RE) in the calculation of sediment delivery load. Furthermore, McCool et al equation, due to a RE of lower than 10% in estimating the sediment delivery load, is the best method for calculation of LS factor.