جستجوی مقالات مرتبط با کلیدواژه "grace satellite" در نشریات گروه "آب و خاک"

The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models. The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. The results showed that the output of the ICA-ANN model had the best fit with the observation data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. Without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy.

Fresh water availability and its shortage is one of the most important issues in the world today that some countries are facing. In the last two decades, Kabul province, Afghanistan, has seen a decrease in water resources under the influence of natural and human factors. The purpose of this research is to use satellite data and remote sensing techniques to investigate changes in surface and underground water resources in Kabul province. For this purpose, the satellite data and products available in the Google Earth Engine in the period 2000 to 2022 and the climate data of ground stations in the period 2006 to 2021 have been used, which include: evaporation data- Transpiration, Enhanced Vegetation Index (EVI), Global Product of Surface Water Areas (JRC), GRACE data, OLS Satellite Night Images, Sentinel 2 Satellite Image, Landsat 7 Satellite Image, Temperature, Humidity and Rainfall Data. The change process of the used data was analyzed through the Mann-Kendall test and the significance level of these data was checked. The supervised classification method was used on the two mentioned images to calculate the area of vegetation, water area, residential areas, and barren lands. The obtained results show the reduction of underground water resources (significant reduction trend of GRACE satellite data) and the reduction of the surface water area changes in Kabul province, under the influence of natural and human factors, which among these factors can reduce He pointed out the amount of rainfall, increase in temperature, increase in evaporation-transpiration, increase in the level of vegetation and physical development of Kabul city and increase in the population using water resources.

Excessive extraction of groundwater has caused most of Iran's groundwater aquifers to face a drop in water level in recent years. This has subject caused the use of most of the aquifers to be prohibited, most of the Qanats have dried up and most of the permanent springs have had a significant reduction in their water supply. Therefore, the investigation of the groundwater level should be given more attention. Various methods and tools have been used to investigate this issue. Artificial intelligence models have been used in most of these studies. Among these intelligence models, Support Vector Regression (SVR) model has performed well. In order to improve the performance of these models, in recent years, the use of pre-processing tools and the formation of hybrid models have been considered. One of these tools is complementary ensemble empirical mode decomposition (CEEMD). In this research, the combination of this tool with the SVR model was used to check the groundwater level in the Aspas aquifer. Then their results were compared with the results of the Gravity recovery and climate experiment (Grace) satellite.

The Aspas subbasin with code 4321 is located northwest of the Tashk-Bakhtegan and Maharlu basin in Fars Province. To check the groundwater level in this sub-basin, the SVR model with 4 kernels include: polynomial kernels, RBF kernel, sigmoid kernel, and linear kernel (Lin) was used. Then discusses the formation of a hybrid model obtained from the combination of CEEMD with the SVR intelligence model. When an initial signal is decomposed using the CEEMD method, and the resulting sub-signals are used as inputs to the SVR intelligence model, the hybrid model of CEEMD-SVR is obtained. Satellite data was used to compare the performance of artificial intelligence models. For this purpose, Grace satellite products with 6 different algorithms were used. The parameters the coefficient of determination (R2), root mean square error (RMSE), and the Akaike information criterion (AIC), was used to examine the efficiency of the methods.

The results showed that intelligent models had better performance than Grace satellite products. Therefore, it is more appropriate to use intelligent models, especially the CEEMD-SVR model, to predict the values of the groundwater level. One of the advantages of using satellite data is that it is available up-to-date. If the satellite data values can be approximated to the observed values (in a similar statistical period) based on a suitable method, the groundwater level data can be estimated in an up-to-date manner.

In this study, the SVR model was used to evaluate the groundwater level changes in the Aspas alluvial aquifer located in the Tashk-Bakhtegan-Maharlu basin. Using observation wells in the area the aquifer groundwater hydrograph was plotted. Changes in groundwater level in the aquifer were estimated using the values of precipitation, temperature, and evaporation parameters obtained from drawing different maps, and groundwater level in the aquifer. The preprocessing tool of CEEMD was used. The results showed that the use of the CEEMD has improved by 3.08% the performance of the SVR model. The GRACE satellite products are used. The comparison of the results of processing algorithms showed that the GFZ processing algorithm had the best performance with a coefficient of determination of 0.71 and an RMSE value of 39.15. In the next step, the performance of the CEEMD-SVR model was compared with the GFZ algorithm. The results showed that the CEEMD-SVR model performed better (R2=0.77, RMSE=25.90) and has the ability to be used for modeling and predicting the groundwater level in aquifers, especially the Aspas aquifer.

Water security includes sustainable use and protection of water systems, protection against water-related hazards, sustainable development of water resources, and preservation of water functions and services for humans and the environment. To investigate water security in the past decades, the WSIM-GLDAS water security model was used, and for assessing available water storage, the GRACE satellite data were employed. Then, a multi-model was analyzed using CMIP6 models to investigate future changes in temperature and potential evapotranspiration (PET) over the country's medium-scale under SSP scenarios. The results showed a decreasing trend in available water storage over the past two decades in Iran. The outcome of the water security model indicated a negative return period of water scarcity across the country. The lowest value of WSIM is -10.1 which is seen in the eastern and central regions of Iran, indicating the low water security of these areas. Climate projections show a significant increase in temperature (0.78 oC/decade under the SSP5-8.5 scenario) and evapotranspiration (153.5 mm/decade under the SSP5-8.5 scenario) in the country. To increase water security, it is necessary to take effective adaptation strategies, the purpose of which is to increase the water storage capacity in the soil (green water) and reduce soil erosion in order to increase the quality of the soil and maintain the storage capacity of reservoirs (blue water). Therefore, the adaptation strategies to climate change and its effect on water redistribution from green water to blue water, and the long-term socio-economic consequences of climate change should be considered in future climate plans of Iran.

Groundwater monitoring has long been considered as one of the main sources of agricultural water supply. . In this regard, indiscriminate harvesting of this God-given resource, as well as the construction of unauthorized wells and lack of rainfall have caused the water level to drop in many aquifers of the country. The plains of Kermanshah province with its good climate and fertile soil are the agricultural and horticultural hubs of Iran. Therefore, in this study, piezometric well data in the period 1360-1397 and GRACE satellite data with a resolution of one degree in the period 2002 to 2020 with JPL, GFZ and CSR methods were used to study the changes in groundwater levels of aquifers in Kermanshah province. The amount of soil moisture was extracted from Google Earth Engine cloud computing environment using GLDAS model with a resolution of one degree. Zoning maps of all aquifers in the province for the years 1395 and 1397 were drawn by kriging method in ARC GIS software. The results showed that a number of aquifers, including Hassanabad-Shian, Sanjabi and Dinehvar are in critical condition. Long-term monthly and annual water level data of aquifers for different plains according to their effective areas have also been calculated and displayed by Thissen method. The map of changes in soil moisture, actual evapotranspiration, and cumulative precipitation in Kermanshah province was extracted by TERRA satellite with a resolution of 0.04 degrees equivalent to 4 km by 4 km, which shows the changes in these parameters in December 2019. The values ​​of TWS groundwater level changes were plotted by subtracting soil moisture values ​​against the aquifer observational data and the changes in groundwater level decreasing trend in Kermanshah province indicate a decrease of -1.5 cm and -3.8 cm in the period 2002 to 2016 by GRACE satellite and observational values respectively While from 2016 to 2020, groundwater level changes have been increasing.

Determining groundwater storage changes, especially in arid and semiarid areas, is a vital issue for groundwater management and planning. GRACE satellite produces changes in water storage with 1 degree spatial resolution using Earth's gravity field changes. In this study, it is tried to evaluate monthly groundwater storage changes between August 2002 and June 2016 in two pixels of one square degree of Khorasan Razavi province, which cover the whole area or parts of 6 aquifers using data from four GRACE satellite information processing centers named CENS, CSR, GFZ and JPL. Utilizing amount of Terrestrial Water Storage (TWS) obtained from GRACE satellite data, with data of soil moisture, snow water equivalent and canopy water storage derived from the GLDAS model which are presented with 1 degree spatial resolution on a monthly basis lead to estimates of monthly groundwater level changes. An estimation of monthly changes in groundwater level of the GLDAS model was obtained at a 25 degrees resolution in order to compare with the results of the GRACE satellite data. Observational data containing monthly groundwater level changes from the piezometric wells of the study area were used to validate the results. The results show that data of GRACE (CENS) with (RMSE = 14.35 and MAE = 12.645) are the weakest estimation of groundwater level changes. While the GRACE (JPL) data with RMSE = 5.708 and MAE = 5.038 shows the best estimation. It should be noted that the GLDAS model data with a resolution of 0.25 degree with an RMSE of 2.350 cm and a MAE of 1.826 cm shows a more appropriate estimate than the GRACE data types. GRACE data (CSR) showed the most favorable prediction of the trend of monthly changes in groundwater level by presenting a trend of -0.089 cm / month.