نشریه مدل سازی و مدیریت آب و خاک
سال دوم شماره 3 (پاییز 1401)

Today due to climate change and fluctuations in the intensity and duration of rainfall in most parts of the world, new approaches to streamflow modeling have an extraordinary role in managing water resources and reducing the risks of floods. Since in some catchments, it is not feasible to measure all the observed quantities required for modeling the streamflow process, it is necessary to choose a simple model that can accurately predict rainfall-runoff using minimal information. Artificial intelligence (AI) models have high efficiency, especially when the accurate estimation of processes is more important than understanding the mechanisms and relationships that create them. Therefore, the AI models and semi-conceptual IHACRES models are used for streamflow modeling and compared with each other. In this study, streamflow modeling for the Gamasiab River, located in western Iran, is presented.

For rainfall-runoff modeling, artificial intelligence (AI) models include artificial neural network (ANN) models of type multi-layer perceptron (MLP), radial basis function neural network (RBF), and long short-term memory model (LSTM) are used. In addition, to better evaluate the AI models, a specialized semi-conceptual rainfall-runoff model called IHACRES is used. The data used in this study include daily data of flow discharge, precipitation, and average temperature for 31 years (September 23, 1986 - September 22, 2017), which is a time series of delayed data and as an input signal used to models. To evaluate the performance of the models, some criteria including Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R) were used.

The results show better performance of ANN, RBF, and LSTM models than the IHACRES model, especially at the peak flow rate, in modeling daily streamflow for the study area. The IHACRES model has performed well in calm and medium flows but has not performed well at flow peaks. In addition, the LSTM model performed better than the other models in estimating the flow rate during the verification period, while the ANN and RBF models performed better than the other models in the calibration phase. Overall, the results indicate that the best data-based model (i.e., LSTM model) has more than twice as good performance in streamflow modeling as the semi-conceptual IHACRES model based on RMSE criteria. In general, the results showed that artificial intelligence models are valuable tools for modeling streamflow fluctuations.

The AI models including, ANN, RBF, and LSTM models, especially in estimating flow peaks, were significantly better than the IHACRES model. The IHACRES model has performed well in calm river streamflow and low and medium discharge but has not performed well at flow peaks. In general, is recommended the LSTM model for modeling the daily streamflow of the study area due to better performance. AI models can model the streamflow more accurately and provide more efficient management of water resources in different regions. In general, the results showed that various AI models are a suitable tool in streamflow modeling, and it suggested that they be more utilized in future research.

One of the main goals of spatial analysis of precipitation at area is to reach the standard project storm (SPS) for that area, through which can be reached the standard project flood (SPF). This analysis includes the characteristics of rainfall depth at a certain area and for a specific duration. Relation between depth and rainfall area which called depth-area-duration (DAD), is shown usually by set of curves that each shows different duration of rainfall. Using these curves, a reduction factor is determined for specific area and is applied to adjust the average point rainfall related to frequency of this project. The present study carried out under topic of investigation and map of depth- area- duration in Lorestan, in an area over 28559/5 Km2 in west part of country.

Weak coefficient of correlation is shown meaningfulness of the relation between rainfall and altitude in different time base. this is resulted from different reasons such as extension of area, lack of transmittal and number of suitable weather stations and different extension and tension of mountain than rain flaw. Considering above points cause those other methods of drawing precipitation maps include interpolation or geostatistical methods including, spline, IDW, kriging and Co-kriging were used.

Weak coefficient of correlation is shown meaningfulness of the relation between rainfall and altitude in different time base. this is resulted from different reasons such as extension of area, lack of transmittal and number of suitable weather stations and different extension and tension of mountain than rain flaw. Considering above points cause those other methods of drawing precipitation maps include interpolation or geostatistical methods including, spline, IDW, kriging and Co-kriging were used.

The results show that to preparing precipitation maps of selecting storms, simple co- kriging (SCK) is a suitable method to calculate the amount of rainfall of selecting storms in lorestan province. So the above way is used for preparing precipitation maps. Resulted from investigation of surface reduction factor of rain fall shows that in time duration 12 and 48 hrs with the increase of each 5000 surface reduction factor reduces for 0.1 in 24 hrs duration within 18000 Km2. This coefficient has a slow decreasing trend and then that is similar to 12 and 48 hrs rainfalls. Assessing the daily rainfall statistics of some of rain gauge stations in somewhere of the province by the Meteorological Organization and the Ministry of Energy, shows that sometimes there is a significant difference between the perception rates recorded by these organizations. Therefore, in order to eliminate the existing defects, it is suggested that the stations occupied by these organizations and their monitoring status be periodically evaluated by the experts of the relevant organizations and possible defects be prevented.

Anzali, Ajigol, Almagol and Alagol international wetlands are important global ecosystems. In this study, the trend of land use changes in these wetlands and its impacts on the distribution of waterfowl and shorebirds. One of the common methods in studying the ecosystem changes of wetlands is the diversity evaluation of waterfowl and shorebirds. Because of the ease of observing birds in nature, it is possible to find any possible changes in the wetlands by continuously studying the species diversity, population changes, and the habitat quality.

Satellite images and field information of wetlands and their land changes were prepared based on available data and field surveys. In order to prepare the vegetation map based on the wetland index, 6 bands of Landsat satellite TM sensor were used to calculate the NVIDA index. Also, using field survey and sampling of plants and wetland index, the predominance of plants in the region was determined. Then, land-use maps in different periods of 20 years (1998-2019) were prepared based on satellite image processing and image classification using the maximum likelihood method. The data obtained during 8 years census and field surveys, the Shannon-Wiener species diversity indices and inversely the Simpson index, Margalf and Manhink indices has been calculated and were used to determine the species richness and Pilo and Simson indices were used to determine the species evenness.

The results showed that the water area in Alagol, Ajigol and Almagol international wetlands has decreased by 1050.8 hectares (53.7%) and in Anzali wetland by 1541 hectares (18.9%). According to the results, in Alagol, Ajigol and Almagol international wetlands, the most changes are related to vegetation of medium density with 273% increase and in Anzali wetland, the most changes are related to increase in herbaceous plants with 270.3%. According to Margalf species richness index, the highest species richness was observed in Abchelikian. Also, the highest species diversity was observed in the genus Murghabian based on Shannon-Wiener index and the reverse of Simpson index. Also, the highest amount of species evennessbased on Pilo index and Simpson index was observed in the Baklanian genus.

The water extent of the wetlands has generally decreased and due to the increase of man-made land uses in the margins of the wetlands, adverse effects on the wetland ecosystem have been caused to vulnerability of wetlands to degradation. As a result of declining water levels in wetlands and the uncontrolled growth of non-native plants and the reduction of dissolved oxygen, the rate of habitat destruction of migratory aquatic animals and birds will increase. Due to the declining trend in biodiversity indicators in recent years as a result of declining water levels and land-use change, increasing the ecological protection of the wetland through management projects such as habitat protection, poaching control and pollution control, is recommended.

Soil salinity is the predominant soil degradation process in arid and semi-arid regions. There are several methods for monitoring salinity, which are mainly measured as points, which will be difficult to generalize to the whole region. In recent years, remote sensing-based methods for measuring salinity have been widely considered. Soil salinity has led to the limitation of agricultural land use patterns. This is a serious environmental hazard that affects the growth of many crops.

After selecting the study area on satellite images and ground visits, a cluster-cinematic sampling network was designed and implemented for surface soil sampling. In this study, pH, EC, SAR and TDS were measured. In this study, in order to use the remote sensing technique to study the temporal and spatial changes of vegetation density in the region, ground data and ETM+ images of Landsat 7 satellite and MODIS images have been used. Pre-processing operations including geometric, radiometric and co-ordination corrections were performed on each of the satellite images. In the next step, the desired vegetation indices, after calculation, are applied to the satellite images and thus the vegetation density pattern map is obtained based on each of these indices. Then 8 different salinity and vegetation indices were studied during the years 2005 to 2021.

The The results of this study showed that LANDSAT-7-ETM + sensor has been able to produce better results due to better spatial resolution than MODIS sensor. Also, among the salinity indices studied, SI3 index in both ETM + and MODIS sensors with RMSE (1.01 and 1.1) and correlation coefficient R (0.98 and 0.86) was able to have the best performance in Have an area. In the study between EC and SAR, both sensors had a high correlation between red and infrared bands.

In a general summary, by examining the information of the synoptic station of the plain, parameters such as temperature and amount of precipitation in the studied period show that by increasing the average temperature and decreasing the amount of precipitation in the region, the surface temperature increases during the year. Recent causes of drought and significant effects of climate change on the prevailing environmental conditions, which in turn will increase the breadth of salinity in the verse.

Sand mines are one of the most important active mines. One of the key properties of sediments is their particle size distribution, which affects many physical and chemical properties such as hydraulic and electrical properties and characteristics related to the transportability by erosive agents. Determining the size of sediment particles in different environments can be used in reclaiming sediments from washing river and mountain sand mines for physical and chemical remediation of soil. By determining the size of sediment particles in different environments, it is possible to measure the reuse of sediments from washing river and mountain sand mines for physical and chemical remediation of soil.

The main aim of this study was to compare the capability of some mathematical models in describing particle size distribution of sediments of 26 sand mines in Urmia. For this purpose, sampling was performed from mine sedimentation ponds. By examining each of the mines, in the field surveyes of sediment ponds, sediment sampling was done. In sampling of each mine, three samples were taken from three different points of stilling pond (entrance, middle and end of the pond) in depth (approximate depth of 20 cm) and composed of sediment accumulation profiles with approximately one kilogram weight. The particle size distribution was determined by hydrometric method. In order to describe the sediments, 4 mathematical models of sediment size distribution including Weibull, Fredlund, Van genuchten and Jacky models were used. Different aspects of models performance were evaluated by some efficiency criteria.

The samples taken from mountain and river mines were in the sandy loam and loamy texture class, respectively, so they have fine to medium grain texture. The results showed the difference between the amount of particles forming river and mountain sediments and there is small difference between river and mountain sediments in terms of the amount of component particles. In mountain sediments, the amount of clay, silt and sand is much more than river sediments and the amount of sand in river sediments is more than mountain sediments. Based on the results, most of the sediments are sand, silt and very fine sand and clay, respectively. Six efficiency coefficients were used to evaluate the accuracy of sediment particle grading models. The results showed that Fredlund model had better performance in describing sediment size distribution than other models.

Analysis of daily, monthly, seasonal and annual trends of precipitation and minimum and maximum temperatures in the period Fredlund model has the lowest error compared to other models and increasing the number of model parameters is not a reason to increase the accuracy of the model.It is suggested that the physical and chemical properties of sediments of mountain and river sand mines be studied separately so that according to different origins and different processes involved in their formation, the feasibility of using these sediments for soil optimization can be investigated.

Understanding the spatial variability of soil texture as one of the most important characteristics of soil is essential for soil and water resources management, productivity and sustainable development. However, in many cases, we face the limitation of field data due to the costs of soil analysis. The aim of this study was to estimate the soil surface texture (percentages of clay, silt and sand proportions) in lack of proper distribution of field data using satellite-based indices and regression modeling.

This study was conducted in Marjan rangelands of Boroujen. Soil samples (80 replicates) were collected from each subplot 2m×2m, and at depth 0–20 cm. Garmin GPS was used to record the coordinates of the sampling locations. Then, Soil samples from three subplots (as one plot 30m×30m) were mixed together and a sample of 500g was transferred to the laboratory. First, the soil samples were first air-dried then passed through a 2mm. Then, the particle size distributions of soil samples were analyzed following the hydrometer method. In order to predict sand proportions spatially from raw spectral bands and bands compositions of Landsat 8 satellite data including particle size index (GSI), Clay Index (CI), Band 4 to Band 7 ratio, Band 6 to Band 7 ratio and Brightness Index (BI) and physiographic variables including DEM and slope were used as auxiliary variables. To map soil texture compositions, we fitted a linear regression model between field observations and GSI index. Soil sand, silt and clay content were extracted from the predicted soil texture map.

Pearson correlation analysis showed that there are a significant relationship (p ≤ 0.05) between GSI and soil texture fractions and CI had a significant relationship with silt and sand. Between the physiographic variables, DEM had a significant correlation with clay, silt and sand, and slope with clay and sand. Therefore, these variables were selected as suitable auxiliary variables for spatial prediction of soil texture fractions using multiple regression. The central and southern parts of the study area, have a higher amount of clay and silt. Most parts of the region have clay and silt between 40-40%. Whereas, low silt and clay content are mostly observed in the north and northeast of the region. Based on sand map, north, northeast and east of the study area had the highest amount of sand (>40%) and the lowest amount of sand was observed in the central and southern parts of the region (sand percentage between 20-25%). The auxiliary variables had good accuracy in spatial prediction of soil texture compositions, especially in limited/inadequate distribution of sampled field data.

The results showed that remote sensing data and topographic properties combined with field data using multiple modeling can be used to better prediction the spatial distribution of soil texture compositions in large scale, when we are faced with data limitations. The generated maps can be used as basic information for environmental management and modeling.

Meymeh River which is located in the southern region of the Ilam Province, Iran, originates from good quality karst aquifer of the Kabir Koh anticline but its water quality decreases during the flow path of the river. The main geological formation at the flow path of the river is Gachsaran formation which partly composed of gypsum and sometimes salty (Halite) rocks. The aim of this study is to determine the contribution of Sioul and Ghadah tributaries in the salinity levels of the Meymeh river water and determining the critical points and also providing remedial solutions for this problem.

In order to determine the locations of quality changes along the Meymeh River, several field visits were arranged and finally 35 sampling stations were determined along the river flow path. Monthly sampling and field measurements were schedualed in the selected stations for measuring the discharge, EC and Temperature for the 2016-2017hyrologic year.  After transferring the water samples to the laboratory, their chemical properties including major cations and anions were measured in the central laboratory of Ilam University.

Meymeh River has several tributaries and its discharge increases gradually. One of its main tributaries is the Sarkadeh River wich collect mainly the water of sulfur spring and Sioul River. The results showed that the Meymeh River quality gradually decreased along its path after originating from the karst formations and entering the Gachsaran formation, (EC at the source and the final station was measured as 500 and 12500 µs/cm, respectively). The results of mass balance showed that 28% Sulfur Springs, 21% Sioul river and 25% gradual changes by Gachsaran formation are effective in the salinity of the Meymeh River. In other words, about 50% of the Meymeh River’s salinity is due to the impact of the Sarkadeh River.

In order to modify the water quality of the Meymeh River, it is necessary to reduce the impact of tributaries which has the highest undesirable effects on the Meymeh River watre quality. Since the Sarkadeh River has the highest impact on the quality of the Meymeh River, the main action was to enhance the water quality of this branch. For this purpose, by constructing channels along the salinity zones of Sioul river, as well as creation of a water transmission pipe line for transfering the water of sulfur springs to the downstream of the Meymeh dam, can reduced more than 50% of the river water salinity and enhance the EC of the Meymeh River from 12500 (current situation) to 5700 µs/cm (doing above activities).