rainfall

Runoff is an important hydrological component in the assessment of water resources. Most water resource applications rely on runoff as an essential hydrologic variable. The hydrology of basins is influenced by many factors, including climate change. Basin discharge estimation is an important step in planning and managing surface water resources, especially in basins that lack reliable flow data. In this study, due to the inappropriate spatial distribution of meteorological stations in the study area of Takab, satellite images and products were used to evaluate the possible effects of climatic factors including rainfall and temperature on runoff. For this purpose, in order to investigate the changes in rainfall and temperature from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. The evaluation results indicate the appropriate accuracy of these satellite products compared to the observed values. Choosing a suitable rainfall-runoff model for the catchment area is important for the efficiency of planning and management of water resources. Also, choosing a model requires recognizing the capabilities and limitations of hydrological models of the catchment area, which requires access to meteorological parameters such as rainfall and temperature. According to the studies, the IHACRES hydrological model has been used to estimate the amount of changes in discharge and runoff in many basins. Its purpose is to help water resources engineers describe the relationship between basin runoff and precipitation.

Finally, the trend of changes in rainfall and temperature was investigated with the non-parametric Mann-Kendall and Sense's slope tests. Examining the rainfall data of the study area of Takab indicates that the highest amount of rainfall occurs in the 3 months of April, March and November respectively. which is approximately equivalent to 45% of the total annual rainfall of the study area and its values are estimated as 53.1, 40.4 and 39.6 mm per month respectively. Also, the highest and lowest average temperatures of the range are in the months of July and January, respectively, which are estimated at 24.2 and -3.4 degrees Celsius, respectively. In the following, the IHACRES model was used to simulate the river discharge using temperature and rainfall data from satellite products. Also, in this study, the IHACRES model was used to predict production runoff under the influence of climate change and evaluate different climate scenarios. For this purpose this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for the coming years until the year 2100 were used.

The flow simulation results from the RCP2.6 scenario indicate that the greatest increase in discharge in the coming period for the Takab study area was estimated for the months of December, November and January. Also, according to the RCP8.5 scenario, the largest flow increase in the future period was calculated in the months of August, July and January, respectively. In addition, the maximum decrease in discharge in both scenarios was simulated in the months of April and May, respectively. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. In order to evaluate the IHACRES model, the determination coefficient (R2), Nash-Sutcliffe efficiency coefficient (ENS), root mean square error (RMSE) and bias error (Bias) were used in the calibration and validation period. According to the obtained results, these values for the 14-year period of model calibration are 0.82 and 0.80 (-), 1.4 (cubic meters per second) and 0.42, respectively. Also, these values for the validation period were calculated as 0.71 and 0.68 (-), 4.7 (cubic meter per second) and 0.1, respectively.

In this study, to investigate the trend of rainfall and temperature changes from 1998 to 2020, TRMM and FLDAS satellite products, respectively, were evaluated using different statistical criteria with Takab synoptic station data. According to the obtained results, the annual changes of rainfall in the entire study area of Takab are incrementally insignificant. Also, in general, the percentage of annual rainfall changes in the highlands is higher than the average annual rainfall in the plains. After examining the trend of temperature and rainfall data, the IHACRES rainfall-runoff model was used to simulate the discharge. After simulating discharge for the statistical period, different climate scenarios were used to predict production runoff. According to the obtained results, according to the RCP2.6 and RCP8.5 climate scenarios, the average annual discharge was predicted to be equal to 8.3 and 1.5 cubic meters per second, respectively. According to the RCP2.6 scenarios, the predicted discharge has an insignificant upward slope. Also, the percentage of annual changes in river flow according to this scenario was calculated as 19.4%. Also, in the examination of the output of the IHACRES model resulting from the RCP8.5 scenarios, it was observed that the future trend of the river is a significant downward trend. In other words, the percentage of annual changes in the simulated discharge according to this scenario was estimated as -68.1%.

The global warming trend has raised concerns about the state of water resources. In this study, to examine the impact of climate change on the precipitation pattern of Ilam Province, the outputs of 12 CMIP6 models were utilized. Considering the SSP1-2.6 and SSP5-8.5 climate change scenarios, precipitation variations in Ilam Province were analyzed up to the year 2050. After clustering the rain gauge stations (Cluster 1: South and East of the province, Cluster 2: North and West of the province) and evaluating the performance of CMIP6 models, the IPSL-CM6A-LR and ACCESS-CM2 models were selected as the best models for Cluster 1, while the IITM-ESM and BCC-CSM2-MR models were chosen for Cluster 2. The model outputs indicate that in the future period (2018-2027), for Cluster 1, under the SSP1-2.6 scenario, the average annual precipitation will decrease by 4.1% to 303.52 mm per year. Under the SSP5-8.5 scenario, this reduction will be 4.7%, bringing annual precipitation to 301.56 mm per year. For Cluster 2, under the SSP1-2.6 and SSP5-8.5 scenarios, the average annual precipitation will increase by 3% and 2.8%, respectively, rising from 431.10 mm per year to 444.04 mm per year (SSP1-2.6) and 443.31 mm per year (SSP5-8.5). By selecting the best probabilistic distribution for each rain gauge station, it was found that under both climate change scenarios and different return periods, the maximum 24-hour precipitation in most cases will decrease in the future period. Therefore, this highlights the importance of developing sustainable water supply strategies for the province.

Climate is a complex system that is changing primarily due to the increase in greenhouse gases. To study the effects of climate change on agricultural, hydrological, and environmental systems, general circulation models (GCMs) are used to simulate climate variables. These models, based on approved Intergovernmental Panel on Climate Change (IPCC) scenarios, enable the modeling of climate parameters over extended periods. Globally, various centers and models simulate future climatic conditions using different emission scenarios, physical structures, and computational approaches. The simulations from CMIP6 GCMs form the foundation for many IPCC conclusions regarding future climate changes. These data are utilized directly or after downscaling to evaluate local and regional climate changes (IPCC, 2021). This study analyzes and predicts trends in precipitation and minimum and maximum temperatures in East Azerbaijan Province under climate change conditions from 2021 to 2100.

This study aims to investigate precipitation and minimum and maximum temperatures and their trends from 2021 to 2100 across stations in Tabriz, Ahar, Jolfa, Maragheh, and Miyaneh. Data from 12 CMIP6 models (ACCESS-CM2, BCC-CSM2-MR, CESM2, CNRM-CM6-1, CanESM5, MIROC6, MRI-ESM2-0, IPSL-CM6A-LR, GISS-E2-1-G, HadGEM3-GC31-LL, NESM3, and NorESM2-MM) were used under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Kling-Gupta Efficiency (KGE) method was applied to identify the best models for simulating precipitation and temperature by comparing historical model data (1989–2018) with observed data from selected stations. Bias correction of model outputs was then used to forecast climate variables under the SSP scenarios. Finally, the mean time series of precipitation and minimum and maximum temperatures for the future period were compared with historical data to quantify changes over the 80-year horizon (2021–2100) for East Azerbaijan Province.

The performance of 12 CMIP6 climate models was evaluated for generating past and present climate data (1989–2018). Based on uncertainty analysis, the BCC-CSM2-MR and MIROC6 models were identified as the best for simulating precipitation and temperature. These models were used, with bias correction, to predict precipitation and minimum and maximum temperatures for the future period (2021–2100) under optimistic, moderate, and pessimistic scenarios for East Azerbaijan Province. The results revealed that in all scenarios, annual temperatures are projected to increase while annual precipitation will decrease. Annual maximum temperatures across the selected stations are expected to increase by 0.57–6.41°C, while annual minimum temperatures will rise by 0.46–4.89°C. Precipitation is projected to decrease by 2.3% to 9.18%. The highest temperature increase and precipitation decrease are expected at Jolfa and Tabriz stations, respectively.

This study demonstrates that CMIP6 models effectively simulate future climate parameters and align well with historical climate data for East Azerbaijan Province. The high accuracy of these simulations makes them suitable for forecasting future climatic conditions and facilitating macro-level management strategies. Such strategies can enhance resource productivity, particularly in water resource management, to address the challenges posed by climate change.

Flood is one of the most frequent and costly natural disasters, and the financial and human losses caused by it every year affect a wide range of countries, especially Iran. Therefore, one of the fields of research to control flood risks is to identify the flooding points of the region. For this reason, the aim of the current research is to identify flood-prone areas in Barzak-e-Kashan basin using DEMATEL-AHP and SVM models. For this purpose, 100 flooding points were identified and recorded during field visits. In the following, 12 factors affecting the occurrence of flood including precipitation, lithology, land use, distance from stream, slope, drainage density, topographic position index, topographic wetness index, topographic roughness index, stream power index, curve number and runoff coefficient were selected for preparing maps of flood-prone areas and their layers were prepared in ArcGIS 10.7.1 and SAGA GIS softwares’ environment. The results showed that the precipitation component with the highest weight equal to 0.211 is the most effective variable on flooding and the runoff coefficient is the most influential factor and has the most relationship with other factors. Also, according to AUC=0.859, the AHP efficiency was evaluated very well and the SVM accuracy was good (AUC= 0.751) in validation phase. The map of flood-prone area also showed that the north, northwest, and west lands of Barzak basin have the highest potential for flooding. The results of the present research can be used as a road map for managers and policy makers to manage flood.

Globally, paulownia (Paulownia fortunei (Seem.) Hemsl.) is one of the most important wood species used in forestry. Despite extensive research on this genus, the influence of climatic factors on the growth and anatomical wood properties of paulownia has not been thoroughly investigated. Therefore, this research aims to examine how climatic factors—such as temperature, precipitation, relative humidity, and sunshine hours—affect radial growth and specific wood anatomical traits of cultivated paulownia trees in Gorgan County, Golestan Province, Northwest Iran.

Five 20-year-old Paulownia fortunei trees were selected from Hashemabad in Gorgan County. These trees were felled in late 2012, and a five-centimeter-thick disk was extracted from each stem base. Strips from the bark to the pith were cut radially from each disk, and the surfaces of the specimens were smoothed using a microtome blade before scanning. To enhance the contrast between the vessels and the background tissue, a charcoal blackening technique was employed prior to scanning. Using images obtained from image processing software, several features were measured: growth ring width, average vessel lumen area, porosity, vessel frequency, vessel area greater than 0.06 mm², radial and tangential diameter of vessels, and ray frequency in each growth ring. To eliminate the effect of juvenile wood—which consists of very wide rings in the early years of growth—five rings near the pith were excluded from measurements and analyses. Consequently, growth rings from 1998 to 2012 (15 years) were evaluated. Pearson correlation was used to investigate the relationships between climatic variables (monthly, bimonthly, seasonal, bi-seasonal, and tri-seasonal) and growth variables (growth rate and quantitative anatomical wood characteristics) from three months prior to growth until the end of the growing season. Additionally, internal relationships among the measured variables were assessed similarly.

The radial growth of paulownia exhibited a decreasing trend with age, particularly notable at seven and thirteen years. Other anatomical features (vascular traits and ray frequency) showed an increasing trend; however, vessel frequency did not exhibit a clear age trend. Correlation analysis revealed that all anatomical characteristics had an inverse relationship with growth rate; thus, as growth ring width increased, other characteristics (e.g., vessel area and vessel diameter) decreased. Vascular traits and ray frequency primarily showed positive correlations with each other, while the correlation between vessel frequency and other variables was weaker. Among climatic factors, total monthly rainfall and average sunshine hours in spring (especially May and June) significantly impacted radial growth and anatomical features of paulownia wood. Climatic factors from three months prior to the growth period (the previous winter) did not show significant correlations with anatomical characteristics; furthermore, correlations between anatomical traits and summer climatic factors were much weaker than those observed in spring. Increased spring rainfall enhanced radial growth while reducing anatomical features such as vessel size and ray frequency; average sunshine hours had a similar but less intense effect on these traits. Temperature and air humidity did not significantly affect the studied characteristics.

Overall, it can be concluded that paulownia has high water and light requirements but shows low sensitivity to temperature in the studied region. The months of May and June (mid to late spring) are particularly influential for radial growth and hydraulic architecture (water transport system) in paulownia trees. Increased precipitation and average sunshine hours promote radial growth but reduce vessel size. Additionally, ray frequency can be considered a valuable wood anatomical characteristic for studying the relationship between climate and tree physiology in future research. 

Climate changes can significantly affect socioeconomic activities and quality of life, especially in countries that are currently facing water tensions. Climate models play a key role in assessing the impact of climate change and developing adaptation and resilience strategies. Considering the importance of food security and then water security in resilience against climate change, as well as the significant contribution of Mazandaran province in the production of agricultural products and food supply of the country, it is very important to examine the drought situation of this province and its climate change process. In this study, therefore, the wet and dry durations in the 20-year base period of Mazandaran province were evaluated using the standardized standard precipitation index. Then, projections of temperature and precipitation at the local scale were made in future periods using the five global circulation models (GCM) available in phase 6 of the climate output project (CMIP6) under three scenarios SSP2.6, SSP4.5, and SSP8.5.

In this research, six meteorological stations, viz. Ramsar, Noshahr, Siyabisheh, Babolsar, and Qarakhil, were selected due to the coverage of the most statistical years and suitable spatial distribution in the region. Time series of precipitation and daily maximum/minimum temperatures were collected for six selected stations in the region with a base statistical period of 20 years (January 1999 to December 2018). After ensuring the quality of the data, the trend of their changes was analyzed using Mann-Kendall and age slope tests. Standard precipitation index values were calculated and evaluated in different intervals. Finally, large-scale data from five general circulation models (ACCESS-CM2, CanESM2, CNRM-CM-6-1, MRI-ESM2-0, and NESM3) were downscaled by the LARS-WG6 climate generator. Thus, predictions of seasonal and annual changes for Tmax, Tmin, and precipitation in two future periods (2040-2060 and 2080-2100) were made using the average of selected GCMs.

As a result of the statistical analysis of the above data, minimum and maximum temperatures increased and precipitation decreased during the standard period, but no significant trend was found at the 0.05 level. The analysis also shows that the state's worst droughts occurred in 2007, 2009, late 2011, early 2012, and 2018, with SPI values below-1.0 at stations. The wettest years in the region are 2004-2006 and 2017. The frequency of wet periods is higher than dry periods for all seasons in the region. In the microscale aspect, the results confirmed the ability of the LARS-WG6 model to simulate temperature more accurately than local precipitation, with more precipitation errors in wet seasons. Among these results, the lowest value of the correlation coefficient (0.941) was obtained for maximum and minimum monthly temperatures, which means that the squared error value is between 1.05 and 3.82°C. The largest differences between modeled precipitation and observations occurred during the rainy season when GCMs underestimated precipitation. The analysis of future climate changes revealed that all five GCMs indicated a continued increase in temperature in the study area. However, differences in the magnitude of signal changes were observed in different GCMs and SSPs. These predicted temperature changes are significant and reliable because all models agree on the direction of temperature change across the province. Overall, the increase in average Tmax and Tmin is significant in SSP8.5 compared to SSP4.5 because of no reduction in greenhouse gas emissions. Thus, the largest mean changes in the SSP8.5 scenario for 2050 and 2090 at the provincial level for maximum temperature are increases of 2.64 and 4.72 °C during spring, and the largest mean changes in minimum temperature were calculated to rise to 2.97 and 4.83 °C during autumn. Future changes in precipitation proved to be more complex and unpredictable than temperature. The largest incremental changes in local precipitation in 2090 under the SSP4.5 (40.5%) and SSP8.5 (51.9%)scenarios were shown by the Ramsar station. In the study area, it is projected 38.86% and 43.95% on average in the feature period (2040-2060) and 45.11% and 65.94% in the feature period (2080-2100) under SSP4.5 and SSP8.5. Thus, the results of this study show that the shift toward wetter seasons at the provincial level in the future will cause more precipitation in the western part of the province.

Examining the drought situation in the base period shows the occurrence of periods with near-normal rainfall in longer intervals, and Sari and Qarakhail stations report more drought than stations in the west of the province. All GCM forecasting models presented the same results in significant warming trends in this province. For precipitation, however, it is suggested to investigate other models in this regard due to the sensitivity of the issue and the uncertainties involved in the issue. Considering the effect of changes in temperature parameters and precipitation on water resources and floods in the region, it is necessary to adopt suitable management strategies for the future to be resilient against climate change.

Awareness of vegetation and climate changes is necessary for the correct planning of ecosystem management. In the present study, the temporal and spatial changes of vegetation cover and also land surface temperature and precipitation parameters in the natural vegetation cover of different bioclimates of Isfahan province from 1986 to 2022 were investigated based on  Google Earth Engine system, Landsat satellite and PERSIANN-CDR data. The trend and the amount of changes of these parameters were obtained with the Mann-Kendall and Sen's slope tests. The results showed that the minimum amount of NDVI vegetation was 0.26 in arid and warm climate in 2022 and its maximum was 0.90 in humid and cold climate in 2002. The lowest and highest of land surface temperature was obtained in humid and cold climate and warm and arid climate as 290.61 and 328.54 Kelvin in 2003 and 2022, respectively. The minimum and maximum precipitation were 88.72 mm and 598.17 mm in 2004 and 2001, respectively, in the semi-arid and warm climate and humid and cold climate. The Z statistics of Mann -Kendall test and the amount of Sen's slope for the parameters of vegetation and temperature in most climates were decreasing and increasing, respectively. A decreasing trend with high statistical significance of precipitation was observed only in humid and cold climate (Z equal to -2.08 with a slope of -3.34). In general, the results showed that the vegetation of the province is decreasing, but the temperature is increasing.

A key effect of reforestation is its impact on the hydrological cycle which can change the quantity and quality of rainfall. The aim of this research was to compare the nutrient elements, pH and EC of rainfall compared to TF of a natural stand of Carpinus betulus L. and a man-made Pinus taeda L. plantation located in the western Hyrcanian region, Astara, north of Iran.

Rainfall and throughfall samplings were done using nine and 18 man-made plastic collectors installed randomly under the canopy and open space of each stands, respectively,. The amounts of EC and pH were measured using EC meter and pH meter. The nutrient amounts of calcium (Ca++) and magnesium (Mg++) were measured by the Titration method and potassium (K+) and sodium (Na+) elements were measured by Flame photometer for three rainfall events during the summer season, 2022. Comparison of means was done by one-way ANOVA and LSD at 95% confidence level.

pH in TF of C. betulus stand was 7 and 6.8 and P. taeda 6.4 and 6.6 in two events which were significantly different from those measured for rainfall (6.6 and 7.1). EC of TF of C. betulus and P. taeda increased by 126.4 percent and 113.2 percent, respectively. The amounts of Ca++ in the TF of the C. betulus stand for three events were 24, 19.6 and 12.5 ppm (mean: 18.7 ppm), respectively, while for corresponding rainfall were measured 11.6, 6.7 and 6.2 (mean: 28.8 ppm), respectively, statically different for all events. Mg++ showed the least change compared to other nutrients elements. The K+ in TF of C. betulus in July rainfall (21.2 ppm) had increased by 11.9 ppm compared to rain (9.3 ppm). The amount of Na+ in TF of man-made P. taeda plantation in the first event (15.6) showed a significant difference with that of Na+ in rainfall (9.3 ppm).

When rainfall hits the canopy of the C. betulus natural stand, in 85 to 90% of the samples of TF, nutrients as well as the pH and EC were significantly changed compared to rainfall. However, this percentage of significant change was 70 to 75% for P. taeda man-mad plantation. In this way, replacement of C. betulus with P. taeda altered the nutrients elements of TF as well as pH and EC by 55%. Understanding the change in the amount of nutrients inputs to the forest ecosystems via TF is essential for choosing the appropriate species for the reforestation of degraded forests since planting new species in an area, in addition to changing the amount of rain that reaches the forest floor, it changes the quality of rainfall.

Depending on rainfall systems and conditions influenced by unprincipled human activities, floods cause a lot of damage to natural resources, settlements, and projects every year. Hydrometric stations are often destroyed during floods or small watersheds lack hydrometric stations, which means that the estimation of runoff and maximum flood discharges requires a suitable method to calculate runoff and flood values in these basins. Prevention of this damage is doubly important when the study area overlooks places with a high density of settlements and urban facilities that can threaten the lives of many residents. The amount of runoff in each sub-basin of the watershed overlooking Malayer City was estimated in this study.

The watershed overlooking the city of Malayer with an area of 14,700 hectares is stretched from the north to the northeast of the city. The processing of digital elevation models identified five sub-basins overlooking the city and field visits. The curve number method was used to estimate runoff height and flood volume in each sub-basin. The most intense rainfall event (88 mm per day) with a return period of 30 years was designed as rainfall, considering the amount of previous rainfall 5 days before this event with a cumulative value of 45.2 mm. To calculate the physical parameters of the watershed in this study, topography, geology, vegetation, soil, and land use maps were digitized using a geographic information system. Simultaneous maps of land use and rainfall were provided using Sentinel-2 images, containing 13 spectral bands and a spatial resolution of 10 meters. Utilizing the SCS method, the layers of land use, vegetation, and soil hydrological groups were combined to produce a curve number map. The value of the curve number was corrected for the wet condition based on the rainfall 5 days before and the location of the basin in the previous wet conditions. A weighted curve number approach for each sub-basin was compared with another approach based on the arithmetic mean of the curve number for each sub-basin, as well as a weighted runoff height approach for each curve number. Kirpich's method was used to calculate the concentration time of each sub-basin. In the next step, using the maximum daily rainfall data from the Malayer synoptic station in the statistical period from 1991 to 2021, the rainfall height values were converted to runoff height using the SCS relationship, and then the peak flood discharge was calculated for each sub-basin.

The lack of permanent vegetation and the presence of annual grasses are among the reasons for the high potential of the sub-basins in runoff production. As a result, the average curve numbers of arithmetic and weighted average methods are 79.09 and 81.46, respectively, which shows the high capacity of the basin in producing runoff. Converting curve number values to peak flood height and flow using two commonly used methods of curve number calculation and comparing it with the results of the weighted runoff height calculation approach for each curve number in the working units of the study area showed no significant difference. Finally, the runoff height maps and the peak discharge of each sub-basin were drawn. The northern sub-basin of the basin with an average runoff height of 44.32 mm and a peak discharge of 168 m3/s has the highest participation in flood discharge toward the city of Malayer, and sub-basin 4 in the south-eastern part of the basin has the least participation in flood generation. The results of the relationship between the area and runoff height showed that the basins with a larger area did not necessarily contribute the most to the occurrence of floods in all three calculation methods of the curve number and runoff height. Other factors also play a role in these results, such as the extent of rocky outcrops and reduction time of concentration due to the high slope as one of the reasons for this problem. The formation of the longest watercourse in the basin with a length of 16314 meters under Sub-basin 3, which has the highest peak flood flow after Sub-basin 2, is one of the discussed technical points that can handle the rapid discharge of the peak flow of about 100 m3/s. However, the study of its longitudinal slope of about 2% indicates the high risk of flooding caused by the lack of evacuation in rains with a high return period, which in turn is a serious risk for the threats caused by floods in the southeastern part of the city. The intersection of the end part of the main waterway with the exit of other sub-basins and the collection of runoff under the upstream basins are other problems that increase the risk of flooding in the downstream areas.

It is suitable to use the curve number method to estimate the amount of runoff produced in the basins overlooking the settlements that do not usually have hydrometric stations. Because of its low-density pastures and rain-fed agriculture, the watershed overlooking Malayer has a high potential for runoff production.

Estimating the amount of surface runoff and studying the relationship between rainfall and runoff is the most important issue in surface water hydrology studies. Using simple methods to estimate runoff in hydrological applications, especially in ungauged watersheds, is of particular importance. Probably the simplest conceptual method for predicting runoff depth is the use of the curve number rainfall-runoff model, which was originally developed to model the depth of runoff caused by rainstorms in small agricultural and rangeland catchments, but so far it has been applied in applications other than those originally intended. Various studies show that this method is still growing and improving and it needs to improve. In the present study, firstly, a review of the studies that investigated the factors involved in the curve number method to estimate the runoff value was done, and one of the main results of these studies is to suggest changing the value of the standard initial loss ratio (that is, changing the value of λ from 0.2 to 0.05) based on extensive field measurements and also considering rainfall-runoff response classes when using the curve number method. Secondly, some remaining challenges and future perspectives regarding this method are mentioned.

The construction of rainwater catchment surfaces as a solution to the lack of livestock drinking water in the northern pastures of Golestan province has been less investigated. In the current study, a Geographic Information System (GIS) has been used to identify suitable areas for collecting rainwater in Kalaleh pastures. Among the effective factors in choosing the appropriate site for the construction of rainwater catchment surfaces the criteria of land use, soil depth, distance from fault, slope, distance from waterways, and distance from cattle breeding were selected. Then, with the help of Boolean logic, the weighting of the selection criteria was carried out. In this method, the unsuitable and restrictive areas were assigned a weight of zero, and suitable areas were assigned a numerical value of one. Prone and non-prone areas for rainwater collection were identified by combining information layers. The results indicated that among the selection criteria, the most restricting factor in the study area was due to the slope which made 94.7% of the unsuitable areas for the construction of a rainwater catchment system to be recognized and 3.5% of the pastures have the necessary potential to implement the system. This result is not certain and achieving a desired result requires the existence of acceptable information and data and the selection of proper methods, techniques, and criteria, the way of utilizing them in the studies of identification of prone sites for the construction of catchment systems seems necessary.

More than 80% of Iran's area is arid and semi-arid. Arid and semi-arid soils have various problems and limitations. One of the most important factors limiting soil fertility and crop production in these areas is the lack of soil organic matter (SOM). In more than 60% of Iran's agricultural lands, the amount of SOM is less than 1%; While its optimal level in soils should be 2-3%. SOM, as one of the most important indicators of soil health, plays a vital role in soil fertility and crop production; So, by increasing the amount of SOM, the physical, chemical, and biological characteristics of the soil and crop yield can be improved. However, the stability and residual effects of the fertilizers and organic wastes in the soil are low. Nowadays, converting organic wastes and residues into biochar and adding them to the soil is one of the new methods of increasing soil organic carbon, which enhance soil fertility and crop yield and has high residual effects due to its high stability. However, due to the specific characteristics of the soils of arid and semi-arid regions, using biochar in these regions is associated with challenges and has received less attention. In this review article, while reviewing the positive effects of biochar on soil quality indicators and crop production, the challenges of using biochar in the soils of arid and semi-arid areas, especially in drylands, and the solutions to overcome these challenges have been reviewed.

In Iran, the climatic conditions are such that even in the rainiest areas of the country, there is a need for groundwater resources, and this demand is increasing every year. Since, groundwater is one of the most valuable water resources in Iran, it is very necessary to predict its changes in order to use it optimally with the aim of sustainable development. One of the most complex hydrological processes in nature is the rainfall-groundwater level process, which is affected by various physical and hydrological parameters. Although, various models have been presented to predict the changes in the groundwater level using the rainfall patterns, but less attention has been paid to the transfer function model. Hence, the main objective of this research is to introduce and use the transfer function (TF) model to predict the monthly groundwater level using rainfall data and to compare its results with ANFIS and Artificial Neural Network (ANN) models.In the present study, 30-year data (1992-2021) of meteorological stations and observation wells in 3 watersheds of Galikesh, Ramian and Mohamadabad were used to model the rainfall and groundwater level. of the Gorganroud river basin.Then, considering that the years closer to the present time have more accurate information about the situation of this time, the years were considered as a forward process in artificial neural networks. The model fitting and prediction of the groundwater level values using rainfall data for the next 12 months was performed with applying three models: Artificial Neural Network (ANN), Adaptive neuro fuzzy inference system (ANFIS), and transfer function (TF). For this purpose, MINITAB SAS, SPSS, and R software were used. Next step, the validation of the values predicted by the models was evaluated using three indices Mean Absolute Distance (MAD), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).The results of the autocorrelation plots of the groundwater level of the wells revealed that all time series have a seasonal trend with a period of 12 months. Based on the cross-correlation plots, it was also found that rainfall has direct effect on the groundwater level in the two watersheds of Galikesh and Mohamadabad with lag time of three months and in the Ramian watershed with a delay of one month. The validation results of the models using three indexes MAD, RMSE and MAPE revealed that the artificial neural network model for predicting the groundwater level using monthly rainfall data in all three investigated watersheds had the most appropriate performance (RMSE=0.0778, 0.0243, 0.0532m) and the ANFIS model is ranked second (RMSE=0.1841, 0.0832, 0.1012m). Although the transfer function model was less accurate than the other two methods (RMSE=0.5711, 0.5023, 0.3234m), but this model has performed well in fitting the monthly groundwater level values. This model is very effective in identifying the delay in the impact between the input and output variables, as well as expressing the model based on which the impact of rainfall can be expressed as a model.The results of this research show that all three models of artificial neural network, ANFIS and transfer function can be used to predict the groundwater level using monthly rainfall values. Consecutive overestimation and underestimation, which increases the error and decreases the performance of the models, was not observed for the three used models. Also, all three models perform well in detecting trends and data changes. However, the artificial neural network model is more accurate than the other models. In addition, when forward process is used in artificial neural network modeling, compared to the case where the complete series of data is used, the efficiency of the model is significantly improved.

Saffron (Crocus sativus L.) has been used as food and medicine for Iranians for a long time, from the ancient to the modern period, and several factors have influenced its production and consumption in different provinces, especially Razavi Khorasan. In order to investigate the trend of cultivated area, stigma production and yield of saffron and also the effect of climatic parameters such as altitude above sea level, average temperature, and precipitation on the cultivation and production of this plant in different cities of Khorasan Razavi Province, a study was conducted by using data of Agricultural Jihad Organization during 1984 to 2020. Results indicated that the highest production and cultivated area in the province of Razavi Khorasan in the studied period were related to Zaveh, Torbat Heydarieh, and Roshtkhar, respectively. Zaveh and Torbat Heydarieh also had the largest portion of irrigated production in the province, with 13.3 and 9.4 %, respectively. Upon comparing the yield of saffron across different cities to the provincial average, it became evident that Gonabad and Torbat Heydarieh secured the first and second positions, respectively, in this comparison. Conversely, Kalat exhibited the lowest percentage in terms of cultivated area, irrigated production, and yield ratio compared to the provincial average. Further analysis revealed that elevation above sea level is positively correlated with both cultivated area and saffron production. Interestingly, there exists a non-linear inverse relationship between annual rainfall and cultivated area, production, and yield of saffron. Moreover, the study found that higher average temperatures are associated with increased saffron production, although the average yield of saffron tends to decrease. Regarding similarities across different cities in Khorasan-Razavi province, an investigation at a 75% similarity threshold indicated the possibility of classifying the province's various regions into five distinct clusters. The cities of Bakharz, Gonabad, Kashmer, Mahvalat, and Roshtkhar were in the first cluster, and the cities of Bajastan, Khaf, Torbat Jam, Neishabur, Bardaskan, and Taibad were in the second cluster, and each of the cities of Torbat Heydarieh and Zaveh also formed two separate branches. The rest of the cities were placed in a cluster. In general, the increasing trend of saffron cultivation and production in Khorasan-Razavi province indicates a greater desire to cultivate this plant by the farmers, considering the climatic and soil conditions of this province.

The Severe shortage of surface and groundwater resources is the most important constraint on sustainable agricultural development in arid and semi-arid regions. Frequent droughts in recent decades have also led to a significant drop in groundwater levels due to uncontrolled abstraction. In the present study, different irrigation scenarios are provided to reduce the withdrawal of water from the aquifer of Kavar plain in Fars province. The results of various studies show that due to climate change, water resources are reduced and irrigation requirements of plants will be increased during the growing season (Goodarzi et al., 2015; Zeinoddini et al., 2019). In this study, to simulate future climate parameters, the outputs of AOGCMs models and emission scenarios RCP2.6, RCP4.5, and RCP8.5 of the Fifth Climate Change Report were used (IPCC,2014). Then irrigation requirements of the study area were calculated by Cropwat software considering the cultivation pattern for the future period. Finally, by defining irrigation scenarios, the simultaneous effects of climate change and cultivation pattern change in the development of irrigation systems (surface and pressurized) on groundwater resources were evaluated.

Climate change is one of the most significant global challenges threatening food security now, in the near and far future. This mainly occurs in the form of increasing temperature, change in rainfall pattern, and increase in extreme weather events. There are strong evidences demonstrating the vulnerability of agriculture sector in arid and semi-arid regions to climate change. This may directly impact on crops growth and production or indirectly impact on their environments. The ability of soil to produce a crop depends on its physical and chemical properties and these properties are directly and indirectly affected by climatic factors such as temperature and rainfall. Most predictions show that in arid and semi-arid regions, including many regions of Iran, climate change will lead to an increase in temperature and a decrease in rainfall. Therefore, considering the importance and role of temperature and humidity in physical and chemical quality indicators of soil and production stability, it seems that the phenomenon of climate change will have adverse effects on soil and then on crop production. Therefore, it is very important to use the necessary solutions to mitigate these adverse effects and adapt to the upcoming conditions. In this article, by reviewing and summarizing the research on the effects of climate change on the characteristics of arid and semi-arid soils, an attempt has been made to provide some kind of foresight of possible changes in the physical, chemical, and biological properties of soil due to climate change.

Ecosystem services are fundamental for human well-being and are the basis of rural livelihoods, particularly for poor people. Rainwater harvesting can serve as an opportunity to enhance ecosystem productivity, thereby improving livelihoods, human well-being, and economies. Rainwater harvesting has been shown to create synergies between landscape management and human well-being. These synergies are particularly obvious when rainwater harvesting improves rainfed agriculture, is applied in watershed management, and when rainwater harvesting interventions address household water supplies in urban and rural areas. Rainwater harvesting has often been a neglected opportunity in water resource management: only water from surface and ground water sources is conventionally considered. Managing rainfall will also present new management opportunities, including rainwater harvesting. Improved water supply, enhanced agricultural production, and sustainable ecosystem services can be attained through adoption of rainwater harvesting with relatively low investments over fairly short time spans (5-10 years). Rainwater harvesting is a coping strategy in variable rainfall areas. In the future climate change will increase rainfall variability and evaporation, and population growth will increase demand on ecosystem services, in particular for water. Rainwater harvesting will become a key intervention in adaptation and reducing vulnerabilities. Moreover, in addition to increasing public knowledge about the ecosystem function of these systems, basic steps should be taken in the audit, evaluation and application of local knowledge of rainwater harvesting in the country.