فصلنامه پژوهش های فرسایش محیطی
سال یازدهم شماره 2 (پیاپی 42، تابستان 1400)

Water scarcity is currently one of the worldchr('39')s challenges, especially in arid and semi-arid regions; This is because population growth and rising living standards require more food and water. Therefore, excessive use of natural resources has led to the destruction of potential agricultural lands and natural resources. The final process is floods, dust, and desertification, as a large part of the fertile agricultural lands have been affected by erosion and have been removed from agricultural use and farmer has migrated to other places. As a result, rural farmers have migrated to other areas due to a lack of income. According to the reports, by climate and land use change, desertification affected 6% of the worldchr('39')s arid regions from 1982 to 2015. Such a process in arid ecosystems has led to the development of soil erosion hotspots. Numerous studies have been conducted by national and international researchers on drought and its impact on water resources. Therefore, changes in the hydrological cycle and the impact on water resources have a significant role in land use and natural ecosystemschr('39') sustainability. Hence to the importance of studying land use changes and determining erosion areas that provides a good basis for control and stabilization of erosion centers in these areas. This study aimed to investigate landuse changes and erosion areas by simulating time series images with a high spatial and temporal resolution during periods of water scarcity, normal and wet in the Nimroz region of Sistan.

The study area is located in the north of Sistan and Baluchestan province and part of Nimroz city and the southern part of Hamoon Lake and borders with Afghanistan and at about 480 meters above sea level. This area is mostly flat and lacks topography and natural features. The average rainfall in this area is 50 mm, that most of which falls in winter. One of the Sistan regionchr('39')s characteristics is 120-day winds, which sometimes reach speeds of more than 120 kilometers per hour, accompanied by much dust. The development of the Sistan region depends on the water inflow from the Helmand River for 1,200 km, which originates from the mountains of northern Afghanistan. For this purpose, at first, using micro-scale exponential methods, time series of satellite images with high spatial and temporal resolution were prepared using Landsat and Modis satellite images. To monitor the changes in erosion region, land use maps related to the years of water scarcity (2002), normal (2009), and floods (2019) were extracted, and by evaluating the classification accuracy of the maps, change detection operations were performed using the post-classification comparison method for these maps.

The results shows that the during the period of water shortage to normal, the highest percentage of changes (35.84%) related to the conversion of barren lands to erosion areas with an area of ​​about 32708.68 hectares. The next rank related to conversion of erosion areas to lands Rangeland with vegetation is less than 10% (25.24%).The results also show that during the water shortage to wet, the highest percentage of changes is equal to 25.38 and 25.98%, respectively, and are related to the conversion of erosion area to water and rangeland area. The results also show that during the flood period, with the increase of floodplain areas in the study area, the area of ​​erosion region has decreased by 60% compared to the water shortage period (equivalent to 8059.36 hectares).

Due to the dry climate and low rainfall in Sistan, floods from Afghanistan only meet the water needs of the regionchr('39')s ecosystems. By studying the volume of incoming floods during periods of water shortage, normal and high water, the results showed significant fluctuations in the volume of incoming floods to the Sistan region during the studied periods. Since land use is more affected by climatic and hydrological factors, they play a significant role in land use changes. The results shows that the during the period of water shortage, more than 46% of the study area is eroded region, which has been mainly related to the conversion of barren landscapes, dried bed of Hamoon wetland, and flood agricultural lands to these areas.Therefore, it can be concluded that the changes in the hydrological conditions of inflows to the Sistan region play an important role in the extent of erosion areas with respect to the water needs of water-dependent ecosystems. Despite the environmental crises in Sistan region and its regression, the improvement of environmental conditions in Sistan region requires the demand of water rights in Sistan region from Afghanistan.

The natural environment is affected by climate, and its changes have different geomorphic forms. The dominance of different climatic phases and changes in the balance of matter and energy cause different shaping systems in the environment. Ancient lakes are one of the geomorphic forms of the Quaternary period, and today many of these lakes are either completely dry or have temporary and sometimes permanent lakes. One of the major functions of geomorphologists in studying ancient environments is to identify and map the shores of ancient lakes that show hydrological changes from wet to dry conditions. These studies are based on the height of lake terraces by reconstructing the volume and dimensions of ancient lakes and studying climatic conditions. Terraces are one of the best evidence of the reconstruction of ancient paleo geomorphological conditions in the coastal environments of the seas and lakes because the water level fluctuation and, consequently, the advancement and regression of water have created them on the coast. By examining the number of terraces, different periods of climate change can be identified, and changes in the extent of lakes can be determined.

This research has been done using the library, field, laboratory, and software studies. After visiting the area, lake terraces were identified for the first time in the area. In the next step, samples taken from these surfaces were analyzed in a laboratory, and their lake nature was confirmed. Then, using remote sensing and mapping techniques based on the height of the recorded lake terraces, the lake area levels were determined, and finally, the limits of these levels were corrected according to other natural factors such as slope, aspect, drainage pattern, and geological studies.

In the Jazmourian region, according to geomorphological evidence, three levels of lake terraces remain, the first of which is located in the west of the current Jazmourian plateau at an altitude of 410 meters above sea level and shows a lake with a depth of 72 meters and an area of ​​11935 square kilometers in the past. It shows that this level is the highest level of an ancient lake in the region that has been identified. In the western part of the hole, the second level of the garrison is located at an altitude of 375 meters above sea level and shows a lake with a depth of 37 meters and an area of ​​6579 square kilometers in the past. It separates from the previous terrace and shows a lake with a depth of 32 meters and an area of ​​5486 square kilometers, which was the lowest limit of the ancient lake, according to the evidence identified in the area. Different climatic conditions in each period in the region have created a different lake area. In fact, in favorable periods, there were lakes with more area and less water salinity, and in unfavorable weather conditions, in terms of rainfall characteristics, the lake was formed with less area and more salinity.

The results show that three levels of terrace in Jazmourian Playa at different heights in the west of the current seasonal lake, which indicates different climatic periods and the expansion of the lake in the past, were identified, and the extent of the ancient lake in this area was restored. Krinsley (1972) proved the past wet conditions in Iran by studying aerial photographs and field observations based on geomorphological, hydrological, and tectonic evidence in Iranian beaches. His studies show that there was a large lake in the Jazmourian hole in the Ice Age with varying width and depth, which is confirmed by the results of this study. Vaezi et al. (2019) also examined the evidence of climate diversity in the Pleistocene-Holocene period based on various geochemical analyses. Their results confirmed the regionchr('39')s wetter climatic conditions in the past, which is consistent with the results of this study. Of course, the lack of financial facilities for dating terraces and determining the exact time of the lake is one of the main problems of paleogeomorphological studies in the world and this research. Iran should be eliminated.

Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined based on human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is changing over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land-use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. Therefore, accurate detection of changes in land surface properties, especially LULC changes have become a key issue for monitoring local, regional, and global resources and environments, Providing a basis for a better understanding of the interactions between humans and natural phenomena and the proper management and use of these terrestrial resources. About the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land-use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost, to be prevented.

The Gamasiab River originates from calcareous springs located 21 kilometers southeast of Nahavand in Hamadan province from the northern slopes of the Greene Highlands known as the Mirab Gamasiab. This river enters Kangavar, Harsin, and Bistoon Kermanshah from the east-west direction of Nahavand and then enters the Faraman area by going around Bistoon and continues its north-south direction after receiving other branches and water. The surface currents of the adjacent basins join the Gharasu. For this study, an approximately 80 kilometers interval from the Gamasiab River and its adjacent lands 5 kilometers from each side was selected. Three images of Landsat for TM, ETM+ and OLI sensors were selected for monitoring of the river adjacent lands and vegetation indices for the years 1987, 2000, and 2017, respectively. NDVI is the normalized difference vegetation index and is the most common vegetation index. SAVI Soil- Adjusted Vegetation Index by Huete (1988) has been developed to use soil optical properties on the canopy reflectance capability. This index has added a factor of L (soil texture correction factor) to the NDVI equation. Radiometric and atmospheric correction images are performed before applying spectral indices. In the process of atmospheric correction, the first step is to calculate the radius value, and from the radius value, the reflectance value is calculated. There are two advantages to using reflectance values compared to radiative values: first, the effect of the cosine angle of the different solar angles can be measured relative to the time difference between the data harvesting, and second, the different amounts of solar radiation outside the atmosphere caused by the differences. The band is spectral, corrected. Atmospheric correction is done to eliminate the effects of the transmission and absorption of electromagnetic waves in the visible and infrared range. In general, each of the terrestrial features has a special spectral sign (spectral signature). These spectral signatures depend on many factors, such as sensing properties, differences in radiation and reception angles, atmospheric and topographic conditions, and imaging time. Because of the factors mentioned above, digital numbers (DN) cannot represent the actual conditions of spectral reflection of the Earth. The purpose of radiometric correction is to remove or neutralize the above effects of the image. After the indexes are applied, the land units have to be separated so that they are threshold on them, which means that we separate the pure classes. So, values between -1 to 0 are considered as wet and water body, values between 0 to 0.3 as soil, and values between 0.3 to 1 as vegetation.

Due to the lack of user interference against the object-oriented and pixel-based classification algorithms, this process (applying spectral indices) uses spectral information of the bands used, as accurate as the radiometric resolution of the sensors used. The results showed that for the NDVI index in 1987 the amount of water land fields was 13% and this value decreased by 0.63% to 12.57% in the year 2000 and 18.71% in the whole study area in 2017. These figures for the SAVI index in the year 1987 amounted to 10.42%, for the year 2000 the value was 10.93%, and for the year 2017 amount was 16.54% of the total area studied. What is certain is the slight change in 2000 relative to 1987 for both indices. Both NDVI and SAVI show an increasing trend of vegetation cover (water land fields) in 2017. These figures show the unprecedented use of river water and groundwater in recent years. Excessive use of groundwater resources results in land-use changes and subsequently physical, chemical, and even biological changes in water resources and land surface. For soil class, it is clear that both NDVI and SAVI indices show slight changes from 1987 to 2000, and for 2017 both indices show decreases in soil class compared to 1987 and 2000. The results show the inefficiency of indices in river and water body extraction in the study area. Principal components analysis was used for river extraction. Consequently, by comparing the indices with the corresponding PCAs it can be said that the river is properly extracted using PCA which can lead to even better results for the wider rivers.

Identifying and discovering the land cover changes can help planners and planners identify effective factors in land-use change and land cover, and have useful planning to control them. High accuracy maps are required for this purpose. The use of spectral indices makes this possible with very high accuracy. The results of this study, in addition to prove the accuracy and efficiency of spectral indices for estimating land cover, showed that during the years of 1987, 2000 to 2017 the soil class reduce and, on the other hand, increased water land fields a general trend This illustrates the general trend of degradation in the region through the replacement dry-land fields than water land fields.

Flood is one of the most complex and destructive natural phenomena that cause significant damage to agriculture, fisheries, housing, and infrastructure and significantly affects social and economic activities (Chang et al., 2008). The relationship between geomorphological and hydrological parameters makes it possible to predict floods in gauged basins and generalize predictions to similar ungauged basins by creating relationships between the geomorphologically similar basins (Jain & Sinha, 2003). Prioritization of areas for flood control projects is a fundamental decision that must be confirmed by studying the areachr('39')s physical, social, and economic conditions and determining the effectiveness of the plans (Djrodjetive & Bruck, 1989). Sub-basins with critical conditions or are close to main rivers, or public facilities (reservoir dams, diversion dams, etc.) prioritize carrying out rehabilitation projects.The region being mountainous, lack of proper past and present management of pastures, very steep slopes and slope instability, relatively severe soil erosion, the existence of rock outcrops with inadequate vegetation, and human interventions are the factors that cause flood problems in the Haji-Bakhtiar watershed. In the current study, the sub-basins in the Haji Bakhtiar watershed will be prioritized based on the factors mentioned above. To this end, two techniques are combined: AHP and VIKOR. 

In order to meet the purpose of the research, the following steps are carried out.- Studying the theoretical foundations by searching library documents and using expert opinions to identify the most important indicators of flood potential in sub-basins
- Collecting and preparing the required climate data and digital layers- Calculating the weights of the indicators by using Analytic Hierarchy Process (AHP)- Prioritizing (15) sub-basins in terms of flood potential using the VIKOR method and preparing a sub-basin potential prioritization map in two cases of 25 and 50 year return period rainfall- Classifying flood potential of sub-basins in 4 classes (very high, high, medium, low) based on the VIKOR method.

Results :

In the AHP method, important flood-producing indices were determined using the Delphi technique (consulting with experts) and 15 questionnaires. The results showed that the indicators affecting flood potential have different weights, based on expertschr('39') judgments, and the compatibility ratio in all questionnaires is less than 0.1. Among the indicators, the area index (final weight 0.44) has the most significant effect on flood potential, and the average slope index (final weight 0.096) has the least effect on flood potential.The range of changes in the VIKOR index of options, based on the AHP-VIKOR method, varies from 0.032 to 0.994. The results of prioritizing flooding potential of sub-basins, based on two rainfall intensities with 25 and 50 return periods, showed that in both cases, sub-basin H12-2 (Palkaneh village) with VIKOR index of 0.994 has the highest priority (Maximum flooding potential) and H9 sub-basin with VIKOR index of 0.032 has the lowest priority (minimum flooding potential).After prioritization in Arc GIS 10.3 environment based on the VIKOR index in two cases of rainfall intensity, the flooding potential of the sub-basins were classified into four classes. The sub-basin flooding potential map results at both return periods showed that, for both rainfall intensities, about 31% of the area had medium flood potential, 53% high flood potential, and 16% of the area had very high flooding potential. Out of 15 sub-basins, seven sub-basins are in moderate flooding condition, seven sub-basins are in high flooding condition, and one sub-basin is in very high flooding condition.

 Discussion & Conclusions :

The results obtained from the AHP method in the present study are based on aggregating the data collected by a questionnaire, which professors and watershed management experts complete. The results show that, among the four indicators affecting the flooding potential of sub-basins, the area index has the most significant impact on the flooding potential of the study area. This finding is consistent with Dehghani et al. (2013) and Ozturk & Batuk (2011). The rainfall intensity index is the next most important factor affecting the flood potential of the study area, which is consistent with the results of Karam and Derakhshan (2012); Dehghani et al. (2013); Yahaya (2008); and Ozturk & Batuk (2011). The curve number index is the third important index, which corresponds to Khosroshahi and Saghafian (2003); Soleimani Sardo et al., (2013). Finally, the fourth index is the average slope index; this result is consistent with the results of Karam and Derakhshan (2012); Dehghani et al. (2013), and Yahaya (2008); Fernandez & Lutz (2010). After calculating the weights of the indicators using the AHP method, the VIKOR method (Asghari Saraskanrood et al., 2015; Khalghi, 2002) was used to prioritize the flood potential sub-basins. The flood classification potential of sub-basins shows the high potential of the region in terms of flood generation. The lands with very high risk need watershed management measures such as preventing soil erosion and destruction, reducing water flow rate, increasing flood concentration-time, creating opportunities for water infiltration in the subsoil and recharge the aquifers, and cultivating suitable plants for the conditions. The geography of the slopes and rangelands restoration should be done to change what makes the region vulnerable to its strengths.

Gypsum, sulfuric acid, and sulfur are used to improve saline and sodic soils. In some cases, animal manures are also used, which varies according to environmental conditions and soil type (Jesus et al., 2019). Industrial gypsum is often used to modify saline and sodic soils due to electrolyte maintenance, physical and hydraulic properties (Keren, 1996), low cost, solubility, and ease of use (Amezketa et al., 2005). Gypsum particle size can also affect gypsum performance. Abdolfattah et al. (2015), using gypsum in three different sizes (<0.5, 1-1.5, and 1-2 mm), found that the use of gypsum with smaller particle size has better performance than large particles. Gypsum can reduce salinity as well as soil sodium. Morete and Hiro (2015) showed that the content of soil gypsum has a significant effect on water retention, as the higher the gypsum content, the holding curve soil water holdings have a steeper range and the soil stores more water. In leaching operations and improvement of saline and sodic soils, a large amount of water is required. Therefore, determining the amount of water required due to the water shortage crisis and the protection of water resources is of particular importance. Therefore, in this study, the effect of gypsum modifiers with different amounts and sizes of particles on the amount of water required for leaching saline and sodic soils with low to medium salinity has been investigated. In general, the main objectives of this research are 1- Determining the optimal size of gypsum particles for leaching with minimum water, 2- Determining the optimal amount of gypsum for leaching with minimum water 3- Determining minimum water required for leaching saline and sodium soils.

After drying, the lumps in the soil samples were pounded using a plastic hammer and a unique rolling pin available in the laboratory to prepare the soil. Finally, the compacted soil sample was passed through a 2 mm sieve. Then, using different amounts of sodium chloride, it was transformed into the soil with low to medium salinity and sodium by trial and error in a laboratory. After applying the treatments mentioned in the text of the article to determine the chemical properties of the soil before washing, five different amounts of gypsum (5, 7.5, 10, 25, 50 g of gypsum per kg of soil) were added to the soil in three repetitions and poured into cylindrical pillars. In the preparation of laboratory columns, polyethylene cylindrical containers with a diameter of 35 and a height of 40 cm were used. To drain the laboratory columns in the bottom of the cylindrical containers, a sand filter with a height of 5 cm was used, and pipes for the water outlet were installed in the same part. After 24 hours of treatments and leaching, soil sampling was performed from two different depths (0-15 and 15-30 cm), and soil chemical properties were measured after six leaching steps. The gypsum is passed through different sieves (4, 2, 0.5, 0.25 mm) to obtain different particle sizes in the next step. The treated soils were then tested with three replications as in the previous step, and their chemical properties were measured. After determining the optimal amount and size of gypsum from the last two stages, the soil was leached in six repetitions in 6 stages. After each leaching, one of the treated soils was removed from the leaching cycle and soil and drainage chemical properties. Its output was measured. Finally, soil and drainage characteristics were compared during the repetition of 6 leaching times to determine which stage the leached soil has the desired optimal conditions. Finally, all the mentioned steps were analyzed using SPSS 19 software.

The results obtained from the application of 10, 25, and 50 g of gypsum per kg of soil showed that if the amount of gypsum used to improve the saline and sodium soils studied is less than 7.5 g of gypsum per kg of soil, the soil retains its salinity and sodium content. If the amount of gypsum used is higher than this amount, the salinity and sodium intensity of the soil will increase. For soil with a depth of 30 cm and a volumetric mass of 1.5 tons per cubic meter, the amount of gypsum required will be about 3.4 tons per hectare. In the continuation of experiments to determine the optimal size of gypsum particles, the tested soil was treated with 7.5 g of gypsum per kg of soil in different particle sizes (0.25, 0.5, 2, and 4 mm). After the significance of the ANOVA test, the average chemical properties of soil in each of the different treatments of gypsum particle size were compared. This finding shows that the optimal gypsum particle size for the study of saline and sodium soils was particle size between 0.25 to 0.5 mm. In the continuation of the research, using the determined optimal amount (7.5 g / kg) and the optimal size of gypsum particle (0.5 mm), the effect of each leaching stage on the soil at two depths (0-15 and 30-15) were examined. According to the results, the lowest amount of ESP is related to saturated flower extract after four washing steps.

This study showed that using 7.5 grams of gypsum per kilogram of soil is the most suitable value for leaching saline and low to medium saline soils (equivalent: 11 tons per hectare to a depth of 10 cm, 16.85 tons per hectare to a depth of 15 cm, and 33.7 tons Per hectare to a depth of 30 cm). The gypsum particle size of 0.5 mm had the best performance in soil remediation. Therefore, it can be stated that adding 7.5 g / kg soil with gypsum particle size (0.5 mm) is the best treatment for soil improvement with low to medium salinity and sodium. This section stated that gypsum treatment with 7.5 g of gypsum per kg of soil and gypsum particle size of 0.5 mm has the best performance after four leaching stages. Finally, in this study, control treatment and gypsum treatment with optimal particle size and size were compared in terms of the effect of different leachates in both surface and deep parts of the soil. Leaching saline and sodium soils without using modifiers causes the soil to become sodic and destroys the soil structure. The results of studies by Noori et al. (2021) have shown that saline and sodium soils that have been partially degraded can be developed for agricultural use by modification with a specific type of treatment and with the best soil management practices.

Nowadays, forecasting and modeling the rainfall-runoff process is essential for planning and managing water resources. Rainfall-Runoff hydrologic models provide simplified characterizations of the real-world system. A wide range of rainfall-runoff models is currently used by researchers and experts. These models are mainly developed and applied for simulation and prediction. They allow decision-makers to make the most effective decision for planning and operation. Rainfall-runoff modeling is essential in flood routing, flood prediction, flood frequency estimation, real-time flood forecasting, warning climate changes, and other cases. Time series analysis includes methods for analyzing and modeling time-series data to extract forecasts and other characteristics of the time-series data. Time series forecasting is using a model to predict future values based on previously observed values. The transfer function is an advanced and multivariate time series model that enables us to utilize other time series to produce response time-series forecasts. Although the rainfall-runoff process has been modeled by various methods so far, less attention has been paid to the transfer function model. The primary purpose of this study is to introduce the transfer function to model the rainfall-runoff process and compare its results to the common time series model (SAIRMA) in Ramian and Galikesh watersheds.

In this research, to model the rainfall-runoff process, the monthly averages of rainfall and discharge time series of Ramian and Galikesh watersheds were used for a period of 36 years (1981-2017). These two watersheds are branches of the Gorganroud River which has an essential role in providing water resources required in Golestan province. The time series homogeneity was examined using Chow`s method. The existence of trends in time series was investigated based on the moving average time series plot, and the existence of seasonal trends was explored using autocorrelation charts. The cross-correlation diagram was used to investigate the relationship between rainfall and runoff time series. The SARIMA and transfer function models were used to predict monthly runoff. Without considering the rainfall time series, the runoff time series was modeled by a SARIMA model. Also, considering the precipitation time series as an input time series, a transfer function is used to model runoff time series as a response time series. The transfer function modeling was performed in three steps, pre-whitening, selecting the appropriate parameters of the transfer function model, and finally fitting a SARIMA model to the residual values. For the SARIMA model, the goodness of fit test was evaluated based on the Box Pierce statistic. For the transfer function model, two indices were computed. The first index investigated the relationship between runoff and rainfall, and the second index performed the goodness of fit test for residual time series. Then, based on the forecasted and fitted values and using MAD, RMSE, MAPE, and E indices, the accuracy, and precision of SARIMA and transfer function models were compared.

 Results :

According to the autocorrelation diagrams, the results showed that the all-time series has a seasonal trend over 12 months. Also, according to the moving average time series, there was no significant shift in rainfall time series, but there was a decreasing trend in the runoff time series. The transformation log(1+Yt)   was used to the monthly average discharges time series. Then, the transfer function and SARIMA models were used to forecast the monthly average discharges for the next 12 months by using Minitab and SAS software. In the next step, the validation of the predicted data and the fitted data of monthly average discharges of two models were evaluated using MAD, RMSE, MAPE, and E indices. The results showed that the transfer function model in both Galikesh and Ramian watersheds has higher precision from the two perspectives of forecasting and model fitting than the Box and Jenkins (SARIMA) model.

Discussion & Conclusions :

In this research, to model the rainfall-runoff process, the monthly average of rainfall and runoff were used in Ramian and Galikesh watersheds. Then SARIMA and transfer function models were used to predict monthly runoff. The results showed that the transfer function model in both Galikesh and Ramian watersheds has higher precision than the SARIMA model from the two forecasting and model fitting perspectives. Changes in rainfall, directly and indirectly, cause changes in runoff, and this effect might be happened either at the same time or with a time lag. The transfer function model can consider the time lags of both time series in forecasting runoff time series. The SARIMA models lack the information of other environmental parameters and apply just the runoff in the present and past times to predict future values. Since the pattern of these parameters changes annually, not considering these changes leads to unreliable forecasts from SARIMA models. However, in transfer function models, it is possible to investigate the effect of more than one environmental parameter on the runoff changes, increasing the accuracy of model fitting and forecasting.

Land-use change has been extensively considered by farmers as an easily available approach with the aim of finding new resources for producing more agricultural products. Deforestation, the most common type of land-use change, has led to the extensive parts of productive lands in different parts of the world being highly degraded. Rainfed farming can be listed as one of the most important substitutes for the forests which have been destroyed. In this scenario, as time goes on, the productivity of the land might be decreased due to the problems arising out of inappropriate land management strategies or inexorable climate changes, leading to the lands being abandoned. Given the prevalence of this issue in the Southwest part of Iran, the present study aimed to monitor the changes of selected soil fertility properties following the oak deforestation in the Miankooh region, Southwest Shahrekord City.  

The study area is located in 31° 44′ N and 50° 34′ E, 150 kilometers far from Shahrekord City, Southwest Iran. The area has a semi-arid climate with an annual average temperature and rainfall of 16.3 °C and 439.5 mm, respectively. This area is naturally covered with semi-dense oak forests, considerable parts of which have been partly destroyed and then assigned to rainfed wheat by local farmers. A substantial loss in crop yield in recent decades, mainly due to the decreased precipitation, has persuaded the young farmers to abandon the less productive lands. Aimed at assessing the impact of land-use change on soil fertility, a total of 30 surface soil samples (0–25 cm) were collected from the lands under wheat cultivation, semi-dense oak forest, and abandoned lands. The soil samples were air-dried and sieved through a 2-mm sieve and then analyzed for soil pH, organic matter content (OM), total nitrogen (N), available phosphorus (P), and exchangeable potassium (K) using standard methods. The data was statistically analyzed applying the Duncan test at the probability level of 5%, one-way ANOVA, and the multivariate approach of the Hotelling test.

The studied soils contained a fairly high content of clay and can be classified as alkaline soils, possibly due to their relatively high content of calcium carbonates. A high soil buffering capacity caused by the accumulation of calcium carbonates in surface layers would have contributed to enabling the studied soil to undergo different uses without any significant changes in soil pH. Following the land-use change from oak forest to rainfed wheat and then to abandoned (uncultivated) lands, soil organic matter and total nitrogen decreased from 2.33% and 0.11% to 1.0% and 0.05%, respectively. This observation may be arisen out of the fact that the soils under wheat cultivation receive a relatively deep plowing almost every year, which can substantially reduce the percentage of soil total N or OM by mixing the rich surface layer of the soil with the less enriched subsurface layer. As far as the abandoned soil is considered, receiving the negligible inputs of organic matter caused a further decrease in soil total N and OM. The investigated soils benefited from acceptable amounts of available P so that the insignificant fall in their P contents due to land-use change may not affect their fertility potential. When it comes to K, unlike soil OM and N, it significantly rose from 615.0 mg/kg in forest lands to 633.4 in the soils under wheat cultivation. It appears that the addition of manure to surface soil by local farmers has provided a good source of this important nutrient. Applying the multivariate approach of the Hotelling test aimed at simultaneous comparing of different land-use types according to all of the studied soil properties, it was observed that the three types of land use investigated could be considered as different units.

Even though deforestation generally leads to soil degradation, some soil fertility properties might remain unchanged or even be improved based on the management strategies applied. In fact, applying purposeful and appropriate techniques in the field may be helpful to alleviate the unavoidable consequences of land-use change. Compared to substituting the semi-dense oak forests with rainfed farming, abandoning the croplands may result in more problematic soils. The pretty fast degradation of the crucial soil fertility properties caused by land-use change gives a serious warning about the possibility of further destruction of soils in the area studied, which may make them entirely unsuitable for any kind of agricultural use. Considering the complexity of soil environment, comparing the soils under different land use in terms of each individual soil property may not necessarily lead to reliable results. Accordingly, multivariate approaches, like the Hotelling test, maybe more consistent with the complex nature of the soil.

A bare surface on forest roads is created due to road construction. This surface is the main source of erosion and sediment yield to streams in forest areas. The increase of sediment in streams causes dramatic damage to the quality of water ecosystems and the life of aquatic organisms. Therefore, road engineers should pay attention not only to the cost of road construction but also to its environmental damage. So, it is necessary to reduce the amount of erosion and sediment produced by these roads. With the accurate prediction of erosion and sediment yield of roads, it is possible to mitigate the negative effects of sedimentation and manage the region, sustainability. In this regard, several models such as WARSEM and SEDMODEL have been introduced to estimate the sediment and to identify the sensitive erosion points. These models are often used to estimate road surface erosion in forest regions. Therefore, the purpose of this study was to evaluate different models in estimating forest road sediment and compare them to field measurements. The findings of the study are useful in finding a suitable and realistic model in the estimation of erosion and sediment of the forest road.

Two prediction models, including WARSEM and SEDMODEL, were used to estimate the amount of annual sediment production of forest roads. For this purpose, 2602 m of roads in compartments 202, 212, and 244 in the Rezaian forestry plan of ZarrinGol forests were selected. The total length of roads in the region was divided into homogeneous units, and then factors of road length and width, geological condition, road surface, traffic, longitudinal road slope, rainfall, and sediment transport were calculated using GIS maps (road layer, river, waterway, geology, edaphic and topography), forestry plan booklet and field measurements. A sediment trap and suitable container were installed at the end of each unit to measure the actual amount of road sediment after each rainfall. The volume of water was measured (in L) after the deposition of sediment in the container, and then the deposited sediment was taken out of the container and placed in an oven. After drying, the amount of sediment was calculated (in g/m2). In the laboratory, the sediment concentration was obtained by filtering the suspended load sample and passing the runoff sample through the Whatman 42 filter paper. The sediment samples were then placed in the oven at 105 °C for 24 hours. The samples were weighed in a Desiccator using a digital scale (one-thousandth accuracy), and the sediment concentration was obtained by dividing the sediment mass (g) by the runoff volume (L).

Results showed that the estimated sediment by SEDMODL and WARSEM for different roads and compartments were 23.87 and 20.07 tons per year, respectively. In addition, the longitudinal slope had a significant effect on the amount of sediment production estimated by the two models WARSEM and SEDMODL, at a probability level of 5%. While this factor has no effect on the amount of sediment measured in real conditions amount of sediment estimated by the models in the slope class of 5-10% was significantly higher than the slope class of 0-5%. There was no significant difference between the amount of sediment estimated by WARSEM and SEDMODL models; however, estimated values were significantly higher than the measured value at the probability level of 5%. Validation of WARSEM and SEDMODL models and their comparison with the field measured value showed a significant difference. Both models estimated the amount of sediment more than the field measured value. The results also showed that the sediment delivery potential estimated by WARSEM and SEDMODL was related to the part of the road located in parcel No. 202.

By calculating the amount of sediment production in the road and various parts using WARSEM and SEDMODL and comparing them with the measured value, it was found that the total amount of sediment estimated by WARSEM and SEDMODL was 20.07 and 23.87 tons per year, respectively. In general, there was no significant difference between the amounts of sediment estimated by WARSEM and SEDMODL, but these estimated values ​​were higher than the field measured value at the probability level of 5%. Moreover, the effect of the longitudinal slope of the road on sediment production was also studied. According to the factors measured by WARSEM and SEDMODL, it is necessary to design roads on geologically resistant formations, improve the pavement quality, decrease the level of sediment delivery and reduce the traffic.