Flow discharge simulation based on land use change predictions

Haraz River is one of the most important rivers in Iran, which has been faced with successive inappropriate land use changes and environmental degradation practices in recent decades. In this way, the impact of land use changes on stream flow generation, evaporation and hydrological processes of the Haraz basin has been studied. Land use maps for the years of 1988, 2000 and 2013 were prepared and assessed for any changes in land use using land change modeler and logistic regression methods. GEOMOD method was also used for accuracy tests of models. The calibration periods of 1988–2000, 1988–2013 and also Markov chain with hard prediction model were applied in order to predict the future land use for 2025. Besides, SWAT model was used to evaluate the watershed-scale impacts of land use change. Evaluating the calibration periods using GEOMOD method and some parameters showed a more accurate prediction for the period of 1988–2013 than the 1988–2000 period. Likewise, the results indicated that the rate of changes from 2013 to 2025 will be decreased in terms of forest and range lands (6751.05 and 168,09.01 ha, respectively) and will be increased in terms of residential areas, irrigated farming, gardens and bare lands up to 1567.2, 1405.68, 3039.38 and 174,05.55 ha, respectively. The assessment of model efficiency showed that the SWAT model has acceptable performance to simulate the flow discharge. Overall, the model outcomes indicated that land use changes lead to increase the average runoff in the study area. As a matter of fact, this issue has significant effects on water resources, economic and social situations, and hence, efficient strategies are needed for an integrated management in the Haraz basin.     

Effect of storm pattern on soil erosion in damaged rangeland; field rainfall simulation approach

Raindrop size,rainfall intensity and runoff discharge affect the detachment and transportation of soil particles.Among these three factors,the rainfall intensity seems to be more important because it can change other two factors.Storm patterns can be determined by changing the rainfall intensity during the storm.Therefore,the objective of this research is to test the influence of storm pattern on runoff,soil erosion and sediment concentration on a rangeland soil slope under field rainfall simulation.Four storm rainfall intensity patterns were selected for examining the effects of variations in storm event characteristics on soil erosion processes.The selected storm patterns were:I(45,55 and 70 mm h-1);II(45,70 and 55 mm h-1);III:(70,55 and 45 mm h-1);and IV(55,45 and 70 mm h1).The last pattern is a new one instead of the uniform pattern which has been sufficiently studied in previous researches.The experiments were conducted in field plots(in Kojour watershed,Mazandaran Province,Iran)with an area of one square meter and an constant slope gradient of 18%,surrounded by galvanised sheets.Following the nonuniform prioritization of the storm patterns for the studied variables,time to runoff(I>II>IV>III),runoff volume(III>IV>II>I),sediment concentration(IV>III>I>II)and soil erosion(III>IV>II>I)),it can be generally inferred that each pattern has specific effect on soil erosion processes during a storm.The results of the general linear model(GLM)test indicated that the effects of storm pattern on time to runoff,total runoff volume,runoff coefficient and soil erosion were significant at a level of 99%.The Duncan test showed that the storm patterns can be divided into three groups of III,IV;II;I(for time to runoff),I,II;IV,III(for runoff coefficient),and I;II;IV,III(for runoff volume and soil erosion).     

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. Th...     

Simulating the effects of land use changes on soil erosion using RUSLE model

The aim of this paper is to evaluate the impacts of land use change on soil loss. Soil loss was quantified using the revised universal soil loss equation model in Darabkola catchment. Land use maps of 1992, 1998 and 2012 were derived from Landsat Thematic Mapper data. The mean annual soil loss was therefore determined for these years. The results showed open-canopy forest area decreased by 36% between 1992 and 1998. Likewise, the decreasing trend of forest lands which are near to residential areas has continued from 1795 ha in 1998 to 1765 ha in 2012. Also the results indicate that the maximum annual soil loss ranged from 5.06, 6.19 and 15.23 ton h^?1 y^?1 in 1992, 1998 and 2012, respectively. Also, by assuming that all watershed conditions and land uses be constant in the future, then the area of close- and open-canopy forest and dry agricultural lands will be 23.23, 2.88 and 29.89 ha in 2040, respectively.     

Land use/cover change and driving force analyses in parts of northern Iran using RS and GIS techniques

To accomplish integrated watershed management and land use planning, it is necessary to study the dynamic spatial pattern of land use and cover change related to socioeconomical and physical parameters. In this study, land use and cover change detection was applied to the Lajimrood Drainage Basin in northern parts of Iran, an area characterized by rich and diversified agricultural and forest mosaic. The main of changes in the study area were forest–arable land transformation, which was only considered in this study. In order to detect these changes, at first, based on 1:25,000 digital topographic maps dated 1967 and 1994 and ETM⁺ satellite image dated 2002, land use map in these three dates were prepared. The results showed that the area with forest land use decreased about 3.2% in transition 1967–2002. Also, arable land increased about 36.9%. We suggested a method to analyze the driving forces and the spatial distribution of land use change. The maps of elevation, slope, and aspect were derived and classified by using digital elevation model (DEM). Also, the maps of distance from road, drainage network, and building area were selected as socioeconomical factors. These maps were overlaid and crossed with land use change map and land use change area ratio was computed. The results showed that the elevation, slope, and aspect were physical effective factors in land use changing. Also, by increasing the distance from building area and roads, deforestation rate was reduced.     

Landslide susceptibility mapping based on frequency ratio and logistic regression models

The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning.     

A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

This study describes the application of logistic regression to rock-fall susceptibility mapping along 11?km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76?% of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57?%. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall.     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.     

The increase of rainfall erosivity and initial soil erosion processes due to rainfall acidification

The drastic growth of population in highly industrialized urban areas, as well as fossil fuel use, is increasing levels of airborne pollutants and enhancing acid rain. In rapidly developing countries such as Iran, the occurrence of acid rain has also increased. Acid rain is a driving factor of erosion due to the destructive effects on biota and aggregate stability; however, little is known about its impact on specific rates of erosion at the pedon scale. Thus, the present study aimed to investigate the effect of acid rain at pH levels of 5.25, 4.25, and 3.75 for rainfall intensities of 40, 60, and 80 mm h^?1 on initial soil erosion processes under dry and saturated soil conditions using rainfall simulations. The results were compared using a two‐way ANOVA and Duncan tests and showed that initial soil erosion rates with acidic rain and non‐acidic rain under dry soil conditions were significantly different. The highest levels of soil particle loss due to splash effects in all rainfall intensities were observed with the most acidic rain (pH = 3.75), reaching maximum values of 16 g m^?2 min^?1. The lowest levels of particle losses were observed in the control plot where non‐acidic rain was used, with values ranging from 3.8 to 8.1 g m^?2 min^?1. Similarly, under saturated soil conditions, the lowest level of soil particle loss was observed in the control plot, and the highest peaks of soil loss were observed for the most acidic rains (pH = 3.75 and pH = 4.25), reaching maximum average values of 40 g m^?2 min^?1. However, for saturated soils with acidic water but with non‐acidic rain, the highest soil particle loss was observed for the control plot for all the rainfall intensities. In conclusion, acidic rain has a negative impact on soils, which can be more intense with a concomitant increase in rainfall intensity. Rapid solutions, therefore, need to be found to reduce the emission of pollutants into the air, otherwise, rainfall erosivity may drastically increase.     

Design,manufacture and calibration of the SARI portable rainfall simulator for field and laboratory experiments

The Simulator of Artificial RaInfall (SARI) rainfall simulator (RS) is a newly designed, constructed and calibrated, portable, two-nozzle RS with low water consumption, accurate measurement, easy management and low cost. The raindrop size distribution and velocity and mean rainfall intensity were measured. The best rainfall spatial distribution was achieved with nozzles separated by 50, 60 and 70 cm, and with oscillation angles of 30, 45 and 60°, at a pressure of 60 kPa. The uniformity coefficient varied from 57 to 61% and rainfall intensity from 48 to 101 mm h^?1. The raindrop diameter varied from 0.2 to 9.9 mm. The raindrop velocity at the optimum pressure of 60 kPa, which was measured with high-speed photography, ranged from 1.1 to 7.1 m s^?1. Comparison with other RSs shows that the SARI simulator is a suitable apparatus to research soil erosion and runoff generation under laboratory and field conditions.