Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio

Application of inclusive multiple model for the prediction of saffron water footprint

Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the Group Method of Data Handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11–25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08)

Study the Effect of Natural and Anthropogenic Factors on The Ground Water Falling in The Minab Plain

IntroductionGroundwater is the main source of fresh water in arid and semi-arid regions, making it resilient to the lack of precipitation. Previously, groundwater was considered an unlimited or, at a minimum, entirely renewable natural resource. However, in recent decades, primarily because of population growth and industrial development, it has been under irreparable pressure, which has resulted in groundwater depletion in most parts of the world, in particular in Iran. In addition to the mentioned factors, climatic events, climatic changes caused by global warming and drought sequences, geological and topographical characteristics are also effective at raising and lowering ground water levels. Several studies have been conducted to investigate factors affecting the depletion of aquifers. Studies such as the investigation of climatic factors and aquifer extraction on changes in the level of the Maharluo lake, changes in groundwater quantity and quality in Sistan and Balochistan and factors affecting the depletion of aquifer storage in Darab have shown that the human and climatic factors have been effective in depleting groundwater. Studies in China have stated that socio-economic and agricultural development, land use changes, and dam construction are the most important factors in depleting groundwater resources. The Minab Plain is one of the fertile areas of Hormozgan province, where excessive groundwater extraction has reduced groundwater levels in the area. This plain also plays an important part in the transfer of water between basins from Minab to Bandar Abbas, so that a large quantity of water is pumped and transferred daily. Therefore, it is important to study the variables that affect the depletion of groundwater reserves in this plain. Considerable research has been carried out in the Minab plain to investigate changes in hydroclimatic variables, land use and groundwater. However, no research was conducted on the effect of these factors on groundwater loss. The aim of this research is to analyze the trend of hydro-climatic variables and groundwater level, to investigate land use changes, to detect the trend of groundwater extraction, water transfer operations from Minab plain to Bandar Abbas and, to investigate its effects on depletion in the Minab plain. Material and MethodsThe study area in the present study is the Minab Plain which the Esteghlal Dam was built on this river in 1983, with a water intake volume of 250 MCM. The data used in the research including the groundwater level, precipitation, discharge, and temperature. The number of 47 piezometric or observation wells have measured water level variations in the Minab plain since 1986, and 20 rain gauge stations have been distributed around case studies, of which 10 active rain gauge stations, six evapotranspiration stations and four hydrometer stations are used with a long statistical period. In the current study, the Mann-Kendall non-parametric method was used to analyze the trend. The point change was determined by the Pettitt’s test. Landsat satellite time series images were used to extract land use classes. Considering the existing land uses in the region, five land uses were considered: agricultural, rock, rangeland, urban and waterbodies. The images were analyzed in three time periods of 1991, 2005 and 2021 using Landsat satellite images. Results and DiscussionThe results indicate that during the 1986-2020 statistical period, the average level of groundwater in the plain fell by 10.19 m. The calculated Mann-Kendall statistic is -6.69, indicating a significant negative trend and, according to the Pettitt test statistics, 2013 was reported as a time of change. With respect to the trend of hydroclimatic variables, temperature and evapotranspiration are increasing at all stations, while precipitation and flow are trending downward which is not statistically significant. In addition, at most stations, change points were from 2001. The results of the land use change survey showed that rangeland decreased by 22%, and irrigated agricultural land and urban land increased by 2.2 and 2.8 times, respectively. In investigating the effect of human factors on groundwater depletion levels, it showed that the number of wells increased from about 100 to 796 wells, 16 wells were drilled in this plain to provide drinking water to Bandar Abbas, and the inter-basin water transfer with a discharge flow of 1200 lit/s, started since 2002 has put double pressure on the storage. The other reason for groundwater depletion is the decrease in aquifer feeding because of the decrease in precipitation, increase in temperature and evaporation, and decrease in infiltration of water from the Minab River, especially after the operation of Esteghlal dam in 1984. The amount of aquifer feed due to runoff infiltration is 12/77 MCM, or 22% of the total aquifer feed. Finally, it can be concluded that natural factors, including a reduction in precipitation and runoff and an increase in temperature, and non-natural factors such as building dams, increasing the allocation of aquifers for agriculture, domestic and industrial purposes, inter-basin transfer water, the increase in agricultural land and residential areas led to a double pressure on groundwater storage in the Minab Plain, resulting in a marked depletion of groundwater levels in the Minab Plain

The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran

One of the most crucial consequences of climate change involves the alteration of the hydrologic cycle and river flow regime of watersheds. This study was an endeavor to investigate the contributions of climate change to maximum daily discharge (MDD). To this end, the MDD simulation was carried out through implementing the IHACRES precipitation-runoff model in the Payyab Jamash watershed for the 21st century (2016-2100). Subsequently, the observed precipitation and temperature data of the weather stations (1980-2011) as well as 4 multi-model outputs of Global Climate Models (GCMs) under the maximum and minimum Representative Concentration Pathways (RCPs) (2016-2100) were utilized. In order to downscale the output of GCMs, Bias Correction (BC) statistical method was applied. The projections for the 21st century indicated a reduction in Maximum Daily Precipitation (MDP) in comparison with the historic period in the study area. The average projected MDP for the future period was 9 mm/day and 5 mm/ day under 2.6 and 8.5 RCPs (4.6% and 2.6% decrease compared with the historical period), respectively. Moreover, the temperature increased in Jamash Watershed based on 2.6 and 8.5 RCPs by 1∘C and 2∘C(3.7% and 7.4% increase compared with the historical period), respectively. The findings of flow simulation for the future period indicated a decrease in MDD due to the diminished MDP in the study area. The amount of this decrease under RCP8.5 was not remarkable (0.75 m3/s), whereas its value for RCP2.6 was calculated as 40m3/s (respectively, 0.11% and 5.88% decrease compared with the historical period)

Relationship between Indigenous Knowledge Development in Agriculture and the Sustainability of Water Resources

The relationship between agricultural knowledge and water management is very important. Indigenous knowledge in agriculture can improve the water crisis situation and alleviate water stress from dry and semi-arid areas. Therefore, the combination of these two impacts can improve the agricultural sector and reduce the effects of drought. The purpose of this study was to investigate the factors affecting indigenous knowledge and the sustainable management of water resources for optimal water use in agriculture in the Sistan region of Iran. Alongside field research and interviews with 40 indigenous experts and experts from the Jihad-e-Agriculture sector of the Sistan region, the required information was collected by means of a questionnaire. Using the fuzzy hierarchy process (FAHP), the factors affecting indigenous knowledge and the sustainable management of water resources for optimal water use in the Sistan region were ranked. The final rankings of the factors influencing indigenous knowledge for optimal agricultural use of water resources indicate that the educational-extensional factor, with a final weight of 0.37, is the first priority, while social factors, government support, economics, farmers’ knowledge, and information, with weights of 0.24, 0.21, 0.13, and 0.03, respectively, are the next priorities. It is recommended that the indigenous knowledge of local authorities be augmented, and that farmers be encouraged to use modern irrigation techniques to optimize the agricultural irrigation of water

Future Rainfall Erosivity over Iran Based on CMIP5 Climate Models

Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran’s climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of more frequent occurrences of heavy and torrential rains. Results also showed that the transformation of annual rainfall was not related to the spatial change of erosion. In the central and southern parts, the intensity of rainfall would increase. Therefore, erosion would be more in the south and central areas

Forecasting of SPI and SRI Using Multiplicative ARIMA under Climate Variability in a Mediterranean Region: Wadi Ouahrane Basin, Algeria

Water resources have always been a major concern, particularly in arid and semiarid parts of the world. Low precipitation and its uneven distribution in Algeria, along with fast population and agriculture activity increase and, particularly, recent droughts, have made water availability one of the country’s most pressing issues. The objectives of the studies reported in this article are to investigate and forecast the meteorological and hydrological drought in Wadi Ouahrane basin (270 km2) using linear stochastic models known as Autoregressive Integrated Moving Average (ARIMA) and multiplicative Seasonal Autoregressive Integrated Moving Average (SARIMA). In particular, data from 6 precipitation stations and 1 hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) for 12 months. Then, the multiplicative ARIMA model was applied to forecasting drought based on SPI and SRI. As a result, the ARIMA model (1,0,1)(0,0,1)12 for SPI and (1,0,1)(1,0,1)12 for SRI were shown to be the best models for drought forecast. In fact, both models exhibited high quality for SPI and SRI of 0.97 and 0.51 for 1-month and 12-month lead time, respectively, based on validation R2. In general, prediction skill decreases with increase in lead time. The models can be used with reasonable accuracy to forecast droughts with up to 12 months of lead time

Avaliação de suscetibilidade de erosão gully na bacia hidrográfica de Kondoran usando algoritmos de aprendizagem de máquina e a seleção de recursos boruta

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.PTDC/GES-URB/31928/2017info:eu-repo/semantics/publishedVersio

Uncertainty Analysis of Hydrological Drought Due to Record Length, Time Scale, and Probability Distribution Functions Using Monte Carlo Simulation Method

Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30- and 49-year lengths from the Minab Dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3-, 6-. 9-, 12-, 24-, and 48-month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty, and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to Normal and Lognormal distributions and the minimum uncertainty to Gamma and Weibull distributions. Furthermore, the increase in both time scale and record length led to the decrease in uncertainty

Meteorological and Hydrological Drought Risk Assessment Using Multi-Dimensional Copulas in the Wadi Ouahrane Basin in Algeria

A persistent precipitation deficiency (meteorological drought) could spread to surface water bodies and produce a hydrological drought. Meteorological and hydrological droughts are thus closely related, even though they are separated by a time lag. For this reason, it is paramount for water resource planning and for drought risk analysis to study the connection between these two types of drought. With this aim, in this study, both meteorological and hydrological drought were analyzed in the Wadi Ouahrane Basin (Northwest Algeria). In particular, data from six rainfall stations and one hydrometric station for the period 1972–2018 were used to evaluate the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI) at multiple timescales (1, 2, 3, 4, …, 12 months). By means of a copula function, the conditional return period for both types of drought was evaluated. Results evidenced that runoff is characterized by high level of temporal correlation in comparison to rainfall. Moreover, the composite index JDHMI (Joint Deficit Hydro-meteorological Index) was evaluated. This index is able to reflect the simultaneous hydrological and meteorological behavior at different timescales of 1–12 months well and can present the probability of a common hydrological and meteorological deficit situation more accurately and realistically compared to precipitation or runoff-based indicators. It was found that, over the analyzed basin, the average severity of combined hydro-meteorological drought (JDHMI) was 10.19, with a duration of 9 months and a magnitude of 0.93