| 1. |
- Haj-Amor, Zied, et al.
(författare)
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Soil salinization and critical shallow groundwater depth under saline irrigation condition in a Saharan irrigated land
- 2017
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Ingår i: Arabian Journal of Geosciences. - : Springer Science and Business Media LLC. - 1866-7511 .- 1866-7538. ; 10:14
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Tidskriftsartikel (refereegranskat)abstract
- In the arid irrigated lands, understanding the impact of shallow groundwater fluctuation on soil salinization has become crucial. Thus, investigation of the possible options for maintaining the groundwater depth for improving land productivity is of great importance. In this study, under saline irrigation condition, the effects of shallow groundwater depth on water and salt dynamics in the root-zone of date palms were analyzed through a particular field and modeling (SWAP) investigation in a Tunisian Saharan oasis (Dergine Oasis). The model was calibrated and validated against the measured soil water content through the date palm root-zone. The good agreement between measured and estimated soil water content demonstrated that the SWAP model is an effective tool to accurately simulate the water and salt dynamics in the root-zone of date palm. Multiple groundwater depth scenarios were performed, using the calibrated SWAP model, to achieve the optimal groundwater depth. The simulation results revealed that the shallow groundwater with a depth of ~80 cm coupled with frequent irrigation (20 days interval) during the summer season is the best practice to maintain the adequate soil water content (>0.035 (cm3 cm−3) and safe salinity level (<4 dS m−1) in the root-zone layer. The results of field investigation and numerical simulation in the present study can lead to a better management of lands with shallow water table in the Saharan irrigated areas.
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| 2. |
- Haj-Amor, Zied, et al.
(författare)
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The consequences of saline irrigation treatments on soil physicochemical characteristics
- 2018
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Ingår i: Euro-Mediterranean Journal for Environmental Integration. - 2365-7448. ; 3:22, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- When saline water is used to irrigate crops in arid environments, appropriate irrigation management should be applied to avoid negatively impacting soil characteristics. In this study, the effects of irrigating date palms with saline water (2.24 g l−1) on soil physicochemical characteristics such as the electrical conductivity (ECe), the pH of the saturated soil paste (pHe), the concentrations of soluble cations (Ca2+, Mg2+, Na+), the sodium adsorption ratio (SAR), the saturated soil hydraulic conductivity (Ks), and the volumetric water content of the soil (θv) were evaluated in a Tunisian Saharan cropland, the Dergine Oasis, during a 4-year period (2012–2015). The effects of three different irrigation treatments of date palms on soil properties were investigated: low treatment (90% of the net irrigation requirement (NIR) of date palms was applied); medium treatment (100% of NIR was applied), and high treatment (110% of NIR was applied). The results showed that the application of saline water for irrigation inevitably has a negative impact on the physicochemical properties of the soil. Irrigation with saline water was observed to have severe negative impacts on the soil characteristics, especially ECe, Na+, Ks, and θv. However, among the three irrigation treatments applied, statistical analysis (Duncan’s multiple range test) indicated that the high treatment significantly (p < 0.05) minimized the degradation of soil characteristics by the saline water; this treatment decreased ECe, Na+, and SAR and increased the water content, θv, of the studied soil.
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| 3. |
- Jalali, Reza, et al.
(författare)
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A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran
- 2024
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Ingår i: Environmental Science and Pollution Research. - 0944-1344. ; 31:29, s. 42088-42110
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Tidskriftsartikel (refereegranskat)abstract
- The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.
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| 4. |
- Kazemzadeh, Majid, et al.
(författare)
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Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed
- 2021
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Ingår i: Environmental Monitoring & Assessment. - : Springer Science and Business Media LLC. - 1573-2959 .- 0167-6369. ; 193
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Tidskriftsartikel (refereegranskat)abstract
- Soil moisture, vegetation cover, and land surface temperature are vital variables in water-energy balance, eco-hydrological processes, and water resources management, which can be influenced by watershed management activities. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature-Vegetation Dryness Index (TVDI) under a biological watershed management practice in the Taleghan paired watershed, namely, treated (TW) and control watersheds (CW), in Alborz province, Iran. In this research, along with the remote sensing techniques, the soil moisture and vegetation cover data were measured and statistically analyzed in the three aspects of both TW and CW during a growth period from May to October 2017. The results indicated that soil moisture, vegetation cover, and land surface temperature values in the paired watershed were significantly different at the 0.01 level during the study period. The increased vegetation cover in the TW had an inverse effect on the land surface temperature and TVDI, while directly impacted the soil moisture content. The average TVDI in the CW was 0.83, while this index was found to be 0.69 in the TW. Unlike the vegetation cover and soil moisture, the results revealed that the southern aspects had the highest TVDI and land surface temperature compared to the northern and eastern aspects of both watersheds. However, the increased vegetation cover as a biological watershed management activity in the steep terrain and mountainous areas of TW led to an increased soil moisture and a decreased land surface temperature and soil dryness. As a result, decreasing soil dryness in the TW can exert vital controls on the water resources and increasing water availability. In the arid and semiarid countries such as Iran, a proper watershed management activity can effectively increase soil moisture and water availability in the watersheds. In particular, the vegetation cover protection and biological practices can be considered as practical solutions in the rehabilitation of exhausted watersheds in arid and semiarid environments.
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| 5. |
- Kordestani, Mojtaba Dolat, et al.
(författare)
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Cartografía del potencial de agua subterránea utilizando un nuevo modelo de conjuntos de minería de datos
- 2019
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Ingår i: Hydrogeology Journal. - : Springer Science and Business Media LLC. - 1431-2174 .- 1435-0157. ; 27:1, s. 211-224
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Tidskriftsartikel (refereegranskat)abstract
- Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.
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| 6. |
- Mohammadi, Maziar, et al.
(författare)
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Human-induced arsenic pollution modeling in surface waters : An integrated approach using machine learning algorithms and environmental factors
- 2022
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Ingår i: Journal of Environmental Management. - : Elsevier BV. - 0301-4797. ; 305
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted regression trees (BRT) and random forest (RF). Based on the sediment quality guidelines (Effects range low) polluted and non-polluted arsenic sediment samples were defined with concentrations >8 ppm and <8 ppm, respectively. Different conditioning factors such as topographical, lithology, erosion, hydrological, and anthro- pogenic factors were acquired to model surface waters’ vulnerability to arsenic. We trained and validated the models using 70 and 30% of both polluted and non-polluted samples, respectively, and generated surface vulnerability maps. To verify the maps to arsenic pollution, the receiver operating characteristics (ROC) curve was implemented. The results approved the acceptable performance of the RF and BRT algorithms with an area under ROC values of 85% and 75.6%, respectively. Further, the findings showed higher importance of precipi- tation, slope aspect, distance from residential areas, and slope length in arsenic pollution in the modeling pro- cess. Erosion, lithology, and land use maps were introduced as the least important factors. The introduced methodology can be used to define the most vulnerable areas to arsenic pollution in advance and implement proper remediation actions to reduce the damages.
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| 7. |
- Mondal, Arun, et al.
(författare)
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Intercomparison of trend analysis of Multisatellite Monthly Precipitation Products and Gauge Measurements for River Basins of India
- 2018
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694. ; 565, s. 779-790
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Tidskriftsartikel (refereegranskat)abstract
- This study compares the precipitation trend from the gridded rain gauge data collected by India Meteorological Department (IMD) and Multisatellite High Resolution Precipitation Products (HRPPs) including Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) version 7, Climate Prediction Center Morphing (CMORPH) version 1.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1 for the river basins of India. The IMD and HRPPs are of the same spatial resolution (0.25° × 0.25°) and extend from 1998 to 2015. The gridded precipitation datasets are compared for 25 river basins of India. TRMM, CMORPH, PERSIANN, and MSWEP datasets accuracy for the river basins are assessed by comparison with IMD using root mean square error (RMSE) and correlation coefficient (CC) methods. The Mann–Kendall (MK) and modified Mann–Kendall (MMK) tests are applied for analyzing the data trend, and the change is detected by Sen's Slope using all datasets for annual and seasonal time periods. Variation in precipitation is high (>20%) in the northern part of India in all datasets. All these basins located at the elevations above 2000 m. The annual and monsoon trend pattern for TMPA, CMORPH, PERSIANN, and MSWEP matched with IMD data in the north, northwest, and central part in 1–2, 22–25, and parts of 3, 12, and 21 river basins (1998–2015). The calculated results implied that the TMPA precipitation product (in terms of accuracy) and PERSIANN (in terms of annual and monsoon trend) show a better agreement with IMD and they can be used in climate studies and hydrological simulation in locations/river basins where the number of rain gauges is not adequate to quantify the spatial variability of precipitation.
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| 8. |
- Naghibi, Seyed Amir, et al.
(författare)
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Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study : Mashhad Plain, Iran)
- 2018
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Ingår i: Water. - : MDPI AG. - 2073-4441. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions.
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| 9. |
- Naghibi, Seyed Amir, et al.
(författare)
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Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms
- 2020
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Ingår i: Natural Resources Research. - : Springer Science and Business Media LLC. - 1520-7439 .- 1573-8981. ; 29:3, s. 1915-1933
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Tidskriftsartikel (refereegranskat)abstract
- Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions.
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| 10. |
- Pakparvar, Mojtaba, et al.
(författare)
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Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region
- 2018
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 63:8, s. 1183-1202
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Tidskriftsartikel (refereegranskat)abstract
- To assess recharge through floodwater spreading, three wells, approx. 30 m deep, were dug in a 35-year-old basin in southern Iran. Hydraulic parameters of the layers were measured. One well was equipped with pre-calibrated time domain reflectometry (TDR) sensors. The soil moisture was measured continuously before and after events. Rainfall, ponding depth and the duration of the flooding events were also measured. Recharge was assessed by the soil water balance method, and by calibrated (inverse solution) HYDRUS-1D. The results show that the 15 wetting front was interrupted at a layer with fine soil accumulation over a coarse layer at the depth of approx. 4 m. This seemed to occur due to fingering flow. Estimation of recharge by the soil water balance and modelling approaches showed a downward water flux of 55 and 57% of impounded floodwater, respectively.
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