Evaluación de la degradación del suelo y sequías en una región árida utilizando índices de sequía, índice de vegetación ajustado al suelo modificado y datos de sensores remotos Landsat

Ain Sefra is part of the Ksour Mountains and it's situated in southwestern Algeria, where the climate is arid. The study area is progressively facing regression and degradation exacerbated by climate change. These trends point to a significant acceleration of desertification and drought and the loss of production systems that play a critical social, ecological, and economic role in the region. To better understand the natural hazard of dryness in Ain Sefra and the impact of climate change, we used various drought indices and remote sensing data. Hence, analyzing precipitation records from 1965 to 2021, through several drought indices, droughts were identified as a recurring phenomenon. Moreover, the frequency of successive dry years is relatively high. There were three most extended continuous dry periods. The first phase lasted seven years from 1980 to 1987, the second twelve years from 1994 to 2006, and the third nine years from 2012 to 2021. Calculation of the Modified Soil-Adjusted Vegetation Index (MSAVI) for five multidate satellite images allowed us to follow the evolution of land use elements in this region from 1977 to 2017. Indeed, the study of these multi-temporal images reveals a considerable growth of sands, moving towards the north and northeast of the zone during the last decades. The combination of drought indices and remote sensing seems to be most promising; whose results are valuable tools for guidance and decision support to local and regional authorities.Ain Sefr, en las montañas Ksour, está situada en el suroeste de Argelia, donde el clima es árido. El área de estudio se enfrenta progresivamente a la regresión y degradación exacerbada por el cambio climático. Estas tendencias apuntan a una aceleración significativa de la desertificación y la sequía y a la pérdida de sistemas de producción que desempeñan un papel social, ecológico y económico crítico en la región. Para comprender mejor el peligro natural de la sequía en Ain Sefra y el impacto del cambio climático, se varios índices de sequía y datos de teledetección. Al analizar los registros de precipitación desde 1965 hasta 2021, a través de varios índices de sequía, se identificaron las sequías como un fenómeno recurrente. Además, la frecuencia de años secos sucesivos es relativamente alta. Hubo tres períodos secos continuos más prolongados. La primera fase duró siete años, de 1980 a 1987, la segunda doce años, de 1994 a 2006, y la tercera nueve años, de 2012 a 2021. El cálculo del Índice de Vegetación Ajustado al Suelo Modificado (MSAVI) para cinco imágenes satelitales multifecha nos permitió seguir la evolución de los elementos de uso del suelo en esta región desde 1977 hasta 2017. De hecho, el estudio de estas imágenes multitemporales revela un crecimiento considerable de arenas, moviéndose hacia el norte y noreste de la zona durante las últimas décadas. La combinación de índices de sequía y sensores remotos parece ser muy prometedores, pues sus resultados son valiosas herramientas de orientación y apoyo a la decisión de los entes locales y regionales.Financial support to perform this study was provided partially by the University Center Salhi Ahmed Naama (Argelia). Antonio Jodar-Abellan acknowledges financial support received form the XTREME Spanish National Project (Ref: PID2019-109381RB-I00/AEI/10.13039/501100011033)

Designing Efficient and Sustainable Predictions of Water Quality Indexes at the Regional Scale Using Machine Learning Algorithms

Water quality and scarcity are key topics considered by the Sustainable Development Goals (SDGs), institutions, policymakers and stakeholders to guarantee human safety, but also vital to protect natural ecosystems. However, conventional approaches to deciding the suitability of water for drinking purposes are often costly because multiple characteristics are required, notably in low-income countries. As a result, building right and trustworthy models is mandatory to correctly manage available groundwater resources. In this research, we propose to check multiple classification techniques such as Decision Trees (DT), K-Nearest Neighbors (KNN), Discriminants Analysis (DA), Support Vector Machine (SVM), and Ensemble Trees (ET) to design the best strategy allowing the forecast a Water Quality Index (WQI). To achieve this goal, an extended dataset characterized by water samples collected in a total of twelve municipalities of the Wilaya of Naâma in Algeria was considered. Among them, 151 samples were examined as training samples, and 18 were used to test and confirm the prediction model. Later, data samples were classified based on the WQI into four states: excellent water quality, good water quality, poor water quality, and very poor or unsafe water. The main results revealed that the SVM classifier obtained the highest forecast accuracy, with 95.4% of prediction accuracy when the data are standardized and 88.9% for the accuracy of the test samples. The results confirmed that the use of machine learning models are powerful tools for forecasting drinking water as larger scales to promote the design of efficient and sustainable water quality control and support decision-plans.This work is part of PRFU project N° E04N01CU450120220001. The authors gratefully acknowledge the support of the General Directorate of Scientific Research and Technological Development (DGRSDT) and to Taif University Researchers Supporting Project TURSP 2020/34, Taif University, Taif, Saudi Arabia

Assessing the Quality of Treated Wastewater for Irrigation: A Case Study of Ain Sefra Wastewater Treatment Plant

This study aimed to assess the water quality parameters in the wastewater treatment plant (WWTP) of Ain Sefra, southwestern Algeria. Various methods were employed to analyze the performance and suitability of the WWTP for irrigation. The results revealed effective removal of nitrates, with levels below the limit set for irrigation water. The dissolved oxygen content showed efficient biological processes and good degradation of organic matter. Phosphate levels were found to be within FAO and Algerian irrigation standards. However, elevated ammonia levels were observed, exceeding typical ranges for irrigation. The suitability of groundwater for irrigation was evaluated by calculating groundwater suitability indices. These indices categorized all samples as either excellent or good based on their Sodium Adsorption Ratio (SAR) and Kelly’s ratio. However, the sodium percentage values raised concerns about potential negative effects on the soil. Some samples were deemed unsuitable for irrigation because of high magnesium hazard and potential salinity values. These findings offer valuable insights into the performance and suitability of treated wastewater for irrigation in the Ain Sefra region. They can inform decision makers and stakeholders involved in agriculture and water management

Análisis del umbral de escorrentía de la cuenca del río Obispo, en la provincia del Carchi (Ecuador)

El presente proyecto analizó la variación de la escorrentía para tres escenarios de cobertura y usos de suelo en la cuenca del Río Obispo, ubicada en Ecuador, en la zona de los Andes. El primer escenario responde a las condiciones actuales; el segundo, plantea condiciones de deforestación; y, el tercero, condiciones de reforestación. Se realizó la caracterización físico-geográfica de la cuenca, la caracterización climática de la zona de estudio, así como el análisis de la escorrentía basado en el número de curva y el umbral de escorrentía para cada uno de los escenarios planteados. Finalmente se obtuvieron los caudales de crecida mediante un modelo hidrológico teórico, utilizando el software HEC-HMS. En concreto, para las condiciones y datos del modelo se trabajó con los valores obtenidos de la caracterización físico-geográfica de la cuenca, número de curva y umbral de escorrentía; además, se generaron los hietogramas de precipitación en función del análisis de lluvias intensas de la zona de estudio

Estimación de la evapotranspiración real, escorrentía superficial y recarga de acuíferos mediante dos modelos hidrológicos en el Sureste de España

En este trabajo se comprobó la capacidad del modelo Soil & Water Assessment Tool (SWAT) para simular el comportamiento hidrológico de una cuenca kárstica en un entorno mediterráneo semiárido (sur este de España). La calibración y validación de SWAT fueron realizadas con registros de caudal de 20 y 10 años. Adicionalmente, los resultados de SWAT fueron contrastados con los del modelo Sistema Integrado de Modelación Precipitación-Aportación (SIMPA), el modelo nacional de gestión de recursos hídricos de España. A tenor de los resultados obtenidos en la optimización del modelo SWAT (calibración y validación), el modelo simula correctamente el balance hídrico de la cuenca estudiada, que en este trabajo es representado espacialmente mediante tres variables principales: la evapotranspiración real, la escorrentía superficial y la recarga de acuíferos.Esta investigación ha sido financiada por la Cátedra del Agua de la Universidad de Alicante y la Diputación Provincial de Alicante (https://catedradelaguaua.org/)

Groundwater Potentiality Assessment of Ain Sefra Region in Upper Wadi Namous Basin, Algeria Using Integrated Geospatial Approaches

Water demand has been increasing considerably around the world, mostly since the start of the COVID-19 pandemic. It has caused many problems for water supply, especially in arid areas. Consequently, there is a need to assimilate lessons learned to ensure water security. In arid climates, evaluating the groundwater potential is critical, particularly because the only source of drinking water and irrigation for the community is groundwater. The objective of this report is to locate and identify probable groundwater basins in the upper Wadi Namous basin’s Ain Sefra area. GIS and RS were used to evaluate the parameters of morphometry and to demarcate groundwater potential zones by using eight different influencing factors, viz., geology, rainfall, height, slope, land cover, land use, and lineaments density are all factors to consider. The analytical hierarchical process (AHP) was used to give weightages to the factors, and definitions within each attribute were sorted in order of priority for groundwater potentiality. The major findings of the research were the creation of groundwater-potential zones in the watershed. The hydrogeological zone of the basin was assessed as follows: very poor (0.56%), poor (26.41%), moderate (44.72%), good (25.22%), and very good (3.1%). The groundwater recharge potential zones are concentrated in low cretaceous locations, according to analytical data. The groundwater potential regions were checked to field inventory data from 45 water locations to corroborate the findings. The qualitative findings and the groundwater inventory data agreed 77.78%, according to the cross-validation study. The produced groundwater potential map might substantially assist in the development of long-term management plans by enabling water planners and decision-makers to identify zones appropriate for the placement of productive wells and reducing investment losses caused by well drilling failures. The results of the study will also serve as a benchmark for further research and studies, such as hydrogeological modeling

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management