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Assessment of methods of the autumn-winter moisture accumulation in poor draining soil in the arid region of Northern Kazakhstan

Vladimir L. Astafyev Pavel G. Ivanchenko
Journal of Water and Land Development | 2021 | No 48 | 115-121 | DOI: 10.24425/jwld.2021.136154
Keywords arid region coulisses chiselling moisture accumulation over crusting soil
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Abstract

In the initial stage of the growing season, the accumulation of autumn and winter precipitation moisture in poorly draining soil in arid conditions in the Northern region of Kazakhstan was a serious production problem. Research methods included measurements of autumn and winter moisture reserves in poorly draining soil and snow on the backgrounds of ordinary stubble, stubble coulisses and tall stubble left after stripper header (continuous combing) with and without autumn chiselling. The study revealed that the use of the continuous combing and stubble coulisses on poor draining soil: (a) sup-ports reserves of moisture in autumn soil; (b) the lack of chiselling leads to increased water runoff and the formation of li-mans in the fields. The use of stubble coulisses during snowy winters allowed moisture reserves in the snow to be increased in comparison with the stubble background. The use of chiselling on the background of stubble coulisses allowed: (a) to reduce runoff moisture loss in poorly draining soil by 35–50% after snowy winters, by 25–35% after little snowy winters, and prevent the formation of limans in the fields; (b) in comparison with the stubble background to increase the total re-serves of autumn-winter moisture in poorly draining soil by 61–105 mm in favourable years, and by 57 mm in years with the low autumn-winter precipitation. The use of chiselling on a stubble background did not significantly affect the total re-serves of autumn-winter moisture in poorly draining soil.
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Bibliography

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Authors and Affiliations

Vladimir L. Astafyev
1
ORCID: ORCID
Pavel G. Ivanchenko
1
ORCID: ORCID
  1. Kostanay Branch of LLC Scientific Production Center of Agricultural Engineering, 110011, Kostanay, Abai Avenue, 34, Kazakhstan

Using RUSLE and GIS for the soil loss assessment in arid regions: The case of the Ain Sefra catchment in the Ksour Mountains, Algeria

Ahmed Melalih Mohamed Mazour
Journal of Water and Land Development | 2021 | No 48 | 205-214 | DOI: 10.24425/jwld.2021.136163
Keywords Ain Sefra watershed Algeria arid region GIS RUSLE soil erosion
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Abstract

The loss of soil quality due to erosion is a global problem, particularly affecting natural resources and agricultural pro-duction in Algeria. In this study, the Revised Universal Soil Loss Equation (RUSLE) is applied to estimate the risk of water erosion in the Ain Sefra arid watershed (Algeria). The coupling of this equation with Geographic Information Systems (GIS) allows to assess and map the soil loss rates. The land erosion is influenced by many control variables, such as the topographic factor of the terrain and the length of slope (LS factor), rainfall erosivity (R factor), sensitivity of soil to erosion (K factor), presence of vegetation (C factor) and the anti-erosion cultivation techniques (P factor). To calculate the average annual soil loss, these five factors were considered and multiplied in the RUSLE Equation. The result shows that the aver-age rate of soil loss is estimated at about 5.2 t·ha–1·y–1 over the whole watershed. This study is the first of its kind in the region and aims to assess the soil loss caused by water erosion processes in this arid zone. Consequently, it is essential to take real intervention measures in these upstream areas in order to combat this scourge, based on priorities ensuring the sustainable management of natural resources in the study area.
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Authors and Affiliations

Ahmed Melalih
1 2
ORCID: ORCID
Mohamed Mazour
3
  1. Abou Bakr Belkaïd University, Faculty of Natural and Life Sciences and theUniverse, BP 230, New campus, Tlemcen, 13000 Algeria
  2. University Center of Ain Temouchent Belhadj Bouchaib, Laboratory of Applied Hydrology and Environment (LHYDENV), Ain Temouchent, Algeria
  3. University Center of Ain Temouchent Belhadj Bouchaib, Institute of Science and Technology, Ain Temouchent, Algeria

Integrating advanced approaches for climate change impact assessment on water resources in arid regions

Barno S. Abdullaeva
Journal of Water and Land Development | 2024 | No 60 | 149-156 | DOI: 10.24425/jwld.2024.149116
Keywords arid regions climate change hydrological modelling machine learning water resources
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Abstract

This research addresses the growing complexity and urgency of climate change’s impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash–Sutcliffe efficiency – NSE RCP2.6 = 0.99, root mean square error – RMSE RCP2.6 = 1.1, and coefficient of determination R 2 RCP2:6= 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions.
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Authors and Affiliations

Barno S. Abdullaeva
1
ORCID: ORCID
  1. Tashkent State Pedagogical University, Vice-Rector for Scientific Affairs, 27 Bunyodkor Ave, 100070, Tashkent, Uzbekistan

Suitability evaluation model for the land reclamation of coal mines in the northern foot of tianshan mountain, Xinjiang

Ruihua Hao Zizhao Zhang Xiaoli Guo Xuebang Huang Zezhou Guo Tianchao Liu
Gospodarka Surowcami Mineralnymi - Mineral Resources Management | 2022 | vol. 38 | No 2 | 131-145 | DOI: 10.24425/gsm.2022.141669
Keywords land reclamation Bayes discriminant analysis cold and arid region suitability level indexes
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Abstract

The ecological environment is significantly vulnerable to coal-mining activities in western China due to the cold and arid climate. The evaluation of land reclamation is therefore a key process that has to be known for the sustainable use of coal resources. A Bayes discriminant analysis method to evaluate the suitability level of land reclamation for coal mine lands in cold and arid regions of western China is presented. Ten factors influencing the suitability of land reclamation were selected as discriminant indexes in the suitability analysis. The data of eighty-four land reclamation units from sixteen coal-mining areas was used as training samples to develop a discriminant analysis model to evaluate the suitability level of land reclamation. The results show that the discriminant analysis model has high precision and the misdiscriminant ratio is 0.02 in the resubstitution process.The suitability levels of land reclamation for eleven sites in two coal mine lands were evaluated by using the model and the evaluation results are identical with that of the practical situation. Our method and findings are significant for decision makers in similar regions who want to prepare for possible strategies for land reclamation in the future.
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Authors and Affiliations

Ruihua Hao
1
ORCID: ORCID
Zizhao Zhang
1 2
Xiaoli Guo
3
Xuebang Huang
1
Zezhou Guo
1
Tianchao Liu
4
  1. School of Geological and Mining Engineering, Xinjiang University, Urumqi, Xinjiang, China
  2. State Key Laboratory for Geomechanics and Deep Underground Engineering, Xinjiang University, Urumqi, Xinjiang, China
  3. Xinjiang Intelligent Check for Security Environmental Protection Technology Co., Ltd, Urumqi, Xinjiang, China
  4. The First Regional Geological Survey Brigade, Xinjiang Bureau of Geo-Exploration & Mineral Development, 466 North Tianjin road, Urumqi, Xinjiang, China

The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters Case study: Seybouse basin, Northeast Algeria

Yamina Aoulmi Nadir Marouf Mohamed Amireche
Journal of Water and Land Development | 2021 | No 50 | 38-47 | DOI: 10.24425/jwld.2021.138158
Keywords artificial neural networks (ANNs) meteorological parameters rainfall-runoff semi-arid region Seybouse basin various input configurations
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Abstract

Over the past two decades, artificial neural networks (ANN) have exhibited a significant progress in predicting and modeling non-linear hydrological applications, such as the rainfall-runoff process which can provide useful contribution to water resources planning and management. This research aims to test the practicability of using ANNs with various input configurations to model the rainfall-runoff relationship in the Seybouse basin located in a semi-arid region in Algeria. Initially, the ANNs were developed for six sub-basins, and then for the complete watershed, considering four different input configurations. The 1st (ANN IP) considers only precipitation as an input variable for the daily flow simulation. The 2nd (ANN II) considers the 2nd variable in the model input with precipitation; it is one of the meteorological parameters (evapotranspiration, temperature, humidity, or wind speed). The third (ANN IIIP,T,HUM) considers a combination of temperature, humidity, and precipitation. The last (ANN VP,ET,T,HUM,Vw) consists in collating different meteorological parameters with precipitation as an input variable. ANN models are made for the whole basin with the same configurations as specified above. Better flow simulations were provided by (ANN IIP,T) and (ANN IIP,Vw) for the two stations of Medjez-Amar II and Bordj-Sabath, respectively. However, the (ANN VP,ET,T,HUM,Vw)’s application for the other stations and also for the entire basin reflects a strategy for the flow simulation and shows enhancement in the prediction accuracy over the other models studied. This has shown and confirmed that the more input variables, as more efficient the ANN model is.
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Authors and Affiliations

Yamina Aoulmi
1
ORCID: ORCID
Nadir Marouf
1
ORCID: ORCID
Mohamed Amireche
1
ORCID: ORCID
  1. University of Larbi-Ben-M’hidi, Faculty of Sciences and Applied Sciences, Department of Hydraulic, Laboratory of Ecology and Environment, PO Box 358, 04000 Oum El Bouaghi, Algeria

Performance analysis of a reservoir in arid region. Case study: Babar reservoir, Aurès region, Algeria

Fatima Z. Tebbi Hadda Dridi Mahdi Kalla
Journal of Water and Land Development | 2018 | No 39
Keywords arid region Babar reservoir Explicit Stochastic Dynamic Programming (ESDP) optimization performance reservoir risk RRV (reliability, resilience, vulnerability)
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Authors and Affiliations

Fatima Z. Tebbi
Hadda Dridi
Mahdi Kalla

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