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Zhang, Y., Liu, X., Yuan, S., Song, J., Chen, W., & Dias, D. (2023). A two-dimensional experimental study of active progressive failure of deeply buried Qanat tunnels in sandy ground. Soils and Foundations, 63(3), 101323.
Abstract: As an ancient underground hydraulic engineering facility, the Qanat system has been used to draw groundwater from arid regions. A qanat is a horizontal tunnel with a slight incline that draws groundwater from a higher location and delivers it to lower agricultural land. During long-term water delivery, the qanat tunnel has experienced different degrees of aging and collapse, which may result in the significant ground settlement and even disasters. This paper developed a two-dimensional laboratory system to investigate the influence of progressive failure on the stability of deeply buried qanat tunnels. The developed system is fully instrumented with a particle image velocimetry (PIV) system and earth pressure and displacement monitoring. A special cylindrical membrane tube is designed and connected to an advanced pressure–volume controller to simulate the step-wise failure process of the tunnel. Three model tests were conducted on a dry sand considering the buried qanat tunnels at three different depths. Experimental results clearly show the progressive evolution of soil arching effect in the dry sand associated with the progressive failure of the tunnels. The failure of the Qanat ground starts from the vault and develops upwards, which is closely related to the evolution of stress contour at three consecutive stages. Ground surface settlement and volume loss corresponding to three burial depths were compared. A deeply buried qanat tunnel has a small effect on surface settlement. Earth pressure evolution on the 2D plane shows the load redistribution when the qanat collapses. The maximum arch and the initial point of the limit state correspond to a volume loss of 12.5 % and 50 %, respectively. For the collapse of the deep buried qanat tunnel, ground earth pressure evolution can be divided into a stress-increasing region, stress-decreasing region, and no redistribution region. Furthermore, a multi trap-door model considering soil expansion is proposed to describe the progressive failure behavior and its effects.
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Weerahewa, J., Timsina, J., Wickramasinghe, C., Mimasha, S., Dayananda, D., & Puspakumara, G. (2023). Ancient irrigation systems in Asia and Africa: Typologies, degradation and ecosystem services. Agricultural Systems, 205, 103580.
Abstract: CONTEXT Ancient irrigation systems (AISs) have been providing a multitude of ecosystem services to rural farming and urban communities in Asia and Africa, especially in arid and semi-arid climatic areas with low rainfall. Many AISs, however have now been degraded. A systematic analysis of AISs on their typologies, causes of degradation, and their ecosystem services is lacking. OBJECTIVE The objective of this review was to synthesize the knowledge on AISs on their typologies, status and causes of degradation, ecosystem services and functions, and identify gaps in research in Asia and Africa. METHOD A critical review of peer-reviewed journal papers, conference and workshop proceedings, book chapters, grey literature, and country reports was conducted. Qualitative and quantitative information from journal papers were used to conceptualize the typologies and analyze the status and causes of degradation, and ecosystems services and functions provided by the AISs. RESULTS AND CONCLUSION Based on the review, we classified AISs into three groups by source of irrigation water: Rainwater harvesting system (RHS) with small reservoirs, ground water based system, and floodwater based system. The RHSs, which used to receive reliable rainfall and managed by well cohesive social organizations for their maintenance and functioning in past, have now been silting due to extreme rainfall pattern and breakdown of the cohesive organizations in recent decades. In ground water based systems, indiscriminate development of deep tube wells causing siltation of channels has been a major challenge. In floodwater irrigation systems, irregular rainfall in the highlands and the breakage of irrigation structures by destructive floods were the main causes of degradation. Lack of maintenance and increased soil erosion, inadequate skilled manpower, and declining support from the government for repair and maintenance were the main causes of degradation of all AISs. The main ecosystem service provided by all AISs is water for agriculture. In tank- and pond-based systems, fish farming is also practiced. Tank irrigation systems provide various types of provisioning, regulatory, cultural and supporting services, especially in India and Sri Lanka. Ground water based systems provide water for domestic purposes and various cultural services. Floodwater based systems provide water for power generation and wildlife habitat maintenance and help in flood control. SIGNIFICANCE The knowledge generated through the review provide evidence-based information, and help aware governments, private sectors and development agencies for improved policy planning and decision making, and prioritizing the restoration, rehabilitation, and management of various AISs.
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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.
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Ollivier, C. C., Carrière, S. D., Heath, T., Olioso, A., Rabefitia, Z., Rakoto, H., et al. (2023). Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar. Journal of Hydrology: Regional Studies, 47, 101400.
Abstract: Study region this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focus the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.
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Zwartendijk, B. W., Ghimire C. P., Ravelona M., Lahitiana J., & van Meerveld H. J. (2023). Hydrometric data and stable isotope data for streamflow and rainfall in the Marolaona catchment, Madagascar, 2015-2016. NERC EDS Environmental Information Data Centre.
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