| Records |
| Author |
Kamruzzaman, M.; Chowdhury, A. |
| Title |
Flash flooding considerations aside: Knowledge brokering by the extension and advisory services to adapt a farming system to flash flooding |
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Journal Article |
| Year |
2023 |
Publication |
Heliyon |
Abbreviated Journal |
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| Volume |
9 |
Issue |
9 |
Pages |
19662 |
| Keywords |
Flash flooding, Knowledge brokering, Extension and advisory services, Farming system, Climate change |
| Abstract |
The development of agriculture sector and livelihood in Bangladesh are threatened by various climatic stressors, including flash flooding. Therefore, Extension and advisory services (EAS) need to navigate the knowledge landscape effectively to connect various farm actors and help secure the optimum benefits of knowledge and information for making rational decisions. However, little is known how EAS can perform this task to combat various effects of climate change. This study investigates the means of brokering knowledge by the EAS to help the farming sector adapt to flash flooding. The research was conducted in the north-eastern part of Bangladesh with 73 staff of the Department of Agricultural Extension (DAE), the largest public EAS in Bangladesh. The results showed that DAE primarily dealt with crop production-related information. However, EAS did not navigate knowledge and information about flash flooding, such as weather forecasting and crop-saving-embankments updates, among the farming actors. Moreover, they missed the broad utilization of internet-based-communication channels to rapidly navigate information and knowledge about possible flash flooding and its adaptation strategies. This article provides some policy implications to effectively support the adaptation of farming system to flash flooding through EAS. |
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2405-8440 |
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THL @ christoph.kuells @ KAMRUZZAMAN2023e19662 |
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235 |
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Bresinsky, L.; Kordilla, J.; Hector, T.; Engelhardt, I.; Livshitz, Y.; Sauter, M. |
| Title |
Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Hydrology X |
Abbreviated Journal |
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| Volume |
20 |
Issue |
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Pages |
100153 |
| Keywords |
Groundwater recharge, Storage, Hydrogeological droughts, Climate change effects, Groundwater management, Mitigation of climate change effects |
| Abstract |
Many studies highlight the decrease in precipitation due to climate change in the Mediterranean region, making it a prominent hotspot. This study examines the combined impacts of climate change and three groundwater demand scenarios on the water resources of the Western Mountain Aquifer (WMA) in Israel and the West Bank. While commonly used methods for quantifying groundwater recharge and water resources rely on regression models, it is important to acknowledge their limitations when assessing climate change impacts. Regression models and other data-driven approaches are effective within observed variability but may lack predictive power when extrapolated to conditions beyond historical fluctuations. A comprehensive assessment requires distributed process-based numerical models incorporating a broader range of relevant physical flow processes and, ideally, ensemble model projections. In this study, we simulate the dynamics of dual-domain infiltration and precipitation partitioning using a HydroGeoSphere (HGS) model for variably saturated water flow coupled to a soil-epikarst water balance model in the WMA. The model input includes downscaled high-resolution climate projections until 2070 based on the IPCC RCP4.5 scenario. The results reveal a 5% to 10% decrease in long-term average groundwater recharge compared to a 30% reduction in average precipitation. The heterogeneity of karstic flow and increased intensity of individual rainfall events contribute to this mitigated impact on groundwater recharge, underscoring the importance of spatiotemporally resolved climate models with daily precipitation data. However, despite the moderate decrease in recharge, the study highlights the increasing length and severity of consecutive drought years with low recharge values. It emphasizes the need to adjust current management practices to climate change, as freshwater demand is expected to rise during these periods. Additionally, the study examines the emergence of hydrogeological droughts and their propagation from the surface to the groundwater. The results suggest that the 48-month standardized precipitation index (SPI-48) is a suitable indicator for hydrogeological drought emergence due to reduced groundwater recharge. |
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2589-9155 |
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THL @ christoph.kuells @ Bresinsky2023100153 |
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223 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
| Title |
Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
Type |
Journal Article |
| Year |
2023 |
Publication |
Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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