Your search found 3 records
1 Mojid, M. A.; Mainuddin, M.; Murad, K. F. I.; Kirby, J. M. 2021. Water usage trends under intensive groundwater-irrigated agricultural development in a changing climate – evidence from Bangladesh. Agricultural Water Management, 251:106873. (Online first) [doi: https://doi.org/10.1016/j.agwat.2021.106873]
Water use ; Trends ; Agricultural development ; Climate change ; Groundwater irrigation ; Sustainability ; Irrigation requirements ; Cropping patterns ; Irrigation water ; Rain ; Soil water balance ; Agricultural planning ; Water demand ; Food security ; Evapotranspiration ; Models / Bangladesh / Rajshahi / Rangpur / Bogura / Chapainawabganj / Joypurhat / Naogaon / Natore / Pabna / Sirajganj / Thakurgaon / Panchagarh / Nilphamari / Lalmonirhat / Kurigram / Gaibandha / Dinajpur
(Location: IWMI HQ Call no: e-copy only Record No: H050326)
(17.00 MB)
Comprehensive information on the past trend of local-level water usage of the cultivated crops is important for agricultural planning and forecasting water needs. This vital information is however deficient for the North-West (NW) region of Bangladesh. We estimated actual crop evapotranspiration (ET), total and crop-usable effective rainfalls (TER and ER, respectively) and irrigation requirement (IR) of 8 major crops and 8 cropping patterns over historical period (1985–2015) by using SWBcropwat model and trends of these water parameters by using MAKESENS tool for the 16 districts of the region. ET of the Rabi crops and cropping patterns revealed significant (p = 0.05) decreasing trends in all districts, the average decrease being 13–31% in different districts. ER decreased significantly for most dry season crops in 4 districts. TER was often greater than ER for Kharif crops, which could not fully utilize TER always because of its non-uniform temporal distributions. IR showed significantly decreasing trend for the Rabi crops in 11 districts and increasing trend for the Kharif crops in 5 districts. Although ET and IR decreased in most cases, their total volumetric quantities showed significantly increasing trends due to expanded irrigated area in 16 districts over time; IR increased by 27–186% in different districts. Because of water scarcity and prospective economic benefit, farmers have been spontaneously adjusting crop selection – shifting from higher-water demanding crops to lower water-demanding crop-cultivation – during the last two decades. Our information would guide planning the agriculture of the NW region by selecting appropriate crops based on sustainable limit of groundwater resources. The employed methodology can evaluate crop suitability periodically for adjustment in any area.
2 Mia, Md. U.; Rahman, M.; Elbeltagi, A.; Abdullah-Al-Mahbub, Md.; Sharma, G.; Islam, H. M. T.; Pal, S. C.; Costache, R.; Towfiqul Islam, A. R. Md.; Islam, Md. M.; Chen, N.; Alam, E.; Washakh, R. M. A. 2022. Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology. Geocarto International, 30p. (Online first) [doi: https://doi.org/10.1080/10106049.2022.2112982]
Flooding ; Risk assessment ; Disaster risk management ; Machine learning ; Blockchain technology ; Neural networks ; Sustainable development ; Floodplains ; Rain ; Forecasting ; Datasets ; Mapping ; Normalized difference vegetation index ; Models / Bangladesh / Brahmaputra River / Jamalpur / Gaibandha / Kurigram / Bogra
(Location: IWMI HQ Call no: e-copy only Record No: H051339)
https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2112982
https://vlibrary.iwmi.org/pdf/H051339.pdf
https://vlibrary.iwmi.org/pdf/H051339.pdf
(5.41 MB) (5.41 MB)
The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.
3 Haque, A.; Shampa; Akter, M.; Hussain, Md. M.; Rahman, Md. R.; Salehin, M.; Rahman, M. 2024. An integrated risk-based early warning system to increase community resilience against disaster. Progress in Disaster Science, 21:100310. [doi: https://doi.org/10.1016/j.pdisas.2023.100310]
Disaster risk reduction ; Flood forecasting ; Communities ; Resilience ; Early warning systems ; Model ; Sustainable Development Goals ; Vulnerability ; Villages ; Indicators ; River water ; Water levels / Bangladesh / Kurigram
(Location: IWMI HQ Call no: e-copy only Record No: H052633)
https://www.sciencedirect.com/science/article/pii/S2590061723000376/pdfft?md5=40313c2dfaa230bcc2d53032aa35f8bf&pid=1-s2.0-S2590061723000376-main.pdf
https://vlibrary.iwmi.org/pdf/H052633.pdf
https://vlibrary.iwmi.org/pdf/H052633.pdf
(9.74 MB) (9.74 MB)
The need to integrate Early Warning System (EWS) with Disaster Risk Reduction (DRR) has long been recognized in several global forums. In the year 2006, the United Nations International Strategy for Disaster Reduction (UNISDR) proposed an Integrated Risk-based EWS (IR-EWS) by integrating four elements: (1) Monitoring and warning service; (2) Risk knowledge; (3) Dissemination and communication; and (4) Response capability. Nearly after two decades of the UNISDR proposal, our study finds that there are still gaps in making IR-EWS operational. Our study also finds that works on conceptualizing integration of resilience against disaster with EWS as part of DRR (in line with SDG-13) has not yet been started. Against this backdrop, in this study we developed an IR-EWS for flood termed as Dynamic Flood Risk Model (DFRM) which contains: (1) simple risk-based warning numbers which are easily understandable and communicable to the community, with risk considered as a proxy for resilience; and (2) capital-based action plans in relation to community capital to reduce disaster risk and increase community resilience against disaster. The DFRM is applied in two flood-prone districts in Bangladesh and found to be acceptable to the community with reasonable accuracy. The model is the customized version of flood for generic IR-EWS. This study can be considered as the first attempt of the next generation IR-EWS where risk is represented by simple warning numbers and where EWS (as part of DRR) can be applied to increase the resilience.
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