Your search found 7 records
1 Lee, S.; Marino, M. A. 1995. Application of the LP-DP approach to Andong and Imha parallel reservoirs, Korea. Water Resources Management, 9(4):315-333.
Reservoirs ; Linear programming ; Water supply ; Water use ; Mathematical models ; Electric power demand / Korea Republic
(Location: IWMI-HQ Call no: PER Record No: H017821)
2 Wallender, W.; Rhoades, J.; Weinberg, M.; Lee, S.; Uptain, C.; Purkey, D. 2002. Irrigated land retirement. Irrigation and Drainage Systems, 16(4):311-326.
Irrigation water ; Wild life ; Irrigated farming ; Land management / USA / California / San Joaquin Valley
(Location: IWMI-HQ Call no: PER Record No: H032577)
3 Lee, S.; Park, E.; Cho, M. J. 2000? Development and application of a GIS based groundwater modeling system. Unpublished report. 24p.
(Location: IWMI-HQ Call no: P 6870 Record No: H034756)
4 Sadoff, C.; Harshadeep, N. R.; Blackmore, D.; Wu, X.; O’Donnell, A.; Jeuland, M.; Lee, S.; Whittington, D. 2013. Ten fundamental questions for water resources development in the Ganges: myths and realities. Water Policy, 15(S1):147-164. [doi: https://doi.org/10.2166/wp.2013.006]
Water resources development ; International waters ; Rivers ; Tributaries ; River basin management ; Integrated management ; Flooding ; Water power ; Water quality ; Water storage ; Reservoir storage ; Flow discharge ; Irrigation water ; Policy making ; Groundwater management ; Sediment ; Climate change ; Ecosystem services ; Economic aspects ; Upstream ; Downstream / South Asia / India / Bangladesh / Nepal / Ganges / Himalayas
(Location: IWMI HQ Call no: e-copy only Record No: H048102)
(0.64 MB)
This paper summarizes the results of the Ganges Strategic Basin Assessment (SBA), a 3-year, multi-disciplinary effort undertaken by a World Bank team in cooperation with several leading regional research institutions in South Asia. It begins to fill a crucial knowledge gap, providing an initial integrated systems perspective on the major water resources planning issues facing the Ganges basin today, including some of the most important infrastructure options that have been proposed for future development. The SBA developed a set of hydrological and economic models for the Ganges system, using modern data sources and modelling techniques to assess the impact of existing and potential new hydraulic structures on flooding, hydropower, low flows, water quality and irrigation supplies at the basin scale. It also involved repeated exchanges with policy makers and opinion makers in the basin, during which perceptions of the basin could be discussed and examined. The study’s findings highlight the scale and complexity of the Ganges basin. In particular, they refute the broadly held view that upstream water storage, such as reservoirs in Nepal, can fully control basinwide flooding. In addition, the findings suggest that such dams could potentially double low flows in the dry months. The value of doing so, however, is surprisingly unclear and similar storage volumes could likely be attained through better groundwater management. Hydropower development and trade are confirmed to hold real promise (subject to rigorous project level assessment with particular attention to sediment and seismic risks) and, in the near to medium term, create few significant tradeoffs among competing water uses. Significant uncertainties – including climate change – persist, and better data would allow the models and their results to be further refined.
5 Kirschke, S.; Avellan, T.; Barlund, I.; Bogardi, J. J.; Carvalho, L.; Chapman, D.; Dickens, Chris W. S.; Irvine, K.; Lee, S.; Mehner, T.; Warner, S. 2020. Capacity challenges in water quality monitoring: understanding the role of human development. Environmental Monitoring and Assessment, 192(5):298. [doi: https://doi.org/10.1007/s10661-020-8224-3]
Water quality ; Monitoring ; Capacity building ; Human resources ; Sustainable Development Goals ; Goal 6 Clean water and sanitation ; Indicators ; Decision making ; Strategies ; Technology ; Financing ; Environmental effects ; Surveys
(Location: IWMI HQ Call no: e-copy only Record No: H049662)
https://link.springer.com/content/pdf/10.1007/s10661-020-8224-3.pdf
https://vlibrary.iwmi.org/pdf/H049662.pdf
https://vlibrary.iwmi.org/pdf/H049662.pdf
(0.81 MB) (828 KB)
Monitoring the qualitative status of freshwaters is an important goal of the international community, as stated in the Sustainable Development Goal (SDGs) indicator 6.3.2 on good ambient water quality. Monitoring data are, however, lacking in many countries, allegedly because of capacity challenges of less-developed countries. So far, however, the relationship between human development and capacity challenges for water quality monitoring have not been analysed systematically. This hinders the implementation of fine-tuned capacity development programmes for water quality monitoring. Against this background, this study takes a global perspective in analysing the link between human development and the capacity challenges countries face in their national water quality monitoring programmes. The analysis is based on the latest data on the human development index and an international online survey amongst experts from science and practice. Results provide evidence of a negative relationship between human development and the capacity challenges to meet SDG 6.3.2 monitoring requirements. This negative relationship increases along the course of the monitoring process, from defining the enabling environment, choosing parameters for the collection of field data, to the analytics and analysis of five commonly used parameters (DO, EC, pH, TP and TN). Our assessment can be used to help practitioners improve technical capacity development activities and to identify and target investment in capacity development for monitoring.
6 Ahmad, M. J.; Cho, G.-H.; Kim, S.-H.; Lee, S.; Adelodun, B.; Choi, K.-S. 2020. Influence mechanism of climate change over crop growth and water demands for wheat-rice system of Punjab, Pakistan. Journal of Water and Climate Change, 18p. (Online first) [doi: https://doi.org/10.2166/wcc.2020.009]
Climate change ; Forecasting ; Crop growth stage ; Water demand ; Wheat ; Rice ; Evapotranspiration ; Irrigation water ; Water requirements ; Crop water use ; Rain ; Models / Pakistan / Punjab
(Location: IWMI HQ Call no: e-copy only Record No: H049883)
(1.19 MB)
Conceptualizing the climate change perspective of crop growth and evapotranspiration (ETc) rates and subsequent irrigation water requirements (IWR) is necessary for sustaining the agriculture sector and tackling food security issues in Pakistan. This article projects the future growth periods and water demands for the wheat-rice system of Punjab. Intense and hotter transitions in the future thermal regimes and erratic monsoon rainfall increments were envisaged. The crop growth rates were accelerated by the probable temperature rise resulting in shortened growth periods. The temperature rise increased the reference evapotranspiration rates; however, the future ETc declined due to reduced growth period and net radiation. Highly unpredictable, but mostly increasing, cumulative seasonal and annual rainfalls were indicative of more effective rainfalls during the future crop seasons. Reduced ETc and increments in seasonal effective rainfalls gave rise to the declining IWR for both crops. The study findings seemingly undermined the harmful climate change influences on the water requirements of the wheat-rice system of Punjab but alarmingly shortening of growth periods indicates a higher crop failure tendency under the projected future thermal regime.
7 Arabameri, A.; Pal, S. C.; Rezaie, F.; Nalivan, O. A.; Chowdhuri, I.; Saha, A.; Lee, S.; Moayedi, H. 2021. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies, 36:100848. [doi: https://doi.org/10.1016/j.ejrh.2021.100848]
Groundwater potential ; Modelling ; Geographical information systems ; Machine learning ; Techniques ; Neural networks ; Remote sensing ; River basins ; Land use ; Land cover ; Landslides / Iran (Islamic Republic of) / Tabriz River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050645)
https://www.sciencedirect.com/science/article/pii/S221458182100077X/pdfft?md5=008d5c28c1313c1b111fb09896b85615&pid=1-s2.0-S221458182100077X-main.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
https://vlibrary.iwmi.org/pdf/H050645.pdf
(14.10 MB) (14.1 MB)
Study region: The present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, and the northern and southern parts of the basin receive the highest amounts.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
Study focus: In this study, we mapped the groundwater potential (GWP) with a new hybrid model combining random subspace (RS) with the multilayer perception (MLP), naïve Bayes tree (NBTree), and classification and regression tree (CART) algorithms. A total of 205 spring locations were collected by integrating field surveys with data from Iran Water Resources Management, and divided into 70:30 for training and validation. Fourteen groundwater conditioning factors (GWCFs) were used as independent model inputs. Statistics such as receiver operating characteristic (ROC) and five others were used to evaluate the performance of the models.
New hydrological insights for the region: The results show that all models performed well for GWP mapping (AUC > 0.8). The hybrid MLP-RS model achieved high validation scores (AUC = 0.935). The relative importance of GWCFs was revealed that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. This study demonstrates that hybrid ensemble models can support sustainable management of groundwater resources.
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