Your search found 11 records
1 Chakravorty, U.; Chen, Y.. 2001. An economic model of the Arenal-Tempisque Watershed. In Hazell, P.; Chakravorty, U.; Dixon, J.; Celis, R. Monitoring systems for managing natural resources: Economics, indicators and environmental externalities in a Casta Rican watershed. IFPRI. Environment and Production Technology Division; World Bank. Environment Department. pp.44-81.
Watershed management ; Models ; Models ; Economic aspects ; Catchment areas ; Livestock ; Pastures ; Forests ; Hydroelectric schemes ; Irrigation water ; Wetlands ; Fisheries ; Deforestation ; Reservoirs ; Sedimentation ; Siltation ; Water pollution ; Irrigated farming / Costa Rica / Arenal-Tempisque Watershed
(Location: IWMI-HQ Call no: P 5845 Record No: H028765)
2 Chen, Y.; Takara, K.; Cluckie, I. D.; de Smedt, F. H. 2004. GIS and remote sensing in hydrology, water resources and environment. Wallingford, UK: International Association of Hydrological Sciences (IAHS). 422p. (IAHS Publication 289)
GIS ; Remote sensing ; Hydrology ; Water resources ; Water management ; Environmental effects ; Flood control ; Flood plains ; Models ; Forecasting ; Watersheds ; Stream flow ; River basins ; Reservoirs ; Catchment areas ; Lakes ; Land use ; Water power ; Dams ; Management information systems ; Wetlands ; Water quality ; Coastal waters ; Ecology ; Pollutant load ; Runoff ; Sediment ; Precipitation ; Desertification ; Soil erosion ; Urban areas ; Irrigation ; Phytoplankton ; Chlorophylls ; Evapotranspiration ; Carbon / China / Netherlands / Croatia / Hungary / Malaysia / USA / England / Wales / Feilaixia Reservoir / Guangdong / Wei River Basin / Upper Yellow River / Meuse Basin / Drava River / Mura River / Pearl River Delta / Taihu Basin / Danube River Basin / Longyangxia Reservoir / Everglades / Three Gorges Area
(Location: IWMI HQ Call no: 526.0285 G000 CHE Record No: H046621)
(0.41 MB)
3 Slika, J. W. F.; Arroyo-Rodriguezb, V.; Aibac, S.-I.; Alvarez-Loayzad, P.; Alvese, L. F.; Ashton, P.; Balvanera, P.; Bastian, M. L.; Bellingham, P. J.; van den Berg, E.; Bernacci, L.; da Conceicao Bispo, P.; Blanc, L.; Bohning-Gaese, K.; Boeckx, P.; Bongers, F.; Boyle, B.; Bradford, M.; Brearley, F. Q.; Hockemba, M. B.-N.; Bunyavejchewin, S.; Matos, D. C. L.; Castillo-Santiago, M.; Catharino, E. L. M.; Chai, S.-L.; Chen, Y.; Colwell, R. K.; Robin, C. L.; Clark, C.; Clark, D. B.; Clark, D. A.; Culmsee, H.; Damas, K.; Dattaraja, H. S.; Dauby, G.; Davidar, P.; DeWalt, S. J.; Doucet, J.-L.; Duque, A.; Durigan, G.; Eichhorn, K. A. O.; Eisenlohr, P. V.; Eler, E.; Ewango, C.; Farwig, N.; Feeley, K. J.; Ferreira, L.; Field, R.; de Oliveira Filho, A. T.; Fletcher, C.; Forshed, O.; Franco, G.; Fredriksson, G.; Gillespie, T.; Gillet, J.-F.; Amarnath, Giriraj; Griffith, D. M.; Grogan, J.; Gunatilleke, N.; Harris, D.; Harrison, R.; Hector, A.; Homeier, J.; Imai, N.; Itoh, A.; Jansen, P. A.; Joly, C. A.; de Jong, B. H. J.; Kartawinata, K.; Kearsley, E.; Kelly, D. L.; Kenfack, D.; Kessler, M.; Kitayama, K.; Kooyman, R.; Larney, E.; Laumonier, Y.; Laurance, S.; Laurance, W. F.; Lawes, M. J.; do Amaral, I . L.; Letcher, S. G.; Lindsell, J.; Lu, X.; Mansor, A.; Marjokorpi, A.; Martin, E. H.; Meilby, H.; Melo, F. P. L.; Metcalfea, D. J.; Medjibe, V. P.; Metzger, J. P.; Millet, J.; Mohandass, D.; Montero, J. C.; de Morisson Valeriano, M.; Mugerwa, B.; Nagamasu, H.; Nilus, R.; Onrizal, S. O.-G.; Page, N.; Parolin, P.; Parren, M.; Parthasarathy, N.; Paudel, E.; Permana, A.; Piedade, M. T. F.; Pitman, N. C. A.; Poorter, L.; Poulsen, A. D.; Poulsen, J.; Powers, J.; Prasad, R. C.; Puyravaud, J.-P.; Razafimahaimodison, J.-C.; Reitsma, J.; dos Santos, J. R.; Spironello, W. R.; Romero-Saltos, H.; Rovero, F.; Rozak, A. H.; Ruokolainen, K.; Rutishauser, E.; Saiter, F.; Saner, P.; Santos, B. A.; Santos, F.; Sarker, S. K.; Satdichanh, M.; Schmitt, C. B.; Schongart, J.; Schulze, M.; Suganuma, M. S.; Sheil, D.; da Silva Pinheiro, E.; Sist, P.; Stevart, T.; Sukumar, R.; Sun, I.-F.; Sunderand, T.; Suresh, H. S.; Suzuki, E.; Tabarelli, M.; Tang, J.; Targhetta, N.; Theilade, I.; Thomas, D. W.; Tchouto, P.; Hurtado, J.; Valencia, R.; van Valkenburg, J. L. C. H.; Van Do, T.; Vasquez, R.; Verbeeck, H.; Adekunle, V.; Vieira, S. A.; Webb, C. O.; Whitfeld, T.; Wich, S. A.; Williams, J.; Wittmann, F.; Woll, H.; Yang, X.; Yao, C. Y. A.; Yap, S. L.; Yoneda, T.; Zahawi, R. A.; Zakaria, R.; Zang, R.; de Assis, R. L.; Luize, B. G.; Venticinque, E. M. 2015. An estimate of the number of tropical tree species. Proceedings of the National Academy of Sciences of the United States of America, 112(24):7472-7477. [doi: https://doi.org/10.1073/pnas.1423147112]
(Location: IWMI HQ Call no: e-copy only Record No: H047084)
4 Deng, H.; Chen, Y.. 2017. Influences of recent climate change and human activities on water storage variations in Central Asia. Journal of Hydrology, 544:46-57. [doi: https://doi.org/10.1016/j.jhydrol.2016.11.006]
Climate change ; Human behaviour ; Water resources ; Water storage ; Water table ; Groundwater extraction ; Precipitation ; Evapotranspiration ; Air temperature ; Glaciers ; Satellite observation ; Models ; River basins ; Mountains / Central Asia / Tian Shan Mountains / Tarim River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H047955)
(4.16 MB)
Terrestrial water storage (TWS) change is an indicator of climate change. Therefore, it is helpful to understand how climate change impacts water systems. In this study, the influence of climate change on TWS in Central Asia over the past decade was analyzed using the Gravity Recovery and Climate Experiment satellites and Climatic Research Unit datasets. Results indicate that TWS experienced a decreasing trend in Central Asia from 2003 to 2013 at a rate of 4.44 ± 2.2 mm/a, and that the maximum positive anomaly for TWS (46 mm) occurred in July 2005, while the minimum negative anomaly ( 32.5 mm) occurred in March 2008–August 2009. The decreasing trend of TWS in northern Central Asia ( 3.86 ± 0.63 mm/a) is mainly attributed to soil moisture storage depletion, which is driven primarily by the increase in evapotranspiration. In the mountainous regions, climate change exerted an influence on TWS by affecting glaciers and snow cover change. However, human activities are now the dominant factor driving the decline of TWS in the Aral Sea region and the northern Tarim River Basin.
5 Wang, X.; Chen, Y.; Li, Z.; Fang, G.; Wang, Y. 2020. Development and utilization of water resources and assessment of water security in Central Asia. Agricultural Water Management, 240:106297. (Online first) [doi: https://doi.org/10.1016/j.agwat.2020.106297]
Water resources development ; Water security ; Assessment ; Agriculture ; Water use ; Water supply ; Water demand ; International waters ; River basins ; Ecological factors ; Socioeconomic environment ; Indicators ; Forecasting ; Models / Central Asia / Kazakhstan / Kyrgyzstan / Tajikistan / Turkmenistan / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H049902)
(2.23 MB)
The utilization of water resources and water security in Central Asia are critical to the stability of the region. This paper assesses the water security of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) by using the projection pursuit model based on particle swarm optimization (PSO-PEE). The results show that the average annual water consumption in Central Asia is about 1255.57 × 108 m3, and the proportion of agricultural water consumption decreased due in large part to the changes of crop planting structure. For the ecological security, Kazakhstan, Tajikistan and Kyrgyzstan have improved their status, but Turkmenistan is getting worse. For the quantity security of water resources, Tajikistan and Kyrgyzstan are relatively safe, whereas Uzbekistan is at risk. For the socio-economic conditions, Kazakhstan scored the highest, while Tajikistan and Uzbekistan scored the lowest, water consumption per 10,000 dollars of GDP across all five countries is relatively high but shows a significant decreasing trend. For the water supply and demand security, the status of Kazakhstan, Kyrgyzstan and Tajikistan are better than that of Turkmenistan and Uzbekistan. Kazakhstan has achieved a relatively safe level (level ) and the degree of water security is high. Kyrgyzstan, Tajikistan and Turkmenistan are only in the basically safe level (level III). Uzbekistan is under significant pressure with regard to water security (level IV), which indicates that the country needs to strictly control population growth and strengthen the comprehensive management of water resources.
6 Chen, Y.; Fang, G.; Hao, H.; Wang, X. 2020. Water use efficiency data from 2000 to 2019 in measuring progress towards SDGs in Central Asia. Big Earth Data, 14p. (Online first) [doi: https://doi.org/10.1080/20964471.2020.1851891]
Water use efficiency ; Sustainable Development Goals ; Agricultural water use ; Water resources ; Evapotranspiration ; Ecosystems ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Datasets / Central Asia / Kazakhstan / Kyrgyzstan / Tajikistan / Turkmenistan / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H050142)
https://www.tandfonline.com/doi/pdf/10.1080/20964471.2020.1851891?needAccess=true
https://vlibrary.iwmi.org/pdf/H050142.pdf
https://vlibrary.iwmi.org/pdf/H050142.pdf
(5.50 MB) (5.50 MB)
Central Asia, located in the hinterland of the Eurasian continent, is characterized with sparse rainfall, frequent droughts and low water use efficiency. Limited water resources have become a key factor restricting the sustainable development of this region. Accurately assessing the efficiency of water resources utilization is the first step to achieve the UN Sustainable Development Goals (SDGs) in Central Asia. However, since the collapse of the Soviet Union, the evaluation of water use efficiency is difficult due to low data availability and poor consistency. To fill this gap, this paper developed a Water Use Efficiency dataset (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Gross Primary Production (GPP) data and the MODIS evapotranspiration (ET) data. The WUE dataset ranges from 2000 to 2019 with a spatial resolution of 500 m. The agricultural WUE was then extracted based on the Global map of irrigated areas and MODIS land use map. As a complementary, the water use amount per GDP was estimated for each country. The present dataset could reflect changes in water use efficiency of agriculture and other sectors.
7 Wang, W.; Chen, Y.; Chen, Y.; Wang, W.; Zhang, T.; Qin, J. 2022. Groundwater dynamic influenced by intense anthropogenic activities in a dried-up river oasis of Central Asia. Hydrology Research, 53(4):532-546. [doi: https://doi.org/10.2166/nh.2022.049]
Groundwater recharge ; Groundwater extraction ; Anthropogenic factors ; River basins ; Groundwater table ; Canals ; Surface water ; Evapotranspiration ; Flow discharge ; Precipitation ; Salinity ; Stable isotopes ; Land cover change / Central Asia / Weigan-Kuqa River Basin / Tarim Basin / Wei-Ku Oasis
(Location: IWMI HQ Call no: e-copy only Record No: H051123)
https://iwaponline.com/hr/article-pdf/53/4/532/1043485/nh0530532.pdf
https://vlibrary.iwmi.org/pdf/H051123.pdf
https://vlibrary.iwmi.org/pdf/H051123.pdf
(1.19 MB) (1.19 MB)
Intense anthropogenic activities in arid areas have great impacts on groundwater process by causing river dried-up and phreatic decline. Groundwater recharge and discharge have become hot spot in the dried-up river oases of arid regions, but are not well known, challenging water and ecological security. This study applied a stable isotope and end-member mixing analysis method to quantify shallow groundwater sources and interpret groundwater processes using data from 186 water samples in the Wei-Ku Oasis of central Asia. Results showed that shallow groundwater (well depth < 20 m) was mainly supplied by surface water and lateral groundwater flow from upstream, accounting for 88 and 12%, respectively, implying surface water was the dominant source. Stable isotopes and TDS showed obviously spatiotemporal dynamic. Shallow groundwater TDS increased from northwest to southeast, while the spatial variation trend of groundwater d18O was not obvious. Surface water and groundwater in non-flood season had higher values of stable isotopes and TDS than those in flood season. Anthropogenic activities greatly affect groundwater dynamics, where land-cover change and groundwater overexploitation are the main driving factors. The findings would be useful for further understanding groundwater sources and cycling, and help restore groundwater level and desert ecosystem in the arid region.
8 Cao, M.; Chen, Y.; Duan, W.; Li, Y.; Qin, J. 2022. Comprehensive evaluation of water–energy–food system security in the China–Pakistan economic corridor. Water, 14(12):1900. (Special issue: Water-Energy-Food Nexus Analysis for Sustainable Resources Management) [doi: https://doi.org/10.3390/w14121900]
Water resources ; Energy consumption ; Food security ; Food systems ; Nexus ; Economic development ; Sustainable development ; Policies ; Indicators ; Water use ; Tube wells ; Precipitation ; Evapotranspiration ; Models / China / Pakistan
(Location: IWMI HQ Call no: e-copy only Record No: H051187)
https://www.mdpi.com/2073-4441/14/12/1900/pdf?version=1655123720
https://vlibrary.iwmi.org/pdf/H051187.pdf
https://vlibrary.iwmi.org/pdf/H051187.pdf
(7.79 MB) (7.79 MB)
The safety of the water–energy–food (WEF) system in the China–Pakistan Economic Corridor (CPEC) is critical to the sustainable development of resources, the economy, and society in the region. This paper uses the projection pursuit model of a real-code accelerated genetic algorithm (RAGA-PP) to comprehensively evaluate the WEF system security of the CPEC for the period 2000–2016. The results show that from 2000 to 2016, the projection value of the WEF system was reduced from 2.61 to 0.53, and the overall system security showed a downward trend. Moreover, the CPEC increased by 6.13 × 107 people, resulting in a rapid decrease in per capita water resources and decreased security of the water resources subsystem. With the rising social and economic development in recent years, the per capita energy consumption has likewise risen, leading to a decline in the energy subsystem. At the same time, the per capita grain output in the study area has increased from 185 to 205 kg, and the safety of the food subsystem has been enhanced. However, the significant increase in irrigated areas (from 1.82 × 1010 to 1.93 × 1010 hectares) has further highlighted the contradiction between the supply and demand of surface water resources, and the number of tube wells increased by 7.23 × 105, resulting in the consumption of a large amount of electricity and diesel resources. The water–energy (WE) subsystem also became less safe. With the implementation of water resources management policies over the past few decades, the proportion of agricultural water consumption dropped from 95.06% in 2000 to 93.97% in 2016, and the safety of the water–food (WF) subsystem increased. Unfortunately, agricultural irrigation consumes a large amount of power resources, leading to a reduction in the security of the energy–food (EF) subsystem. The research results from the present study could provide a scientific basis for the coordinated development of WEF systems across the CPEC region.
9 Xu, X.; Chen, Y.; Zhou, Y.; Liu, W.; Zhang, X.; Li, M. 2023. Sustainable management of agricultural water rights trading under uncertainty: an optimization-evaluation framework. Agricultural Water Management, 280:108212. (Online first) [doi: https://doi.org/10.1016/j.agwat.2023.108212]
Water rights ; Uncertainty ; Optimization methods ; Evaluation ; Water resources ; Irrigation water ; Hydrological cycle ; Models ; Evapotranspiration ; Economic benefits ; Water supply ; Water demand ; Water use ; Indicators ; Water footprint ; Carbon footprint ; Water allocation ; Sustainable development ; Rice / China / Heilongjiang
(Location: IWMI HQ Call no: e-copy only Record No: H051718)
https://www.sciencedirect.com/science/article/pii/S037837742300077X/pdfft?md5=3053b49293b0c5e8a5380876d7685ede&pid=1-s2.0-S037837742300077X-main.pdf
https://vlibrary.iwmi.org/pdf/H051718.pdf
https://vlibrary.iwmi.org/pdf/H051718.pdf
(5.41 MB) (5.41 MB)
The optimal allocation of agricultural water rights is of great importance in promoting the efficient management of water resources in irrigation areas. In the process of agricultural water rights allocation, problems develop when the dynamics and uncertainties caused by changes in water cycle elements are ignored. To balance socioeconomic development and environmental protection, this study develops a model framework for evaluating and optimizing the synergistic management of agricultural water rights allocation trading under multiple uncertainties (AWRAS-TCME). The model is capable of reflecting the dynamic changes in meteorological and hydrological factors such as rainfall, evapotranspiration and runoff and quantitatively measures the synergistic effect of multidimensional objectives of the economy-society-resources-environment on water rights allocations and transactions. The AWRAS-TCME model integrates a two-level multiobjective nonlinear programming model and a projection tracking model into a framework to measure the fairness and economic benefits of water rights allocation based on an analysis of the sustainability of water rights prices in multiple dimensions, fully considering the influence of uncertainties in hydrological and social systems. The model was applied to an actual irrigation area, and the results showed that (1) total optimized water rights allocation was reduced by 4.7–20.9% at different levels of water supply and demand; (2) the total volume of water rights transfer among regions was increased by 4.8%-12.9%, and the trading volume of the water rights market was increased to account for 5%-16.2% of the total revenue; and (3) the optimal net income of water rights allocation was increased by 1.2%-3.3%, and the equity of water rights allocation was increased by 0.06–0.09. The developed model promotes the sustainable utilization of agricultural water resources in irrigated areas.
10 Rizwan, M.; Li, X.; Chen, Y.; Anjum, L.; Hamid, S.; Yamin, M.; Chauhdary, J. N.; Shahid, M. A.; Mehmood, Q. 2023. Simulating future flood risks under climate change in the source region of the Indus River. Journal of Flood Risk Management, 16(1):e12857. [doi: https://doi.org/10.1111/jfr3.12857]
Climate change ; Flooding ; Risk ; Precipitation ; Stream flow ; Land cover ; Climate models ; Aquifer / Pakistan / India / Afghanistan / Upper Indus River Basin / Jhelum River Basin / Kabul River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051719)
https://onlinelibrary.wiley.com/doi/epdf/10.1111/jfr3.12857
https://vlibrary.iwmi.org/pdf/H051719.pdf
https://vlibrary.iwmi.org/pdf/H051719.pdf
(7.52 MB) (7.52 MB)
Pakistan experiences extreme flood events almost every year during the monsoon season. Recently, flood events have become more disastrous as their frequency and magnitude have increased due to climate change. This situation is further worsened due to the limited capacity of existing water reservoirs and their ability to absorb and mitigate peak floods. Thus, the simulation of stream flows using projected data from climate models is essential to assess flood events and proper water resource management in the country. This study investigates the future floods (in near future and far future periods) using the integrated flood analysis system (IFAS) model under the RCP2.6, RCP4.5, and RCP8.5 climate change scenarios. Downscaled and bias corrected climatic data of six general circulation models and their ensemble were used in this study. The IFAS model simulated the stream flow efficiently (R2 = 0.86–0.93 and Nash–Sutcliffe efficiency = 0.72–0.92) in the Jhelum River basin (JRB), Kabul River basin (KRB), and upper Indus River basin (UIRB) during the calibration and validation periods. The simulation results of the model showed significant impact of projected climate change on stream flows that will cause the mean monthly stream flow in the JRB to be lower, while that of the KRB and UIRB to be higher than that of the historical period. The highest flow months are expected to shift from May–June (Jhelum basin) and June–July (Kabul basin) to April–May with no changes in the UIRB. Higher frequencies of low to medium floods are projected in the KRB and UIRB, while the JRB expects fewer flood events. Based on the results from the IFAS model, it is concluded that stream flow in the study area will increase with several flood events.
11 Karim, F.; Penton, D. J.; Aryal, S. K.; Wahid, S.; Chen, Y.; Taylor, P.; Cuddy, S. M. 2024. Large scale water yield assessment for sparsely monitored river basins: a case study for Afghanistan. PLOS Water, 3(4):e0000165. [doi: https://doi.org/10.1371/journal.pwat.0000165]
Water yield ; Assessment ; Monitoring ; River basins ; Precipitation ; Runoff ; Models ; Stream flow ; Water resources ; Water availability ; Drainage systems ; Evapotranspiration ; Glaciers ; Case studies / Afghanistan / Panj-Amu River Basin / Kabul River Basin / Harirod-Murghab River Basin / Helmand River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052767)
https://journals.plos.org/water/article/file?id=10.1371/journal.pwat.0000165&type=printable
https://vlibrary.iwmi.org/pdf/H052767.pdf
https://vlibrary.iwmi.org/pdf/H052767.pdf
(4.25 MB) (4.25 MB)
This paper presents results from a study on water yield assessment across five major river basins of Afghanistan. The study was conducted using GR4J and GR4JSG precipitation-runoff models. The river basins were divided into 207 subcatchments and each subcatchment was divided into multiple functional units. The model was calibrated using observed streamflow data from 2008 to 2015 and validated over the 2016 to 2020 period. Model parameters were calibrated for an unregulated subcatchment in each basin and calibrated parameters from the best-performing subcatchment were transferred to other subcatchments. Results show that modelled water yield across the five basins varies from 0.3 mm in the Helmand basin to 248 mm in the Panj-Amu basin, with an average of 72.1 mm for the entire country. In the period of 2008 to 2020, area averaged water yield in the five basins varies from 36 to 174 mm. For the same period, mean annual precipitation for the entire country is 234.0 mm, indicating a water yield of 30.8%. The nation-wide average water yield of 72.1 mm is equivalent to 46.3 billion cubic meters (BCM) of surface water for the country. In addition, about 28.9 BCM generates annually in the neighbouring Tajikistan and Pakistan from snow and glaciers of the Hindu-Kush mountains. The elevated northern parts of Afghanistan, including parts of neighbouring Tajikistan are the primary water source. Water yield across the country varies between years but there is no consistent increasing or decreasing trends. About 60 to 70% of flow occurs between March to June. The study identified the high water yield areas and investigated variability at monthly, seasonal, and annual time scales. An importance finding is the large spatial and temporal variability of water yield across the basins. This information is crucial for long-term water resources planning and management for agricultural development.
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