Your search found 29 records
1 Zhang, Y.. 1988. The study field water management for salinity control by computer simulation. In Proceedings of the International Conference on Irrigation System Evaluation and Water Management, Wuhan, China, 12-16 September 1988: Vols.1 & 2. Wuhan, China: Wuhan University of Hydraulic and Electrical Engineering. pp.419-430.
(Location: IWMI-HQ Call no: 631.7.8 G000 PRO Record No: H06687)
2 Zhang, Y.; Wang, J. 1991. Stochastic dynamic programming for regional exploitation of shallow groundwater resources. In ICID, The Special Technical Session Proceedings, Beijing, China, April 1991. Vol.1-B: Operation of irrigation systems. New Delhi, India: ICID. pp.77-82.
(Location: IWMI-HQ Call no: ICID 631.7 G000 ICI Record No: H014730)
(Location: IWMI-HQ Call no: P 5899 Record No: H029017)
(Location: IWMI-HQ Record No: H031180)
(Location: IWMI-HQ Call no: P 6747 Record No: H034049)
(Location: IWMI-HQ Call no: P 6866 Record No: H034720)
7 Wang, G.; Wei, J.; Zhang, Y.. 2004. Analysis of the sustainability of the development of a small phreatic aquifer in Northern China. Water International, 29(4):467-474.
(Location: IWMI-HQ Call no: PER Record No: H036714)
(Location: IWMI-HQ Call no: PER Record No: H033710)
(Location: IWMI-HQ Call no: PER Record No: H038284)
10 Chen, D.; White, R.; Li, Y.; Zhang, J.; Li, B.; Zhang, Y.; Edis, R.; Huang, Y.; Cai, G.; Wei, Y.; Zhu, A.; Hu, K.; Li, G.; Zhu, Z. 2006. Conservation management of water and nitrogen in the North China Plain using a GIS-based water and nitrogen management model and agricultural decision support tool. In Willett, I. R.; Gao, Z. (Eds.) Agricultural water management in China: Proceedings of a workshop held in Beijing, China, 14 September 2005. Canberra, Australia: ACIAR. pp.26-38.
(Location: IWMI-HQ Call no: 631.7 G592 WIL Record No: H039219)
11 He, Y.; Su, Z.; Jia, L.; Zhang, Y.; Roerink, G.; Wang, S.; Wen, J.; Hou, Y. 2005. Estimation of daily evapotranspiration in Northern China Plain by using MODIS/TERRA images. In Owe, M.; D’Urso, G. (Eds.). Remote Sensing for Agriculture, Ecosystems, and Hydrology VII. Proceedings of The International Society of Optical Engineering (SPIE), Bruges, Belgium, 20-22 September 2005. Vol.59761. 59761M-1:12p.
(Location: IWMI-HQ Call no: P 7708 Record No: H039591)
12 Yu, Q.; Zhang, Y.; Tang, W. 2007. The Three Gorges Project and flood control of the Yangtze River. In Ranade, P. S. (Ed.). Rivers, dams and development: Issues and dilemmas. Punjagutta, Hyderabad, India: Icfai University Press. pp.159-172.
(Location: IWMI HQ Call no: 627.8 G000 RAN Record No: H040675)
(Location: IWMI HQ Call no: e-copy only Record No: H046873)
(608 KB)
Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.
14 Zhang, Y.; Holzapfel, C.; Yuan, X. 2013. Scale-dependent ecosystem service. In Wratten, S.; Sandhu, H.; Cullen, R.; Costanza, R. (Eds.). Ecosystem services in agricultural and urban landscapes. Chichester, UK: John Wiley. pp.107-121.
(Location: IWMI HQ Call no: e-copy only Record No: H047092)
(0.08 MB)
The scale-dependent feature of ecosystem services is embodied in the scale dependency of ecosystem provider, ecosystem beneficiary, ecosystem service measurement and ecosystem service management. This study discusses each scale-dependent feature of ecosystem services, and two typical case studies are presented to illustrate the scale dependency of ecosystem service. One case deals with a park in one of the world’s largest and most developed metropolitan area (New York), which represents local and regional ecosystem services of green space in an urbanized area. The other case covers the Tibet plateau, which represents a nature-dominated ecosystem that provides ecosystem services with both regional and global significance. Such hierarchically structured ecosystem services underline the importance of understanding ecosystem service in an integrated and comprehensive perspective.
(Location: IWMI HQ Call no: e-copy only Record No: H049582)
(0.78 MB)
Climate change affects the livelihood of farmers in a variety of ways. Farmers’ indigenous knowledge influences their perception of climate-related issues. A perception-based, semi-structured questionnaire survey of 530 households was performed to gather information about the awareness of, indicators for, and determinants of climate change. The survey covered three ecological regions of Nepal. The statistical analysis was done with a chi-square ( 2) test and a binary logistic regression (BLR) model to screen farmers’ perception of climate change. This study shows that socio-economic and agricultural characteristics of the farmers directly influence their perception of climate change. Farmers have identified climate change indicators in various forms, e.g., an increase in temperature (99.2% of those surveyed), a decrease in precipitation (98.9%), and an increase in climate-induced diseases and pests (96.8%) for agricultural crops. Observed precipitation (- 16.093 mm/year; p = 0.055) and temperature (0.0539 °C/year; p = 0.007) between 2000 and 2015 are both consistent with farmers’ perception. The selected independent variables are significantly correlated with the dependent variables, as confirmed by the BLR model, where 2 = 83 with p = 0.002. The BLR shows there is a strong relationship between farmers’ perception of climate change and the group of descriptive variables, with a coefficient of determination of 85%. The biophysical characteristics and impact variables were the most important determinants. It is important that organizations and policymakers in Nepal develop adaptation strategies that improve the livelihoods of farmers. These strategies include introducing drought-tolerant crops, developing disease- and pest-tolerant seeds, constructing irrigation systems, and building hospitals.
(Location: IWMI HQ Call no: e-copy only Record No: H049774)
(7.14 MB)
Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal models by progressively refining the shadow detection result with two attention-based interacting modules – Shadow Detection Module (SDM) and Shadow Classification Module (SCM). To facilitate model training and validation, we also created a Shadow Semantic Annotation Database (SSAD) using the 1 m resolution (National Agriculture Imagery Program) NAIP aerial imagery. The SSAD comprises 103 image patches (500 × 500 pixels each) containing various types of shadows and six major land-cover classes – building, tree, grass/shrub, road, water, and farmland. Our results show an overall accuracy of 90.6% and Kappa of 0.82 for RSAM to extract the six land-cover classes within shadows. The model performance was stable along the urban-rural gradient, although it was slightly better in rural areas than in urban centers or suburban neighborhoods. Findings suggest that RSAM is a robust solution to eliminate the effects in high-resolution mapping both from cast and self shadows that have not received equal attention in previous studies.
(Location: IWMI HQ Call no: e-copy only Record No: H049885)
(0.65 MB) (668 KB)
In recent decades, many algorithms have been developed for the retrieval of water quality parameters using remotely sensed data. However, these algorithms are specific to a certain geographical area and cannot be applied to other areas. In this study, feature-orientated principal component (PC) selection, based on the Crosta method and using Landsat Thematic Mapper (TM) for the retrieval of water quality parameters (i.e., total suspended sediment concentration (TSM) and chlorophyll a (Chla)), was carried out. The results show that feature-orientated PC TSM, based on the Crosta method, obtained a good agreement with the MERIS-based TSM product for eight Landsat TM images. However, the Chla information, selected using the feature-orientated PC, has a poor agreement with the MERIS-based Chla product. The accuracy of the atmospheric correction method and MERIS product may be the main factors influencing the accuracy of the TSM and Chla information identified by the Landsat TM images using the Crosta method. The findings of this study would be helpful in the retrieval of spatial distribution information on TSM from the long-term historical Landsat image archive, without using coincident ground measurements.
18 Liu, D.; Wang, X.; Aminjafari, S.; Yang, W.; Cui, B.; Yan, S.; Zhang, Y.; Zhu, J.; Jaramillo, F. 2020. Using InSAR [Interferometric Synthetic Aperture Radar] to identify hydrological connectivity and barriers in a highly fragmented wetland. Hydrological Processes, 14p. (Online first) [doi: https://doi.org/10.1002/hyp.13899]
(Location: IWMI HQ Call no: e-copy only Record No: H049975)
(3.71 MB) (3.71 MB)
Hydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high-resolution and large-scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)-based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long-time scales using remote sensing technique.
(Location: IWMI HQ Call no: e-copy only Record No: H050288)
(11.70 MB)
Water-saving technologies have long been seen as an effective method to reduce irrigation water use and alleviate regional water shortage. However, growing reports of more severe water shortage and increasing application of water-saving technologies across the world have necessitated reassessment of agricultural water-saving. This study develops a simple method based on satellite-based ET partitions to estimate water withdrawal, water consumption and return flow from the 1980s to 2010s, and quantifies water-savings across globe and four hot-spot irrigated areas at both field and regional scales based on water accounting framework. The results show that global irrigation water flows keep increasing from the 1980s to 2010s, with over 50% increase from the expansion in irrigated lands. While water-saving technologies are found mainly applied in originally old irrigated lands, traditional flooding irrigation is still dominant in newly-developed irrigated lands. Non-beneficial water consumption (soil evaporation) is effectively reduced by water-saving technologies, but return flow has increased at the same time. At field scale, water-saving technologies fail to save water because the accumulated increased return flow is more than the accumulated decreased non-beneficial water consumption. At regional scale, however, water is saved because the return flow percolated to fresh aquifers is seen as beneficial rather than loss. At the same time, the accumulated increase of beneficial water consumption (crop transpiration) exceeds regional water savings, which explains the paradox between wide application of water-saving technologies and more severe regional water shortage. This study provides key new evidence for the paradox of irrigation efficiency and helps reconsidering water-saving technologies and their impacts on regional water resources.
(Location: IWMI HQ Call no: e-copy only Record No: H050394)
(2.59 MB) (2.59 MB)
Ganga-Brahmaputra-Meghna (GBM) river basin is the third-largest and one of the most populated river basins in the world. As climate change is affecting most of the hydrometeorological variables across the globe, this study investigated the existence of climate change signal in all four climatological seasons in the GBM river basin and assessed the contribution of anthropogenic activities, i.e., Greenhouse Gases (GHGs) emission in the change. Significant decreasing trends in the monsoon and a small increase in pre-monsoon precipitation were observed. Negligible change was detected in post-monsoon and winter season precipitation. CMIP5 GCMs were used for climate change detection, change point estimation, and attribution studies. Support Vector Machine (SVM) regression method was adopted to downscale GCM variables at the local scale. Monte-Carlo simulation approach was used to detect changes in different seasons. The climate change ‘signals’ were detectable after the year 1980 using Signal to Noise ratio (SNR) method in the majority of central and north-western regions. The change point was detectable only in annual monsoon precipitation at the basin level. Attribution analysis indicated >50% contribution of anthropogenic activities (GHGs) to annual monsoon precipitation changes. So, there is high confidence that monsoon precipitation in GBM has significantly changed due to anthropogenic activities. Different mitigation and adaption measures are also suggested, which may be adopted to manage the growing demand and water availability in the basin.
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