Your search found 51 records
1 Thenkabail, Prasad; Biradar, Chandrashekhar; Turral, Hugh; Noojipady, Praveen; Li, Yuanjie; Vithanage, Jagath; Dheeravath, Venkateswarlu; Velpuri, Manohar; Schull, M.; Cai, Xueliang; Dutta, Rishiraj. 2006. An irrigated area map of the world (1999) derived from remote sensing. Colombo, Sri Lanka: International Water Management Institute (IWMI). 65p. (IWMI Research Report 105) [doi: https://doi.org/10.3910/2009.105]
(Location: IWMI-HQ Call no: IWMI 631.7.1 G000 THE Record No: H039270)
(1.57MB)
This document summarizes the materials and methods used to create a series of maps of irrigated areas of the world using remote sensing approaches. These maps are complementary to existing statistics (FAO-Aquastat) and the GISderived maps (FAO/University of Frankfurt Global irrigated area map). The document also provides details of how the estimates of global irrigated areas in one main season (net) and more than one season (intensity or annualized) were derived.
2 Thenkabail, Prasad S.; Biradar, C. M.; Noojipady, P.; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Y. J.; Velpuri, M.; Gumma, Murali Krishna; Pandey, Suraj. 2007. Sub-pixel area calculation methods for estimating irrigated areas. Sensors, 7: 2519-2538.
(Location: IWMI HQ Call no: IWMI 631.7.1 G000 THE Record No: H040450)
3 Cai, Xueliang; Cui, Y.; Dong, B. 2007. Estimating pond storage capacity using remote sensing and GIS: A case study in Zhanghe irrigation scheme, southern China. In Proceedings of the 2nd International Symposium on Methodology in Hydrology, held in Nanjing, China, October – November, 2005. IAHS Publication 311, 2007. 6p.
Ponds ; Water storage ; Topography ; GIS ; Remote sensing / China / Hubei Province / Zhanghe Irrigation Scheme
(Location: IWMI HQ Call no: IWMI 631.7.1 G592 CAI Record No: H040567)
There are numerous ponds in Southern China and it is very difficult to evaluate their contribution to local water users because of their small size and wide variance. This paper presents two methods for estimating pond storage capacity using RS/GIS. One is to estimate pond storage capacity as a function of the topographic factors. An indicator, PV, pond storage capacity per unit rice area of 86 villages, is calculated, and then the correlation between PV, slope, altitude and drainage density is calculated to estimate the pond storage capacity of whole study area. The other method is to extrapolate estimated results from a high resolution image of small scale to a low resolution image of large scale area. With field information and IKONOS image (1 m), the pond storage of a small scale area is estimated using a pond surface area–volume function, and then the value is extrapolated to large scale area with reference to a LandSat ETM image (14.25 m). The two methods are applied to Zhanghe irrigation district, and the application results show that the results are acceptable.
4 Roost Nicolas; Cai, Xueliang; Molden, David; Cui, Y. L. 2008. Adapting to intersectoral transfers in the Zhanghe Irrigation System, China: Part I - In-system storage characteristics. Agricultural Water Management, 95(6): 698-706.
Farm ponds ; Recharge ; Hydrology ; Water storage ; Water reuse ; Remote sensing ; Irrigation canals ; Irrigation systems ; Reservoirs ; Water allocation / China / Zhanghe Irrigation System / Yangtze River Basin
(Location: IWMI HQ Call no: 631.7.1 G592 ROO Record No: H040568)
The Zhanghe Irrigation System (ZIS), in Central China, has drawn attention internationally because it managed to sustain its rice production in the face of a dramatic reallocation of water to cities, industries and hydropower uses. Ponds, the small reservoirs ubiquitous in the area, are hypothesized to have been instrumental in this. Ponds are recharged by a combination of return flows from irrigation and runoff from catchment areas within the irrigated perimeter. They provide a flexible, local source of irrigation water to farmers. This paper assesses the storage capacity and some key hydrological properties of ponds in a major canal command within ZIS. Using remote sensing data (Landsat and IKONOS) and an area–volume relationship based on a field survey, we obtained an overall pond storage capacity of 96 mm (per unit irrigated area). A comparative analysis between 1978 and 2001reveals that part of this capacity results from a very significant development of ponds (particularly in the smaller range of sizes) in the time interval, probably as a response to rapidly declining canal supplies. We developed a high-resolution digital elevation model from 1:10,000 topographic maps to support a GIS-based hydrological analysis. Pond catchments were delineated and found to extensively overlap, forming hydrological cascades of up to 15 units. In a 76-km2 area within the irrigation system, we found an average of close to five ‘connected’ ponds downstream of each irrigated pixel. This high level of connectivity provides opportunities for multiple reuses of water as it flows along toposequences. A fundamental implication is that field ‘losses’ such as seepage and percolation do not necessarily represent losses at a larger scale. Such scale effects need to be adequately taken into account to avoid making wrong assumptions about water-saving interventions in irrigation.
5 Roost Nicolas; Cai, Xueliang; Turral, Hugh; Molden, David; Cui, Y. L. 2008. Adapting to intersectoral transfers in the Zhanghe Irrigation System, China: Part II – Impacts of in-system storage on water balance and productivity. Agricultural Water Management, 95(6): 685-697.
Farm ponds ; Irrigation systems ; Reservoirs ; Water balance ; Simulation models ; Rice ; Crop production ; Irrigation canals ; Groundwater ; Drainage ; Evapotranspiration ; Water distribution / China / Zhanghe Irrigation System / Yangtze River Basin
(Location: IWMI HQ Call no: 631.7.1 G592 ROO Record No: H040569)
This paper investigates the impacts of farm ponds in a context of declining supplies in a major canal command within the Zhanghe Irrigation System (ZIS), in Central China. As dam supplies have been diverted to higher-valued uses (hydropower, cities and industry), farmers have responded by constructing small storages within their fields. These farm ponds have given them sufficient flexibility in water supply to practice varying forms of alternate wetting and drying irrigation for rice without compromising yields and incomes. Ponds are recharged by a combination of return flows from irrigation and runoff from catchment areas within the irrigated perimeter. Various scenarios of water supply incorporating the main reservoir, in-system reservoirs, farm ponds and irrigation practices were simulated using the OASIS model. OASIS integrates surface and groundwater flows, and contains a crop growth module to aggregate the impacts of different water management regimes. The modelling and sensitivity analysis show that further reductions in main reservoir supplies will have a negative effect on rice production in dry and average years, and that ponds have played a crucial role in adapting agriculture to reduced canal supplies. The flexibility allowed by the ponds has resulted in increased water productivity, except in high rainfall years, but net depletion has not decreased, as local supplies have substituted for water from the main reservoir. The study demonstrates the importance of properly accounting for return flows and the necessity to understand crop production in relation to the actual depletion of water (as evapotranspiration) within an irrigation system.
6 Platonov, Alexander; Thenkabail, Prasad; Biradar, Chandrashekhar M.; Cai, Xueliang; Gumma, Murali Krishna; Dheeravath, Venkateswarlu; Cohen, Y.; Alchanatis, V.; Goldshlager, N.; Ben-Dor, E.; Vithanage, Jagath; Manthrithilake, Herath; Kendjabaev, S.; Isaev, S. 2008. Water productivity mapping (WPM) using Landsat ETM+ data for the irrigated croplands of the Syrdarya River Basin in Central Asia. Sensors, 8:8156-8180.
Water productivity ; Mapping ; Remote sensing ; Water use ; Crops ; Productivity ; Crop yield ; Models ; Evapotranspiration ; Irrigated farming ; River basins / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041566)
The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET) and “cold” pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.
7 Sharma, Bharat R.; Amarasinghe, Upali A.; Cai, Xueliang. 2009. Assessing and improving water productivity in conservation agriculture systems in the Indus-Gangetic Basin. Invited lead paper presented at the 4th World Congress on Conservation Agriculture Innovations for Improving Efficiency, Equity and Environment, Session 1.4, Irrigated systems, National Academy of Agricultural Sciences, NASC Complex, Pusa, New Delhi, India, 4-7 February 2009. 12p.
River basins ; Water productivity ; Rainfed farming ; Irrigation systems ; Supplemental irrigation ; Water delivery ; Water storage / Pakistan / India / Nepal / Bangladesh / Indo-Gangetic Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041863)
(0.24 MB)
8 Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Noojipady, P.; Dheeravath, Venkateswarlu; Li, Yuan Jie; Velpuri, M.; Reddy, G. P. O.; Cai, Xueliang; Gumma, Murali Krishna; Turral, Hugh; Vithanage, Jagath; Schull, M.; Dutta, R. 2008. A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium. Colombo, Sri Lanka: International Water Management Institute (IWMI) 62p. [doi: https://doi.org/10.5337/2011.0024]
(Location: IWMI HQ Call no: e-copy only Record No: H042115)
(3.00 MB) (3MB)
9 Cai, Xueliang; Thenkabail, P. S.; Biradar, C. M.; Platonov, Alexander; Gumma, Murali Krishna; Dheeravath, V.; Cohen, Y.; Goldlshleger, F.; Ben-Dor, E.; Alchanatis, V.; Vithanage, Jagath; Anputhas, Markandu. 2009. Water productivity mapping using remote sensing data of various resolutions to support more crop per drop. Journal of Applied Remote Sensing, 3(033557). 23p. [doi: https://doi.org/10.1117/1.3257643]
Water productivity ; Crops ; Water use ; Evapotranspiration ; Mapping ; Remote sensing ; Models / Central Asia / Kyrgyzstan / Tajikistan / Uzbekistan / Kazakhstan / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042408)
(4.07 MB)
The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m3, 34% had moderate WP of 0.3-0.4 kg/m3, and only 11% area had high WP > 0.4 kg/m3. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.
10 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Li, Yuan Jie; Velpuri, N. M.; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Turral, H.; Cai, Xueliang; Vithanage, Jagath; Schull, M. A.; Dutta, R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14):3679-3733. [doi: https://doi.org/10.1080/01431160802698919]
(Location: IWMI HQ Call no: e-copy only Record No: H042409)
(18.23 MB)
A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997–1999, (b) Syste`me pour l’Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961–2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST-SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) ‘zoom-in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAMwas produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252Mha from season one, (b) 174Mha from season two and (c) 41Mha from continuous yearround crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).
11 Cai, Xueliang; Sharma, Bharat R. 2009. Remote sensing and census based assessment and scope for improvement of rice and wheat water productivity in the Indo-Gangetic Basin. Science in China Series E: Technological Sciences, 52(11):3300-3308. [doi: https://doi.org/10.1007/s11431-009-0346-3]
Remote sensing ; Water productivity ; Cropping systems ; Rice ; Wheat ; Evapotranspiration ; Models ; Mapping ; River basins / South Asia / India / Pakistan / Bangladesh / Nepal / China / Afghanistan / Indus River Basin / Ganges River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042410)
(0.50 MB)
Understanding of crop water productivity (WP) over large scale, e.g., river basin, has significant impli-cations for sustainable basin development planning. This paper presents a simplified approach to combine remote sensing, census and weather data to analyze basin rice and wheat WP in In-do-Gangetic River Basin, South Asia. A crop dominance map is synthesized from ground truth data and three existing LULC maps. National statistics on crop area and production information are collected and the yield is interpolated to pixel level using moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI). Crop evapotranspiration is mapped using simplified surface energy balance (SSEB) model with MODIS land surface temperature products and meteorological data collected from 56 weather stations. The average ET by rice and wheat is 368 mm and 210 mm respectively, accounting for only 69% and 65% of potential ET, and 67% and 338% of rain-fall of the crop growth period measured from Tropical Rainfall Measurement Mission (TRMM). Average WP for rice and wheat is 0.84 and 1.36 kg/m3 respectively. WP variability generally follows the same trend as shown by crop yield disregarding climate and topography changes. Sum of rice-wheat water productivity, however, exhibits different variability leading to better understanding of irrigation water management as wheat heavily relies on irrigation. Causes for variations and scope for improvement are also analyzed.
12 Cai, Xueliang; Cui, Y. 2009. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. In Chinese. Transactions of the Chinese Society of Agricultural Engineering, 25(8):124-130.
Remote sensing ; Irrigated land ; Crop management ; Rice ; Wheat / China / Zhanghe Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H042411)
(1.13 MB)
Crop planting structure extraction in irrigated areas includes a range of dynamic parameters which require proper spatial and temporal resolution remotely sensed data. The paper seeks to extract crop planting structure by employing multi-temporal images from multi-sensors. Landsat enhanced thematic mapper plus (ETM+) images and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) monthly data were res-merged to produce a mega data tube, which was then classified using ISO cluster algorithm. Spectral signature of each class was extracted and identified using spectral matching technique taking crop coefficient curve as reference. In the way Zhanghe Irrigation system in southern China was classified into four classes: rice-rapeseed rotation, rice-wheat rotation, single summer crops, and double economic crops. Accuracy assessment suggests good agreement with statistical data and 91% classification accuracy when using IKONOS high resolution images as Ground Truth data. The application demonstrates the method a cost-efficient and robust approach to extract crop planting structure at irrigation system scale.
13 Cai, Xueliang; Cui, Yuanlai. 2009. A simplified ET mapping algorithm and its application in irrigation district. In Chinese. Journal of Irrigation and Drainage, 28(2):51-54.
Remote sensing ; Mapping ; Evapotranspiration ; Irrigated land / China / Zhanghe irrigation district
(Location: IWMI HQ Call no: PER Record No: H041477)
(0.50 MB)
14 Li, Y. J.; Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Gangalakunta, O. R. P.; Cai, Xueliang. 2009. A history of irrigated areas of the world. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.13-37. (Taylor & Francis Series in Remote Sensing Applications)
Irrigated land ; History ; Irrigation programs ; Statistics / China / India / Egypt / Peru / Indus River Basin / Tigris River Basin / Euphrates River Basin
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042418)
(1.04 MB)
15 Biradar, C. M.; Thenkabail, P. S.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Turral, H.; Cai, Xueliang; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Xiao, X. 2009. Global map of rainfed cropland areas (GMRCA) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.357-389. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042430)
(1.40 MB)
16 Cai, Xueliang; Sharma, Bharat R. 2010. Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin. Agricultural Water Management, 97(2):309-316. [doi: https://doi.org/ 10.1016/j.agwat.2009.09.021]
Rice ; Crop yield ; Mapping ; Evapotranspiration ; Water productivity ; Water use ; Models ; Remote sensing ; River basins / Pakistan / India / Nepal / Bangladesh / Indus River / Ganges River
(Location: IWMI HQ Call no: e-copy only Record No: H042489)
(0.56 MB)
Crop consumptive water use and productivity are key elements to understand basin watermanagement performance. This article presents a simplified approach tomap rice (Oryza sativa L.) water consumption, yield, and water productivity (WP) in the Indo-Gangetic Basin (IGB) by combining remotely sensed imagery, national census and meteorological data. The statistical rice cropped area and production data were synthesized to calculate district-level land productivity, which is then further extrapolated to pixel-level values using MODIS NDVI product based on a crop dominance map. The water consumption by actual evapotranspiration is estimated with Simplified Surface Energy Balance (SSEB) model taking meteorological data and MODIS land surface temperature products as inputs. WP maps are then generated by dividing the rice productivity map with the seasonal actual evapotranspiration (ET) map. The average rice yields for Pakistan, India, Nepal and Bangladesh in the basin are 2.60, 2.53, 3.54 and 2.75 tons/ha, respectively. The average rice ET is 416 mm, accounting for only 68.2% of potential ET. The average WP of rice is 0.74 kg/m3. The WP generally varies with the trends of yield variation. A comparative analysis of ET, yield, rainfall and WP maps indicates greater scope for improvement of the downstream areas of the Ganges basin. The method proposed is simple, with satisfactory accuracy, and can be easily applied elsewhere.
17 Cai, Xueliang; Thenkabail, P. S. 2010. Using remote sensing to assess crop water productivity. SPIE, 3p. [doi: https://doi.org/10.1117/2.1201002.002576]
Water productivity ; Assessment ; Estimation ; Remote sensing ; Models ; Mapping ; Crop management ; Water use ; Cotton
(Location: IWMI HQ Call no: e-copy only Record No: H042729)
http://spie.org/x39199.xml?highlight=x2420&ArticleID=x39199
https://vlibrary.iwmi.org/pdf/H042729.pdf
https://vlibrary.iwmi.org/pdf/H042729.pdf
(1.47 MB)
Crop consumptive water use, biophysical parameters, and water productivity values can be mapped to support ‘more crops per drop.’
18 Sharma, Bharat R.; Amarasinghe, Upali; Cai, Xueliang; de Condappa, D.; Shah, Tushaar; Mukherji, Aditi; Bharati, Luna; Ambili, G.; Qureshi, Asad Sarwar; Pant, Dhruba; Xenarios, Stefanos; Singh, R.; Smakhtin, Vladimir. 2010. The Indus and the Ganges: river basins under extreme pressure. Water International, 35(5):493-521. (Special Issue on "Water, Food and Poverty in River Basins, Part 1" with contributions by IWMI authors). [doi: https://doi.org/10.1080/02508060.2010.512996]
River basins ; Groundwater management ; Electrical energy ; Water productivity ; Irrigation water ; Rice ; Wheat ; Evapotranspiration ; Cropping systems ; Water governance ; Watercourses ; Water conservation ; Water costs ; Water policy ; Multiple use ; Rural poverty / India / Pakistan / Nepal / Bangladesh / Indus River Basin / Ganges River Basin / Bhakra Irrigation System
(Location: IWMI HQ Call no: PER Record No: H043246)
http://www.tandfonline.com/doi/pdf/10.1080/02508060.2010.512996
https://vlibrary.iwmi.org/pdf/H043246.pdf
https://vlibrary.iwmi.org/pdf/H043246.pdf
(8.90 MB) (1.77MB)
The basins of the Indus and Ganges rivers cover 2.20 million km2 and are inhabited by more than a billion people. The region is under extreme pressures of population and poverty, unregulated utilization of the resources and low levels of productivity. The needs are: (1) development policies that are regionally differentiated to ensure resource sustainability and high productivity; (2) immediate development and implementation of policies for sound groundwater management and energy use; (3) improvement of the fragile food security and to broaden its base; and (4) policy changes to address land fragmentation and improved infrastructure. Meeting these needs will help to improve productivity, reduce rural poverty and improve overall human development.
19 Cai, Xueliang; Sharma, Bharat R.; Matin, Mir Abdul. 2010. Current status and scope for improvement of agricultural water productivity in the Indo-Gangetic River Basin. Paper presented at the 3rd International Perspective on Current and Future State of Water Resources and Environment, Chennai, India, 5-7 January 2010. Paper No.270. 7p.
Rice ; Wheat ; Cultivation ; Water productivity ; Irrigated farming ; Evapotranspiration ; Remote sensing / South Asia / Nepal / India / Pakistan / Bangladesh / China / Afghanistan / Indo-Gangetic River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H043387)
(0.54 MB)
This paper assesses the agricultural water consumption and productivity of the predominant crop paddy rice and wheat for the Indo-Gangetic river basin (IGB) in South Asia. A new approach was adopted in the study to integrate census, remote sensing and weather data to assess crop water productivity (WP) across large scale. The average paddy field ET for rice for major growing period of June 10 to October 15 is 416 mm, which is 70% of rice potential evapotranspiration (ETp, equals to ET0*Kc). Average rice water productivity is 0.74 kg/m3. The average evapotranspiration (ETa) and WP of wheat is 299 mm and 0.94 kg/m3 respectively. Significant variations were observed for the ETa, yield and WP of rice and wheat. The scope for improvement of water productivity could be assessed by comparing “hot” and “bright” spots in consultation with factors such as rainfall and topography. It is found while improving yield in long term will finally lead to improved WP, reducing non-beneficial ET from low yield areas is a effective approach to improve WP in short term. Integrated land, crop and water management is the key to sustainable development of the region.
20 Cai, Xueliang; Karimi, Poolad; Masiyandima, Mutsa; Sally, Hilmy. 2010. Agricultural water productivity in the Limpopo River Basin: more produce per drop? In Institute of Water and Sanitation Development. 11th WaterNet/WARFSA/GWP-SA Symposium, Victoria Falls, Zimbabwe, 27-29 October 2010. IWRM for national and regional integration: where science, policy and practice meet: water and land. Harare, Zimbabwe: Institute of Water and Sanitation Development (IWSD) pp.5-18.
River basins ; Water use ; Crop production ; Precipitation ; Remote sensing ; Water balance ; Evapotranspiration ; Water productivity / Africa / Limpopo River Basin / Southern Africa
(Location: IWMI HQ Call no: e-copy only Record No: H043388)
http://www.waternetonline.ihe.nl/11thSymposium/WaterandLandFullPapers2010.pdf
https://vlibrary.iwmi.org/pdf/H043388.pdf
https://vlibrary.iwmi.org/pdf/H043388.pdf
(1.03 MB)
The increasing water scarcity and food demand has put enormous pressure on water management in the Limpopo basin, where rainfed agriculture predominates. This study analyzed basin water consumption against precipitation generated from remote sensing imagery integrated with weather data, which was linked to crop water productivity maps. The time series actual ET (ETa) and reference ET (ETo) maps were then overlaid together with precipitation data from Tropical Rainfall Measurement Mission (TRMM) to assess the evolution of water balance components in the basin. The relation between water balance components and water productivity were then analyzed to assess the factors affecting water productivity and the scope for improvement. The basin average ETa is 779 mm, only 46% of ETo. The ETa of cropland varies significantly across the basin, which is attributed to varying water availability conditions. The basin crop water productivity is very low with great variation, which could be explained by low yields as a result of variable rainfall patterns and lack of other production inputs. The fluctuant prices of maize at local market also had significant impact on water productivity.
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