Alexander Platonov

Using remote sensing data for water depletion assessment at administrative and irrigation-system levels: case study of the Ferghana Province of Uzbekistan

Efforts aimed at improving regional water management are often frustrated due to the paucity of reliable and consistent information. This paper assesses the scope for remote sensing information, freely available on the Internet, to help water managers in obtaining reliable and consistent information at large (sub) system level. In the Ferghana province of Uzbekistan, belonging to the Syr-Darya river basin, water is managed across administrative units rather than along hydrological boundaries (the basin approach). Using recent developments in the field of remote sensing application in water management, this paper shows that remote sensing tools can help in improving water management in three ways: (a) by providing information on the existing patterns of water use; (b) by identifying the weaknesses in the approach to water management; and (c) by assisting in identifying the potential areas where there are opportunities for water savings or improving water use efficiency.

Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia

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.

Water productivity mapping methods using remote sensing

The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM) (m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at

From monocentric ideal to polycentric pragmatism in the Syr Darya: searching for second best approaches.

0.5/m3 followed by wheat with

Supplementing Farm-level Water Productivity Assessment by Remote Sensing in Transition Economies

0.33/m3 and rice at

Soil Salinity Mapping Using Multi-Temporal Satellite Images in Agricultural Fields of Syrdarya Province of Uzbekistan

0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with only about 10% area in high WP.

Improving water governance in Central Asia through application of data management tools

While best practice in water management typically calls for the use of a basin-level approach, specific guidance in the absence of basin-level management is fairly scant. This paper reviews the experience of the Syr Darya basin to identify insights related to second best practices for water management at scales below the basin level. This paper first presents the causes for the disintegration of river basin management within the Syr Darya, which include both changes in operation of the Toktogul reservoir and rising water demands due to shifts in agricultural production and land ownership. Focus is then devoted specifically to small transboundary tributaries, where bottom-up cooperation has continued or reemerged in recent times. This paper concludes by highlighting the limitations to singular focus on sub-basins and tributaries, suggesting a balance between more intense cooperation and water control on tributaries and a loose overarching framework at the basin level.

Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

Abstract Increasing water scarcity in the downstream areas of several river basins demands improved water management and conservation in the upper reaches. Improved management is impossible without proper monitoring at various levels. In the Aral Sea Basin, monitoring is carried out, albeit largely by under-paid staff; however, the water flows to farms and fields remain largely unmeasured due to poor infrastructure and lack of proper measurement facilities. Any conclusions drawn, and therefore the policies devised for water conservation, remain largely ineffective. Contrary to the prevalent human methods of monitoring, the potential for standard monitoring of large areas at field- or farm-level in terms of water depletion and yield, using the Surface Energy Balance Algorithm for Land (SEBAL), and eventually leading to water productivity calculations, is explored here using multi-source public and non-public remote sensing data combinations. The results show reasonable levels of accuracy and indicate areas needing improvements and further investigation and refinement. Farm-level maps of various water depletions and productivities provide a practical view of the performance for the mesolevel. Finally, the link between the field, farm, district, and province is briefly addressed in order to provide a methodology using water depletion assessment at smaller level to upscale it to a sub-basin scale.

A water accounting procedure to determine the water savings potential of the Fergana Valley

Almost 50% of the irrigated lands of Central Asia are affected by different levels of salinity. In extreme cases, the most severely affected lands are abandoned, while moderately saline lands produce low crop yields. Rehabilitation of the saline lands could have significant implications on productivity of irrigated lands as well as positive impacts on the environment. The assessment of the trend and the scale of salinity are crucial element in the development of a remediation/rehabilitation strategy. The traditional approach for soil salinity mapping is extremely costly and has low level of precision. The chapter discusses the approach in developing the soil salinity maps by analysis of vegetation stress from multi-temporal remote sensing data for irrigated areas.

Water productivity mapping using remote sensing data of various resolutions to support more crop per drop

Contemporary water management decisions use many sources and forms of data. The paper discusses the implementation results of data management activities in the water sector carried out in five countries of the Central Asia region. Geoinformation systems, remote sensing tools and databases have been applied worldwide for improving water resources management with differing levels of success. Water management organisations, equipped with data management tools will have better capacities to adapt their decision-making in the changing availability and scarcity of water resources. Application of data management tools for improving collection, storage and processing of data and information are a first step towards improved water governance.