Yann Chemin

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.

Spatiotemporal Fusion of Rice Actual Evapotranspiration With Genetic Algorithms and an Agrohydrological Model

Monitoring of water consumption in irrigation systems has become increasingly important for water managers in the actual trend of integrated water management. Low spatial resolution (LSR) satellite remote sensing has already proven the capacity of monitoring evapotranspiration (ETa) over large areas at high temporal frequencies, by which monitoring for large irrigation systems can be satisfied. However, smaller pixel size is still required for more local management while keeping the return period within a few days. High spatial resolution (HSR) satellite imagery is indeed available for calculation of ETa and has already been used in many studies. However, its practical return period is a major drawback to its implementation for monitoring irrigation systems. This paper is perusing into the use of genetic algorithms to assimilate parameters of an agrohydrological model called soil-water-air-plant for each of the pixels of HSR images contained into one single pixel of an LSR multitemporal image. The methodology developed and experimented here is trying to take advantage of the spatial content of HSR images and the temporal content of LSR images by fusing them by the process of data assimilation

Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas

Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use.

Combining remotely sensed surface energy fluxes and GIS analysis of groundwater parameters for irrigation system assessment

Abstract Despite being necessary for effective water management, the assessment of an irrigation system requires a large amount of input data for the estimation of related parameters and indicators, which are seldom measured in a regular and reliable manner. In this work, spatially distributed surface energy balance fluxes and geographical information systems analysis of multiple groundwater parameters were used to estimate water availability, supply, and demand, in order to calculate water-accounting indicators. This methodology was used to evaluate the performance of an irrigation system in the Pinios river basin (Greece) at two selected years of high and low water availability. Time series of archived satellite images and groundwater measurements have been used for past years to support comparative analyses, due to the limited availability of actual water measurements. The resulting maps from the proposed methodology show that the performance of the irrigation system varied across space and time due to differences in its characteristics and changes in its operation, driven by fluctuation of water availability and the response of stakeholders to water depletion. Irrigation districts with unsustainable water management were identified and, together with those with slow and/or limited groundwater recharge, were brought to the attention of water managers. The observed differences in the system operation between the wet and dry years were attributed not only to the hydrological conditions of each year, but also to the changing behaviour of farmers and the improvement actions of the water managers.

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

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.

Spatio-ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Irrigation Water Consumption Through Remote Sensing. Comparison At Different Scales In Zhanghe, Irrigation System, China

Despite being particularly essential for improving the productivity of agriculture, water management in large-scale imgation systems is often lacking of even the basic information. Techniques for estimation of the water consumption have recently been developed using the low-cost satellite images covering large agricultural areas. Appealing as it may be, the new techniques raise the question of accuracy, especially in the context of the measurement scale. Comparison of different scales of images and their influence in the calculation of the global volumes of water evaporated is giving critical information on the applicability of these advanced tools in a water management perspective. The Surface Energy Balance Algorithm for Land (SEBAL), calculating evaporation of the physical environment from various satellite sensors, has been applied to Landsat 7 ETM+ in the irrigation system of Zhanghe, in the Hubei Province of China. The Landsat 7 ETM+ image has been aggregated to various levels of spatial resolution, reaching down to the equivalent of NOAA AVHRR satellite, which is widely used as it is free of cost. Volumes of water consumption were compared at different pixel sizes. The quantitative relation of water consumption at different scales is assessed, leading to the determination of the optimum measurement scale in the Zhanghe imgation system. A discussion is then giving the research orientations of the scale assessment of evaporation and the further implications in applied research for water management aided by satellite images.

Vegetation growth zonation of Sri Lanka for improved water resources planning

Abstract Water resources planning depends on the physical environments, notably on the vegetation and hydrological conditions in river basins. Vegetation growth—both natural and human induced—has a strong dynamic behavior which, especially at larger scales, is difficult to survey in the field. Low cost imagery from the NOAA–AVHRR satellite provides new opportunities to estimate vegetation development and biomass production. Composite maps of biomass production were compiled as the primary basis to establish a growth zone classification. A total of 92 vegetation growth zones were delineated by visual interpretation. Digital vector maps of land use and soil type, as well as digital raster maps of moisture availability index, soil moisture and actual evapotranspiration have been explored to describe vegetation growth zones from satellite data. These vegetation growth zones will contribute more to water resources planning than existing agro-ecological zonations and land use mapping, because vegetation growth is based on the environment and has strong linkages with the hydrological processes occurring in different parts of the basin. It provides a vehicle to relate water demand and water use in river basins to various types of vegetation.

Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations

The surface energy balance algorithm for land (SEBAL) has been successfully applied to estimate evapotranspiration (ET) and yield at different spatial scales. However, ET and yield patterns have never been investigated under highly heterogeneous conditions. We applied SEBAL in a salt-affected and water-stressed maize field located at the margin of the Venice Lagoon, Italy, using Landsat images. SEBAL results were compared with estimates of evapotranspiration by the Food and Agriculture Organization (FAO) method (ETc) and three-dimensional soil-plant simulations. The biomass production routine in SEBAL was then tested using spatially distributed crop yield measurements and the outcomes of a soil-plant numerical model. The results show good agreement between SEBAL evapotranspiration and ETc. Instantaneous ET simulated by SEBAL is also consistent with the soil-plant model results (R2 = 0.7047 for 2011 and R2 = 0.6689 for 2012). Conversely, yield predictions (6.4 t/ha in 2011 and 3.47 t/ha in 2012) are in good agreement with observations (8.64 t/ha and 3.86 t/ha, respectively) only in 2012 and the comparison with soil-plant simulations (8.69 t/ha and 5.49 t/ha) is poor. In general, SEBAL underestimates land productivity in contrast to the soil-plant model that overestimates yield in dry years. SEBAL provides accurate predictions under stress conditions due to the fact that it does not require knowledge of the soil/root characteristics.