The Water Framework Directive in Europe requires extending metering and water abstraction controls to accurately satisfy the necessary water resource requirements. However, in situ measurement instruments are inappropriate for large irrigation surface areas, considering the high investment and maintenance service costs. In this study, Remote Sensing-based Irrigation Water Accounting (RS-IWA) (previously evaluated for commercial plots, water user associations, and groundwater water management scales) was applied to over 11 Spanish river basin districts during the period of 2014–2018. Using the FAO56 methodology and incorporating remote sensing basal crop coefficient time series to simulate the Remote Sensing-based Soil Water Balance (RS-SWB), we were able to provide spatially and temporally distributed net irrigation requirements. The results were evaluated against the irrigation water demands estimated by the Hydrological Planning Offices and published in the River Basin Management Plans applying the same spatial (Agricultural Demand Units and Exploitation Systems) and temporal (annual and monthly) water management scales used by these public water managers, ultimately returning ranges of agreement (r2 and dr) (Willmott refined index) of 0.79 and 0.99, respectively. Thus, this paper presents an operational tool for providing updated spatio-temporal maps of RS-IWA over large and diverse irrigation surface areas, which is ready to serve as a complementary irrigation water monitoring and management tool.
Mapping irrigated surfaces and the crops growing on these surfaces by using Remote Sensing is a well known first relevant step to contribute to water governance at different scales ranging from farm, irrigation scheme and, by scaling-up, to the whole river basin. These maps provide a first estimation of the spatially distributed water flows about evapotranspiration and irrigation water requirements based on the cropping agronomic knowledge. During the last 20 years, annual maps of crops and irrigated surfaces elaborated by using time series of multispectral satellite imagery have been in the basis of a successful water management of a big groundwater body placed in the Southern Spain. Threatened by over-exploitation, this aquifer extends over around 10,000 km 2 of land surface, in the middle Júcar river basin, supporting around 100,000 ha of irrigated crops, and providing drinking water for 150,000 inhabitants, with competing uses from downstream users. This paper describes the main learned lessons. In addition, the paper tackles a necessary further step in the context of the current requirements for water governance of this aquifer: the direct remote sensing-based water accounting, by quantifying agricultural water flows (e.g. rainfall, irrigation, evapotranspiration, drainage and recharge, at practical spatial and temporal scales for water governance purposes). This RS-based WA approach relies on dense time series of multispectral imagery acquired by the multisensor constellation formed by Landsat 8 and Sentinel-2, jointly with meteorological data. By this, we discuss the technical and non-technical feasibility to rely monitoring water abstraction on this RS-based WA approach, providing the basis for a hybrid system.
In dealing with intrinsically imprecise-valued magnitudes, a common rating scale type is the natural language-based Likert. Along the last decades, fuzzy scales (more concretely, fuzzy linguistic scales/variables and fuzzy ratig scales) have also been considered for rating values of these magnitudes. A comparative descriptive analysis focussed on the variability/dispersion associated with the magnitude depending on the considered rating scale is performed in this study. Fuzzy rating responses are simulated and associated with Likert responses by means of a`Likertization' criterion. Then, each`Likertized' datum is encoded by means of a fuzzy linguistic scale. In this way, with the responses available in the three scales, the value of the dierent dispersion estimators is calculated and compared among the scales.
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