The precision of satellite-based irrigation quantification in the Indus and Ganges basins

Abstract

Abstract. Even though irrigation is the largest direct anthropogenic interference with the terrestrial water cycle, limited knowledge on the amount of water applied for irrigation exist. Quantification of irrigation via evapotranspiration (ET) or soil moisture residuals between remote sensing models and hydrological models, with the latter acting as baselines of natural conditions without the influence of irrigation, have successfully been applied in various regions. Here, we implement an novel ensemble methodology to estimate the precision of ET-based net irrigation quantification by combining different ET and precipitation products in the Indus and Ganges basins. A multi-model calibration of 15 models independently calibrated to simulate natural rainfed ET was conducted prior to the irrigation quantification. Based on the ensemble average, the 2003–2013 net irrigation amounts to 246 mm/year (78 km3/year) and 115 mm/year (76 km3/year) in Indus and Ganges basin, respectively. Net irrigation in Indus basin is evenly split between dry and wet period, whereas 73 % of net irrigation occurs during the dry period in Ganges basin. We found that although annual ET from remote sensing models varied by 91.5 mm/year, net irrigation precision was within 25 mm/season during the dry period, which emphasizes the robustness the applied multi-model calibration approach. Net irrigation variance was found to decrease as ET uncertainty decreased, which related to the climatic conditions, i.e. high uncertainty under arid conditions. A variance decomposition analysis showed that ET uncertainty accounted for 81 % of the overall net irrigation variance and that the influence of precipitation uncertainty was seasonally dependent, i.e. with an increase during the monsoon season. The results underline the robustness of the framework to support large scale sustainable water resource management of irrigated land.

An inter-comparison of approaches and frameworks to quantify irrigation from satellite data

“…on the quantification of net irrigation from ET residuals, which is the irrigation water loss to the atmosphere (Koch et al, 2020;Kragh et al, 2022). The total irrigation amount cannot be quantified from ET residuals alone as some of the water is bound within the soil column to prevent the soil moisture content to reach the wilting point.…”

“…The main purpose of using a rainfed hydrological model is to estimate how much of the observed ET can be explained by the precipitation input and thereby is embedded within the baseline, making sure that when larger amounts of precipitation input are available the irrigation estimates will be low if the baseline is well calibrated. PET estimates can vary substantially between readily available remote sensing datasets or estimates from meteorological data, which ultimately can have a profound impact on the irrigation components if not accounted for in the calibration (Kragh et al, 2022).…”

“…Other studies have compared satellite remote sensing models and hydrological models to extract soil moisture changes associated with a non-precipitation source of water to estimate irrigation (Zaussinger et al, 2019;Zohaib and Choi, 2020). Koch et al (2020) and (Kragh et al, 2022) further compared remote sensing model retrievals of actual ET from irrigated cropland areas with a rainfed hydrological model calibrated against the remote sensing model retrieval of actual ET over rainfed cropland (hence with natural vegetation) to counteract the influence of a possible https://doi.org/10.5194/hess-2023-142 Preprint. Discussion started: 8 June 2023 c Author(s) 2023.…”