Preprints
https://doi.org/10.5194/hess-2022-307
https://doi.org/10.5194/hess-2022-307
 
12 Sep 2022
12 Sep 2022
Status: this preprint is currently under review for the journal HESS.

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

Søren Julsgaard Kragh1, Rasmus Fensholt2, Simon Stisen1, and Julian Koch1 Søren Julsgaard Kragh et al.
  • 1Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen, 1350, Denmark
  • 2Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, Denmark

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.

Søren Julsgaard Kragh et al.

Status: open (until 21 Nov 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-307', Anonymous Referee #1, 14 Sep 2022 reply

Søren Julsgaard Kragh et al.

Søren Julsgaard Kragh et al.

Viewed

Total article views: 336 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
268 62 6 336 1 4
  • HTML: 268
  • PDF: 62
  • XML: 6
  • Total: 336
  • BibTeX: 1
  • EndNote: 4
Views and downloads (calculated since 12 Sep 2022)
Cumulative views and downloads (calculated since 12 Sep 2022)

Viewed (geographical distribution)

Total article views: 310 (including HTML, PDF, and XML) Thereof 310 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Sep 2022
Download
Short summary
This study investigates the precision of irrigation estimates from a global hotspot of unsustainable irrigation practice, the Indus and Ganges basins. We show that by comparing satellite and rainfed hydrological model estimates of evapotranspiration, irrigation water use can be estimated with high precision. We believe that our work can support sustainable water resource management as it addresses the uncertainty of a key component of the water balance that remains challenging to quantify.