Anthropogenic Water Use (AWU) has been declared as an Essential Climate Variable (ECV). Even though the term AWU encompasses several water uses, it is mainly represented by agricultural water allocated for irrigation purposes, which largely prevails on industrial and domestic water uses, thus representing a primary source of anthropogenic interference with the “natural” hydrological cycle. More than 70% of global freshwater withdrawals are attributable to irrigation; in some countries, the rates of water depletions linked to such practices reach up to 90% of the available freshwater (McDermid et al., 2023). Additionally, the impact of irrigation is expected to further increase in the next decades given the projected climate change scenarios associated with the foreseen population growth and consequent rising of food demand and living standards.


The overarching objective of Climate Change Initiative – Anthropogenic Water Use (CCI-AWU) precursor project is to derive long-term (at least over twenty years, since early 2000s) AWU time series for selected regions using several approaches exploiting remote sensing observations, as a proof-of-concept of the feasibility towards a proper AWU ECV product. In particular, AWU is more specifically intended as agricultural water allocated for irrigation, which represents the largest anthropogenic water use, thus making irrigation being the most impactful human activity on the hydrological cycle.

About the project

The monitoring of AWU dynamics has recently been enhanced through advancements in remote sensing technologies and Data Assimilation (DA) systems. Within this context, the CCI-AWU project aims to develop the AWU ECV by exploiting Earth Observations (EO) in the following ways:

  • Creating coarse-scale/long-term (e.g., 20 years) AWU datasets for selected pilot locations (e.g., regional/basin scale) to advance understanding of human disturbances on land-atmosphere interaction processes.
  • Assessing the capacity of coarse-scale (mainly microwave) EO data to inform irrigation retrieval algorithms and advanced land surface models for irrigation applications, thereby advancing the closure of the water cycle from a climate perspective.
  • Developing tools and techniques to properly characterize the uncertainties associated with each AWU dataset. This includes validating AWU products against benchmark data and cross-comparing developed products to enhance uncertainty characterization.
  • Utilizing AWU estimates to: (1) enhance agro-hydrological modeling and provide insights into water management practices, (2) facilitate closure of the water budget, and (3) conduct a comprehensive comparison between satellite-derived and model-derived AWU estimates.