Abstract

AbstractReliable accounting of agricultural water use is critical for sustainable water management. However, the majority of agricultural water use is not monitored, with limited metering of irrigation despite increasing pressure on both groundwater and surface water resources in many agricultural regions worldwide. Satellite remote sensing has been proposed as a low‐cost and scalable solution to fill widespread gaps in monitoring of irrigation water use in both developed and developing countries, bypassing the technical, socioeconomic, and political challenges that to date have constrained in situ metering. In this paper, we show through a systematic meta‐analysis that the relative accuracy of different satellite‐based irrigation water use monitoring approaches remains poorly understood, with evidence of large uncertainties when water use estimates are validated against in situ irrigation data at both field and regional scales. Subsequently, we demonstrate that water use measurement errors result in large economic welfare losses for farmers and may negatively impact ability of policies to limit acute and nonlinear externalities of irrigation abstraction on both the environment and other water users. Our findings highlight that water resource planners must consider the trade‐offs between accuracy and costs associated with different water use accounting approaches. Remote sensing has an important role to play in supporting improved agricultural water accounting—both independently and in combination with in situ monitoring. However, greater transparency and evidence is needed about underlying uncertainties in satellite‐based models, along with how these measurement errors affect the performance of associated policies to manage different short‐ and long‐term externalities of irrigation water use.

Highlights

  • Agriculture is the main sectoral user of freshwater worldwide

  • We show through a systematic meta-analysis that the relative accuracy of different satellite-based irrigation water use monitoring approaches remains poorly understood, with evidence of large uncertainties when water use estimates are validated against in situ irrigation data at both field and regional scales

  • Of the 26 studies that estimate irrigation water use, we find that only 7 compare estimates of irrigation water use against real-world in situ observations at field or farm scales (Figure 3) that are the primary unit of management for agricultural water use in most regions

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Summary

Introduction

Agriculture is the main sectoral user of freshwater worldwide. As competition over limited water resources increases, policymakers in many regions are seeking to limit agricultural water withdrawals and incentivize improvements in the productivity of irrigation water use. Economic, and political challenges associated with in situ metering, researchers and water managers are seeking alternative solutions for monitoring distributed agricultural water use to support regulation, planning, and management. One such approach is through use of satellite remote sensing. Errors in satellite-based monitoring and accounting of irrigation water use have potential to generate significant negative impacts on farmers (e.g., if their water use is overestimated leading to unfair restrictions or penalties) and water resource sustainability (e.g., if agricultural water users are incorrectly allowed to exceed intended abstraction limits). Our findings show that there are a number of technical and institutional challenges for use of remote sensing for water use monitoring and accounting and highlight the need for greater research focus on validation and assessment of trade-offs between monitoring accuracy and costs to support effective and judicious use of satellite data in agricultural water management and policy

Study Selection and Synthesis
Study Characteristics and Estimation Approaches
Sources of Measurement Error and Uncertainty
Economic and Hydrologic Impacts of Measurement Errors
Choice of Monitoring Approach Involves Trade-Offs Between Accuracy and Costs
Conclusions
Findings
Data Availability Statement
Full Text
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