Soil Moisture Data Assimilation to Estimate Irrigation Water Use

Kumar, Sujay V.; Small, Eric; Livneh, Ben; Abolafia-Rosenzweig, Ronnie

Citeable URL: https://scholar.colorado.edu/concern/articles/f7623d608

Abstract

Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different conﬁgurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm−3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle ﬁlter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance. Plain Language Summary Irrigated agriculture is the world's largest consumer of global freshwater producing more than 40% of global food, yet the amount of water being used in irrigation remains largely unknown. This paper presents and evaluates a new method to estimate the amount of water used in irrigation that involves giving computer models of the land surface different amounts of information on soil moisture and then evaluating how well irrigation can be predicted. We show that the method can accurately predict daily irrigation magnitude so long as the model simulation of soil moisture is closely in line with observations. The method is also generally robust to common sources of error in a NASA satellite‐based soil moisture. However, when differences between simulated soil moisture from operational models and satellite‐based soil moisture are too large, then the method will require pre‐ or post‐processing to correct errors between the two sources. This study provides a useful step toward producing new estimates of irrigation while highlighting the importance of improving the realism of simulated soil moisture.

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