Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use. (4th November 2022)

• Record Type: Journal Article
• Authors: Nie, Wanshu; Kumar, Sujay V.; Peters‐Lidard, Christa D.; Zaitchik, Benjamin F.; Arsenault, Kristi R.; Bindlish, Rajat; Liu, Pang‐Wei
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-04
• Subjects: irrigation -- data assimilation -- leaf area index -- Noah‐MP; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2022MS003040
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on‐ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor‐based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo −1 and 10–82 g m −2 mo −1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation‐informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states. Plain Language Summary: Agricultural irrigation accounts for more than 70% of freshwater use over the globe and can impact local and regional water resources, crop productivities, and climate and weather systems. Investigating impact of irrigation heavily relies on models, which vary in terms of modeling structure, input data sources, assumptions, etc. Given these variations, models are often subject to large uncertainties in estimating irrigation water use. The goal of this study is to explore the potential to integrate satellite observations of vegetation conditions with models to improve estimates of irrigation and its impact across the Contiguous United States. We find that integrating satellite observations is helpful in correcting simulation of vegetation growth, leading to a better estimation of irrigation water use and its impact on surface soil moisture, evapotranspiration, and agricultural productivities. These results underscore the effectiveness of using satellite vegetation observations to improve irrigation modeling. Key Points: Moderate Resolution Imaging Spectroradiometer leaf area index (LAI) data are assimilated into the Noah‐MP land surface model to inform simulation of irrigation schedules and volumes Irrigation simulations without LAI assimilation overestimate irrigation amount and increase the BIAS for evapotranspiration (ET) and GPP Assimilating LAI to constrain irrigation shows the overall best performance for surface soil moisture, ET, and GPP