Abstract

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015) method) and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort.

Highlights

  • Land surface models are widely used tools in simulating and predicting the Earth’s water and energy budgets over a wide range of spatiotemporal scales (Rodell et al, 2004; Haddeland et al, 2011; Getirana, 2014; Xia et al, 2012a, b, Xia et al, 2016a, b)

  • This division was later adopted by Xia et al (2012a) and Tian et al (2014) when land surface models were evaluated over the conterminous US (CONUS)

  • Land surface models are capable of capturing the large-scale pattern of ET, significant biases were found at finer spatiotemporal scales (Parr et al, 2015, 2016; Wang et al, 2016), which propagate to influence other components of the hydrological cycle including runoff and soil moisture (Parr et al, 2015)

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Summary

Introduction

Land surface models are widely used tools in simulating and predicting the Earth’s water and energy budgets over a wide range of spatiotemporal scales (Rodell et al, 2004; Haddeland et al, 2011; Getirana, 2014; Xia et al, 2012a, b, Xia et al, 2016a, b). Wang et al.: Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5 mann and Saulo, 2015), analyzing soil moisture variability (Cheng et al, 2015), studying the impact of soil moisture on dust outbreaks (Kim and Choi 2015), and improving the data quality of in situ soil moisture observations (Dorigo et al, 2013; Xia et al, 2015) These model-based estimates of land surface fluxes and state variables are considered an important surrogate for observations, as observational data for some components of the global water and energy cycles are scarce in many regions of the world, and lack spatial and temporal continuity where they do exist. Land surface models are subject to large uncertainties. Haddeland et al (2011) compared 11 models in simulating evapotranspiration (ET), and found that the global ET on the land surface ranges from 415 to 586 mm yr−1 and that the runoff ranges from 290 to 457 mm yr−1. Xia et al (2012a, b, 2016a, b) documented a large disparity among the four models in NLDAS phase 2 (NLDAS-2) at both the continental and basin scales, and showed that the Mosaic and Sacramento Soil Moisture Accounting (SAC-SMA) models tend to overestimate ET whereas the Noah and Variable Infiltration Capacity (VIC) models tend to underestimate ET

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