Challenging a Global Land Surface Model in a Local Socio-Environmental System

Kyla M Dahlin,Danica L Lombardozzi,Michael Abraha,David E Reed,Jiquan Chen,Gabriela Shirkey,Donald Akanga,Cheyenne Lei

doi:10.3390/land9100398

Abstract

Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

Highlights

Across much of the world, human impacts are an important component of the land surface [1].Growth and loss of urban populations [2], changes in certain land areas and habitability [3], and changing agricultural and forestry practices [4,5] have all changed the ways in which energy and matter move through and among landscapes
When comparing the time series, the average annual NOAA values were significantly correlated with the Community Land Model (CLM) values (Figure 3A,B)
We show that the CLM plant functional types (PFTs) distribution, adapted from Land Use Harmonized version 2 (LUH2), does a good job of generally representing the crop fraction in this region in recent decades (~40%), but underestimates the amount of forests and urban areas, assuming instead that a large fraction of this landscape is grass

Summary

Introduction

Across much of the world, human impacts are an important component of the land surface [1].Growth and loss of urban populations [2], changes in certain land areas and habitability [3], and changing agricultural and forestry practices [4,5] have all changed the ways in which energy and matter move through and among landscapes. In an Earth system modeling context, human impacts are mostly represented as changes in the footprint of urban areas and the parameters of those urban areas, changes in the extent and types of agricultural practices, changes in nitrogen deposition, and changes in global atmospheric CO2 concentration [6]. These changes over time can in turn impact water movement through a landscape [7], albedo and heat distribution [8], and the carbon cycle through agricultural practices (e.g., tilling, irrigation, fertilization; [4]), nitrogen fertilization [9], and land conversion [10]. LSMs can be coupled to climate, sea ice, and ocean models to create Earth system models (ESMs), which represent all of the Earth’s major biophysical processes using only a few external inputs—typically incoming solar radiation, atmospheric CO2 and land use/land cover changes [13]

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Discussion

Conclusion