Model Library

Dynamic Land Ecosystem Model (DLEM)

Model name: Dynamic Land Ecosystem Model (DLEM)

Developed by: researchers at the Ecosystem Dynamics and Global Ecology Laboratory, Auburn University, with significant contributions from Dr. Xueping Lu, Dr. Shuxian Li, and their team (Last update: 2023)

Model type: Distributed, deterministic, process-based, terrestrial ecosystem watershed model

Computational requirements: Windows/Linux

Software requirements: GIS: optional

Link to download model

  • Contact: Dr. Zihao Bian (tianhan@auburn.edu)

Capabilities and Limitations:

Capabilities

  • DLEM simulates water flow and nitrogen (N) fluxes from various land ecosystems (crops, grasslands, forests) to streams and rivers (Zhang et al., 2022);
  • DLEM considers dissolved organic N, dissolved inorganic N, and particulate organic N (Zhang et al., 2022, Liu et al., 2013; Lu et al., 2018; Tian et al., 2010; Yu et al., 2020);
  • DLEM simulates carbon and nitrogen cycling, water balances, and vegetation dynamics across managed ecosystems, such as agricultural lands (Zhang et al., 2022);
  • DLEM evaluates agricultural practices, such as fertilizer application, tile drainage, and crop yield improvements (Lu et al., 2018).

Limitations

  • In DLEM, snowfall does not directly induce N leaching or loading but affects soil moisture in early spring due to melting, altering water and N yield; this delayed effect also applies to extreme snowfall (Zhang et al., 2022);
  • DLEM may neglect erosion processes beyond overland flow, such as gully erosion, landslides, and floodplain erosion (Bian et al., 2023);
  • DLEM ignores within-grid spatial variability (Bian et al., 2023);
  • DLEM simplifies the transport of groundwater and soil N exchange with groundwater (Terziotti et al., 2018; Pan et al., 2017).
  • DLEM does not simulate vegetation dynamics under a changing chemical climate, or forest species shift (Sun et al., 2023).

Model Inputs and Outputs:

Inputs

  • River network, Land use, Soil data, Meteorological data, Atmospheric compositions, Geomorphology data, Water quality data.

Outputs

The model simulates CO2-related parameters, non-CO2 GHG related parameters, hydrological (surface and subsurface runoff, evapotranspiration, soil moisture, and river discharge) parameters, and Nitrogen-related parameters.

Examples:

References

Zhang, J., Lu, C., Crumpton, W., Jones, C., Tian, H., Villarini, G., et al. (2022). Heavy precipitation impacts on nitrogen loading to the Gulf of Mexico in the 21st century: Model projections under future climate scenarios. Earth's Future, 10, e2021EF002141. https://doi.org/10.1029/2021EF002141

Li, W., Chen, X., Xu, S., et al. (2024). Effects of storm runoff on the spatial–temporal variation and stratified water quality in Biliuhe Reservoir, a drinking water reservoir. Environmental Science and Pollution Research, 31, 19556–19574. https://doi.org/10.1007/s11356-024-32431-w

Objectives

The goals of this study are to (a) examine the patterns of future heavy precipitation across the MARB under two climate scenarios; (b) estimate the contributions of annual total precipitation (TP), HP events, non-HP events, and no-precipitation days to water yield and N yield across the MARB by the end of the century; and (c) identify the spatial hotspots of increases in water yield and N yield driven by precipitation intensity change.

The objectives of the study were to understand long-term riverine nitrogen dynamics under external forces, identify main contributors to nitrogen loading, evaluate the efficacy of water pollution control policies, and use a model to quantify nitrogen exports from the Chesapeake Bay Watershed while analyzing the contributions of various environmental factors.

Other resources: DLEM-CNP model extends the Dynamic Land Ecosystem Model (DLEM) by integrating P processes and C–N–P interactions (Bian et al., 2022).