The Noah-MP (Noah Multi-Physics or Multi-Parameterization) land surface model (LSM) (https://ral.ucar.edu/model/noah-multiparameterization-land-surface-model-noah-mp-lsm) is a sophisticated, open-source tool widely used in hydrology and climate research, especially when it comes to water-related studies (https://github.com/NCAR/noahmp). Its main strength lies in its ability to simulate a variety of complex land surface processes, such as evapotranspiration, snowpack dynamics, soil moisture, and runoff, all of which are crucial for understanding water movement through the environment. Being open-source, Noah-MP is maintained by a global community of researchers and developers, making it both flexible and accessible for scientists across different domains. This community-driven effort ensures that the model remains up-to-date with the latest advancements in hydrological and climate science, while also benefiting from peer reviews and continuous improvements.
Noah-MP offers a detailed framework for simulating land surface processes with a variety of physics-based parameterizations to capture key elements like soil physics, groundwater, evapotranspiration (ET), and vegetation dynamics. Diving into the specifics, the model allows for fine-tuned control over the physical processes, making it particularly useful for water-related research (https://www.jsg.utexas.edu/noah-mp/).
Noah-MP provides several soil physics schemes to model the vertical movement of water through the soil column. The model includes options like the Richards equation, which simulates unsaturated water flow through soil based on the soil moisture content and hydraulic conductivity. This approach can account for soil texture and layer depth, making it useful for simulating infiltration, soil water retention, and groundwater recharge. Alternatively, a simpler free-drainage scheme is available for studies where detailed soil moisture dynamics are less critical. The ability to select different soil parameterizations allows researchers to focus on the processes most relevant to their particular study, whether it be large-scale hydrological cycles or localized agricultural water use.
Noah-MP’s treatment of groundwater is also highly customizable. It supports the simulation of both shallow and deeper groundwater systems. For shallow groundwater, the model integrates dynamic water table depth to capture the exchange of water between the soil column and underlying aquifers. This is particularly useful in regions where groundwater is a critical component of the hydrological cycle or where interactions between surface water and groundwater significantly influence water availability. The model can simulate how changes in precipitation, surface runoff, and soil moisture affect the recharge and depletion of aquifers. This feature is vital for long-term water management studies, particularly in areas dependent on groundwater for irrigation or municipal water supplies.
The model provides multiple ways to simulate ET, with particular emphasis on the control exerted by surface properties like roughness length. Noah-MP includes parameterizations such as the Monin-Obukhov similarity theory, which is used to calculate turbulent fluxes of heat and moisture based on atmospheric stability and surface roughness. Roughness length, in turn, is influenced by the vegetation type, surface moisture, and terrain, which directly affect how water and energy are transferred between the land surface and the atmosphere. In addition to Monin-Obukhov, Noah-MP also supports the Chen 97 scheme, which calculates surface resistance and aerodynamic resistance based on vegetation parameters, making it more suitable for regions where vegetation cover plays a significant role in controlling ET. This option could allow researchers to simulate how changes in vegetation affect water loss to the atmosphere.
One of the advanced features of Noah-MP is its support for dynamic vegetation, which allows for real-time simulation of vegetation changes in response to climatic conditions. Vegetation not only affects ET through transpiration but also influences roughness length and albedo, both of which are important for energy fluxes at the land-atmosphere interface. Dynamic vegetation schemes in Noah-MP can account for processes like leaf area growth, carbon allocation, and vegetation mortality, making it useful for long-term climate impact studies where land cover changes might influence water availability. By simulating the seasonal cycles of vegetation (e.g., leaf-out and senescence), Noah-MP provides a more realistic representation of how ecosystems interact with water and energy cycles, particularly under changing climate conditions.
One of the key advantages of Noah-MP is its modular design, which allows it to couple with a wide array of atmospheric and hydrological models, such as the Weather Research and Forecasting (WRF) model. This makes it scalable and highly adaptable for different applications, from small watershed studies to large-scale climate simulations. In ALICE-LAB, for instance, we leverage Noah-MP’s capabilities to conduct high-resolution simulations on supercomputers, which enables us to explore water dynamics over vast regions with unprecedented detail. The model’s core is written in Fortran, a programming language known for its computational efficiency, and it supports parallel computing, allowing simulations to run faster and handle large datasets. This is particularly useful when modeling complex water systems over long periods, as it significantly reduces computational time without sacrificing accuracy.
In ALICE-LAB, Noah-MP is used to simulate hydrometeorological variables at high spatial resolution (e.g., 1 km) across various regions globally. The model is driven by meteorological forcing data from ECMWF Reanalysis Version 5 (ERA-5; https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). We spin up the model for approximately 400 years to ensure that all initial states reach equilibrium, thereby minimizing potential biases in the simulation results. The specific parameter configurations and physical processes applied is described below.
ALICE-LAB: Asian Land Information for Climate and Environmental Research Laboratory
PCR-GLOBWB, developed by Utrecht University, models global water cycles and includes human water use, making it invaluable for studying water scarcity and sustainability. ALICE-LAB leverages this model with GRACE satellite data to assess global water resources and inform climate resilience strategies.
WRF-Hydro integrates weather and hydrological data to simulate streamflow, crucial for flood forecasting and water management. ALICE-LAB uses it to generate high-resolution, 1-km streamflow forecasts in Thailand, enhancing flood preparedness and water resource planning.