Coupling Biocrusts and Vegetation Dynamics to Improve Predictions of Dryland Change

Dr. Zhang, Yu – LANL/NMC Research Scientist

(1) Develop deep learning approaches to detect and characterize biocrust communities using remote sensing data and create the first high resolution, large-scale biocrust database for the drylands in the southwest U.S.
(2) Develop a coupled biocrust-vegetation model, and use this model to identify the spatial signature of vegetation patterning signaling impending ecosystem change and to predict consequent dryland changes to climatic forces.

This NSF sponsored project lead by UC Davis

Collaborative Research: Testing Controls on Source, Sink, and Lifetime of Atmospheric Water withNumerical Tags and Stable Isotope Ratios

Dr. Fiorella, Richard – NMC Research Scientist, LANL Research Scientist

Work seeks to establish direct, causal relationships between the physical mechanisms relevant to isotopic composition and the actual composition of water vapor and precipitated water. A new “water tagging” capability is developed which allows simulated water vapor to record information about conditions under which it evaporated.
This project is sponsored by the National Science Foundation (NSF)
Hydrology Project

Experimental Framework for Testing the National Water Model: Operationalizing the Use of Snow Remote Sensing in Alaska

Katrina Bennett, NMC Research Scientist, University of Alaska Affiliate
Ryan Crumley, LANL Postdoctoral Researcher

Snow is an important driver to the hydrologic regime, and accurately observing snow is essential to understanding hydrological processes in Alaska. There is a fundamental lack of data to populate, test and validate models to simulate Alaskan snow regimes and, therefore, the physical mechanisms and process parameterizations that are important to predict Alaskan hydrology are not well under-stood. Improvements to the efficiency, effectiveness, and accuracy of obtaining observational datasets, paired with the knowledge and tools to fuse this data into models, will significantly enhance our ability to capture snowpack processes and simulate streamflow in Alaska, as well as in other cold region, data sparse areas around the country.

This project will develop a near-real-time data stream of simple, first order assimilations of remotely sensed snow cover extent and snow water equivalent data in Alaska and operationalize components of this work for use in NOAA’s Community Hydrologic Prediction System (CHPS) framework. We will then transfer this knowledge to the National Water Model (NWM) system and run experiments using the NWM model for key study watersheds representing a range of hydro-climatic regimes in Alaska.

The goal of these experiments is to validate NWM model skill and recommend physics and parameter improvements to better capture the processes critical to hydrologic prediction in Alaska, focused on the modeling challenges of deep snowpack, glaciers, and permafrost. These conditions are not common in the initial NWM CONUS implementation and therefore lack even a baseline evaluation.

This project is sponsored by NOAA

Photo by Katrina Bennett