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Zhi, Wei

Publications and source records attributed to Zhi, Wei.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

BioRT‐HBV 1.0: A Biogeochemical Reactive Transport Model at the Watershed Scale

Abstract Reactive Transport Models (RTMs) are essential tools for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent watershed‐scale RTMs have integrated ecohydrological and biogeochemical interactions between surface and subsurface. These emergent, watershed‐scale RTMs are often spatially explicit and require extensive data, computational power, and computational expertise. There is however a pressing need to create parsimonious models that require minimal data and are accessible to scientists with limited computational background. To that end, we have developed BioRT‐HBV 1.0, a watershed‐scale, hydro‐biogeochemical RTM that builds upon the widely used, bucket‐type HBV model known for its simplicity and minimal data requirements. BioRT‐HBV uses the conceptual structure and hydrology output of HBV to simulate processes including advective solute transport and biogeochemical reactions that depend on reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. The model uses time series of weather (air temperature, precipitation, and potential evapotranspiration) and initial biogeochemical conditions of subsurface water, soils, and rocks as input, and output times series of reaction rates and solute concentrations in subsurface waters and rivers. This paper presents the model structure and governing equations and demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, BioRT‐HBV can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous‐to‐measure subsurface, and their influence on the observed stream or river chemistry and solute export. With its parsimonious structure and easy‐to‐use graphical user interface, BioRT‐HBV can be a useful research tool for users without in‐depth computational training. It can additionally serve as an educational tool that promotes pollination of ideas across disciplines and foster a diverse, equal, and inclusive user community.

Sadayappan, Kayalvizhi↗
DOE OSTI2024

Hydrology Outweighs Temperature in Driving Production and Export of Dissolved Carbon in a Snowy Mountain Catchment

Abstract Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed‐scale reactive‐transport model BioRT‐HBV and stream data from a snow‐dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m 2 /year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m 2 /year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m 2 /year, compared to 0.12 ± 0.02 gC/m 2 /year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m 2 /year) than in the deep subsurface (0.36 ± 0.02 gC/m 2 /year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly‐observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often‐overlooked but growing importance of deeper processes in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

58 GEOSCIENCES↗
DOE OSTI2022

BioRT-Flux-PIHM v1.0: a biogeochemical reactive transport model at the watershed scale

Watersheds are the fundamental Earth surface functioning units that connect the land to aquatic systems. Many watershed-scale models represent hydrological processes but not biogeochemical reactive transport processes. This has limited our capability to understand and predict solute export, water chemistry and quality, and Earth system response to changing climate and anthropogenic conditions. Here we present a recently developed BioRT-Flux-PIHM (BioRT hereafter) v1.0, a watershed-scale biogeochemical reactive transport model. The model augments the previously developed RT-Flux-PIHM that integrates land-surface interactions, surface hydrology, and abiotic geochemical reactions. It enables the simulation of (1) shallow and deep-water partitioning to represent surface runoff, shallow soil water, and deeper groundwater and of (2) biotic processes including plant uptake, soil respiration, and nutrient transformation. The reactive transport part of the code has been verified against the widely used reactive transport code CrunchTope. BioRT-Flux-PIHM v1.0 has recently been applied in multiple watersheds under diverse climate, vegetation, and geological conditions. This paper briefly introduces the governing equations and model structure with a focus on new aspects of the model. It also showcases one hydrology example that simulates shallow and deep-water interactions and two biogeochemical examples relevant to nitrate and dissolved organic carbon (DOC). These examples are illustrated in two simulation modes of complexity. One is the spatially lumped mode (i.e., two land cells connected by one river segment) that focuses on processes and average behavior of a watershed. Another is the spatially distributed mode (i.e., hundreds of cells) that includes details of topography, land cover, and soil properties. Whereas the spatially lumped mode represents averaged properties and processes and temporal variations, the spatially distributed mode can be used to understand the impacts of spatial structure and identify hot spots of biogeochemical reactions. The model can be used to mechanistically understand coupled hydrological and biogeochemical processes under gradients of climate, vegetation, geology, and land use conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Learning for Hydro-Biogeochemistry Processes

Focal Area(s): The white paper focuses on areas 2 and 3. The proposed deep learning modeling approach can provide forecasting capabilities and mechanism-based understanding of terrestrial and aquatic hydro- biogeochemical processes at watershed and river basin scales. Knowledge gleaned here can also provide insights for Earth System Models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗