Results for “land surface model” — Page 14

DOE OSTI2022

Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

Spatial heterogeneity in methane (CH 4 ) flux requires a reliable upscaling approach to reach accurate regional CH 4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH 4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH 4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH 4 flux in early growing seasons. The simulated monthly CH 4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m −2 s −1 and 0.782 using the DF algorithm, but 0.007 μmol m −2 s −1 and 0.758 using HF and 0.007 μmol m −2 s −1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH 4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH 4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH 4 flux for all five tower domains despite relatively weak differences in simulated CH 4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH 4 flux at both plot and landscape scales at a high temporal resolution, which should be applied to other landscapes. Integrating land surface models with an appropriate algorithm provides a powerful tool for upscaling CH 4 flux in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Mapping wall-to-wall fractional cover of Arctic tundra plant functional types in Alaska using 20-m spatial resolution satellite imagery and harmonized plot observations

Estimates of fractional cover (fCover) across given land surfaces are used to assess, and often model, vegetation composition and diversity, which are crucial for understanding the health and functioning of terrestrial ecosystems. Remote sensing provides a useful means for scaling local, plot-measured fCover estimates to regional scales. Leveraging a recently synthesized and harmonized plot database, this study generated wall-to-wall maps of fCover for six Alaskan-Arctic plant functional types (PFT), including non-vascular plants, forbs, graminoids, and deciduous and evergreen shrubs, using 20-m satellite data (Sentinel-1, Sentinel-2, ArcticDEM) using a machine learning regression approach, specifically the random forest (RF) algorithm, which is well-suited for handling nonlinear relationships and high-dimensional satellite datasets. This study additionally addressed the spatio-temporal inconsistencies e.g., sampling scale, plot size, and collection year in plot measured fCover by adopting a multivariate outlier detection approach—Cook’s distance—to identify high-quality plots for model training and validation. Our approach achieves high accuracy (R 2 = 0.59–0.93, root mean squared errors = 0.02–0.10 for all PFTs) between plot-observed and satellite-derived fCover when using high-quality plot samples. The mapped fCover characterizes the spatial patterns of different PFTs across the tundra biome at a 20-m resolution, providing key information needed for improved representation of Arctic tundra vegetation in terrestrial biosphere models to better understand climate-vegetation feedback across the Arctic tundra.

Arctic tundra↗

DOE OSTI2020

Relating land surface, water table, and weathering fronts with a conceptual valve model for headwater catchments

Knowing little about how porosity and permeability are distributed at depth, we commonly develop models of groundwater by treating the subsurface as a homogeneous black box even though porosity and permeability vary with depth. One reason for this depth variation is that infiltrating meteoric water reacts with minerals to affect porosity in localized zones called reaction fronts. We are beginning to learn to map and model these fronts beneath headwater catchments and show how they are distributed. The subsurface landscapes defined by these fronts lie subparallel to the earth-air interface but with lower relief. They can be situated above, below, or at the water table. These subsurface landscapes of reaction are important because porosity developed from weathering can control subsurface water storage. In addition, porosity often changes at the weathering fronts, and when this affects permeability significantly, the front can act like a valve that re-orients water flowing through the subsurface. We explore controls on the positions of reaction fronts under headwater landscapes by accounting for the timescales of erosion, chemical equilibration, and solute transport. One strong control on the landscape of subsurface reaction is the land surface geometry, which is in turn a function of the erosion rate. In addition, the reaction fronts, like the water table, are strongly affected by the lithology and water infiltration rate. We hypothesize that relationships among the land surface, reaction fronts, and the water table are controlled by feedbacks that can push landscapes toward an “ideal hill”. In this steady state, reaction-front valves partition water volumes into shallow and deep flowpaths. Furthermore, these flows dissolve low- and high-solubility minerals, respectively, allowing their reaction fronts to advance at the erosion rate. This conceptualization could inform better models of subsurface porosity and permeability, replacing the black box.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska

The availability of supercomputers and state-of-science datasets has made it possible to conduct large-scale land simulations at an ultrahigh-resolution. This study reported a computational framework for land surface simulation using the E3SM land model (ELM) at an unprecedented resolution (1 km x 1 km gridcell). The ultrahigh-resolution ELM (uELM) simulation framework includes three parts: (1) high-resolution atmospheric forcing and surface properties dataset generation, (2) massive gridcell-based simulation, and (3) large-scale simulation results analysis. Additionally, we implemented the uELM simulation framework and completed the first 1 km x 1 km terrestrial ecosystem simulation (from 1850 to 2014) over the Seward Peninsula in Alaska (78,000 km 2 ). The experiment contained two phases: a spin-up simulation and a transient simulation, and required five weeks of calculations using 320 cores in a 44-node Linux HPC computer. It created approximately 1.3 TB of data from the transient simulation alone (1850 - present). We selected sample results (monthly and daily simulation outputs) to illustrate the temporal and spatial variations of several variables in high-latitude Arctic ecosystems’ water, energy, and carbon cycles. At last, we summarized the lessons learned and proposed new developments for full-scale uELM simulations over the entire North American continent (approximately 22,000,000 km 2 ).

54 ENVIRONMENTAL SCIENCES↗

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 OSTI2022

Quantifying the City-Scale Impacts of Impervious Surfaces on Groundwater Recharge Potential: An Urban Application of WRF–Hydro

Decades of urbanization have created sprawling, complex, and vulnerable cities, half of which are located in water-scarce areas. With the looming effects of climate change, including increasing droughts and water shortages, there is an urgent need to better understand how urbanization impacts the water cycle at city scale. Impervious surfaces disrupt the natural flow of water, affecting groundwater recharge in water-scarce cities, such as Los Angeles, looking to harness local water resources. In the face of growing water demand, informing on opportunities to maximize potential groundwater recharge can help increase cities’ resilience. WRF–Hydro, a physics-based hydrological modeling system, capable of resolving atmospheric, land surface, and hydrological processes at city scale, is adapted to represent urban impervious surfaces. The modified model is used to assess the hydrological implications of historical urbanization. Pre- and post-urban scenarios are used to quantify the impacts of impervious surfaces on the local water budget. Our results show that urbanization in LA has vastly decreased the potential for groundwater recharge, with up to half of the water inflow being redirected from infiltration in highly urbanized watersheds, while doubling surface runoff’s share of the city’s water budget, from ~15% to 30%. This study not only sheds light on the role of imperviousness on groundwater recharge in water-scarce cities, but also offers a robust and transferable tool for the management of urban land and water resources.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona

Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Using Homogenized Models to Explore the Effect of Fracture Densities on Weathering

Despite its importance, only a few researchers have incorporated the effects of fracturing into models of reactive transport for rock weathering. Here we explore 2D simulations that describe weathering under conditions of diffusive and advective transport within heterogeneous media consisting of rocky blocks and fractures. In our simulations, the Darcy velocities vary in space and time and depend on weathering processes within the rock matrix. We explore simulations with saturated and unsaturated flow for weathering bedrock that consists of blocks separated by inert or weathered material. The simulations show that a simplified homogenized model can approximate exact solutions for some of the simulated columns and hills and can allow exploration of coupling between flow and reaction in fractured rock. These hillslope simulations document that, even in the presence of 2D water flow, i) an increase in fracture density results in faster weathering advance rates; and ii) the water table locates deeper for a rock system that is weathered and fractured rather than weathered and unfractured. Some of these patterns have also been observed for natural systems. But these simulations also highlight how simplified models that do not use appropriate averaging of heterogeneities can be inaccurate in predicting weathering rate for natural systems. For example, if water flows both vertically and laterally through the vadose zone of a hill, then a prediction of the depth of regolith that is based on modeling strictly unidirectional downward infiltration will be unrealistically large. Likewise, if the fracture density observed near the land surface is used in a model to predict depth of weathering for a system where the fracture density decreases downward, the model will overestimate regolith depth. Learning how to develop accurately homogenized models could thus enable better conceptual models and predictions of weathering advance in natural systems.

58 GEOSCIENCES↗

DOE OSTI2024

Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales

Evapotranspiration (ET) is a pivotal component in catchment-scale water balance and is essential for informed watershed management. Nevertheless, uncertainties in ET observation or modeling have been hindering effective water resources management. This study addresses this gap by establishing a robust, generalized linear relationship between ET and gross primary productivity (GPP) at the catchment scale. We test the linearity of the relationships between monthly GPP and ET data at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., yielding Pearson’s r ≥ 0.6 for 97% of the 380 catchments. We then develop a regionalization strategy to parameterize this GPP-ET relationship at the catchment scale by identifying and utilizing the linkages between the parameter values and extensively available hydroclimatic and landscape data. We demonstrate the efficacy of the proposed GPP-ET relationship and parameter regionalization strategy by their combined predictive capacity, where the predicted monthly GPP matches well with remote-sensing-based GPP product, achieving Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. In addition, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, suggesting that the new relationship is transferable across the site, catchment, and regional scales. Furthermore, our findings are valuable for improving remote-sensing-based estimation of monthly ET and diagnosing coupled water–carbon simulations in land surface and Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Lake Huron Enhances Snowfall Downwind of Lake Erie: A Modeling Study of the 2010 New Year’s Lake Effect Snowfall Event

In the Northeast and Great Lakes regions of the United States, the influence of multiple lakes on the overlying air can greatly affect lake-effect snowfall over downwind communities. To assess the impact of Lake Huron on snowfall downwind of Lake Erie, we simulated a lake-effect snow event which occurred from 1 to 6 January 2010 using the Regional Atmospheric Modeling System (RAMS). We found that the presence of Lake Huron enhances snowfall downwind of Lake Erie by almost 20% and leads to much heavier local snowfall totals than when Lake Huron is not present. This increase in snowfall is due to a lake-to-lake (L2L) convective band, as secondary circulations associated with lake-effect convection form over Lake Huron and persist overland between the lakes before reintensifying over Lake Erie. As these secondary circulations move over Lake Erie, lifting within the upward branch of the circulations accelerates the development of convection within the L2L band. Surface fluxes and convection over Lake Huron deepen the boundary layer, promoting deeper vertical development of this L2L band over Lake Erie. However, although this boundary layer modification strengthens the L2L band, we found that it actually reduced lake-effect snowfall produced by wind-parallel bands (WPB) in other parts of the lake. This indicates that boundary layer modification from upstream lakes may impact L2L bands differently than WPB which are not connected to upstream secondary circulations.

Lake effects↗

DOE OSTI2020

The Role of Subsurface Flow on Evapotranspiration: A Global Sensitivity Analysis

Abstract Water resources are impacted by water‐energy balance fluxes at the land surface, most notably evapotranspiration ( ET ), the largest component of surface energy balance. While integrated hydrologic models show promise in quantifying the nonlinear dynamics at this interface, the model results are plagued by parametric uncertainties. Given the high computational demand of running multiple parameter spaces of these models, little is known about how these uncertainties propagate into land surface processes. In this work, we perform a global sensitivity analysis by computing Sobol and AMAE indices using a surrogate model constructed with a polynomial chaos expansion to assess the impacts of the subsurface physical properties on ET . We do so by modeling a semisynthetic test case. Our results show that the effects of vertical hydraulic conductivity, porosity, and the water retention curve parameter Van Genuchten α mainly control ET . However, we note that while evaporation ( E ) shows behavior similar to the ET with high sensitivity to the parameters controlling the flow in the unsaturated zone, transpiration ( T ) is very sensitive to the saturated zone parameters and groundwater flow, especially during periods without rain. Our results also show that heterogeneities in land cover impact the flow in the saturated zone. Furthermore, our work demonstrates that reduced‐order models can be developed to make integrated models more accessible for rigorous sensitivity analyses and calibration purposes.

Maina, Fadji Zaouna↗

DOE OSTI2022

Aridity-Dependent Land Surface Skin Temperature Biases in CMIP5/6

Land surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, in this study, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite-based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi-arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land-atmosphere interactions. These biases need to be corrected or considered while using models for future projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Abstract Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km 2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

Science & Technology - Other Topics↗

DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗

DOE OSTI2022

MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks

Endorheic drainage basins, those inland basins not connected directly to ocean, are essential for hydrological modeling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. Within many hydrological model frameworks, river basins are defined by digital river networks through their flow direction and connectivity datasets. Here we present an improvement to gridded flow direction data and its derivatives produced from upscaled global 5 and 15 arc minute MERIT networks. We explicitly label endorheic and exorheic drainage basins and alter the delineation of endorheic basins by merging small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion of those basins since most of the merged catchments were inside other larger endorheic areas. We developed and present here the endorheic basin delineation method. This method performs an analysis of the contributing river and basin geometry relative to the location of the flow end point (i.e. potential endorheic lake), proximity of the latter to the drainage basin boundary and the elevation difference between the basin's lowest point and potential spillover location at the basin boundary. The new digital river network was validated using the University of New Hampshire Water Balance Model by comparing the water balance of endorheic inland depressions with modeled accumulation of water in their inland lakes based on the observed historical climate drivers used by WBM.

Earth Systems↗

DOE OSTI2023

MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks

Endorheic drainage basins, those inland basins not connected directly to ocean, are essential for hydrological modeling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. Within many hydrological model frameworks, river basins are defined by digital river networks through their flow direction and connectivity datasets. Here we present an improvement to gridded flow direction data and its derivatives produced from upscaled global 5 and 15 arc minute MERIT networks. We explicitly label endorheic and exorheic drainage basins and alter the delineation of endorheic basins by merging small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion of those basins since most of the merged catchments were inside other larger endorheic areas. We developed and present here the endorheic basin delineation method. This method performs an analysis of the contributing river and basin geometry relative to the location of the flow end point (i.e. potential endorheic lake), proximity of the latter to the drainage basin boundary and the elevation difference between the basin's lowest point and potential spillover location at the basin boundary. The new digital river network was validated using the University of New Hampshire Water Balance Model by comparing the water balance of endorheic inland depressions with modeled accumulation of water in their inland lakes based on the observed historical climate drivers used by WBM.

Earth Systems↗

DOE OSTI2023

MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks

Endorheic drainage basins, those inland basins not connected directly to ocean, are essential for hydrological modeling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. Within many hydrological model frameworks, river basins are defined by digital river networks through their flow direction and connectivity datasets. Here we present an improvement to gridded flow direction data and its derivatives produced from upscaled global 5 and 15 arc minute MERIT networks. We explicitly label endorheic and exorheic drainage basins and alter the delineation of endorheic basins by merging small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion of those basins since most of the merged catchments were inside other larger endorheic areas. We developed and present here the endorheic basin delineation method. This method performs an analysis of the contributing river and basin geometry relative to the location of the flow end point (i.e. potential endorheic lake), proximity of the latter to the drainage basin boundary and the elevation difference between the basin's lowest point and potential spillover location at the basin boundary. The new digital river network was validated using the University of New Hampshire Water Balance Model by comparing the water balance of endorheic inland depressions with modeled accumulation of water in their inland lakes based on the observed historical climate drivers used by WBM.

Earth Systems↗

DOE OSTI2023

MERIT-Plus Dataset: Delineation of endorheic basins in 5 and 15 min upscaled river networks

Endorheic drainage basins, those inland basins not connected directly to ocean, are essential for hydrological modeling of global and regional water balances, land surface water storage, gravity anomalies, sea level rise, etc. Within many hydrological model frameworks, river basins are defined by digital river networks through their flow direction and connectivity datasets. Here we present an improvement to gridded flow direction data and its derivatives produced from upscaled global 5 and 15 arc minute MERIT networks. We explicitly label endorheic and exorheic drainage basins and alter the delineation of endorheic basins by merging small inland watersheds to the adjacent host basins. The resulting datasets have a significantly reduced number of endorheic basins while preserving the total land portion of those basins since most of the merged catchments were inside other larger endorheic areas. We developed and present here the endorheic basin delineation method. This method performs an analysis of the contributing river and basin geometry relative to the location of the flow end point (i.e. potential endorheic lake), proximity of the latter to the drainage basin boundary and the elevation difference between the basin's lowest point and potential spillover location at the basin boundary. The new digital river network was validated using the University of New Hampshire Water Balance Model by comparing the water balance of endorheic inland depressions with modeled accumulation of water in their inland lakes based on the observed historical climate drivers used by WBM.

Earth Systems↗