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DOE OSTI2020

NNSS Soils Monitoring: Plutonium Valley (CAU 366) FY2019

Desert Research Institute (DRI) conducted a field assessment of the potential for radionuclide-contaminated soil to be transported from the Plutonium Valley Dispersion Sites Contamination Area (CA) because of both wind and storm water runoff. This activity supported U.S. Department of Energy (DOE) Environmental Management Nevada Program (EM NV) efforts to establish post-closure monitoring plans for the Plutonium Valley Dispersion Sites Corrective Action Unit (CAU) 366. The DRI task was intended to identify the likely mechanism(s) of transport and determine the meteorological conditions that might cause the movement of radionuclide-contaminated soils. The emphasis of the work was on collecting sediment transported by channelized storm runoff and measuring airborne dust concentrations and associated wind conditions. These data will facilitate an appropriate post-closure monitoring program. In 2011, DRI installed meteorological monitoring stations north and south of the Plutonium Valley CA—known as Pu Valley North and Pu Valley South, respectively—as well as a fluvial sediment sampling station within the CA. Since installation, temperature, wind speed, wind direction, relative humidity, precipitation, solar radiation, barometric pressure, soil temperature, volumetric soil moisture content, and airborne particulate concentrations have been collected at both meteorological stations. The maximum, minimum, and average or total (as appropriate) for each of these parameters were recorded for each 10-minute interval. The sediment sampling station included an automatically activated sampling pump with collection bottles for suspended sediment, which was activated when sufficient flow was present in the channel, and passive traps for bedload material that was transported down the channel during runoff events. This report presents data collected from these stations during fiscal year (FY) 2019. During the FY2019 (October 1, 2018, through September 30, 2019) reporting period, the warmest month was July and the coldest month was February. Monthly total solar radiation was highest in June or July (depending on the station) and lowest in December. Monthly mean wind speeds were highest during February. At the Pu Valley North station, winds were commonly northerly, northwesterly, and southerly. At the Pu Valley South station, the wind direction was more variable and winds frequently came from most directions, excluding westerly and northwesterly winds. Winds above 15.0 miles per hour (mph) (24.1 kilometers per hour [km/hr]) were frequently southerly at both stations, and less-frequent strong winds were northerly (at the Pu Valley South station) or northwesterly (at the Pu Valley North station). Monthly average relative humidity ranged from 18 percent in August to 62 percent in January and February. Monthly total precipitation ranged from zero at both stations in September to 2.23 inches (in) (56.6 millimeters [mm]) during February at the Pu Valley South station and 2.09 in (53.1 mm) during March at the Pu Valley North station. From October 1, 2018, through September 30, 2019, the total precipitation was 8.23 in (209 mm) and 8.15 in (207 mm) at the Pu Valley South and Pu Valley North stations, respectively. The largest daily precipitation totals of 1.46 in (37.1 mm) and 1.14 in (29.0 mm) occurred at the Pu Valley South and Pu Valley North stations, respectively, on March 6, 2019. However, the ISCO autosampler was not activated during any storm event in FY2019 because the channel water depth measurements were insufficient for suspended sediment sample collection. Additionally, because no significant flow through the channel was indicated, no bedload samples were collected. Light breezes of 0.0 mph to 5.0 mph (0.0 km/hr to 8.0 km/hr) occurred most frequently (approximately 55 percent to 57 percent of the time). The frequency of occurrence diminished exponentially as the wind speed increased such that winds in excess of 25.0 mph (40.2 km/hr) occurred less than 0.01 percent of the time. Winds in excess of 15.0 mph (24.1 km/hr) were most commonly southerly, which is likely controlled by the topography of the valley because topographic highs define the east and west sides of the valley and converge toward the north.The concentrations of PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 micrometers [μm]) and PM10 (particulate matter with an aerodynamic diameter ≤10 μm) in the air generally increase as wind speed increases. However, the particle profiler at the Pu Valley South station regularly malfunctioned, which resulted in poor data quality and a limited period of record within FY2019. Therefore, discussion of the annual dust observations is mostly limited to the Pu Valley North station. At the Pu Valley North station, significant increases in windblown dust concentrations were observed when wind speeds exceeded 15.0 mph (24.1 km/hr), especially during southerly winds. When all wind speeds were considered, high dust concentrations at the Pu Valley North station were frequently associated with northerly, northwesterly, or southerly winds. The majority of PM10 transport at the Pu Valley North station occurred when winds were blowing northerly and northwesterly because of the high frequency of winds up to 15.0 mph (24.1 km/hr). The ratio of PM10 to PM2.5 is a qualitative indicator of the proximity of dust sources to the observation point and the values of the PM10 to PM2.5 ratio tend to be higher nearer to the source. The PM10 to PM2.5 ratio increased from 1.2 to 2.8 with higher wind speeds at the Pu Valley North station. This increase suggests that some locally sourced PM10 was present in the air when local winds exceeded 15.0 mph (24.1 km/hr). Local dust suspension is interpreted during major dust events when peaks in the PM10 to PM2.5 ratio occur simultaneously with the maximum wind speed and the peak PM10 concentration. Analysis of the five major dust events in FY2019 revealed that northerly winds were dominant in three events and southerly winds were dominant in two events. Local suspension of dust within Plutonium Valley is suspected to have occurred during two of the five major events (April 9, 2019, and September 28 to 29, 2019), and a nonlocal source of dust is presumed for the three other major dust events (October 14, 2018; May 16, 2019; and September 2, 2019). The three suspected regional dust transport events exhibited winds that typically did not exceed the 15.0 mph (24.1 km/hr) wind speed threshold for local dust transport and featured PM10 to PM2.5 ratios that indicate large-scale regional dust transport. Similar event characteristics (e.g., wind speed and PM10 concentration) were observed at air monitoring stations approximately 70 mi (113 km) to the northwest within the Tonopah Test Range (TTR). Therefore, these three regional events may not reflect transport from the Plutonium Valley CA. Routine monitoring of meteorological and hydrologic parameters at Plutonium Valley has been discontinued at the request of EM NV, effective at the conclusion of FY2019. Environmental data acquisition has been completed, and both Pu Valley North and Pu Valley South meteorological stations within Plutonium Valley as well as the ISCO autosampler station within the CA were removed on October 14, 2019.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Vegetation water sources in California's Sierra Nevada (USA) are young and change over time, a multi-isotope ( δ 18 O, δ 2 H, 3 H) tracer approach.

Sierra Nevada forests transpire a significant amount of California's water resources, sparking interest in applying forest management to improve California's water supply. Determining the source water of evapotranspiration enables forest managers to make informed decisions. To this end, a significant interest in critical zone science is to develop new methods to work across time scales to predict subsurface water storage and use. Here, forest vegetation accessed young water and switched sources depending on availability, suggesting that forest drought vulnerability may depend on the range of water sources available (rain, snowmelt and deeply stored water). This finding also suggests that changes in transpiration rates may have immediate effects on water sources in close proximity to vegetation, and delayed effects on storage and runoff. New δ 18 O, δ 2 H and 3 H data were used to track precipitation, runoff, evapotranspiration and storage through the critical zone seasonally, including seasons where evapotranspiration and snowmelt were in phase (winter snowmelt) and out of phase (seasonally dry summer). The main source of this headwater catchment's runoff is derived from its meadow saturated zone water, which was dominated by snowmelt. Water that originated as snowmelt contributed to transpiration, unless other sources, such as recent rain, became available. In cases where xylem δ 18 O and δ 2 H signatures matched those of deeper saturated zone water, 3 H data showed that xylem water was distinctly younger than the deep saturated zone water. During 2016, which experienced relatively normal snowpack in winter and seasonally dry summer conditions, mean summer saturated zone water and vegetation water were similar in δ 18 O, –12.4 ± 0.04 ‰ and – 12.5 ± 0.3 ‰, respectively, but were distinctly different in 3 H, 5.5 ± 0.2 pCi/L and 13.7 ± 1.1 pCi/L, respectively. While δ 18 O shows that vegetation and meadow saturated zone water have similar origins, 3 H shows they have dissimilar ages.

58 GEOSCIENCES↗
DOE OSTI2020

Planning green infrastructure placement based on projected precipitation data

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimizing green infrastructure placement under precipitation uncertainty

Increased urbanization, infrastructure degradation, and climate change threaten to overwhelm stormwater systems across the nation, rendering them ineffective. Green Infrastructure (GI) practices are low cost, low regret strategies that can contribute to urban runoff management. However, questions remain as to how to best distribute GI practices through urban watersheds given precipitation uncertainty and the variable hydrological responses to them. We develop stochastic programming models to determine the optimal placement of GI practices across a set of candidate locations in a watershed to minimize the total expected runoff under medium-term precipitation uncertainties. Specifically, we first develop a two-stage stochastic programming model. Next, we reformulate this model using perturbed parameters to reduce the requisite computational time and extend it to multi-stage. In addition, we introduce constraints that allow for incorporating sub-catchment-level runoff reduction considerations. Furthermore, we account for hydrological connectivity in the watershed using an underlying acyclic connectivity graph of sub-catchments and incorporate various practical considerations into the models. In addition, we develop a systemic approach to downscale the existing daily precipitation projections into hourly units and efficiently estimate the corresponding hydrological responses. These advancements are brought together in a case study for an urban watershed in a mid-sized city in the U.S., where we perform sensitivity analyses, evaluate the importance of the considered constraints, and provide insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The impact of multi-sensor land data assimilation on river discharge estimation

River discharge is one of the most critical renewable water resources. Accurately estimating river discharge with land surface models (LSMs) remains challenging due to the difficulty in estimating land water storages such as snow, soil moisture, and groundwater. While data assimilation (DA) ingesting optical, microwave, and gravity measurements from space can help constrain theses storage states, its impacts on runoff and eventually river discharge are not fully understood. In this study, by taking advantage of recently published land DA results that jointly assimilate eight different combinations of observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), we quantify to what degree multi-sensor land DA improves the river discharge simulation skills over 40 global river basins, and investigate the complementary strengths of different satellite measurements on river discharge. To be more specific, river discharge is updated by feeding gridded runoff from the eight multi-sensor DA simulations into a vector-based river routing model named the Routing Application for Parallel computatIon of Discharge (RAPID). Our modeling results, including 7-year simulations at 177,458 river reaches globally, are used to study the seasonal to interannual variability of river discharge. It is found that assimilating GRACE has the greatest impact on global runoff patterns, leading to the most pronounced improvements in spatial river discharge in the middle and high latitudes with the R 2 increased by 0.16. The seasonal variation of spatial discharge is most skillful during the boreal summer. However, our evaluation also shows model and DA still struggle to generate reasonable variability and averaged discharge over permafrost regions. Finally, by assessing how different satellites add value to discharge forecasts, this study paves the way for more advanced multi-sensor satellite data assimilation to predict the terrestrial hydrological cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Nitrate loading projection is sensitive to freeze-thaw cycle representation

Climate change can have substantial impacts on nitrogen runoff, which is a major cause of eutrophication, harmful algal blooms, and hypoxia in freshwaters and coastal regions. We examined responses of nitrate loading to climate change in the Upper Mississippi River Basin (UMRB) with an enhanced Soil and Water Assessment Tool with physically based Freeze-Thaw cycle representation (SWAT-FT), as compared with the original SWAT model that employs an empirical equation. Driven by future climate projections from five General Circulation Models (GCMs) from 1960 to 2099 under the Representative Concentrations Pathways (RCP) 8.5 scenario, we analyzed changes in riverine nitrate loadings, as well as terrestrial surface and subsurface contributions of the UMRB in the 21st century relative to the baseline period of 1960–1999. By the end of the 21st century, the original SWAT model predicted about a 50% increase in riverine nitrate loadings which is nearly twice as much as that estimated by SWAT-FT (ca. 25%). Such a large difference in projected nitrate changes can potentially mislead mitigation strategies that aim to reduce nitrogen runoff from the UMRB. Further analysis shows that the difference between the original SWAT model and SWAT-FT led to substantial discrepancies in the spatial distribution of surface and subsurface nitrate loadings in the UMRB. In general, SWAT-FT predicted more nitrate leaching for northwestern parts of the UMRB which are more sensitive to freeze-thaw cycle, mainly because SWAT-FT simulated less frequent frozen soils. This study highlights the importance of using physically based freeze-thaw cycle representation in water quality modeling. Design of future nitrogen runoff reduction strategies should include careful assessment of effects that land management has on the freeze-thaw cycles to provide reliable projection of water quality under climate change.

Wang, Qianfeng↗
DOE OSTI2020

Parameterizing Perennial Bioenergy Crops in Version 5 of the Community Land Model Based on Site‐Level Observations in the Central Midwestern United States

Abstract With projected expansion of biofuel production at a global scale, there is a pressing need to develop adequate representation of bioenergy crops in land surface models to help effectively quantify the biogeophysical and biogeochemical effects of its associated land use changes. This study implements two new perennial bioenergy crops, Miscanthus and switchgrass, into the Community Land Model Version 5 based on site‐level observations from the midwestern United States by modifying parameters associated with photosynthesis, phenology, allocation, decomposition, and carbon cost of nitrogen uptake and integrating concomitantly land management practices. Sensitivity analyses indicate that carbon and energy fluxes of the perennial crops are most sensitive to photosynthesis and phenology parameters. Validation of simulated fluxes against site‐level measurements demonstrates that the model is capable of capturing the overall patterns of energy and carbon fluxes, as well as physiological transitions from leaf emergence to senescence. Compared to annual crops, perennial crops feature longer growing season, greater leaf areas, and higher productivity, leading to increased transpiration, lower annual runoff, and larger carbon uptake. The model simulations suggest that with higher CO 2 assimilation rates and lower demands for nutrients and water, high‐yielding perennial crops are promising alternatives of bioenergy feedstocks compared to traditional annual crops not only for mitigating climate change but also for environmental conservation purposes by reducing fertilizer application and therefore alleviating surface‐ and ground‐water contaminations. Although the local‐scale simulations shed light on potential benefits of using perennial grasses as bioenergy feedstocks, quantifying consequences of their plantations at larger scales warrants additional investigation.

Cheng, Yanyan↗
DOE OSTI2021

Vegetation Response to Rising CO 2 Amplifies Contrasts in Water Resources Between Global Wet and Dry Land Areas

Rising atmospheric CO 2 impacts on vegetation physiological processes can alter land feedbacks on precipitation and water resources, but understanding of regional differences in these changes is uncertain. We investigate the impact of rising CO 2 on land water resources for different wetness levels using four Earth system models. We find an overall tendency of runoff to increase across all wetness levels. However, runoff increases in wet regions are much larger than those in dry regions, especially in wet seasons. This substantial amplification of contrasts between wet and dry regions increases at 3% per 100 ppm increase in CO 2 relative to the historical period, reaching 18% for a quadrupling of CO 2 , quantified by a new wetting contrast index (WCI). Physiological effects suppress evapotranspiration more in wet than dry regions, which has a larger contribution than radiative forcing to the amplification of runoff contrast, reshaping the spatial distribution of future land water resources.

58 GEOSCIENCES↗
DOE OSTI2024

Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning

Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models—particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs. We present a novel differentiable routing method (δMC-Juniata-hydroDL2) that mimics the classical Muskingum-Cunge routing model over a river network but embeds an NN to infer parameterizations for Manning's roughness (n) and channel geometries from raw reach-scale attributes like catchment areas and sinuosity. The NN was trained solely on downstream hydrographs. Synthetic experiments show that while the channel geometry parameter was unidentifiable, n can be identified with moderate precision. With real-world data, the trained differentiable routing model produced more accurate long-term routing results for both the training gage and untrained inner gages for larger subbasins (>2,000 km2) than either a machine learning model assuming homogeneity, or simply using the sum of runoff from subbasins. The n parameterization trained on short periods gave high performance in other periods, despite significant errors in runoff inputs. The learned n pattern was consistent with literature expectations, demonstrating the framework's potential for knowledge discovery, but the absolute values can vary depending on training periods. The trained n parameterization can be coupled with traditional models to improve national-scale hydrologic flood simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uniting Surface Properties With Hydrodynamic Roughness in Shallow Overland Flow Models

Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or “roughness scheme”—describing the functional relationship between flow resistance and flow depth/velocity—and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's n). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation-based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely-used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Machine Learning Calibration of Groundwater Table Depth in ELM: Impact on Land Surface Hydrology and Land‐Atmosphere Fluxes

Accurate representation of groundwater table depth (GWTD) is crucial for simulating hydrological cycling in Earth system models (ESM). Nevertheless, there is a notable gap in the literature regarding the validation of GWTD simulations in ESMs and their subsequent impact on downstream hydrological components. This study explores the calibration of parameterization of global GWTD using machine learning within the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Despite achieving significant gains in simulating GWTD through calibration, offline ELM simulations unexpectedly show that these improvements do not translate to substantial enhancements in model performance for other key hydrological variables, including soil moisture (SM), runoff, groundwater contribution to runoff or base flow index (BFI), and evapotranspiration and its partitioning. The performance in SM and runoff was even degraded in some regions, while BFI was mostly overestimated. Although there is significant improvement in GWTD within the critical range of 1–5 m, where groundwater traditionally influences land surface energy fluxes, these improvements occurred mostly in humid areas where the impact of GWTD on surface processes is minimal. Although the impacts of model calibration are generally small in offline ELM simulations, coupled land-atmosphere simulations exhibit much stronger responses to GWTD calibration, highlighting the role of land-atmosphere feedbacks in Earth system modeling. These findings underscore the need for integrated calibration strategies that simultaneously optimize multiple hydrological variables. However, if a single-variable approach is necessary, it is crucial to establish clear priorities for calibration, identifying the most critical variables that have the greatest impact on overall model performance.

Fang, Yilin [Pacific Northwest National Laboratory↗
DOE OSTI2022

Pervasive alterations to snow-dominated ecosystem functions under climate change

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. So our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Understanding the distinct impacts of MCS and non-MCS rainfall on the surface water balance in the central US using a numerical water-tagging technique

Warm-season rainfall associated with mesoscale convective systems (MCSs) in the central US is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multi-parameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April to August (1997-2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface-subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10 percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lighter intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture-precipitation feedbacks and ecohydrology.

Hu, Huancui↗
DOE OSTI2024

Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C

Drainage structures are used to capture stormwater runoff in streets and highways in urban environments. These drainage structures, which typically consist of catch basins with grates, inlets, or combination grates/inlets, collect stormwater runoff and discharge through buried conveyance systems. They are strategically placed for public safety in curb and gutter systems to provide efficient drainage of water from roadways and thus reduce the risk of hydroplaning. The performance of drainage structures is measured in terms of hydraulic efficiency, which is defined as the percentage of flow captured by the basin as compared to the total flow drainage to the structure. Understanding of the performance of these drainage structures helps designers properly space inlets to promote an economic design that ensures the safety of the traveling public. The current design methodology used to determine drainage structure follows guidelines established in the current Michigan Department of Transportation (MDOT) Drainage Manual (2006). The guidelines in the MDOT Drainage Manual were modeled after the Federal Highway Administration’s (FHWA) Hydraulic Engineering Circular 22 “Urban Drainage Design” (HEC-22). HEC-22 includes empirically derived equations to calculate the interception capacity of drainage structures for several commonly used grate configurations, such as the parallel bar, curved vane and tilt bar grates, which are based on a research study performed by Burgi et al. in the 1970s. MDOT uses several drainage structures to capture runoff that are detailed as Standard Plans. Many of these drainage structures utilize sinusoidal type grates that are not described in HEC-22. Physical modeling of these structures has been limited, posing the need to have them analyzed to verify their capture efficiency. Current MDOT practice is to assume a similar sized reticuline grate, as described in HEC-22, for capture efficiencies. Until recently, evaluating the hydraulic performance of drainage structures was limited to physical modeling in a hydraulics laboratory. With advances in engineering software and computing power, computational fluid dynamics (CFD) modeling has become a more cost-effective alternative. The Federal Highway Administration (FHWA) provides states the option to evaluate their drainage structures using CFD through the Transportation Pooled Fund Program. This study, “Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C,” was carried out using the pooled fund. MDOT’s Cover C was chosen as the first test candidate, given its similar sinusoidal pattern to other MDOT grates, but it is typically used for high-volume, higher speed applications. A similar version, Cover CX, is used on interstate highways but does not have traverse bars for bicycle safety. Additional grates may be considered for evaluation in the future.

42 ENGINEERING↗
DOE OSTI2026

Computational Analysis of Hydraulic Efficiency of Michigan DOT Covers J and K

Drainage structures are used in urban street and highway systems to capture stormwater runoff. These structures typically consist of catch basins fitted with grates, inlets, or combination grate/inlet configurations that collect runoff and convey it through buried drainage systems. They are strategically placed within curb-and-gutter systems to enhance public safety by efficiently removing water from roadways and thereby reducing the risk of hydroplaning. The performance of drainage structures is commonly evaluated in terms of hydraulic efficiency, defined as the percentage of flow captured by the basin relative to the total flow reaching the structure. Understanding the hydraulic performance of these structures allows designers to properly space inlets, resulting in cost-effective designs that also help ensure the safety of the traveling public. MDOT uses a variety of drainage structures for runoff capture, as documented in Michigan Department of Transportation (MDOT) Drainage Manual. Many of these structures incorporate sinusoidal-type grates that are not addressed in HEC-22. Because physical modeling of these structures has been limited, further analysis is needed to verify their capture efficiency. Under current MDOT practice, the capture efficiency of these grates is estimated by assuming performance similar to that of a comparably sized reticuline grate described in HEC22. The first phase of this effort, titled Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C, focused on evaluating the hydraulic performance of MDOT’s Cover C grate. Cover C was selected as the initial test candidate because its sinusoidal pattern is representative of other MDOT grates, while it is typically used in high-volume, higher-speed applications. A similar version, Cover CX, is used on interstate highways but does not include transverse bars for bicycle safety. The current second phase of the study expands this work to evaluate MDOT’s Covers J and K. These grates were selected for additional analysis to further assess the hydraulic performance of MDOT drainage structures that are not directly represented by grate configurations in HEC-22. The results of this phase will build on the findings from the Cover C analysis and support improved understanding of the capture efficiency of MDOT’s standard drainage grates. This report is intended to serve as a companion document to the earlier study, Computational Analysis of Hydraulic Efficiency of Michigan DOT Cover C [3]. The present work applies the same overall CFD-based evaluation approach to MDOT Covers J and K and compares the resulting performance trends with those previously identified for Cover C. In particular, both studies assess on-grade interception efficiency, sag-location hydraulic capacity, and the effects of partial obstruction relative to HEC-22-based design estimates.

42 ENGINEERING↗
DOE OSTI2023

CMIP6-based Multi-model Streamflow Projections over the Conterminous US

This dataset presents an ensemble of streamflow projections based on the hydroclimate projections dataset supporting the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). The six-member General Climate Model (GCM) ensemble from the Coupled Models Intercomparison Project phase 6 (CMIP6) downscaled using statistical (i.e., DBCCA) and dynamical (i.e., RegCM) and bias-corrected using two meteorological reference observations (Daymet & Livneh) are driven through two calibrated hydrologic models (VIC & PRMS) to simulate projected future hydrologic responses. This leads to the production of an ensemble of hydroclimate projections, including total runoff (surface runoff and baseflow) for 1980–2019 baseline and 2020–2059 near-term future periods under multiple emission scenarios at 1/24° (~4 km) spatial resolution across the CONUS. The total runoff projections are routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model to produce an ensemble of streamflow projections for both periods across 2.7 million NHDPlusV2 stream reaches in the CONUS.

13 HYDRO ENERGY↗
DOE OSTI2023

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a 1/8° spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816. Evaluations carried out in this study and previous investigations show that the ensemble per forms well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS es timations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.

54 ENVIRONMENTAL SCIENCES↗