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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Statistical and Hydrological Evaluation of TRMM-Based Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the Latest Satellite Precipitation Products 3B42V7 Ready for Use in Ungauged Basins?

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001-2010) daily rain gauge dataset reveals that: 1) 3B42V7 generally improves upon 3B42V6s underestimation both for the whole basin (bias from -41.15 to -8.38) and for a 0.250.25 grid cell with high-density gauges (bias from -40.25 to 0.04), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and 2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already well-performing 3B42V7 except for a reduction in bias (from -26.98 to -4.81). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in-situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins.

TRMM↗
NASA NTRS2018

A Multi-Satellite Assimilation and Modeling Platform to Construct a Global and Complete view of the Hydrologic Cycle

What captivates me about the research field of satellite remote sensing of the earth system science is the opportunity to help human beings by addressing challenging questions related to the current and future availability of natural resources for domestic, agricultural, and industrial needs. For example, a continuously varying climate poses a threat to the water resource, not only in developing countries, but also in economically well-developed regions, creating the urgent need to characterize and predict its spatial and temporal availability. Specifically, my curiosity is driven by the challenge of merging cutting-edge space technology and earth observations (i.e., remote sensing) with state-of-the-art models for the purpose of improving our scientific knowledge about the variability and the change of the hydrologic cycle.The only practical way to observe the land surface processes (e.g., hydrologic cycle) on continental to global scales is via satellite remote sensing. Though remote sensing can make spatially comprehensive measurements of various components of the land surface system, it cannot provide direct information on the entire system, and the measurements represent only a snapshot in time. Physical based models may be used to continuously predict the temporal and spatial earth processes, but these predictions are often poor, due to model initialization, parameter and meteorological forcing errors, and inadequate model physics and/or resolution. Thus, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses of both) to provide a superior land surface state estimate. This is the goal of land surface data assimilation. Data assimilation provides a better estimate of the environmental states than either models or observations could individually do. The broad concept of data assimilation can be applied to various disciplines such as hydrology, ecology, environmental hazards, agriculture and economy.My research is targeted to integrating multiple satellite information having multi-sensor and multi-resolution assimilation within today's state-of-the-art hydrologic models. Multi-sensor and multi-resolution assimilation techniques represent a necessary milestone in future earth science applications because they offer the capability of comprehensively integrating disparate types of earth observations via deep-learning techniques. A multi-satellite assimilation and modeling platform will ultimately provide a robust and complete dataset to more fully understand earth's dynamics. In fact, the multi-satellite assimilation platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.My recent work has focused on recent multi-sensor data assimilation techniques targeted at improving snow, soil moisture, groundwater, and terrestrial water storage hydrological states.

Girotto, Manuela↗
NASA NTRS2022

The Simulation and Subseasonal Forecasting of Hydrological Variables: Insights from a Simple Water Balance Model

Past work has shown that a land surface model’s (LSM’s) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the U.S., and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in-situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in Nature and can serve as potentially useful targets for further LSM development.

Water Balance Model↗
NASA NTRSDate not supplied

Insights Into the Hydrology of the Congo Peatlands Through Land Surface Modeling and Data Assimilation

The 16.8 million ha of peatlands in the Cuvette Centrale wetland complex in the Congo Basin is one of the largest peatland regions on Earth but still highly understudied. Understanding the hydrological functioning of these peatlands and the effects of external disturbances thereon remains a major challenge. Recent research suggested fundamental hydrological differences between the Congo peatlands and the well-studied Southeast Asian peatlands. The Congo peatlands have a doming gradient that is up to ten times smaller, and they are influenced by river hydrology to some extent. In this study, we explore the Congo peatland hydrology through land surface modeling and data assimilation. We build upon our recently developed tropical PEATCLSM module (Apers et al., 2022) that was parameterized based on data from Southeast Asian peatlands due to the lack of field data from other tropical peatland regions. In a first step, we derive Congo-specific peat hydraulic and discharge function parameters from a scalar parametrization of water level dynamics in the Congo peatlands, using observed water level data at two locations. These Congo-specific parameters differ considerably from the original literature-based parameters from Southeast Asian peatlands. In a second step, we apply our original and Congo-specific parameters in an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture and Ocean Salinity (SMOS) mission. The data assimilation results are used in two ways. First, the effect of these parameters on the simulated peatland hydrology and the observation-minus-forecast Tb residuals is evaluated. It is hypothesized that the new parameters reduce the previously reported modeling errors over the Congo peatlands and reduce the residuals in Tb as well. Second, we analyze the data assimilation diagnostics to learn about other model improvement possibilities. In preliminary results, we found long periods of temporally autocorrelated total water storage increments (difference of pre- and post-update) that coincided with anomalies in river stages measured upstream of the peatlands. Since PEATLCSM neglects possible river influence, this concurrence suggests that the typically used grid-based approach of land surface models should be combined with a river routing scheme over the Congo peatlands.

Sebastian Apers↗
NASA NTRS2023

Probabilistic Hydrological Estimation of LandSlides (PHELS): Global Ensemble Landslide Hazard Modelling

In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7-day antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.

Anne Felsberg↗
NASA NTRS2022

Assimilation of NASA’s Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System

he NASA LIS/WRF-Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi-scale surface hydrological modeling capabilities of the WRF-Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub-surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF-Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open-loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF-Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

Hydrologic modeling↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
NASA NTRS2013

Assessment of Evolving TRMM-Based Real-Time Precipitation Estimation Methods and Their Impacts on Hydrologic Prediction in a High-Latitude Basin

The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest dataset of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.

hydrologic prediction↗
NASA NTRS2016

Optimal Reorganization of NASA Earth Science Data for Enhanced Accessibility and Usability for the Hydrology Community

A long-standing "Digital Divide" in data representation exists between the preferred way of data access by the hydrology community and the common way of data archival by earth science data centers. Typically, in hydrology, earth surface features are expressed as discrete spatial objects (e.g., watersheds), and time-varying data are contained in associated time series. Data in earth science archives, although stored as discrete values (of satellite swath pixels or geographical grids), represent continuous spatial fields, one file per time step. This Divide has been an obstacle, specifically, between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. and NASA earth science data systems. In essence, the way data are archived is conceptually orthogonal to the desired method of access. Our recent work has shown an optimal method of bridging the Divide, by enabling operational access to long-time series (e.g., 36 years of hourly data) of selected NASA datasets. These time series, which we have termed "data rods," are pre-generated or generated on-the-fly. This optimal solution was arrived at after extensive investigations of various approaches, including one based on "data curtains." The on-the-fly generation of data rods uses "data cubes," NASA Giovanni, and parallel processing. The optimal reorganization of NASA earth science data has significantly enhanced the access to and use of the data for the hydrology user community.

data rods↗
NASA NTRS2022

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

crop phenology↗
NASA NTRS2024

Improving the Fire Weather Index System for Peatlands Using Peat-Specific Hydrological Input Data

The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWIpeat, was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWIpeat should enable improved monitoring of fire risk in boreal peatlands.

Canadian Fire Weather Index↗
DOE OSTI2025

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation

Study region: The Dnieper and Dniester Rivers of Ukraine. Study focus: The ongoing conflict in Ukraine has caused disruptions to electricity generation, of which hydroelectric sources contribute approximately 9 % to the country’s needs. With the takeover of the Zaporizhzhia nuclear power plant by enemy forces, the loss of the Kakhovka hydroelectric dam, and the future impacts of the conflict on electricity generation unclear, it may be valuable for the Ukrainian government to better understand how it could leverage hydroelectric power sources in the near future. Unfortunately, measurements of river discharge throughout Ukraine ceased data collection in the late 1980’s to early 1990’s. To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. New hydrological insights for the region: We ran new algorithms on 144 WorldView-2 and WorldView-3 satellite images to map rivers and extract width, one of which was validated against river gauge data located along the same river but in a neighboring country. Hydrologic models using two climate scenarios found minimal change in annual discharge at all sites, but magnitude and timing of peak discharge showed a moderate trend. The results suggest that hydropower is underutilized in Ukraine.

13 HYDRO ENERGY↗
DOE OSTI2025

Characterizing How Meteorological Forcing Selection and Parameter Uncertainty Influence Community Land Model Version 5 Hydrological Applications in the United States

Despite the increasing use of large-scale Land Surface Models (LSMs) in predicting hydrological responses in extreme conditions, there's a critical gap in understanding the uncertainties in these predictions. This study addresses this gap through a detailed diagnostic evaluation of the uncertainties arising from meteorological forcing selection and model parametrization in hydrological simulations of the Community Land Model version 5 (CLM5). CLM5 is configured at a spatial scale of about 12-km to simulate runoff processes for 464 headwater watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) dataset to be representative of physiographic and climatic gradients across the conterminous United States. For each watershed, CLM5 is driven by five commonly used gridded forcing datasets in combination with a large ensemble (> 1200) of key CLM5 hydrologic parameters. Our results suggest that uncertainty in CLM5 runoff simulations resulting from both forcing and parametric sources is markedly higher in arid regions, e.g., Great Plains and Midwest regions. Uncertainty in low flow is dominated by parametric uncertainty, while the selection of meteorological forcing contributes more dominantly to high flow and seasonal flows during fall and spring. Our analysis also demonstrates that the selection of forcing datasets and the metrics used to calibrate CLM5 significantly impact the model’s predictive accuracy in extreme event severity for both floods and droughts. Overall, the results from this study highlight the need to understand and account for forcing and parametric uncertainties in CLM5 simulations, particularly for hazard and risk assessments addressing hydrologic extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hydrological Control on Soil Redox Condition and Carbon Loss of Coastal Wetland Under Sea-Level Rise

Coastal wetlands are critical carbon sinks with their biogeochemical and ecological functioning shaped by dynamic hydrological conditions that are increasingly influenced by climate change. A key unresolved question is how hydrologic flow, vegetation response, and rising sea levels interact to regulate soil redox condition and carbon loss in coastal wetlands. Using a field-tested hydrological–biogeochemical–ecological modeling framework, we reveal how the interplay between terrestrial groundwater discharge and tidal fluctuations generates complex groundwater flow patterns at the terrestrial–aquatic interface, and how these patterns modulate soil redox conditions, in turn influencing soil organic matter decomposition and carbon loss. Notably, rising sea levels suppress soil CO2 emissions while reducing lateral dissolved carbon losses, thereby enhancing litter carbon sequestration under anoxic conditions. As vegetation responds to sea-level rise and carbon inputs diminish, litter carbon subsequently declines. These findings underscore a critical hydrological control on carbon cycling, advancing our understanding of coastal ecosystem resilience in a warming world.

Chen, Kewei [ORNL] (ORCID:000000032580514X)↗
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 OSTI2026

When Do Riverine Systems “Feel the Burn”? Simulating How Burn Extent and Severity Modulate Hydrologic Controls on Biogeochemical Export

Wildfires impact terrestrial landscapes and downstream river corridors through shifts in vegetation and soil properties leading to downstream hydrologic and water quality impacts. The magnitude of these impacts depend on a complex and interconnected set of wildfire, landscape, and aquatic processes. Here, we isolate the impact of post-fire hydrologic changes on streamflow, nitrate, and dissolved organic carbon using the Soil and Water Assessment Tool (SWAT) model. We explore how responses differ across burn severity and area burned in two test basins: a humid forested basin and a semi-arid mixed land use basin. We ran 1830 wildfire simulations testing impacts of area burned, burn severity, and post-fire precipitation on streamflow, nitrate, and dissolved organic carbon. Our work suggests that area burned thresholds differ with burn severity and analyte. Additionally, post-fire transport of dissolved organic carbon was sensitive to both area burned and severity, while nitrate was primarily sensitive to area burned. Despite a muted (−9.5 to 5.7 mm yr −1 change) hydrologic response in the semi-arid basin, the model predicted large (7%–288% increase) shifts in dissolved organic carbon, suggesting that post-fire shifts in flow pathways and soil properties are key in its response. The limited shifts in nitrate responses in the simulations highlight that terrestrial post-fire transformations, rather than hydrologic changes, may control the increases in stream nitrate often observed post-fire. As wildfire regimes are shifting, improving understanding of post-fire nutrient export responses is critical to protect freshwater resources and aquatic ecosystems.

Wampler, Katherine A. [Pacific Northwest National ↗
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 OSTI2026

Data for Soil Oxygen Dynamics: A Key Mediator of Tile Drainage Impacts on Coupled Hydrological, Biogeochemical, and Crop Systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

Modeling↗