Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data
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A scalable framework for quantifying field-level agricultural carbon outcomes
Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N 2 O and CH 4 ) and changes in soil carbon stock are defined here as “agricultural carbon outcomes”. Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes = Environment (E) × Management (M) × Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agricultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the “System-of-Systems” solution. Such a “System-of-Systems” solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) systematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of individual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accuracy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R&D) priorities to achieve different components of the “System-of-Systems” solution, thus accomplishing the Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.
54 ENVIRONMENTAL SCIENCES↗
Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics
Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.
54 ENVIRONMENTAL SCIENCES↗
Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models
Abstract Increasing temperature and water cycle changes due to warming climate may increase the frequency and intensity of wildfires. Fire emission projections are useful for informing strategies for adaptation and mitigation of fire impacts on societies and ecosystems. Here, we construct a neural network (NN) model explained by the Shapley Additive explanation to predict fire PM 2.5 emissions change and understand their drivers over the contiguous US (CONUS) in the mid‐21st century under a high greenhouse gas emissions scenario (SSP5‐8.5). Using future meteorology and leaf area index (LAI) simulated by eight global climate models from the Coupled Model Intercomparison Project Phase 6, future population density, and present‐day land use and land cover (LULC) as input to the NN model, the total fire PM 2.5 emissions over CONUS are projected to increase by 4%–75% (model spread). Among different regions, fire emissions in the western US are projected to increase more significantly in June‐July‐August than in other seasons and regions, with the median ratios of future to present‐day fire emissions ranging from 1.67 to 2.86. The increases in fire emissions are mainly driven by increasing normalized temperature (23%–29%) and decreasing soil moisture (2%–10%) in the future. When future LULC change is considered, the projected fire emissions further increase by 58%–83% over the western US compared to projections without LULC change because of future increases in vegetation fraction. The results highlight the important role of warmer temperature, decreasing soil moisture, and LULC change in increasing fire emissions in the future.
54 ENVIRONMENTAL SCIENCES↗
Glacier ice archives nearly 15,000-year-old microbes and phages
Background Glacier ice archives information, including microbiology, that helps reveal paleoclimate histories and predict future climate change. Though glacier-ice microbes are studied using culture or amplicon approaches, more challenging metagenomic approaches, which provide access to functional, genome-resolved information and viruses, are under-utilized, partly due to low biomass and potential contamination. Results We expand existing clean sampling procedures using controlled artificial ice-core experiments and adapted previously established low-biomass metagenomic approaches to study glacier-ice viruses. Controlled sampling experiments drastically reduced mock contaminants including bacteria, viruses, and free DNA to background levels. Amplicon sequencing from eight depths of two Tibetan Plateau ice cores revealed common glacier-ice lineages including Janthinobacterium, Polaromonas, Herminiimonas, Flavobacterium, Sphingomonas, and Methylobacterium as the dominant genera, while microbial communities were significantly different between two ice cores, associating with different climate conditions during deposition. Separately, ~355- and ~14,400-year-old ice were subject to viral enrichment and low-input quantitative sequencing, yielding genomic sequences for 33 vOTUs. These were virtually all unique to this study, representing 28 novel genera and not a single species shared with 225 environmentally diverse viromes. Further, 42.4% of the vOTUs were identifiable temperate, which is significantly higher than that in gut, soil, and marine viromes, and indicates that temperate phages are possibly favored in glacier-ice environments before being frozen. In silico host predictions linked 18 vOTUs to co-occurring abundant bacteria (Methylobacterium, Sphingomonas, and Janthinobacterium), indicating that these phages infected ice-abundant bacterial groups before being archived. Functional genome annotation revealed four virus-encoded auxiliary metabolic genes, particularly two motility genes suggest viruses potentially facilitate nutrient acquisition for their hosts. Finally, given their possible importance to methane cycling in ice, we focused on Methylobacterium viruses by contextualizing our ice-observed viruses against 123 viromes and prophages extracted from 131 Methylobacterium genomes, revealing that the archived viruses might originate from soil or plants. Conclusions Together, these efforts further microbial and viral sampling procedures for glacier ice and provide a first window into viral communities and functions in ancient glacier environments. Such methods and datasets can potentially enable researchers to contextualize new discoveries and begin to incorporate glacier-ice microbes and their viruses relative to past and present climate change in geographically diverse regions globally.
59 BASIC BIOLOGICAL SCIENCES↗
Cycles-L: A Coupled, 3-D, Land Surface, Hydrologic, and Agroecosystem Landscape Model
Managing landscapes to increase agricultural productivity and environmental stewardship can be informed by spatially-distributed models that operate at spatial and temporal scales that are intervention-relevant. This paper presents Cycles-L, a landscape-scale agroecosystem and hydrologic modeling system, using as a test case a watershed in Pennsylvania. Cycles-L emerges from melding the landscape and hydrology structure of Flux-PIHM, a 3-D land surface hydrologic model, with the agroecosystem processes in the Cycles model. Consequently, Cycles-L can simulate processes affected by topography, soil heterogeneity, and management practices, owing to its physically-based hydrology that can simulate horizontal and vertical transport of solutes with water. The model was tested at a 730-ha experimental watershed within the Mahantango Creek watershed. Cycles-L simulated well stream water and mineral nitrogen discharge (Nash-Sutcliffe coefficient 0.55 and 0.60, respectively) and grain yield (root mean square error 1.2 Mg ha -1 ). Cycles-L outputs are as good or better than those obtained with the uncoupled Flux-PIHM (water discharge) and Cycles (grain yield) models. Modeled spatial patterns of nitrogen fluxes like denitrification illustrate the combined control of crop management and topography. For example, denitrification is almost twice as high when simulated with Cycles-L than when simulated with Cycles 1-D. Due to its spatial and temporal resolution, Cycles-L fills a gap in the availability of models that operate at a scale relevant to evaluate interventions in the landscape. Cycles-L can become a central component in tools for climate change scenario analysis, precision agriculture, precision conservation, and artificial intelligence-based decision support systems.
54 ENVIRONMENTAL SCIENCES↗
Spatio–Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology
Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow–CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event–scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process–based models without sacrificing fidelity.
54 ENVIRONMENTAL SCIENCES↗
Terpenes modulate bacterial and fungal growth and sorghum rhizobiome communities
ABSTRACT Terpenes are among the oldest and largest class of plant-specialized bioproducts that are known to affect plant development, adaptation, and biological interactions. While their biosynthesis, evolution, and function in aboveground interactions with insects and individual microbial species are well studied, how different terpenes impact plant microbiomes belowground is much less understood. Here we designed an experiment to assess how belowground exogenous applications of monoterpenes (1,8-cineole and linalool) and a sesquiterpene (nerolidol) delivered through an artificial root system impacted its belowground bacterial and fungal microbiome. We found that the terpene applications had significant and variable impacts on bacterial and fungal communities, depending on terpene class and concentration; however, these impacts were localized to the artificial root system and the fungal rhizosphere. We complemented this experiment with pure culture bioassays on responsive bacteria and fungi isolated from the sorghum rhizobiome. Overall, higher concentrations (200 µM) of nerolidol were inhibitory to Ferrovibrium and tested Firmicutes. While fungal isolates of Penicillium and Periconia were also more inhibited by higher concentrations (200 µM) of nerolidol, Clonostachys was enhanced at this higher level and together with Humicola was inhibited by the lower concentration tested (100 µM). On the other hand, 1,8-cineole had an inhibitory effect on Orbilia at both tested concentrations but had a promotive effect at 100 µM on Penicillium and Periconia . Similarly, linalool at 100 µM had significant growth promotion in Mortierella , but an inhibitory effect for Orbilia . Together, these results highlight the variable direct effects of terpenes on single microbial isolates and demonstrate the complexity of microbe-terpene interactions in the rhizobiome. Importance Terpenes represent one of the largest and oldest classes of plant-specialized metabolism, but their role in the belowground microbiome is poorly understood. Here, we used a “rhizobox” mesocosm experimental set-up to supply different concentrations and classes of terpenes into the soil compartment with growing sorghum for 1 month to assess how these terpenes affect sorghum bacterial and fungal rhizobiome communities. Changes in bacterial and fungal communities between treatments belowground were characterized, followed by bioassays screening on bacterial and fungal isolates from the sorghum rhizosphere against terpenes to validate direct microbial responses. We found that microbial growth stimulatory and inhibitory effects were localized, terpene specific, dose dependent, and transient in time. This work paves the way for engineering terpene metabolisms in plant microbiomes for improved sustainable agriculture and bioenergy crop production.
59 BASIC BIOLOGICAL SCIENCES↗
A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes
Hydro-climatic extremes, such as droughts and floods, have most likely increased due to climatic change and could lead to severe impacts on socio-economic, structural and environmental sectors. With nearly 4000 publications, the Soil and Water Assessment Tool (SWAT) is clearly one of the most extensively used ecohydrological models worldwide. The model has been widely used for projecting the impacts of future hydro-climatic changes, but application for extreme streamflow conditions is still rarely reported. To date, SWAT application reviews have focused on compilations of SWAT studies for specific or relatively new applications such as eco-hydrological modelling, ecosystem services, sub-daily simulations, and pesticide fate and transport simulations. However, no existing SWAT review studies have focused on simulation of hydro-climatic extremes. Furthermore, this research aims to bridge this gap by compiling and reviewing the findings of studies reporting SWAT hydro-climatic extremes including highlighting the performance and future research needs. A total of 111 articles have been identified since 1999; most of these studies were conducted in the United States and China. These articles can be divided into extreme flow assessments, drought studies, flood studies, drought and flood studies, SWAT coupling with other models, and SWAT improvements. Most of the extreme performance assessment studies reported “satisfactory ”performance, with a particular emphasis on peak flow comparisons. Future research needs regarding this topic include: (1) a unified SWAT extreme performance assessment framework; (2) SWAT improvements that result in improved replication of peak and low flows; (3) reliability assessment of global and satellite products for SWAT extreme simulations; (4) bias correction of CMIP6 and regional climate projections; (5) comparison of SWAT + and SWAT for extreme flow simulations in different types of basins; (6) development of an extreme flow module within an overall SWAT modelling system; and (7) integration of artificial intelligence within SWAT modelling.
54 ENVIRONMENTAL SCIENCES↗
An approach to sulfate geoengineering with surface emissions of carbonyl sulfide
Sulfate geoengineering (SG) methods based on lower stratospheric tropical injection of sulfur dioxide (SO 2 ) have been widely discussed in recent years, focusing on the direct and indirect effects they would have on the climate system. Here a potential alternative method is discussed, where sulfur emissions are located at the surface or in the troposphere in the form of carbonyl sulfide (COS) gas. There are two time-dependent chemistry–climate model experiments designed from the years 2021 to 2055, assuming a 40 Tg-Syr -1 artificial global flux of COS, which is geographically distributed following the present-day anthropogenic COS surface emissions (SG-COS-SRF) or a 6 Tg-Syr -1 injection of COS in the tropical upper troposphere (SG-COS-TTL). The budget of COS and sulfur species is discussed, as are the effects of both SG-COS strategies on the stratospheric sulfate aerosol optical depth (~Δτ=0.080 in the years 2046–2055), aerosol effective radius (0.46 µm), surface SO x deposition (+8.9 % for SG-COS-SRF; +3.3 % for SG-COS-TTL), and tropopause radiative forcing (RF; ~-1.5 W m –2 in all-sky conditions in both SG-COS experiments). Indirect effects on ozone, methane and stratospheric water vapour are also considered, along with the COS direct contribution. According to our model results, the resulting net RF is –1.3 W m –2 , for SG-COS-SRF, and –1.5 W m –2 , for SG-COS-TTL, and it is comparable to the corresponding RF of –1.7 W m –2 obtained with a sustained injection of 4 Tg-Syr -1 in the tropical lower stratosphere in the form of SO 2 (SG-SO2, which is able to produce a comparable increase of the sulfate aerosol optical depth). Significant changes in the stratospheric ozone response are found in both SG-COS experiments with respect to SG-SO2 (~5 DU versus +1.4 DU globally). According to the model results, the resulting ultraviolet B (UVB) perturbation at the surface accounts for –4.3 % as a global and annual average (versus –2.4 % in the SG-SO2 case), with a springtime Antarctic decrease of –2.7 % (versus a +5.8 % increase in the SG-SO2 experiment). Overall, we find that an increase in COS emissions may be feasible and produce a more latitudinally uniform forcing without the need for the deployment of stratospheric aircraft. However, our assumption that the rate of COS uptake by soils and plants does not vary with increasing COS concentrations will need to be investigated in future work, and more studies are needed on the prolonged exposure effects to higher COS values in humans and ecosystems.
54 ENVIRONMENTAL SCIENCES↗
PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management
The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).
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Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics
Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.
54 ENVIRONMENTAL SCIENCES↗
Scalable deep learning for watershed model calibration
Watershed models such as the Soil and Water Assessment Tool (SWAT) consist of high-dimensional physical and empirical parameters. These parameters often need to be estimated/calibrated through inverse modeling to produce reliable predictions on hydrological fluxes and states. Existing parameter estimation methods can be time consuming, inefficient, and computationally expensive for high-dimensional problems. In this paper, we present an accurate and robust method to calibrate the SWAT model (i.e., 20 parameters) using scalable deep learning (DL). We developed inverse models based on convolutional neural networks (CNN) to assimilate observed streamflow data and estimate the SWAT model parameters. Scalable hyperparameter tuning is performed using high-performance computing resources to identify the top 50 optimal neural network architectures. We used ensemble SWAT simulations to train, validate, and test the CNN models. We estimated the parameters of the SWAT model using observed streamflow data and assessed the impact of measurement errors on SWAT model calibration. We tested and validated the proposed scalable DL methodology on the American River Watershed, located in the Pacific Northwest-based Yakima River basin. Our results show that the CNN-based calibration is better than two popular parameter estimation methods (i.e., the generalized likelihood uncertainty estimation [GLUE] and the dynamically dimensioned search [DDS], which is a global optimization algorithm). For the set of parameters that are sensitive to the observations, our proposed method yields narrower ranges than the GLUE method but broader ranges than values produced using the DDS method within the sampling range even under high relative observational errors. The SWAT model calibration performance using the CNNs, GLUE, and DDS methods are compared using R 2 and a set of efficiency metrics, including Nash-Sutcliffe, logarithmic Nash-Sutcliffe, Kling-Gupta, modified Kling-Gupta, and non-parametric Kling-Gupta scores, computed on the observed and simulated watershed responses. The best CNN-based calibrated set has scores of 0.71, 0.75, 0.85, 0.85, 0.86, and 0.91. The best DDS-based calibrated set has scores of 0.62, 0.69, 0.8, 0.77, 0.79, and 0.82. The best GLUE-based calibrated set has scores of 0.56, 0.58, 0.71, 0.7, 0.71, and 0.8. The scores above show that the CNN-based calibration leads to more accurate low and high streamflow predictions than the GLUE and DDS sets. Our research demonstrates that the proposed method has high potential to improve our current practice in calibrating large-scale integrated hydrologic models.
54 ENVIRONMENTAL SCIENCES↗