The sensitivity of simulated streamflow to individual hydrologic processes across North America

Mai, Juliane; Craig, James R.; Tolson, Bryan A.; Arsenault, R.J.

doi:10.1038/s41467-022-28010-7

Cited by 23 publications

(17 citation statements)

References 77 publications

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“…Raven is a robust and highly generalized object‐oriented flexible modeling framework platform. It supports flexible customization in terms of a wide range of model structures, watershed discretization, process representations, forcing function estimation and interpolation methods and other numerical algorithms, which provides a standardized modeling platform and allows various types of hydrological modeling investigations, such as model structure sensitivity/uncertainty analysis (Chlumsky et al., 2021; Mai et al., 2022) and model inter‐comparison (Mai et al., 2021). Raven conveniently unifies the format for both models' input and output files.…”

Section: Methodsmentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

Self Cite

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Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.

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Section: Methodsmentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

Self Cite

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“…Studies in cold climates with seasonal snowpack have emphasized the role of snow accumulation and melting in GWR (Aygün et al, 2020; Dubois et al, 2022, 2021a; Greenwood & Buttle, 2018; Morgan et al, 2021; Wright & Novakowski, 2020; Young et al, 2019). Winter‐related model parameters (snowmelt coefficient, melting temperature, freezing soil) are also known to be very sensitive when simulating cold region hydrology (Dubois et al, 2021a; Mai et al, 2022; Nemri & Kinnard, 2020). More research therefore needs to be dedicated to refining the calibration of winter‐related model parameters in LC change contexts.…”

Section: Discussionmentioning

confidence: 99%

“…Winter-related model parameters (snowmelt coefficient, melting temperature, freezing soil) are also known to be very sensitive when simulating cold region hydrology (Dubois et al, 2021a;Mai et al, 2022;Nemri & Kinnard, 2020). More research therefore needs to be dedicated to refining the calibration of winter-related model parameters in LC change contexts.…”

Section: Limitations and Recommendationsmentioning

confidence: 99%

Impact of land cover changes on Long‐Term Regional‐Scale groundwater recharge simulation in cold and humid climates

Dubois

Larocque

Brunner

2023

Hydrological Processes

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In cold and humid climates, warming temperatures will result in longer growing seasons, leading to land cover changes that could have long-term impacts on groundwater recharge (GWR), in addition to the direct impacts of climate change. The objective of this study was therefore to investigate whether land cover (LC) changes need to be considered when simulating long-term regional-scale potential GWR in cold and humid climates by (1) quantifying how LC changes impact simulated GWR and (2) quantifying the combined impacts of LC and climate changes on the future GWR changes. Using the region of southern Quebec (Canada) as a case study and a water budget model, this work proposes an innovative coupling of land cover change scenarios and specific future climate conditions to simulate spatially distributed transient GWR over the 1951-2100 period. The results showed that including LC changes in long-term GWR simulations produced statistically significant increases in GWR compared to using a constant LC through time (average of +13 mm). Massive afforestation taking place on agricultural lands simulated for one of the scenario chains (RCP4.5) increased GWR by reducing runoff during the snow-dominated period (average À 17 mm). The results also showed that GWR was more sensitive to climate change for scenarios that included intense land cover changes. Additionally, the spatial distribution of the LC changes influenced their simulated impacts on GWR. Considering that the methodology was computationally feasible and entirely transferrable to the new CMIP6 ensemble, LC changes should be considered systematically in long-term groundwater resources simulations.climate change, cold and humid climates, groundwater recharge, HydroBudget Model, land cover change, regional-scale | INTRODUCTIONConsidering the central role of groundwater recharge (GWR) in sustainable groundwater management (Brunner et al., 2004;Foster & Ait-Kadi, 2012;Wada et al., 2010), numerous long-term simulations of GWR spanning several decades have been undertaken to anticipate possible future conditions, as reviewed for example in Atawneh et al. (2021), Larocque et al. (2019), and Smerdon (2017. In cold climate regions, warming temperatures have the potential to dramatically impact both the land cover (LC) and the hydrology (Arctic Climate Impact Assessment, 2004;Aygün et al., 2020;Pi et al., 2021). In cold regions, the main recharge period is currently during the spring thaw.

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“…This is complementary to other model diagnostic and development work that aims to understand model sensitivity and why models improve/degrade with changes. Recent studies have applied sensitivity analyses that consider both parametric and structural uncertainties to identify the water cycle components streamflow predictions are most sensitive to (Mai et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Benchmarking High-Resolution, Hydrologic Performance of Long-Term Retrospectives in the United States

Towler

Foks

Dugger

et al. 2022

Preprint

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Abstract. As high-resolution hydrologic models become more widespread, there is a pressing need for systematic evaluation and documentation of their performance. This paper develops and demonstrates a benchmark statistical design that evaluates the long-term performance of two process-oriented, high-resolution, continental-scale hydrologic models that have been developed to assess water availability and risks in the United States (US): the National Water Model v2.1 application of WRF-Hydro (NWMv2.1) and the National Hydrologic Model v1.0 application of the Precipitation-Runoff Modeling System (NHMv1.0). The evaluation is performed on 5,390 streamflow gages from 1983 to 2016 (~33 years) at a daily time step, including both natural and human-impacted catchments, representing one of the most comprehensive evaluations over the conterminous US. The benchmark consists of a suite of metrics for overall performance, their components, and hydrologic-specific signatures. Overall, the model applications show similar performance, with better performance at sites that are less disturbed by human activities, particularly in the West. Both model applications exhibit better performance in the Northeast, Southeast, Pacific Northwest, and high elevation sites in the West. Relatively worse performance is found in the Central region, Southwest, and lower-elevation West. Both models overestimate streamflow volumes at disturbed gages in the West, which could be attributed to not accounting for human activities, such as active management. Both models underestimate flow variability, especially the highest flows. The model applications showed differences in estimation of low flows, with consistent overestimation by the NWMv2.1, and both over- and under-estimation by the NHMv1.0. This benchmark provides a baseline to document performance and measure the evolution of each model application.

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The sensitivity of simulated streamflow to individual hydrologic processes across North America

Cited by 23 publications

References 77 publications

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Impact of land cover changes on Long‐Term Regional‐Scale groundwater recharge simulation in cold and humid climates

Benchmarking High-Resolution, Hydrologic Performance of Long-Term Retrospectives in the United States

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