The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

Shuai, Pin; Mital, Utkarsh; Coon, Ethan T.; Dwivedi, Dipankar

doi:10.5194/hess-26-2245-2022

Cited by 20 publications

(17 citation statements)

References 47 publications

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“…Additionally, due to the large difference between native data and model instance resolution, it is likely that the effects of disaggregation of precipitation and temperature lapsing are main drivers for differences in streamflow estimates between model instances. However, Shuai et al (2022) found that by comparing the same forcing product with native implementations at various spatial resolutions the effect on streamflow estimates was relatively small. This was not the case for distributed variables in the basin (e.g.…”

Section: Relative Model Instance Differencesmentioning

confidence: 99%

Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Aerts

Hut²,

Giesen³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling-related challenges that remain unsolved. To the user, in light of model result interpretation, finer-resolution output might imply an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the form of varying spatial resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances are derived at three spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (Kling–Gupta efficiency (KGE) score) of the three model instances resulted in only a statistical difference between the 3 km and 200 m streamflow estimates. However, an assessment of sampling uncertainty shows high uncertainties surrounding the KGE score throughout the domain. This makes the conclusion based on the statistical testing inconclusive. The results do indicate strong locality in the differences between model instances expressed by differences in KGE scores of on average 0.22 with values larger than 0.5. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help to further understand the challenges that need to be resolved for hyper-resolution hydrological modelling.

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Section: Relative Model Instance Differencesmentioning

confidence: 99%

Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Aerts

Hut²,

Giesen³

et al. 2022

Hydrol. Earth Syst. Sci.

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show abstract

“…Thus, with high-resolution topography data, the ATS model is able to reproduce the hydrograph at the outlet reasonably well. This has been demonstrated across multiple catchments 63 . The good fit might further result from the fact that the catchment response is heavily affected by the inflow boundary conditions.…”

Section: Discussionmentioning

confidence: 70%

“…With elevation that spans more than 1200 m across the LT, orographic effects may enhance such localized precipitation events and thus, the precipitation within the catchment may show high variability that is not accounted for in this study. The importance of spatially heterogenous precipitation on catchment response has been reported by Shuai and collaborators 63 .…”

Section: Discussionmentioning

confidence: 71%

Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface–subsurface interactions

Özgen‐Xian

Molins

Johnson

et al. 2023

Sci Rep

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We computationally explore the relationship between surface–subsurface exchange and hydrological response in a headwater-dominated high elevation, mountainous catchment in East River Watershed, Colorado, USA. In order to isolate the effect of surface–subsurface exchange on the hydrological response, we compare three model variations that differ only in soil permeability. Traditional methods of hydrograph analysis that have been developed for headwater catchments may fail to properly characterize catchments, where catchment response is tightly coupled to headwater inflow. Analyzing the spatially distributed hydrological response of such catchments gives additional information on the catchment functioning. Thus, we compute hydrographs, hydrological indices, and spatio-temporal distributions of hydrological variables. The indices and distributions are then linked to the hydrograph at the outlet of the catchment. Our results show that changes in the surface–subsurface exchange fluxes trigger different flow regimes, connectivity dynamics, and runoff generation mechanisms inside the catchment, and hence, affect the distributed hydrological response. Further, changes in surface–subsurface exchange rates lead to a nonlinear change in the degree of connectivity—quantified through the number of disconnected clusters of ponding water—in the catchment. Although the runoff formation in the catchment changes significantly, these changes do not significantly alter the aggregated streamflow hydrograph. This hints at a crucial gap in our ability to infer catchment function from aggregated signatures. We show that while these changes in distributed hydrological response may not always be observable through aggregated hydrological signatures, they can be quantified through the use of indices of connectivity.

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“…We leveraged an existing ATS setup at the Coal Creek watershed (Shuai et al, 2022). The Watershed Workflow package (Coon and Shuai, 2021) was used to delineate the mesh, the surface land covers, and the subsurface characteristics of the watershed.…”

Section: Ats Model Setupmentioning

confidence: 99%

Knowledge-Informed Deep Learning for Hydrological Model Calibration: An Application to Coal Creek Watershed in Colorado

Jiang

Shuai

Sun

et al. 2022

Preprint

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Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (~50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2016 to 2018. Preliminary MI analysis on 50 realizations resulted in a down-selection of seven out of fourteen ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using all observations, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash-Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.65 to 0.80 when calibrating against Q alone. Using ET observation, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multi-year analysis and show that Q is best simulated (NSE: 0.85) by including in calibration the dry year flow dynamics that shows more sensitivity to subsurface characteristics than the other wet years. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

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The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

Cited by 20 publications

References 47 publications

Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

Understanding the hydrological response of a headwater-dominated catchment by analysis of distributed surface–subsurface interactions

Knowledge-Informed Deep Learning for Hydrological Model Calibration: An Application to Coal Creek Watershed in Colorado

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