Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export

Son, Kyongho; Lin, Laurence; Band, Lawrence E.; Owens, Emmet M.

doi:10.1002/hyp.13412

Cited by 19 publications

(19 citation statements)

References 49 publications

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“…This lends support to future analyses that consider sensitivity analysis of alternative model structures and parameters to discover dominant processes, as in Mai et al (2020) and Koo et al (2020a). The selected parameters across water quantity and quality-focused metrics would likely be different if TN concentrations were estimated from a process-based model, as in the dynamic mode of RHESSys, instead of statistically as a function of streamflow using WRTDS (e.g., RHESSys and WRTDS estimations are compared in Son et al (2019)).…”

Section: Determining Opportunities For Parameter Reductionmentioning

confidence: 78%

“…Even though the results are conditional on the specific parameter ranges (Shin et al, 2013), climatic input data and model outputs (Shields and Tague, 2012), and structural equations selected (Son et al, 2019), the resulting parameter identification should be generally useful to inform future studies that use RHESSys or other ecohydrologic models.…”

Section: Hydrologic Model Description: Rhessysmentioning

confidence: 99%

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Guidance on evaluating parametric model uncertainty at decision-relevant scales

Smith

Lin

Quinn

et al. 2021

Preprint

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Abstract. Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

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Section: Determining Opportunities For Parameter Reductionmentioning

confidence: 78%

Section: Hydrologic Model Description: Rhessysmentioning

confidence: 99%

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Smith

Lin

Quinn

et al. 2021

Preprint

Self Cite

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“…(4) RHESSys model (Son et al, 2019) The Regional Hydro-Ecological Simulation System (RHESSys) is a biophysically based, spatially explicit model that has the capacity to simulate both climate and vegetation change impacts and feedbacks between hydrologic processes and ecosystem C and nutrient cycling.…”

Section: Mechanistic Model Methodsmentioning

confidence: 99%

A review of mass flux monitoring and estimation methods for biogeochemical interface processes in watersheds

Gao

Yang

2020

J. Geogr. Sci.

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The magnitude of mass flux is closely associated with biogeochemical watershed processes, which can generate a considerable amount of pertinent information. Moreover, both the accuracy and precision of mass flux estimation results directly affects the perception of the ecological environmental status, which in turn affects both the formulation and implementation of river basin management planning. In practical applications, the true value of flux is unknown and can only be estimated. Flux results obtained using different monitoring and estimation methods also differ significantly. However, in existing studies on mass flux associated with biogeochemical watershed interfaces, the application of monitoring and estimation methods lacks uniform criteria or references. Accordingly, this study summarizes and deconstructs results from recent studies on biogeochemical watershed interface processes and compares the advantages, disadvantages and applicability of the monitoring and estimation methods used by these studies. This particular study is intended to be used as a reference for the selection of flux calculation methods.

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“…RHESSys mainly simulates water pollutants of dissolved nitrogen, dissolved organic carbon, ammonia nitrogen, and nitrate-nitrogen (Figure 8). RHESSys has been applied to simulate the dissolved organic nitrogen distribution [47] and the soluble organic carbon distribution [48] to support local regulation of water quality. Previous studies also suggested that the model performance can be improved by coupling with groundwater and phenology sub-models.…”

Section: Water Qualitymentioning

confidence: 99%

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

Chen

Liu

et al. 2020

Water

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As the Regional Hydro-Ecological Simulation System (RHESSys) is a tool to simulate the interactions between ecological and hydrological processes, many RHESSys-based studies have been implemented for sustainable watershed management. However, it is crucial to review a RHESSys updating history, pros, and cons for further improving the RHESSys and promoting ecohydrological studies. This paper reviewed the progress of ecohydrological studies employing RHESSys by a bibliometric analysis that quantitatively analyzed the characteristics of relevant studies. In addition, we addressed the main application progress, parameter calibration and validation methods, and uncertainty analysis. We found that since its release in 1993, RHESSys has been widely applied for basins (<100 km2) within mainly seven biomes. The RHESSys model has been applied for evaluating the ecohydrological responses to climate change, land management, urbanization, and disturbances, as well as water quality and biogeochemical cycle. While most studies have paid their attention on climate change, the focus has shifted to the application for land management in recent years. This study also identified many challenges in RHESSys such as the inaccessible data and parameters, oversimplified calibration approach, few applications for large-scale watersheds, and limited application fields. Therefore, this study proposed a set of suggestions to overcome the limitations and challenges: (1) Developing a new approach for parameter acquisition and calibration from multi-source data, (2) improving the applicability for a large-scale basin, and (3) extending the scope of application fields. We believe RHESSys can improve the understandings of human–environment relationships and the promotion of sustainable watersheds development.

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Modelling the interaction of climate, forest ecosystem, and hydrology to estimate catchment dissolved organic carbon export

Cited by 19 publications

References 49 publications

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Guidance on evaluating parametric model uncertainty at decision-relevant scales

A review of mass flux monitoring and estimation methods for biogeochemical interface processes in watersheds

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

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