A coupled surface–subsurface modeling framework to assess the impact of climate change on freshwater wetlands

Yu, Xuan; Bhatt, G.; Duffy, Christopher; Wardrop, Denice H.; Najjar, Raymond G.; Ross, Andrew; Rydzik, Matthew

doi:10.3354/cr01348

Cited by 10 publications

(11 citation statements)

References 58 publications

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“…Penn State Integrated Hydrologic Model future projections for this study were based on the SRES A2 emissions experiment using the MRI‐CGCM2.3.2 climate model calibrated with observational data from weather stations in the Susquehanna River Basin (Yu et al. ). Each watershed model is composed of smaller modeling units referred to as triangular irregular networks (TINs; Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Climate change has also been found to drive seasonal changes in wetland hydrology and water stress that impact wetlands across regions (Dawson et al 2003). The heterogeneity expected at the finer spatial and temporal scales of watershed or season has been revealed by high-resolution spatial and temporal models (Yu et al 2015), confirming the need for careful attention to variability and how it is manifested across scales. Thus, for the development of a management-relevant wetland vulnerability framework, we examined the sources of variability in vulnerabilities to climate change across spatial (ecoregion, small watershed), temporal (annual, seasonal), and organizational (broad, national wetland types, regionally based HGM types, ecoregionspecific HGM types) scales.…”

Section: Introductionmentioning

confidence: 92%

“…The Penn State Integrated Hydrologic Model (PIHM; Qu andDuffy 2007, Bhatt et al 2008) uses nationally available datasets, including USDA SSURGO soils data, land cover, USGS stream network and elevation data, and forcing variables from NASA Land Data Assimilation Systems (NLDAS) to construct watershed models, which we used to simulate historic (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998) and project future (2046-2065) hydrologic regimes (www.pihm.psu.edu). Penn State Integrated Hydrologic Model future projections for this study were based on the SRES A2 emissions experiment using the MRI-CGCM2.3.2 climate model calibrated with observational data from weather stations in the Susquehanna River Basin (Yu et al 2015). Each watershed model is composed of smaller modeling units referred to as triangular irregular networks (TINs; Fig.…”

Section: Construction Of Hydrologic Change Scenariomentioning

confidence: 99%

“…Finer resolution groundwater data were obtained using interpolation calculations in ESRI ArcGIS. Details regarding the PHIM modeling of our study watersheds can be found in Yu et al (2015). The model output used in this study is a measure of daily groundwater elevation above a constraining layer of bedrock.…”

Section: Construction Of Hydrologic Change Scenariomentioning

confidence: 99%

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Assessing the relative vulnerabilities of Mid‐Atlantic freshwater wetlands to projected hydrologic changes

et al. 2019

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Wetlands are known to provide a myriad of vital ecosystem functions and services, which may be under threat from a changing climate. However, these effects may not be homogenous across ecosystem functions, wetland types, ecoregions, or meso‐scale watersheds, making broad application of the same management techniques inappropriate. Here, we present a relative wetland vulnerabilities framework, applicable across a range of spatial and temporal scales, to assist in identifying effective and robust management strategies in light of climate change. We deconstruct vulnerability into dimensions of exposure and sensitivity/adaptive capacity, and identify relevant measures of these as they pertain to the attributes of wetland extent and plant community composition. As a test of the framework, we populate it with data for three primary hydrogeomorphic wetland types (riverine, slope, and depression) in seven small watersheds across four ecoregions (Ridge and Valley, Piedmont, Unglaciated Plateau, and Glaciated Plateau) in the Susquehanna River watershed in Pennsylvania. We use data generated from the SRES A2 emissions experiment and MRI‐CGCM2.3.2 climate model as input to the Penn State Integrated Hydrologic Model to simulate future exposure to altered hydrologic conditions in our seven watersheds, as expressed in two hydrologic metrics: % time groundwater levels occur in the upper 30 cm (rooting zone) during the growing season, and median difference between spring and summer mean water levels. We then examine the spatial and temporal scales at which each of the components of vulnerability (exposure and sensitivity/adaptive capacity) shows significant relative differences. Overall, we find that relative differences in exposure persist at a very fine spatial grain, exhibiting high variability even among individual watersheds in a given ecoregion. For temporal scale, we find strong seasonal but weak annual relative differences in exposure resulting from a magnification of summer dry‐down combined with winter and spring wet periods becoming wetter. Sensitivities/adaptive capacities show significant differences among wetland types. A comparison between our anticipated hydrologic alterations under climate change and historical changes in hydrology due to anthropogenic disturbance indicates potential shifts in hydrologic patterns that are far beyond anything that wetland managers have experienced in the past.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Construction Of Hydrologic Change Scenariomentioning

confidence: 99%

Section: Construction Of Hydrologic Change Scenariomentioning

confidence: 99%

See 2 more Smart Citations

Assessing the relative vulnerabilities of Mid‐Atlantic freshwater wetlands to projected hydrologic changes

et al. 2019

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“…Such models (see recent literature review by Fatichi et al [2016] and Maxwell et al [2014]) are essential tools to understand watershed hydrology [De Schepper et al, 2015], coastal salinization [Yang et al, 2013], subsurface storage [Niu et al, 2014], and climate change impacts assessment [Maxwell and Kollet, 2008], to name a few. Furthermore, due to the comprehensive and physical representation of hydrological processes, coupled surface-subsurface models are of interest to many other research communities (e.g., wetland ecosystem [Fossey et al, 2015;Golden et al, 2014;Yu et al, 2015a] and geomorphology [Paola et al, 2006;Zhang et al, 2016]). Collaboration between hydrologic modelers and other scientists has proven to be a key method to answer synthetic and integrated research questions on water-related research with physical, ecological, and social sciences [Brooks et al, 2015;Clark et al, 2015;Penn et al, 2016;Sutanudjaja et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Open science in practice: Learning integrated modeling of coupled surface‐subsurface flow processes from scratch

Duffy

Rousseau

et al. 2016

Earth and Space Science

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Integrated modeling of coupled surface-subsurface flow and ensuing role in diverse Earth system processes is of current research interest to characterize nonlinear rainfall-runoff response and also to understand land surface energy balances, biogeochemical processes, geomorphological dynamics, etc. A growing number of complex models have been developed for water-related research, and many of these are made available to the Earth science community. However, relatively few resources have been made accessible to the potentially large group of Earth science and engineering users. New users have to invest an extraordinary effort to study the models. To provide a stimulating experience focusing on the learning curve of integrated modeling of coupled surface-subsurface flow, we describe use cases of an open source model, the Penn State Integrated Hydrologic Model, PIHM. New users were guided through data processing and model application by reproducing a numerical benchmark problem and a real-world watershed simulation. Specifically, we document the PIHM application and its computational workflow to enable intuitive understanding of coupled surface-subsurface flow processes. In addition, we describe the user experience as important evidence of the significance of reusability. The interaction shows that documentation of data, software, and computational workflow in research papers is a promising method to foster open scientific collaboration and reuse. This study demonstrates how open science practice in research papers would promote the utility of open source software. Addressing such open science practice in publications would promote the utility of journal papers. Further, popularization of such practice will require coordination among research communities, funding agencies, and journals.

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Role of meteorological controls on interannual variations in wet‐period characteristics of wetlands

Liu

Kumar

2016

Water Resources Research

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Many ecological functions of wetlands are influenced by wet‐periods, i.e., the time interval when groundwater table (GWT) is continuously near the land surface. Hence, there is a crucial need to understand the controls on interannual variations of wet‐periods. Given the scarcity of long‐term measurements of GWT in wetlands, understanding variations in wet‐periods using a measurement approach alone is challenging. Here we used a physically based, fully distributed hydrologic model, in synergy with publicly available hydrologic data, to simulate long‐term wet‐period variations in 10 inland forested wetlands in a southeastern US watershed. A Bayesian regression and variable selection framework was then implemented to (a) evaluate the extent to which the simulated wet‐periods can be estimated and predicted by precipitation (Ppt) and potential evapotranspiration (PET) and (b) infer the relative roles of seasonal Ppt and PET. Our results indicate that wet‐period start date and duration could vary by more than 6 months during the 32 year simulation period. Remarkably, 60–90% of these variations could be captured using regressions based on seasonal Ppt and PET in most wetlands. Effects of seasonal meteorological conditions on wet‐period variations were found to be nonuniform, which indicate that the annual variables may not explain interannual variations in wet‐periods. The Bayesian framework was able to predict wet‐period variations with errors smaller than 1 month at a 90% confidence level. The presented framework provides a minimalistic approach for estimating and predicting wet‐period variations in wetlands and may be used to understand the future responses of associated ecological functions in wetlands.

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A coupled surface–subsurface modeling framework to assess the impact of climate change on freshwater wetlands

Cited by 10 publications

References 58 publications

Assessing the relative vulnerabilities of Mid‐Atlantic freshwater wetlands to projected hydrologic changes

Assessing the relative vulnerabilities of Mid‐Atlantic freshwater wetlands to projected hydrologic changes

Open science in practice: Learning integrated modeling of coupled surface‐subsurface flow processes from scratch

Role of meteorological controls on interannual variations in wet‐period characteristics of wetlands

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