Application of a Coupled Vegetation Competition and Groundwater Simulation Model to Study Effects of Sea Level Rise and Storm Surges on Coastal Vegetation

Teh, Su Yean; Turtora, Michael; Jiang, Jiang; Pearlstine, Leonard G.; Smith, Thomas J.; Koh, Hock Lye

doi:10.3390/jmse3041149

Cited by 17 publications

(10 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, vegetation dynamics are very sensitive to climate change in arid and semi-arid areas (Zhang et al, 2019a;Zhang et al, 2019b;Shao et al, 2019a). Using remote sensing data to dynamically monitor the interannual variation of long-term sequence vegetation has become a prevailing research topic (Ernst-Brock et al, 2019;Teh et al, 2015). Some scholars have used different vegetation indices and models to simulate and monitor changes in vegetation dynamics (Cui et al, 2018;Elhakeem & Elshorbagy, 2015;Herrmann et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Zhuang

Feng

et al. 2020

PeerJ

View full text Add to dashboard Cite

Background Vegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change. Methods In this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation. Results The Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%. Conclusions Our results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Zhuang

Feng

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…, Teh et al. ) or facilitation effects of local salinity reduction by plants along salinity gradients (Callaway ), as we had no data to calibrate such processes and refrained from adding another level of complexity to our model.…”

Section: Methodsmentioning

confidence: 99%

“…In IBC-grass_coast, two parameters were added to account for resistance to inundation and tolerance to salinity, introducing a trade-off, as salt-tolerant plants resistant to inundation suffer higher respiratory costs. We did not, however, include feedbacks of the vegetation on salinity (e.g., Sternberg et al 2007, Teh et al 2015 or facilitation effects of local salinity reduction by plants along salinity gradients (Callaway 2007), as we had no data to calibrate such processes and refrained from adding another level of complexity to our model. Apart from plant trait data collected within the COM-TESS project (seed mass, dry mass of plant components [leaves, stems, flowers, and seeds], maximum individual plant mass, specific leaf area, spacer length and biomass, and releasing height and canopy height), additional speciesspecific trait values for model parameterization were taken from trait databases BiolFlor (K€ uhn et al 2004), CloPla (Klime sov a and de Bello 2009), and LEDA (Kleyer et al 2008) to parameterize 33 species in total (Appendix S4: Table S2).…”

Section: Process-based Modelmentioning

confidence: 99%

“…Despite their differences in complexity, spatiotemporal resolution, and process detail, all models applied in this context (e.g., Done 1987, van de Koppel and Rietkerk 2004, Cordonnier et al 2008) are process-based models (PBM). What makes PBMs inherently fit for resilience studies, and sets them apart from statistical models, is their potential for continuous simulation over time, as one time step depends on the conditions of previous time steps.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel model coupling approach for resilience analysis of coastal plant communities

Schibalski

Körner

Maier

et al. 2018

Ecological Applications

View full text Add to dashboard Cite

Resilience is a major research focus covering a wide range of topics from biodiversity conservation to ecosystem (service) management. Model simulations can assess the resilience of, for example, plant species, measured as the return time to conditions prior to a disturbance. This requires process-based models (PBM) that implement relevant processes such as regeneration and reproduction and thus successfully reproduce transient dynamics after disturbances. Such models are often complex and thus limited to either short-term or small-scale applications, whereas many research questions require species predictions across larger spatial and temporal scales. We suggest a framework to couple a PBM and a statistical species distribution model (SDM), which transfers the results of a resilience analysis by the PBM to SDM predictions. The resulting hybrid model combines the advantages of both approaches: the convenient applicability of SDMs and the relevant process detail of PBMs in abrupt environmental change situations. First, we simulate dynamic responses of species communities to a disturbance event with a PBM. We aggregate the response behavior in two resilience metrics: return time and amplitude of the response peak. These metrics are then used to complement long-term SDM projections with dynamic short-term responses to disturbance. To illustrate our framework, we investigate the effect of abrupt short-term groundwater level and salinity changes on coastal vegetation at the German Baltic Sea. We found two example species to be largely resilient, and, consequently, modifications of SDM predictions consisted mostly of smoothing out peaks in the occurrence probability that were not confirmed by the PBM. Discrepancies between SDM- and PBM-predicted species responses were caused by community dynamics simulated in the PBM and absent from the SDM. Although demonstrated with boosted regression trees (SDM) and an existing individual-based model, IBC-grass (PBM), our flexible framework can easily be applied to other PBM and SDM types, as well as other definitions of short-term disturbances or long-term trends of environmental change. Thus, our framework allows accounting for biological feedbacks in the response to short- and long-term environmental changes as a major advancement in predictive vegetation modeling.

show abstract

“…For this purpose, a hydrology-salinity-vegetation model known as MANTRA (Teh et al 2013) was developed by coupling a spatially explicit model (MANHAM) for simulation of vegetation community dynamics along coastal salinity gradients with SUTRA, a USGS's groundwater flow and transport model. MANTRA has been applied to a Coot Bay Hammock along the southwestern coast of Everglades National Park to project the possible future changes in such coastal hammocks under sea level rise and storm surges (Teh et al 2015). This simulation study underscores that three conditions are necessary for a hardwood hammock to undergo a regime shift leading to a mangrove community; sufficiently severe damage to the existing hammock to open a gap to allow growth of invading propagules, a large input of salinity persisting for a long enough period of time to favor growth of mangrove propagules in competition with remaining freshwater vegetation, and an input of enough mangrove propagules to allow mangroves to be present in sufficient number to influence the future soil salinity.…”

Section: Introductionmentioning

confidence: 99%

MANTRA-O18: An Extended Version of SUTRA Modified to Simulate Salt and δ¹⁸O Transport amid Water Uptake by Plants

et al. 2018

View full text Add to dashboard Cite

Sea level rise and the increasing landward intrusion of storm surges pose the threat of replacement of salinity-intolerant vegetation of important coastal habitats by salinity-tolerant vegetation. Therefore, a means is needed to better understand the processes that influence this vegetation shift and to aid in the management of coastal resources. For this purpose, a hydrology-salinity-vegetation model known as MANTRA was developed by coupling a spatially explicit model (MANHAM) for simulation of vegetation community dynamics along coastal salinity gradients with SUTRA, a USGS groundwater flow and transport model. MANTRA has been used to project possible future changes in Coot Bay Hammock in southern Florida under conditions of gradually rising sea level and storm surges. The simulation study concluded that feasibility exists of a regime shift from hardwood hammocks to mangroves subject to a few conditions, namely severe damage to the existing hammock after a storm surge and a sufficiently persistent high salinity condition and high input of mangrove seedlings. Early detection of salinity stress in vegetation may facilitate sustainable conservation measures being applied. It has been shown that the δ 18 O value of water in the xylem of trees can be used as a surrogate for salinity in the rooting zone of plants, which is difficult to measure directly. Hence, the model MANTRA is revised into MANTRA-O18 by including the δ 18 O of the tree xylem dynamics. A simulation study by MANTRA-O18 shows that effects of increasing salinization can be detected many years before the salinity-intolerant trees are threatened with replacement.

show abstract

Application of a Coupled Vegetation Competition and Groundwater Simulation Model to Study Effects of Sea Level Rise and Storm Surges on Coastal Vegetation

Abstract: Global climate change poses challenges to areas such as low-lying coastal zones, where sea level rise (SLR) and storm-surge overwash events can have long-term effects on vegetation and on soil and groundwater salinities, posing risks of habitat loss critical to

Cited by 17 publications

References 36 publications

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Novel model coupling approach for resilience analysis of coastal plant communities

MANTRA-O18: An Extended Version of SUTRA Modified to Simulate Salt and δ¹⁸O Transport amid Water Uptake by Plants

Contact Info

Product

Resources

About

Application of a Coupled Vegetation Competition and Groundwater Simulation Model to Study Effects of Sea Level Rise and Storm Surges on Coastal Vegetation

Abstract: Global climate change poses challenges to areas such as low-lying coastal zones, where sea level rise (SLR) and storm-surge overwash events can have long-term effects on vegetation and on soil and groundwater salinities, posing risks of habitat loss critical to

Cited by 17 publications

References 36 publications

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Novel model coupling approach for resilience analysis of coastal plant communities

MANTRA-O18: An Extended Version of SUTRA Modified to Simulate Salt and δ18O Transport amid Water Uptake by Plants

Contact Info

Product

Resources

About

MANTRA-O18: An Extended Version of SUTRA Modified to Simulate Salt and δ¹⁸O Transport amid Water Uptake by Plants