Plant hydraulic traits reveal islands as refugia from worsening drought

Abstract Relatively mesic environments within arid regions may be important conservation targets as ‘climate change refugia’ for species persistence in the face of worsening drought conditions. Semi-arid southern California and the relatively mesic environments of California’s Channel Islands provide a model system for examining drought responses of plants in potential climate change refugia. Most methods for detecting refugia are focused on ‘exposure’ of organisms to certain abiotic conditions, which fail to assess how local adaptation or acclimation of plant traits (i.e. ‘sensitivity’) contribute to or offset the benefits of reduced exposure. Here, we use a comparative plant hydraulics approach to characterize the vulnerability of plants to drought, providing a framework for identifying the locations and trait patterns that underlie functioning climate change refugia. Seasonal water relations, xylem hydraulic traits and remotely sensed vegetation indices of matched island and mainland field sites were used to compare the response of native plants from contrasting island and mainland sites to hotter droughts in the early 21st century. Island plants experienced more favorable water relations and resilience to recent drought. However, island plants displayed low plasticity/adaptation of hydraulic traits to local conditions, which indicates that relatively conserved traits of island plants underlie greater hydraulic safety and localized buffering from regional drought conditions. Our results provide an explanation for how California’s Channel Islands function as a regional climate refugia during past and current climate change and demonstrate a physiology-based approach for detecting potential climate change refugia in other systems.

Download Full-text

Carbon flux response and recovery to drought years in a hemi-boreal peat bog between different vegetation types

10.5194/egusphere-egu2020-13187 ◽

2020 ◽

Author(s):

James Benjamin Keane ◽

Sylvia Toet ◽

Phil Ineson ◽

Per Weslien ◽

Leif Klemedtsson

Keyword(s):

Climate Change ◽

Plant Traits ◽

Net Ecosystem Exchange ◽

Calluna Vulgaris ◽

High Water ◽

Future Climate ◽

Peat Bog ◽

Blanket Bog ◽

Drought Conditions ◽

Ghg Fluxes

Peatlands are a globally important store of approximately 500 Gt carbon (C), with northern blanket bogs accumulating ca. 23 g C m-2 y-1 from undecomposed organic material due to prevailing cool wet conditions. As a sink of carbon dioxide (CO2) they act as an important brake on anthropogenic climate change, but in the warming climate the likelihood of drought will increase. However, it is unknown how drought will affect the GHG balance of peatlands: dryer, warmer conditions will likely reduce net ecosystem exchange (NEE) of CO2 and increase soil respiration, potentially tipping these landscapes from sinks to sources of C. High water tables mean blanket bogs are major source of methane (CH4), an important greenhouse gas (GHG) with a global warming potential (GWP) 34 times that of CO2 over 100 years, but this may change in the future climate. It is further expected that the changing climate will alter blanket bog species composition, which may also influence the GHG balance, due to differences in plant traits such as those which form aerenchyma, e.g. Eriophorum vaginatum (eriophorum) and non-aerenchymatous species, e.g. Calluna vulgaris (heather). In order to understand how these important C stores will respond to climate change, it is vital to measure GHG responses to drought at the species level. &#160;&#160;We used an automated chamber system, SkyLine2D, to measure NEE and CH4 fluxes near-continuously from an ombrotrophic blanket peat bog. Five general ecotypes were identified: sphagnum (Sphagnum spp), eriophorum, heather, water and mixtures of species, with five replicates of each sampled. We followed the fluxes of CO2 throughout 2017- 2019 and CH4 throughout 2017- 2018, hypothesising that GHG fluxes would significantly differ between ecotypes. In 2018, the bog experienced drought conditions, allowing the comparison of NEE between drought and non-drought years, and the potential to recover the following year. Contemporaneous measurements of environmental variables were collected to infer details regarding the drivers of GHG fluxes.We found significant differences in CH4 emissions between ecotypes, F= 2.71, p< 0.02, ordered high to low: eriophorum > sphagnum > water > heather> mix, ranging from ca. 1.5 mg CH4-C m-2 d-1 to 0.5 mg CH4-C m-2 d-1. There were no significant differences in NEE between ecotypes, F= 0.54, p> 0.7, however, under 2018 drought conditions all ecotypes were net sources of CO2. We will also present NEE from 2019, when precipitation levels returned to typical conditions. Our results indicate that drought and shifts in vegetation composition under future climate may alter the C balance of hemi-boreal and potentially act as a positive feedback to climate change in a long-term scenario.

Download Full-text

Comparison of univariate and multivariate bias-adjusting methods for hydrological impact assessment under climate change conditions

10.5194/egusphere-egu2020-17387 ◽

2020 ◽

Author(s):

Jorn Van de Velde ◽

Bernard De Baets ◽

Matthias Demuzere ◽

Niko Verhoest

Keyword(s):

Climate Change ◽

Impact Assessment ◽

Climate Model ◽

Temporal Structure ◽

Regional Climate ◽

Multivariate Methods ◽

Rainfall Runoff ◽

Early 21St Century ◽

Hydrological Impact ◽

Input Variables

Climate change is one of the largest challenges currently faced by society, with an impact on many systems, such as hydrology. To locally assess this impact, Regional Climate Model (RCM) data are often used as an input for hydrological rainfall-runoff models. However, RCMs are still biased in comparison with the observations. Many methods have been developed to adjust this, but only during the last few years, methods to adjust biases in the variable correlation have become available. This is especially important for hydrological impact assessment, as the hydrological models often need multiple locally correct input variables. In contrast to univariate bias-adjusting methods, the multivariate methods have not yet been thoroughly compared. In this study, two univariate and three multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To do this, the methods are calibrated in the late 20th century (1970-1989) and validated in the early 21st century (1998-2017), in which the effect of climate change is already visible. The variables adjusted are precipitation, evaporation and temperature, of which the resulting evaporation and precipitation are used as an input for a rainfall-runoff model, to allow for the validation of the methods on discharge. The methods are also evaluated using indices based on the calibrated variables, the temporal structure, and the multivariate correlation. For precipitation, all methods decrease the bias in a comparable manner. However, for many other indices the results differ considerable between the bias-adjusting methods. The multivariate methods often perform worse than the univariate methods, a result that is especially pronounced for temperature and evaporation.

Download Full-text

Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1207-2007 ◽

2007 ◽

Vol 11 (3) ◽

pp. 1207-1226 ◽

Cited By ~ 14

Author(s):

B. Hingray ◽

N. Mouhous ◽

A. Mezghani ◽

K. Bogner ◽

B. Schaefli ◽

...

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Climate Models ◽

Regional Climate ◽

Climate Change Impacts ◽

Future Climate ◽

Climate Scenarios ◽

Change Impact ◽

Global Mean ◽

Potential Climate Change

Abstract. A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-of-the-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) cannot, however, be used to simulate such a large number of scenarios. This paper presents a methodology for obtaining future climate scenarios through a simple scaling methodology. The projections of several key meteorological variables obtained from a few regional climate model runs are scaled, based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (1961–1990) and a future period (2070–2099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The results obtained show that future climate conditions will have a significant influence on the performance of the system and that the uncertainty induced by the inter-RCM variability will contribute to much of the uncertainty of the prediction of the total impact. These CSRs cover the area considered in the 2001–2004 EU funded project SWURVE.

Download Full-text

An Investigation on Spatiotemporal Variability of Drought Events under Different Climate Change Scenarios, Case Study: Rakai District

10.20944/preprints202108.0483.v1 ◽

2021 ◽

Author(s):

Peter Wasswa ◽

Jane Tanner ◽

Geoffrey Sabiiti ◽

Moses Ojara ◽

Harriette Okal ◽

...

Keyword(s):

Climate Change ◽

Time Scales ◽

Hot Spot ◽

Regional Climate ◽

Spatiotemporal Variability ◽

Trend Test ◽

Severe Drought ◽

Climate Change Scenarios ◽

Rakai District ◽

Drought Conditions

Drought occurrences in Rakai district take a strange model and it has been rampantly increasing causing reduced income levels for farmers, reduced farm yields, increased food insecurity and migration, wetland degradation, illness and loss of livestock. The purpose of this study was to investigate past and future characteristics of drought due to climate change in Rakai district. Datasets used include dynamically downscaled daily precipitation and temperature data from Coordinated Regional Climate Downscaling Experiment (CORDEX) at 0.44°×0.44° resolution over the Africa domain. R software (Climpact2 package), was used to generate SPI values, Mann Kendall trend test and Inverse Distance Weighting methods were used to examine temporal and spatial drought characteristics respectively. Results depicted more extreme and severe drought conditions for SPI12 under historical compared to SPI3,Kakuto, Kibanda and Lwanda sub counties were the most drought hot spot areas, positive trends of drought patterns for both time scales were observed, though only significant under SPI12. Projected results revealed extreme and severe drought conditions will be observed under RCP8.5 SPI12, and the least will be under RCP8.5 SPI3 and SPI12. Results further reveal that Kakuto, Kibanda, Kiziba, Kacheera, Kyalulangira, Ddwaniro and Lwanda sub counties will be the most drought hot spot sub counties across all time scales. Generally projected results reveals that the district will experience more drought conditions under RCP8.5 compared to RCP4.5 for time scale SPI12 and therefore urgent actions are needed.

Download Full-text

Global Archaeology

Anthropology ◽

10.1093/obo/9780199766567-0260 ◽

2021 ◽

Author(s):

Claire Smith ◽

Heather Burke

Keyword(s):

Climate Change ◽

Information And Communication Technologies ◽

Forced Migration ◽

Free Markets ◽

Local Conditions ◽

Early 21St Century ◽

Local Case ◽

The World ◽

Big Picture ◽

The Impact

Global archaeology is the archaeology of globalization, documenting and unearthing the material markers of its origins, trajectories, manifestations, and repercussions. In the 1980s, when globalization first developed as a major force, there was a sense of excitement, along with some foreboding. Closely connected to ideals of democracy, individualism, capitalism, and free markets, globalization promised to break down barriers between people in different parts of the world, open borders, improve communications, and create new opportunities. Enhanced understandings and greater world peace seemed an inevitable outcome. However, in the early 21st century, it is clear that the world is undergoing profound environmental, economic, and social transformations, and that many of these changes are problematic. Globalization has exacerbated old stresses and dislocations and created new issues of serious concern. Inequality continues to grow, both within and between countries. Racism, discrimination, and exclusion are hot issues, as are nationalist backlashes against migrants. The lifestyles of people in First World countries are prompting changes in climate that may actually drown some Pacific nations. Archaeology functions as a tool for analyzing the material correlates of the shifts and schisms wrought by globalization. Descending from various strands of archaeological research that seek in one way or another to promote a better potential future, calls for a more “present- and future-oriented archaeology” (Harrison 2011, p. 144) have come from a variety of sub-disciplinary directions, including contemporary archaeology, Indigenous archaeology, and historical archaeology. Archaeologists can record sites on islands that may soon vanish due to climate change. They can identify the manner in which material inequities visually communicate and reinforce economic inequalities. They can identify disconnections and dislocations and provide new insights into social change as it takes place. They can highlight injustices that are normalized by contemporary values and, through this, provide impetus for a different future. Accordingly, this chapter focuses on those branches of archaeology that address the impact of globalization. The topics range from climate change and colonialism to forced migration and the influence of information and communication technologies. Underlying them all are issues of inequality, social justice, ethics, and human rights. Some publications focus on the “big picture” changes that have been wrought by globalization, while others provide in-depth local case studies. All are joined by a common perspective that values the assessment of local conditions in terms of how they are shaped by global circumstances.

Download Full-text

Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model

Climate Dynamics ◽

10.1007/s00382-005-0072-x ◽

2005 ◽

Vol 25 (7-8) ◽

pp. 815-835 ◽

Cited By ~ 128

Author(s):

S. C. Pryor ◽

R. J. Barthelmie ◽

E. Kjellström

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Energy Resources ◽

Northern Europe ◽

Change Impact ◽

Wind Energy Resources ◽

Potential Climate Change

Download Full-text

Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods

10.5194/hess-2020-639 ◽

2020 ◽

Author(s):

Jorn Van de Velde ◽

Matthias Demuzere ◽

Bernard De Baets ◽

Niko E. C. Verhoest

Keyword(s):

Climate Change ◽

Impact Analysis ◽

Climate Model ◽

Hydrological Cycle ◽

Temporal Structure ◽

Regional Climate ◽

Rainfall Runoff ◽

Late 20Th Century ◽

Climate Conditions ◽

Early 21St Century

Abstract. Climate change is one of the biggest challenges currently faced by society, with an impact on many systems, such as the hydrological cycle. To locally assess this impact, Regional Climate Model (RCM) simulations are often used as input for hydrological rainfall-runoff models. However, RCM results are still biased with respect to the observations. Many methods have been developed to adjust these biases, but only during the last few years, methods to adjust biases that account for the correlation between the variables have been proposed. This correlation adjustment is especially important for compound event impact analysis. As a simple example of those compound events, hydrological impact assessment is used here, as hydrological models often need multiple locally unbiased input variables to ensure an unbiased output. However, it has been suggested that multivariate bias-adjusting methods may perform poorly under climate change conditions because of bias nonstationarity. In this study, two univariate and three multivariate bias-adjusting methods are compared with respect to their performance under climate change conditions. To this end, the methods are calibrated in the late 20th century (1970–1989) and validated in the early 21st century (1998–2017), in which the effect of climate change is already visible. The variables adjusted are precipitation, evaporation and temperature, of which the former two are used as input for a rainfall-runoff model, to allow for the validation of the methods on discharge. Although not used for discharge modelling, temperature is a commonly-adjusted variable in both uni- and multivariate settings and therefore important to take into account. The methods are also evaluated using indices based on the adjusted variables, the temporal structure, and the multivariate correlation. For precipitation, all methods decrease the bias in a comparable manner. However, for many other indices the results differ considerably between the bias-adjusting methods. The multivariate methods often perform worse than the univariate methods, a result that is especially notable for temperature and evaporation. As these variables have already changed the most under climate change conditions, this reinforces the opinion that the multivariate bias-adjusting methods are not yet fit to cope with nonstationary climate conditions. Although the effect is slightly dampened by the hydrological model, our analysis still reveals that, to date, the simpler univariate bias-adjusting methods are preferred for assessing climate change impact.

Download Full-text