\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Spatial co-localization of extreme weather events: a clear and present
danger}



\author[1]{Rosalind Dodd}%
\author[2]{David Chadwick}%
\author[2]{Ian Harris}%
\author[3]{Adrian Hines}%
\author[3]{Dan Hollis}%
\author[4,5]{Theo Economou}%
\author[6]{Dylan Gwynn-Jones}%
\author[6]{John Scullion}%
\author[7]{David Robinson}%
\author[2]{D.L. Jones}%
\affil[1]{Lincoln University}%
\affil[2]{Bangor University}%
\affil[3]{Met Office}%
\affil[4]{Met Office Hadley Centre}%
\affil[5]{University of Exeter}%
\affil[6]{Aberystwyth University}%
\affil[7]{Centre for Ecology and Hydrology}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Extreme weather events have become a dominant feature of the narrative
surrounding changes in global climate. with large impacts on ecosystem
stability, functioning and resilience, however, understanding of their
risk of co-occurrence at the regional scale is lacking. Based on the UK
Met Office's long-term temperature and rainfall records, we present the
first evidence demonstrating significant increases in the magnitude,
direction of change and spatial co-localization of extreme weather
events since 1961. Combining this new understanding with land use
datasets allowed us to assess the likely consequences on future
agricultural production and conservation priority areas. All land uses
are impacted by the increasing risk of at least one extreme event and
conservation areas were identified as hotspots of risk for the
co-occurrence of multiple event types. Our findings provide a basis to
regionally guide land use optimisation, land management practices and
regulatory actions preserving ecosystem services against multiple
climate threats.%
\end{abstract}%



\sloppy




\textbf{Abstract}

\textbf{Extreme weather events have become a dominant feature of the
narrative surrounding changes in global climate. with large impacts on
ecosystem stability, functioning and resilience, however, understanding
of their risk of co-occurrence at the regional scale is lacking. Based
on the UK Met Office's long-term temperature and rainfall records, we
present the first evidence demonstrating significant increases in the
magnitude, direction of change and spatial co-localization of extreme
weather events since 1961. Combining this new understanding with land
use datasets allowed us to assess the likely consequences on future
agricultural production and conservation priority areas. All land uses
are impacted by the increasing risk of at least one extreme event and
conservation areas were identified as hotspots of risk for the
co-occurrence of multiple event types. Our findings provide a basis to
regionally guide land use optimisation, land management practices and
regulatory actions preserving ecosystem services against multiple
climate threats.}

Recent large flood and drought events have received global media
attention. For example, unprecedented winter rainfall across the UK in
2013/14 resulted in extreme flooding and storm surges with large areas
of agricultural land under water for more than 80 days (ADAS, 2014),
while over 60 \% of the state of California's land area was under
varying severity of drought from 2011 to 2017 (U.S. Government, 2018).
Flooding and drought can have large economic impacts; the World Economic
Forum has rated extreme weather events as the most significant risk
facing humanity (World Economic Forum, 2018). Losses to the UK
agricultural sector of \selectlanguage{ngerman}£180 million were reported as a result of the
1995 drought and associated heatwave (Environment Agency, 2006), while
the 2013/14 flood led to losses of over £20 million (ADAS, 2014).
Similarly, the total economic impact of the European heatwave in 2013
was estimated at 11 billion Euros (Environment Agency, 2006), while
extreme snow was estimated to cost the US economy up to \$3 billion in
2016 (Lee and Lee, 2019). Natural ecosystems are also vulnerable, for
example, record heat and dry conditions in 2010/2011 led to a sudden
collapse of large areas of Australian eucalypt forest previously
considered to be resilient to drought (Matusick et al., 2013).
Furthermore, the hot and dry conditions of 2018-19 in the UK resulted in
unprecedented wildfires in the globally rare moorland habitat with 135
individual fires burning 29,334 ha of land (E.F.F.I.S., 2019). In 2019,
hot and dry conditions in Australia resulted in the generation of
mega-fires with unprecedented sizes and number of fires covering at
least 3.8 million ha of temperate forest (Nolan et al. 2020)

While there is a wealth of evidence that temperatures are increasing,
the pattern for rainfall is uncertain (IPCC, 2013) but predicted to
become temporally uneven with the majority of annual precipitation
totals occurring in a small number of intense events (Pendergrass and
Knutti, 2018). For many regions of the UK, climate models and historical
observations indicate that the frequency, intensity (Thompson et al.
2017; Kendon et al. 2014) and duration (Roudier et al. 2016) of winter
rainfall has increased, along with the incidence and intensity of short
burst summer downpours (ADAS, 2014) and the kinetic energy of autumn
rainfall (Alfieri et al., 2015). Models also predict an increase in the
frequency of short-term droughts of three to six months in duration
(Burke et al. 2010). These all have implications for agriculture,
conservation and human health.

To date, the majority of studies investigating the risk of extreme
weather events have focused on the global or national scale, and often
only on a single event type. There is greater uncertainty in changes at
the regional scale where the immediate impacts will be felt (Marotzke et
al. 2017). Spatial variation in weather patterns can be large and
analysis at the national scale masks regional differences in the risk of
occurrence and the expected event type (Zampieri et al., 2017).
Furthermore, extreme events might not occur in isolation and there are
an increasing number of examples of direct transitions from one extreme
weather regime to another (e.g. flood to drought or \emph{vice versa} )
(Mahony and Cannon, 2018; Swain et al. 2018; Loecke et al., 2017). In
the UK, heavy spring rainfall in 2012 led to 78 days of flooding, while
98 days of official drought were declared the following summer which the
media dubbed `the wettest drought on record' (Channel 4 News, 2012). In
2019 there were 5,600 flood warnings across England while groundwater
reserves were depleted in 25 areas (The Guardian,
2019)\textsuperscript{(} Such events have highlighted the need for
stakeholders, including farmers, water companies, forestry and
environmental protection and conservation bodies to prepare for the
possibility of both flooding and drought within the same year. So called
`compound events' have been identified by the World Climate Research
Program as a research priority (Alexander et al. 2016) and are likely to
have disproportionately severe impacts on ecosystems, potentially
tipping ecosystem functions into new trajectories (Johnstone et al.
2016).

To safeguard vulnerable ecosystems and the services they provide,
adaption in management may be required. However, the specific strategy
employed will vary depending on the event type. For example, the
re-introduction of grazing livestock to moorland could reduce fire risk
during dry, hot summers but could also increase the risk of compaction
during wet periods increasing subsequent flood risk. Similarly, planting
trees to sequester carbon may increase fire risk under dry conditions
leading to a potential reduction in air quality, water quality and human
health if planted in the wrong place (Lui et al. 2017).

To advise stakeholders and guide policy we need to understand the
regional risk posed by different (single and multiple) extreme events
and identify where they might impact delivery of ecosystem services
(e.g. food security, biodiversity, carbon storage) by different land use
types. In this study, we utilised the historical UK weather record held
by the UK Met Office National Climate Information Centre to examine, for
the first time, the change in frequency and distribution of, and
interaction between, indicators of four weather extremes; extreme heat,
extreme cold, high rainfall and low rainfall, based on thresholds
indicative of heatwaves, cold snaps, floods and droughts, between two
time periods 1961-1988 and 1989-2016. We integrated the results from
this analysis with national land cover data to identify extreme weather
hotspots in relation to ecosystem type and their ability to deliver
different ecosystem services.

These datasets were statistically interrogated to answer four key
questions: (1) Has the frequency of extreme events in the UK increased
between the two time periods? (2) Are there hotspots where the annual
risk of occurrence for two or more event types has increased? (3) Are
there areas of the UK where the probability of occurrence of two or more
types of event \emph{within the same year} has increased? and (4) Are
some vulnerable ecosystems more exposed to changes in risk of increased
numbers of events occurring than others?

Through this analysis, we provide evidence for the perceived increase in
the frequency of extreme events across the UK. To date, most studies of
this nature have focused on the incidence, or impact, at the national
scale. Our results show strong regional variation in the direction and
magnitude of change enabling the production of national risk maps which
can be used by stakeholders to guide land management and policy that
promotes adaptation to protect the delivery of ecosystem services from
different land uses.

Our analysis shows that between the two 28-year periods of high
resolution meteorological records there has been a notable change in the
frequency of threshold exceedance across the UK with strong regional
response patterns (Fig. 1). Temperature metrics showed the largest and
most widespread response but the direction of change varied. For extreme
heat events, there was a significant increase in the mean number of
events during the last 28 years, with the south east of England
experiencing the largest change, corresponding to on average 1.87
additional events each year. Significant increases (0.68--1.36) in the
mean number of extreme events also occurred across most of England,
except the north west and across the east of Northern Ireland, and the
far north of Scotland. Concurrently, the frequency of extreme cold
events decreased across all regions except for much of Wales and small
regions of south west England and northern Scotland. The magnitude of
change was greater than that for heat extremes, ranging from 1--2.3
fewer events each year. Response patterns in rainfall extremes were
weaker than for temperature; this is consistent with the large body of
research showing mixed results for predicted changes in rainfall
patterns across the globe (Alfieri et al. 2015). However, the results
show a significant increase in wet extremes ranging from 1.0 - 1.6
additional events each year in western Scotland to 0.8 - 1.0 additional
events in the Welsh border region, along parts of the south coast of
England and East Anglia, and in western Northern Ireland. The change in
extreme dry events was small with no significant increase overall and a
decrease of 0.9 events in the far north for Scotland. However, a strong
spatial pattern in extreme dry events did emerge, reflecting the changes
in heat events with an increase of up to 0.5 events in south east
England.

These changes in threshold exceedances for temperature and rainfall
provide statistical evidence underpinning the perceived increase in UK
heatwaves, floods and droughts over the past decade and provide insight
into which regions are most at risk. While the changes in temperature
drivers relate directly to heat waves or cold snaps, the use of
precipitation as a proxy for flood or drought events is less robust.
However, an increase in extremely wet periods in Scotland, parts of
southern England and Wales and Northern Ireland will heighten flood
risk. Furthermore, runoff extremes have been shown to increase more
quickly than precipitation extremes in a warming climate, and increases
in rainfall are likely to underestimate the risk of flash flood events
(Yin et al., 2018). These results corroborate the recent analysis of
observed river discharge trends between 1960 and 2010 which found the
largest increase in flood discharge in these areas (Yin et al., 2018).
Similarly, drought risk is a function of both rainfall and temperature
with prolonged high temperatures exacerbating soil dryness and providing
feedback loops further reducing rainfall, increasing surface
temperatures and promoting fire risk(Teuling, 2018). Seasonal analysis
of changes in extreme dry events revealed that the greatest change
occurs during spring (Fig. S1) when new season growth begins, and is a
vital period for sufficient soil moisture supply for agricultural crops.
Spring drought has been shown to be more detrimental to plant production
compared to summer drought conditions across a range of ecosystems (Song
et al., 2019). Increases in dry spring events may be exacerbated by a
spatially coupled increase in the number of periods of suitable winter
growing conditions utilising water reserves built up during preceding
wetter seasons (Fig. 2). Whilst not statistically significant
(at\emph{p} \textless{} 0.05), the indicative combination of i)
increased dry events with ii) an increase in heat events, and iii)
increased winter growing periods, points towards a heightened drought
risk in the future, especially in the south east of England where these
metrics showed the greatest increase. Furthermore, the probability that
a heat event and a dry event will occur within the same year was high
and ranged from 0.80 to 0.98 in this area (Fig. S2). Although the
evidence for increased extreme dry events, from this analysis is weak,
it corroborates recent modelling indicating high drought vulnerability
in East of England based on reported historical agricultural impacts
(Parsons et al., 2019)\textsuperscript{{[}30{]}}

The environmental impact of this increased frequency in extreme events
depends on the land use and the biodiversity and ecosystem services it
is expected to deliver. The response may vary, in magnitude and
direction, based on the type of ecosystem and the dominant services it
provides (Table 1, Table S1). We grouped the UK land cover categories
(Rowland et al. 2015) into four broad classes each providing specific
ecosystem services and levels of biodiversity: (1) Agriculture,
incorporating arable/horticultural and improved grasslands
(provisioning), (2) Woodlands, incorporating broadleaf and coniferous
woodlands (provisioning, regulating and biodiversity), (3) Conservation,
incorporating National Parks and Sites of Special Scientific Interest
(SSSIs) (supporting regulating and biodiversity), (4) Carbon stores,
incorporating heathland, heath grasslands and bogs (regulating).

The reduction in frequency of cold events (i.e. less frosts and snow)
shows an impact across all ecosystem types, ranging from 64\% of all the
land in SSSIs to \textgreater{}80\% of the total area under arable land
use, respectively. Simplistically, it might be assumed that a reduction
in winter cold events would be beneficial. However, many plants rely on
low winter temperatures for vernalisation and warmer winters can cause
increased pest and disease risk, loss of cold acclimation,
asynchronicity of biological lifecycles and increased runoff (Table 1).

Agricultural systems and broadleaf forests represented the largest
proportion of the total land area at increased risk of extreme heat
events and the arable sector in particular appears to the most affected
with 83\% of the total area at risk (Fig. 3a). This reflects the large
dominance of arable land use in the East of England. Furthermore, recent
research suggests that heat extremes have a larger impact on grain
yields than extremes in precipitation, highlighting the risk to arable
systems (Vogel et al., 2019) and, hot dry spells can influence
agricultural water use, especially under cropping. In the period between
2000 and 2017, the highest 2 years for abstraction for the purpose of
spray irrigation correspond with the lowest 2 years of annual levels of
rainfall (DEFRA, 2019a). Temperature extremes also dominated in improved
grasslands, with 56\% of the total area exposed to increase risk of
extreme heat which directly impacts on livestock production. However,
the proportion of grassland exposed to increases in extreme rainfall,
and therefore flooding, was greater than in arable systems. Soil carbon
(C) stores and coniferous forests appear to be most at risk of extreme
rain and flooding, with increased frequency of events occurring across
35--55\% of the total area. Forests are commonly proposed as mitigation
strategies to reduce flood risk through interception of rainfall and
increased soil infiltration (Stratford et al., 2017). However, extreme
rainfall events often override this increased infiltration capacity and
the potential to reduce the severity of major floods is limited. When
flooding does occur, the impact can be severe in commercial forestry
operations with largescale erosion and damage downstream from woody
debris. For soil C stores, reduced extreme cold and extreme rainfall
present the largest risk. This has large implications for the C cycle
and is likely to increase the release of soil C and decrease
sequestration through and increase in wet-drying cycles and microbial
respiration and increased erosion losses (Petrakis et al., 2017;
Reichstein et al., 2013; Kim et al., 2012; Schimel et al., 2007) (Table
1). Our analysis also indicates that large expanses of upland bog or
lowland fen peat are located in regions experiencing higher
temperatures, droughts and therefore potential fire risk. These events
threaten to exacerbate greenhouse gas emissions and destabilization of
terrestrial C stores.

Specific regions of the UK show a significant increase in frequency of
more than one extreme event type (Fig. 4). Risk hotspots, with
significant increased frequency of three threshold exceedances are
identified in the south coast of England, areas in the Welsh borders and
the north east of England, highlighting areas most at risk of unexpected
ecosystem response and largescale impacts on function (Table 1). Land of
high nature value appears to be at most risk of multiple extreme event
types with all three stress indicators increasing in frequency in 24 and
21\% of the total area covered by National Parks and SSSIs (Fig. 3b).
Due to the importance of these sites as niche habitats for rare or
endangered species this could have severe impacts on biodiversity and
genetic resources. This was seen following the 1995 UK drought which led
to a shift in butterfly communities from vulnerable specialised species
to widespread generalist species (De Palma et al., 2017).

Exposure to an extreme event can make ecosystems more susceptible to a
subsequent stress, magnifying impacts (Hohner et al., 2019; Ksudhal et
al., 2018; Mazdiyasni and AghaKouchak, 2015; Zscheischler et al., 2014)
with the potential to decrease the threshold by which climatic metrics,
such as precipitation amount, generate an extreme event, such as flood
or drought (DEFRA, 2019). Our results show that the overall UK mean
increase in the probability of all four event types occurring with the
same year low at 0.275. However, the impact on ecosystem function would
likely be extreme. The increase in frequency of extreme heat events was
the dominant driver of the response pattern, with the highest
probabilities in the south east of the UK and the lowest probabilities
in Scotland, Wales, Northern Ireland and north-west England (Table S2;
Fig. S2).

To illustrate the impact on agriculture, we have taken the UK arable
sector as a case study since the combination of adverse weather
conditions can magnify the impacts on agricultural production. In
particular, the combination of extreme wet spells and extreme dry spells
within the same year has been shown to be particularly detrimental for
crops. In 2017, there was an 8.3\%, 17\% and 19\% reduction in income in
England from key three crops, wheat, sugar beet and potatoes
respectively. This was attributed, in part, due to reduced yields caused
by wet spring conditions, hot dry summer and heavy autumn rains during
harvest\textsuperscript{{[}45{]}}. Reductions in yields reduced the
export value of wheat by 73\% and 84\% in 2017 and 2018 respectively,
and increased the import expenditure by 38\% and 79\% respectively
(DEFRA, 2018). The majority of the UK's arable and horticultural land
area is in the East of England, with 28\% of total wheat production and
62\% of sugar beet production located in the South East, and East Anglia
accounting for one third of England's potato crop (DEFRA, 2018). The
probability that extreme hot, dry and wet events will occur within the
same year is highest for this region of the country and ranges from
0.69--0.99 (Fig. S2) highlighting the vulnerability of this sector to
future climatic risk.

Globally, societies are facing unprecedented and complex threats to food
and water security, infrastructure and well-being due to climate change.
The increased frequency of multiple extreme events across different land
uses identified by our analysis is likely to have detrimental impacts on
the ecosystem service provision. While some benefits to service
provision have been identified, these are likely to be out-weighed by
the negative impacts (Table 1). In May 2019, the UK government declared
a state of climate emergency that was swiftly followed by Ireland,
France and Canada. Furthermore, large-scale land use change has been
identified as a strategy for the UK to meet its emission reductions in
the Paris Agreement (Committee on Climate Change, 2018), and its recent
target of net zero emissions by 2050. The evidence herein provides vital
information on the vulnerability of different areas and economic sectors
to climate extremes and should be used by UK policy makers, farm
advisers and environmental agencies to develop adaption strategies and
land use change policy tailored to the specific extreme event threat,
based on location and ecosystem type. This research highlights the
importance of considering the change in exposure of land to
(combinations of) extreme weather at the regional scale and adoption of
a similar approach in other countries could inform the safeguarding of
the vital ecosystem services on which society depends.



\textbf{Methods}

\textbf{Dataset used in this study}

We used the 5 km scale historical UK weather record held by the UK Met
Office's National Climate Information Centre (Perry and Hollis, 2005).
This gridded dataset covers the whole of the UK and includes daily
maximum and minimum temperature and rainfall data from observation
stations from 1960 to 2016.

We developed indices relating to the risk of occurrence of four extreme
weather events, namely (i) heat waves, (ii) cold snaps, (iii) extreme
rainfall (flood), and (iv) low rainfall (drought). We employed a
threshold approach and for each grid point extracted the frequency each
year that the five day rolling mean temperature or rainfall exceeded
this threshold for a set number of days. We split the resulting dataset
into two 27 year time periods, 1961--1988 and 1989--2016, reflecting the
Met Office's definition of long-term averages for weather data of 30
years (World Meteorological Organization, 2017), while keeping two
discrete time periods of equal length.

\textbf{Setting extreme weather thresholds}

With the exception of the index relating to drought, thresholds were set
based on deviation from the mean value of the whole dataset for each
grid point. Maximum daily temperature or rainfall above the
95\textsuperscript{th} percentile and minimum daily temperatures below
the 5\textsuperscript{th} percentile were considered extreme (World
Meteorological Organization, 2017a\textsuperscript{{[}49{]}}.
Temperature and rainfall conditions are spatially variable across the UK
and utilising percentiles as the threshold instead of a fixed value
allows for regional variation in normal conditions. What is considered
an extreme temperature or rainfall amount in one location may be
relatively normal for another and it is likely that the largest impact
on ecosystem function occurs when conditions are outside the norm rather
than at a fixed value (Zhang et al., 2011).

Using this approach, the following thresholds were proposed as an event
metric for extreme heat, cold and rainfall based on recommendations
provided in the draft guidelines on the definition and monitoring of
extreme weather and climate events produced by the World Meteorological
Organization (WMO) (World Meteorological Organization, 2018a).

\emph{Heat:} The number of times each year where the 5-day rolling mean
of the maximum temperature exceeds the 95\textsuperscript{th} percentile
of the whole dataset for 3 or more days.

\emph{Cold:} The number of times each year where the 5-day rolling mean
of the minimum temperature is below the 5\textsuperscript{th} percentile
of the whole dataset for 3 or more days.

\emph{Extreme rainfall:} The number of times each year where the 5-day
rolling mean of the daily rainfall total is above the
95\textsuperscript{th} percentile for 3 or more days.

\emph{Low rainfall:} The number of times each year where the 5-day
rolling mean of total daily precipitation was below 1 mm for 14 days or
more, based on a historical definition of agricultural drought used in
Britain of rainfall below 1 mm for more than 15 days (Heim et al.,
2002).

For this study, extreme rainfall was used as a proxy for flood risk.
While it is recognised that flood generation encompasses many complex
variables, including the hydrology and topography of the landscape, we
focus on rainfall totals as an indicator of the change in risk
potential. Daily rainfall totals in the preceding 0 to 3 days was shown
to be the best predictor of river flood events across the Swiss Alps
(Froidevaux et al., 2015). In the UK the total rainfall over 3 days was
linked to 40 year maximum peak river discharge and recorded flood events
in 3 out of 4 studied river catchments (Lavers et al., 2011). In China,
persistent extreme precipitation events, considered to indicate high
damage potential were defined as daily precipitation total above 50 mm
for 3 or more days (Chen and Zhai, 2013). Similarly to the flood index,
we used rainfall as a proxy indicator for drought risk. However,
prolonged dry spells, rather than a deviation from the minimum rainfall
long-term average are likely to be more significant in reducing soil
moisture content and increasing risk of drought.

\textbf{Data analysis}

To investigate how the risk of each event type occurring within a year
has changed between the two time periods, we plotted the change in the
number of events between 1961--1988 and 1989--2016 on a gridded map of
the UK, using output from the following model:

\emph{Single extreme weather event models:} Generalized Additive Models
or GAMs (Wood, 2017) were adopted as the modelling framework to
characterise the trends in extreme event frequency. This
well-established class of models allows for flexible characterisation of
the spatio-temporal variability of a modelled environmental variable and
has been used extensively to characterise natural hazards (Youndman and
Economou, 2017) and in modelling environmental variables more generally
(Wood, 2017). The data extracted relates to counts of events
\(y_{s,t}\)in grid cell \(s\) and year
\(t\). To capture the variability of these counts in space
and time, we assume a Poisson distribution with mean\(\mu_{s,t}\):
the mean count in cell \(s\) and year \(t\).
This mean is then characterised as a function of \(s\) and
\(t\) in the following way:

log(\selectlanguage{greek}\emph{μ\textsubscript{s,t}}\selectlanguage{english} ) = \selectlanguage{greek}\emph{µ\textsubscript{0}}\selectlanguage{english}
+\emph{f\textsubscript{T}} (\emph{t} ) + \emph{f\textsubscript{S}}
(\emph{s} ) + \emph{f\textsubscript{S,T}} (\emph{s,t} )

The three unknown functions \emph{f} (\textsuperscript{.}) were all
assumed smooth in the sense of capturing spatial and temporal variation
that does not change too extremely in neighbouring locations or points
in time. Much more extreme variation was captured by the random element
of the model (i.e. the Poisson variability). The one dimensional
function \emph{f\textsubscript{T}} (\emph{t} ) of time (in years) was
used to capture the overall temporal trend in the counts across space,
whereas \emph{f\textsubscript{S}} (\emph{s} ), a two-dimensional
function of longitude and latitude was used to capture overall spatial
variability (across time). Lastly, the three
dimensional\emph{f\textsubscript{S,T}} (\emph{s,t} ) captured
spatio-temporal variability, in the sense of allowing for different
spatial patterns for each time point (year). This captured inter-annual
variability in the spatial patterns exhibited by
\emph{y\textsubscript{s,t}} . Such models were estimated using the
statistical language R (R Core Team, 2019) and the package mgcv (Wood et
al., 2016).

The model was used to estimate event counts \emph{y\textsubscript{s,t}}
using the simulation from the predictive distribution\emph{p}
(\emph{y\textsubscript{s,t}} ). This distribution captures both the
Poisson variability in the counts as well as the uncertainty in
estimating the three unknown functions. From this, we computed the
distribution of the difference in mean counts between the two time
periods, i.e. mean count in 1989-2016 less the mean count in 1961-1988.
This difference was plotted as a Z score in figure 1 and figure 2 and
figure S1. Probabilities where this difference is not zero at the 5\%
significance level are termed significant (analogous to a p value
\textless{} 0.05).

The impact of rainfall on soil moisture is controlled to some extent by
seasonality of resource use. Additionally, the impact of soil moisture
deficit on plant response is related to growth stage. Therefore, we also
investigated the change in dry spells at the seasonal time scale. To do
this, we split each year into four, three-month time periods; Spring
(March, April, May), Summer (June, July, August), Autumn (September,
October, November) and Winter (December, January, February), and carried
out the above data analysis on the defined threshold for low rainfall in
each season.

\textbf{Multiple event interactions}

To investigate how the potential for the interaction of different
extreme events types has changed, we employed two methods to answer two
slightly different questions.

Are there areas of the UK where the annual risk of occurrence at an
individual grid point has increased between the two time periods
(1961--1988 and 1989--2016) for two or more of the classes of extreme
event?

To investigate this question we overlaid the grid points from the single
event analysis to determine those points where there was a significant
increase in two or more event metrics.

Are there areas of the UK where the risk of two or more different types
of extreme event occurring at an individual grid point within a single
year has increased between the two time periods?

To investigate this question we extended the methodology used for the
single events to allow for dependence between them, and investigated how
the probability of events of two or more types occurring within a single
year has changed over the two time periods.

\textbf{Multiple extreme weather event models}

To quantify the correlation between the counts of the various stress
events we used the single event models to detrend the data for each
event metric and create a transformed data set which does not exhibit
spatio-temporal variability. Using the transformed data, the dependency
across the various event metrics was quantified using correlation. The
single event Poisson models were used to transform the original
data\emph{y\textsubscript{s,t}} (for each stress) to the scale of a
Gaussian random variable with mean zero and variance one. At that scale,
all spatial and temporal variability has been factored out and the
sample correlations between the transformed counts for each event are
estimates of the dependency between each event.

A modified simulation technique was employed to sample from the
predictive distribution of the counts for each event, allowing for the
correlation between them. Firstly, we generated random samples of the
data at the detrended scale, respecting the correlation between the
event metrics at this scale. Then, we transformed these samples back to
original scale of the data to obtain a set of simulated counts in each
grid cell and year, thus maintaining both the spatio-temporal
variability in each event but also the correlation between event
metrics.

The thresholds were set as the sample mean of each event metric across
all grid cells and years. The joint probability that the annual mean
count of two or more event categories exceeds a particular threshold was
then determined. Comparison of differences in these probabilities
between 1961--1988 and 1989--2016 lie in the region between -1 and 1,
and conveys information about whether the risk of two or more stress
events occurring within one year has increased. Significant changes are
ones that are above 0.05 or below -0.05.

\textbf{Spatial mapping of the extreme weather event datasets}

Data were exported from R as ascii text files with grid cell centroid
locations provided as absolute integer coordinates in British National
Grid projection to facilitate import into ArcGIS 10.5 for visualisation
and further analyses. Null values (NA) representing offshore locations
were recoded to (-9999), ensuring compliance with numeric format prior
to import. The point locations were plotted and then spatially joined to
a pre-calculated vector 5 km grid, whereupon joined null values and
their corresponding grid squares were identified and removed. The
resulting datasets were then used to create thematic maps.

Geoprocessing (clipping) was used to extract underlying published land
cover data (Rowland et al, 2017). The resulting land cover data required
planimetric areas to be re-calculated, and these were subsequently
summarized by ecosystem type and aggregate area. The UK land cover
categories (Rowland et al. 2017) were grouped into four broad classes
each providing specific ecosystem services and levels of biodiversity:
(1) Agriculture, incorporating arable/horticultural and improved
grasslands (provisioning), (2) Woodlands, incorporating broadleaf and
coniferous woodlands (provisioning, regulating, and biodiversity), (3)
Conservation, incorporating National Parks and Sites of Special
Scientific Interest (SSSIs) (supporting, regulating and biodiversity),
(4) Carbon stores, incorporating heathland, heath grasslands and bogs
(regulating).

Where the analyses had revealed significant change, a field attribute
selection was used to identify the corresponding grid squares,
extracted, and then exported as separate geospatial datasets. To
facilitate further quantification of land cover types affected, the
boundaries between resulting significant grid squares were dissolved, so
that only the perimeters of aggregated squares remained. These two
datasets were combined to produce a map for each of the four land cover
categories overlain with areas of significant increase in frequency of
each extreme event metric.

\textbf{References}

ADAS, (2014), The Economic Impact of 2014 Winter Floods on Agriculture
in England. ADAS: Wolverhamption, UK. p. 46pp.

Alexander, L., X. Zhang, G. Hegerl, S. Senevirantne, A. Behrangi, E.
Fischer, (2016), Implementation Plan for WCRP Grand Challenge on
Understanding and Predicting Weather and Climate Extremes. The
''Extremes Grand Challenge'', W.C.R. Program, Editor. Geneva. pp. 33

Alfieri, L., P. Burek, L. Feyen, and G. Forzieri, (2015), Global warming
increases the frequency of river floods in Europe. \emph{Hydrology and
Earth System Sciences} , 19: 2247-2260.

Burke, E.J., R.H. Perry, and S.J. Brown, (2010), An extreme value
analysis of UK drought and projections of change in the
future.\emph{Journal of Hydrology} , 388: 131-143.

Channel 4 News. (2012), \emph{The wettest drought on record} . {[}cited
2018 14/05/18{]}; Available from:
https://www.channel4.com/news/the-wettest-drought-on-record

Chen, Y. and P. Zhai, (2013), Persistent extreme precipitation events in
China during 1951-2010\emph{.} \emph{Climate Research} , 57: 143-153.

Committee on Climate Change. (2018), Land use: Reducing emissions and
preparing for climate change. 2018\emph{, Committe on Climate Change} .
pp 1 - 100.

DEFRA, (2019), Water Abstraction Statisitics: England 2000 - 2017. p. 4.

DEFRA, (2019), The Future of Farming and Environment Evidence
Compendium. p. 122.

DEFRA, (2018) \emph{Agriculture in the U.K. 2018} . p. 119.

De Palma, A., R.L.H. Dennis, T. Brereton, S.R. Leather, and T.H. Oliver,
(2017), Large reorganizations in butterfly communities during an extreme
weather event. \emph{Ecography,} 40: 577-5852

E.F.F.I.S. (2019); Available from:
http://effis.jrc.ec.europa.eu/static/effis\_stats/effis-estimates/GB,
(acessed 12/07/19)

Environment Agency, (2006), The impact of climate change on severe
droughts. Major droughts in England and Wales from 1800 and evidence of
impact. Bristol UK. p. 54pp.

Fowler, H.J. and C.G. Kilsby, (2003) A regional frequency analysis of
United Kingdom extreme rainfall from 1961 to 2000. \emph{International
Journal of Climatology} , 23: 1313-1334.

Froidevaux, P., J. Schwanbeck, R.M. Weingartner, C. Chevalier, O.
Martins, (2015) Flood triggering in Switzerland: The role of daily to
monthly preceding precipitation\emph{.} \emph{Hydrology and Earth System
Sciences,} 19: 3903-3924.

Heim, R.R., (2002), A Review of Twentieth-Century Drought Indices Used
in the United States\emph{.} \emph{Bulletin of the American
Meteorological Society,} 83:1149-1166.

Hohner, A.K., C.C. Rhoades, P. Wilkerson, F.L. Rosario-Ortiz,
(2019)\emph{,} Wildfires Alter Forest Watersheds and Threaten Drinking
Water Quality\emph{.} \emph{Accounts of Chemical Research} , 52:
1234-1244.

IPCC, (2013), Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, T.F. Stocker, et al.,
Editors. Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA. p. 1535pp.

Johnstone, J.F., C.D. Allen, J.F. Franklin, L.E. Frelich, B.J. Harvey,
P.E. Higuera \emph{et al.,} (2016), Changing disturbance regimes,
ecological memory, and forest resilience. \emph{Frontiers in Ecology and
the Environment} 14: 369-378.

Kaushal, S.S., A.J. Gold, S. Bernal, and J.L Tank, (2018), Diverse water
quality responses to extreme climate events: an
introduction.\emph{Biogeochemistry,} 141: 273-279.

Kendon, E.J., N.M. Roberts, H.J. Fowler, M.J. Roberts, S.C. Chan, and
C.A. Senior, (2014) Heavier summer downpours with climate change
revealed by weather forecast resolution model. \emph{Nature Climate
Change} , 4: 570-576.

Kim, D.G., R. Vargas, B. Bond-Lamberty, and M.R. Turetsky, (2012),
Effects of soil rewetting and thawing on soil gas fluxes: a review of
current literature and suggestions for future
research.\emph{Biogeosciences} , 9: 2459-2483.

Lavers, D.A., R.P. Allan, E.F. Wood, G. Villarini, D.J. Brayshaw, A.J.
Wade, (2011), Winter floods in Britain are connected to atmospheric
rivers. \emph{Geophysical Research Letters} , 38. L23803

Lee, M. and J. Lee, (2019), Trend and Return Level of Extreme Snow
Events in New York City\emph{.} The \emph{American Statistician} .
DOI:~10.1080/00031305.2019.1592780

Liu, X., L.G. Huey, R.J Yokelson, V. Selimovic, I.J. Simpson, M. M\selectlanguage{ngerman}üller,
J.L. Jimenez, \emph{et al.} , (2017), Airborne measurements of western
U.S. wildfire emissions: Comparison with prescribed burning and air
quality implications. \emph{Journal of Geophysical Research:
Atmospheres} , 122: 6108-6129.

Loecke, T.D., A.J. Burgin, D.A. Riveros-Iregui, A.S. Ward, S.A. Thomas,
C.A. Davis, et al., (2017), Weather whiplash in agricultural regions
drives deterioration of water quality. \emph{Biogeochemistry,} 133:
7-15.

Mahony, C.R. and A.J. Cannon, (2018), Wetter summers can intensify
departures from natural variability in a warming climate. \emph{Nature
Communications} , 9: 783.

Marotzke, J., C. Jakob, S. Bony, P.A. Dirmeyer, P.A. O'Gorman, E.
Hawkins, et al., (2017), Climate research must sharpen its
view\emph{.Nature Climate Change,} 7: 89-91.

Matusick G., K.X. Ruthrof, C.N. Brouwers, B. Dell, and G.E.J. Hardy,
(2013), Sudden forest canopy collapse corresponding with extreme drought
and heat in a mediterranean-type eucalypt forest in southwestern
Australia. \emph{European Journal of Forest Research.} 132: 497-510.

Mazdiyasni, O. and A. AghaKouchak, (2015), Substantial increase in
concurrent droughts and heatwaves in the United States\emph{.Proceedings
of the National Academy of Sciences of the United States of America,}
112: 11484-11489.

Nolan, R.H., M.M. Boer, L. Collins, V. Resco de Dios, H. Clarke, M.
Jenkins, \emph{et al.} (2020) Causes and consequences of eastern
Australia's 2019 - 2020 season of mega-fires. \emph{Global Change
Biology} . 26: 1039 - 1041.

Parsons, D.J., R. Dolores, T. Maliko and I.P. Holman, (2019), Regional
variations in the link between drought indices and reported agricultural
impacts of drought. \emph{Agricultural Systems} , 173: 119-129.

Pendergrass, A.G. and R. Knutti, (2018) The Uneven Nature of Daily
Precipitation and Its Change\emph{.} \emph{Geophysical Research Letters}
, 45: 11980 - 11988.

Perry, M. and D. Hollis, (2005), The generation of monthly gridded
datasets for a range of climatic variables over the
UK.\emph{International Journal of Climatology,} 25: 1041-1054.

Petrakis, S., A. Seyfferth, J. Kan, S. Inamdar, and R. Vargas, (2017),
Influence of experimental extreme water pulses on greenhouse gas
emissions from soils. \emph{Biogeochemistry} , 133: 147-164.

R Core Team, (2019), R: A language and environment for statisical
computing. 2019, \emph{R Foundation for Statiscal Computing:} Vienna,
Austria.

Reichstein, M., M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I.
Seneviratne \emph{et al.,} (2013) Climate extremes and the carbon
cycle\emph{.} \emph{Nature,} 500: 287.

Roudier, P., J.C.M. Andersson, C. Donnelly, L. Feyen, W. Greuell, and F.
Ludwig, (2016) Projections of future floods and hydrological droughts in
Europe under a +2°C global warming. \emph{Climatic Change} , 135:
341-355.

Rowland, C.S., D. Morton, L. Carrasci Tornero, G. McShane, A. O'Niel,
and C. Wood, (2017), Land Cover Map 2015 (vector, GB). 2017, NERC
Environmental Information Data Centre.

Schimel, J., T.C. Balser, and M. Wallenstein, (2007) Microbial
stress-response physilogy and its implications for ecosystem
function.\emph{Ecology} , 88: 1386-1394.

Song, L., Y. Li, X. Wu, B. Guo, X. Tang, W. Shi, \emph{et al.,} (2019)
Divergent vegetation responses to extreme spring and summer droughts in
Southwestern China. \emph{Agricultural and Forest Meteorology} , 279:
107703.

Stratford, C., J. Miller, A. House, G. Old, M. Acreman, M.A.
Duenas-Lopez, \emph{et al.} , (2017), Do trees in UK-relevant river
catchments influence fluvial flood peaks? Centre for Ecology and
Hydrology. 46 pp.

Swain, D.L., B. Langenbrunner, J.D. Neelin, and A. Hall, (2018)
Increasing precipitation volatility in twenty-first-century
California\emph{. Nature Climate Change,} 8: 427-433.

The Guardian. 2019 was a bad year for floods and drought in England, say
charities. 2020. Available from
https://www.theguardian.com/environment/2020/mar/16/2019-was-bad-year-for-floods-and-drought-in-england-say-charities.

Thompson, V., N.J. Dunstone, A.A. Scaife, D.M. Smith, J.M. Slingo, S.
Brown, et al., (2017) High risk of unprecedented UK rainfall in the
current climate\emph{.} \emph{Nature Communications} , 8: 107.

Teuling, A.J., (2018), A hot future for European droughts. \emph{Nature
Climate Change} , 8: 364-365.

U.S. Government, (2018), U.S Drought Portal. {[}cited 2018 7/11/18{]};
Available from: https://www.drought.gov/drought/states/california.

Vogel, E., M.G. Donat, L.V. Alexander, M. Meinshausen, D.K. Ray, D.
Karoly, \emph{et al.} , (2019) The effects of climate extremes on global
agricultural yields. \emph{Environ. Res. Lett.} , 14: 054010.

Wood, S.N., (2017), Generalized additive models: an introduction with R.
2nd Edition ed. Texts in Statistical Science. Boca Raton,
Florida:\emph{CRC Press} . 146.

Wood, S.N., N. Pya, and B. Säfken, (2016), Smoothing Parameter and Model
Selection for General Smooth Models. \emph{Journal of the American
Statistical Association} . 111: 1548-1563.

World Economic Forum, (2018), The Global Risks Report 2018. Geneva. p.
pp66.

World Meteorological Organization\emph{,} (2017), WMO guidelines on the
claculation of climate normals. \emph{World Meterological
Organization,}Geneva, Switzerland. p. 29.

World Meteorological Organization, (2018) Draft guidelines on the
definition and monitoring of extreme weather and climate
events,\emph{World Meterological Organization} : Geneva. p. 43pp.

Yin, J., P. Gentine, S. Zhou, S.C. Sullivan, R. Wang, Y. Zhang, \emph{et
al.,} (2018), \emph{L} arge increase in global storm runoff extremes
driven by climate and anthropogenic changes. \emph{Nature
Communications} , 9: 4389.

Youngman, B.D. and T. Economou, (2017), Generalised additive point
process models for natural hazard occurrence\emph{.Environmetrics} , 28:
e2444.

Zampieri, M., A. Ceglar, F. Dentener, and A. Toreti, (2017). Wheat yield
loss attributable to heat waves, drought and water excess at the global,
national and subnational scales. \emph{Environ. Res. Let.} . 12: 064008.

Zhang, X., L. Alexander, G.C. Hegerl, P. Jones, A.K. Tank, T.C.
Peterson, \emph{et al} ., (2011) Indices for monitoring changes in
extremes based on daily temperature and precipitation data. \emph{Wiley
Interdisciplinary Reviews: Climate Change} , 2: 851-870.

Zscheischler, J., A.M. Michalak, C. Schwalm, M.D. Mahecha, D.N.
Huntzinger, M. Reichstein, \emph{et al.} , (2014) Impact of large-scale
climate extremes on biospheric carbon fluxes: An intercomparison based
on MsTMIP data\emph{.} \emph{Global Biogeochemical Cycles} , 28:
585-600.

\textbf{Acknowledgements} All authors acknowledge the financial support
provided by the Welsh Government and Higher Education Funding Council
for Wales through the Sêr Cymru National Research Network for Low
Carbon, Energy and Environment (SCNR-LCEE). We thank Alison
Kingston-Smith, Dimitra Loka, Felicity Hayes, and Mike Humphreys for
initial discussions on this project. We also acknowledge the valuable
feedback provided by David Thomas and the SCNR-LCEE Management Board on
the weather scenarios used here.

Figures and tables:

Main Text:

Figure 1: Change in the annual frequency of threshold exceedance between
the period 1961 - 1988 and 1989 - 2016. Positive numbers denote an
increase and negative numbers denote a decrease. A value of 1.0
corresponds to one additional event per year and a value of - 1.0
corresponds to one fewer event per year. Areas of significant change
(p\textless{}0.05) are denoted by hatching.

Figure 2: Change in the frequency of spells of (a) winter growing
conditions and (b) spring dry spells between the period 1961 - 1988 and
1989 - 2016. Significant areas of change (p\textless{}0.05) denoted by
hatching.

Figure 3: Total area (ha) of vulnerable ecosystem category exposed to a
significant increase in the frequency of a) single stress event types
and b) multiple stress event types.

Figure 4: Co-occurrence of a significant increase in the frequency of
threshold exceedance of each event type at the p \textless{} 0.05
significant level (a) and the interaction with vulnerable land use
category: agriculture (b), woodlands (c), Conservation areas (d) and
carbon stores (e).

\selectlanguage{english}\emph{Table 1| Impact of the most prevalent extreme weather events on
the main ecosystem services delivered within each land use type. The
main ecosystem service is given in brackets where P is provisioning, R
is regulation, S is supporting and C is cultural.}

Supplementary document

\emph{Figure S1: Change in the frequency of extreme dry events between
the period of 1961 -- 1988 and 1989---2016 during each meteorological
season. Significant areas of change ( p \textless{} 0.05) denoted by
hatching.}

\emph{Figure S2: Change in the joint probability that the annual mean
count of two or more event categories exceed their respective thresholds
between the period 1961---1988 and 1989---2016. These values lie in the
region of -- 1.0 to 1.0 and convey information on the change in the risk
of (a) two, (b) three or (c) four extreme events occurring within the
same year. Significant change was inferred for probabilities above 0.05
or below --0.05.}

\selectlanguage{english}\emph{~Table S1| Summary of the risk and benefits of different extreme
event stress on ecosystem service delivery based on and expert-led
comprehensive review of the literature.}

\selectlanguage{english}\emph{Table S2| Summary statistics of the change in probability that all
four extreme event thresholds will be exceeded within the same year for
the UK as a whole and for the individual regions defined by the Met
Office in the accompanying figure.}

\textbf{Hosted file}

\verb`Main text table.docx`  available at \url{https://authorea.com/users/327697/articles/455154-spatial-co-localization-of-extreme-weather-events-a-clear-and-present-danger}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

