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BMeteorol. Z. (Contrib. Atm. Sci.), PrePub DOI 10.1127/metz/2021/1101 PrePub Article© 2021 The authors

Climate changes and their impact on selected sectors of thePolish-Saxon border region under RCP8.5 scenarioconditionsBartłomiej Miszuk∗, Mariusz Adynkiewicz-Piragas, Agnieszka Kolanek, Iwona Lejcus,Iwona Zdralewicz and Marzenna Stronska

Institute of Meteorology and Water Management – National Research Institute, Wrocław, Poland

(Manuscript received June 14, 2021; in revised form August 31, 2021; accepted September 16, 2021)

AbstractClimate changes are one of the most important factors affecting various spectrum of the human activity andnatural environment. They can significantly impact technical infrastructure, modify structures of cultivation,and have an influence on species structure. Furthermore, some of the changes may also negatively affect thehuman organism which consequently influence health and tourism issues. The region of Polish-Saxon borderis characterized by a high variability in terms of land use and natural environment. Thus, the problem ofclimate changes is one of the most important issues in this area. The goal of this paper was to assessed theimpact of climate changes on the sectors of biodiversity, forestry, agriculture, transport, tourism, and publichealth, considering the aspects of sensitivity and risk assessment. The results of climate changes indicatedobserved or projected significant changes in thermal, precipitation, snow and storm conditions. The analysison sensitivity and risk showed a high spatial variability depending on sector. The northern part of the regionis usually endangered in the context of biodiversity and forestry, while the highest risk and sensitivity fortourism are noticed in the mountains. In the case of transport and public health, climate changes can usuallyaffect them in densely populated areas, whereas the central part of the region is most at risk for the sectorof agriculture. The results of this research can be a basis for further analysis related to adaptation to climatechanges.

Keywords: climate changes, sensitivity, risk assessment, Lower Silesia, Saxony

1 Introduction1

Climate changes are currently one of the most im-2

portant processes in Central Europe. They have a sig-3

nificant impact on the human life, technical infras-4

tructure, natural environment and economy. In Ger-5

many, air temperature in the 20th century increased by6

0.8–1.0 °C (Zebisch et al., 2005), while in 1881–20197

rose by 1.6 °C (Kaspar and Friedrich, 2021; Im-8

bery et al., 2021). The total increase in mean air9

temperature in Poland since 1951 amounted to 2 °C10

(IMGW-PIB, 2021; Ustrnul et al., 2021). In the case11

of precipitations, the changes were characterized by12

a high spatial variability. No statistically significant13

trends were usually noticed for the regions of Germany14

and Poland (Zebisch et al., 2005; Marosz et al., 2011;15

DWD, 2020; Łupikasza and Małarzewski, 2021).16

However, in some cases, an increase in the frequency17

of strong precipitations and droughts was either ob-18

served or projected (Kundzewicz and Jania, 2007,19

Hänsel and Matschullat, 2009; Schwarzak et al.,20

2015; Somorowska, 2016, Umweltbundesamt, 2019;21

Pinskwar et al., 2019; Pinskwar and Chorynski,22

2021).23

∗Corresponding author: Bartłomiej Miszuk, Institute of Meteorology andWater Management – National Research Institute, ul. Parkowa 30, 51-616Wrocław, Poland, Bartlomiej.Miszuk@imgw.pl

The analysis carried out in the study focused on 24

the Polish-Saxon trans-border area, located in Central 25

Europe and bordering with Czechia in the south. The 26

challenges posed by climate changes in this region 27

are very important because of a significant variety in 28

terms of land use, economy, social aspects and the ge- 29

ographical factor that can affect numerous sectors. Most 30

of the region is used for agriculture or forestry pur- 31

poses (Lünich et al., 2014). There are also health re- 32

sorts located in the area, while the mountains are at- 33

tractive for tourists. The results of the research car- 34

ried out for 1971–2010 within KLAPS and NEYMO 35

projects showed that mean annual air temperature in the 36

region increased by 1.0–1.2 °C (Mehler et al., 2014; 37

Pluntke et al., 2016). The positive trends were also 38

noticed for heat and summer days occurrence and for 39

the indices concerning vegetation. Consequently, this 40

caused changes in biological state (Chmielewski et al., 41

2005) and bioclimate conditions which had a significant 42

impact on tourism and health-related issues (Mehler 43

et al., 2014; Miszuk et al., 2016; Miszuk, 2021). Simul- 44

taneously, the frequency of extreme precipitations and 45

dry periods increased, especially in the summer season 46

(Lünich et al., 2014). This confirmed the results carried 47

out for different periods (Hänsel and Matschullat, 48

2006; Łupikasza et al., 2011; Hänsel et al., 2019). The 49

climate projections indicate further increase in air tem- 50

© 2021 The authorsDOI 10.1127/metz/2021/1101 Gebrüder Borntraeger Science Publishers, Stuttgart, www.borntraeger-cramer.com

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2 B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region Meteorol. Z. (Contrib. Atm. Sci.)PrePub Article, 2021

perature and the intensification of heat stress in the fol-51

lowing decades (Schwarzak et al., 2014; Miszuk et al.,52

2016). Furthermore, precipitation totals can addition-53

ally decrease by the end of the 21st century (Lünich54

et al., 2014; Pluntke et al., 2016; Adynkiewicz-Pira-55

gas and Miszuk, 2020), especially under RCP8.5 sce-56

nario.57

In Germany, extreme weather events caused infras-58

tructure losses amounting to C 3.1 billion (Umwelt-59

bundesamt, 2019), while in Poland, the losses trig-60

gered by a single heavy rainfall episode in an ur-61

ban area could reach as much as PLN 130 million62

(C 28 million) (IOS, 2018). As a result, the problem63

of climate changes and their impact on different sec-64

tors is one of the most important issues undertaken65

within the UE policies (i.e. Siddi, 2020; EC, 2021)66

The studies focusing on these aspects indicated critical67

threats to biodiversity (IPCC, 2002; Reid, 2006), em-68

phasized a high significance of thermal, precipitation69

and wind conditions in the sector of transport (Colin70

et al., 2016; Christodoulou and Demirel, 2018, UN-71

ECE, 2020) and highlighted the impact of heat stress72

on public health and tourism (Amelung et al. 2007;73

Paci, 2014; IPCC, 2014, Scott et al. 2019). In the case74

of forestry and agriculture, they concerned the influ-75

ence of climate conditions on crop yield and species76

structure (Spathelf et al., 2013; IPCC, 2014; EEA Re-77

port, 2019). Similar analysis for these sectors were con-78

sidered in both German and Polish studies and strate-79

gies (Zebisch et al. 2005; Schröter et al. 2006; Ger-80

man Strategy for Adaptation to Climate Change,81

2008; Hoy et al., 2011; Kundzewicz and Matczak,82

2012; IOS, 2013, 2018; Ministerstwo Srodowiska,83

2013; Kundzewicz et al., 2018; Schliep et al., 2018;84

Mücke and Litvinovitch, 2020).85

In order to examine the impact of climate change on86

economic, social and environmental aspects, risk analy-87

sis are carried out (i.e., Schröter et al., 2006, Settele88

et al., 2010; Buth et al., 2015; Berry et al., 2018; IOS,89

2018, Fronzek et al., 2019; Kahlenborn et al., 2021).90

Besides the evaluation of climate changes, they often91

consider the issues of sensitivity and vulnerability, con-92

cerning the respondence of a system to climate changes93

and the extent to which climate changes can damage a94

system (IPCC, 1996). Risk levels can be assessed us-95

ing risk matrix which was frequently applied for risk96

assessment purposes (i.e., Smolarkiewicz et al., 2011;97

Duijm, 2015).98

The objective of this study is to evaluate the im-99

pact of climate changes on the sectors of biodiversity,100

forestry, agriculture, transport, tourism and public health101

in the Polish-Saxon border area, using RCP8.5 scenario102

and considering the evaluation of sensitivity and risk re-103

lated to climate changes. The aspects of risk assessment104

for water management and energy production were pre-105

sented by Adynkiewicz-Piragas and Miszuk (2020).106

The risk analysis for the selected sectors considered107

the evaluation of probability referring to the changes in108

particular meteorological indices and the consequences109

of the changes for each of the sectors. The results of 110

this research based on the outcomes of the projects of 111

‘TRANSGEA – Cross-border co-operation in local ac- 112

tions to adapt to climate changes’ and ‘WIKT – Support 113

for measures related to climate protection in the cross- 114

border region’, accomplished within the Programme 115

2014–2020 INTERREG V-A Poland-Saxony. 116

2 Materials and methods 117

2.1 Research area 118

The analysis concerned the evaluation of climate con- 119

ditions, sensitivity to climate changes and risk as- 120

sessment for 172 communes located in nine Polish 121

and two German districts (Fig. 1). Climate condi- 122

tions and probability of their changes were examined 123

based on both current and projected data. The evalu- 124

ation was carried out for different hypsometric zones: 125

lowlands (< 150 m a.s.l.), uplands (151–300 m a.s.l.), 126

mountain foreland (301–600 m a.s.l.) and mountains 127

(> 600 m a.s.l.). Each commune was assigned to a spe- 128

cific zone depending on its mean altitude. Similar hypso- 129

metric classes were selected for the purposes of KLAPS 130

project that concerned climate conditions in the Polish- 131

Saxon region (Mehler et al., 2014). As there are only 132

a few meteorological stations in the region and its sur- 133

roundings where at least several meteorological vari- 134

ables are measured, each of the zones was represented 135

by one meteorological station. The analysis carried out 136

within KLAPS and NEYMO projects and further stud- 137

ies showed that the direction of climate changes and 138

their statistical significance are usually similar within 139

a given hypsometric level, while differences are more 140

often noticed in terms of the magnitude of changes 141

(Lünich et al. 2014; Mehler et al., 2014; Pluntke 142

et al., 2016; Miszuk, 2021). Analysis of sensitivity (and 143

consequently risk assessment) was carried out for each 144

commune separately. 145

2.2 Meteorological data and climate 146

projections 147

Climate analysis was carried out using meteorological 148

data from DWD (Germany) and IMGW-PIB (Poland) 149

stations in the region. This concerned daily records of 150

air temperature, precipitations, frequency of storms and 151

snow cover for 1971–2015 period. Data from four sta- 152

tions was taken into consideration. Each station was 153

located in a different hypsometric zone – Legnica 154

(122 m a.s.l.) represented the lowlands, while the sta- 155

tions of Görlitz (238 m a.s.l.), Jelenia Góra (342 m a.s.l.) 156

and Sniezka (1603 m a.s.l.) corresponded to uplands, 157

mountain foreland and mountains, respectively. For the 158

purposes of storm characteristics for the uplands, data 159

from Zielona Góra (192 m a.s.l.) was used due to no 160

records available from Görlitz. Based on the data, analy- 161

sis related to the course of mean annual values of partic- 162

ular thermal, precipitation, storm and snow indices in 163

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 3

Figure 1: The Polish-Saxon border region, its districts and hypsometric zones.

the considered multiannual period was carried out. The164

trends were examined from the perspective of their di-165

rections and statistical significance (level of 0.05) us-166

ing linear regression analysis. They were also verified167

with the Mann Kendall test. The data was examined168

from the perspective of its completeness and reliabil-169

ity. Homogeneity was tested with the Standard Normal170

Homogeneity Test (SNHT) (Alexandersson, 1986;171

Alexandersson and Moberg, 1997). The correlation172

coefficient was also calculated for the thermal, precipi-173

tation, snow and storm data between particular stations174

as well as between these stations and the stations lo-175

cated in the adjacent regions – Dresden (227 m a.s.l.)176

and Wrocław (120 m a.s.l.). Strong relationships, char-177

acterized by a statistical significance, were noticed for178

the thermal, precipitation and storm data. In the case179

of snow cover frequency, no significant correlation was180

found between the stations located lower down (includ-181

ing Dresden and Wrocław) and Sniezka. Such a situa-182

tion results from the totally different snow regime in the183

highest parts of the Sudetes Mountains when compared184

to the lower hypsometric zones.185

Furthermore, changes in climate conditions for the186

future periods were examined. They based on climate187

projections carried out by Climate & Environment Con-188

sulting Potsdam GmbH (Kreienkamp et al., 2013) for189

the needs of the NEYMO and KLAPS projects. The ba-190

sis for the projections development were global models191

simulations (ECHAM5 MPI-OM and MPI-ESM-LR).192

Regional Climate Model of WETTREG was used in193

terms of downscaling. Two approaches regarding down-194

scaling can be distinguished: dynamical (which uses re-195

gional climate models to carry out regional informa-196

tion consistent to the large scale data gained from the197

general circulation models) and statistical (concerning198

statistical relations between regional data and selected199

parameters of the general circulation models to access200

changes on a local scale) (Belli and Haberlandt,201

2012). WETTREG is a statistical type of model that202

was developed to evaluate climate projections for the re- 203

gion of Central Europe. The process of downscaling is 204

in this case related to the assessment of circulation con- 205

ditions, a stochastic weather generator and a statistical 206

regression method. In the case of circulation conditions, 207

WETTREG defines weather in various classes, depend- 208

ing on regarded meteorological factors. The stochastic 209

weather generator enables a development of various pro- 210

jections that are independent from each other and char- 211

acterized by equal probability. Statistical regression is 212

connected with calculations of parameters based on the 213

modeled simulations. Because of different approaches 214

related to downscaling, the simulations based on WET- 215

TREG model can differ from those carried out using dy- 216

namical downscaling. The differences can be noticeable 217

especially for precipitation projections. 218

In this study, WETTREG2013 method was used 219

which establishes statistical relationships between atmo- 220

spheric variables and climatic observations at the exist- 221

ing stations for 1971–2000 (Kreienkamp et al., 2013; 222

Pluntke et al., 2016). This period was considered in the 223

model validation as well as to compare the current and 224

projected data. In the case of the projections, RCP8.5 225

scenario was chosen. Although this scenario is gener- 226

ally characterized by a low probability, current trends 227

for some thermal indices in the discussed region suggest 228

that the conditions may change in the future in the way 229

presented within this scenario (Adynkiewicz-Piragas 230

and Miszuk, 2020). The recent report on global climate 231

changes showed that greenhouses gases concentration 232

has additionally risen since 2011, contributing to the 233

further increase in air temperature (IPCC, 2021). Thus, 234

it can be assumed that climate changes intensity, espe- 235

cially in terms of thermal conditions, are currently more 236

relevant to the pessimistic scenarios than to the opti- 237

mistic ones. Consideration of the worst possible scenario 238

can also contribute to the development of a vast range of 239

adaptation measures that would take into account each 240

aspect of the climate change impact. 241

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Table 1: Selected thermal, precipitation, storm and snow indices for the considered sectors in terms of 1971–2015 observation period (green)and the projections for RCP8.5 for 2071–2100 (red).

Sector Tmax Tmean Tmax > 30 °C RR RR > 10 mm Stormy days SN > 10 cm

Biodiversity o o o oForestry o o o oAgriculture o o o oTransport o o o oTourism o o o oPublic health o o o o

In the analysis, projections concerning three realiza-242

tions for RCP8.5 were used. Each realization consisted243

of 10 runs. The values of selected indices were calcu-244

lated for each run for 2071–2100 and compared to the245

reference period of 1971–2000. Based on these results,246

mean differences for each realization were assessed.247

The range between the differences for particular realiza-248

tions was the basis for the evaluation of possible climate249

changes in the final decades of the 21st century. As pro-250

jections of precipitation totals developed within WET-251

TREG model are less accurate than for thermal condi-252

tions and show distinct trends for the summer and win-253

ter seasons (Umweltbundesamt, 2007), calculations of254

the totals for the warm half-year and the summer sea-255

son were additionally carried out. This results from the256

fact that precipitations in the growing season are cru-257

cial for the sectors affected by this variable (biodiversity,258

forestry, agriculture).259

2.3 Sectors of the Polish-Saxon region260

In the paper, a potential impact of climate changes on261

social-economic and environmental issues was exam-262

ined for several sectors that play an important role in263

the region. The characteristics of climate conditions con-264

cerned the changes that are unfavorable for selected sec-265

tors. In the case of the current conditions, the character-266

istics focused on the changes in selected meteorological267

variables in 1971–2015, while the climate projections268

concerned 2071–2100 period for RCP8.5 scenario. The269

results of the research of climate changes for both ob-270

servation and projected periods were the basis for the271

evaluation of probability of climate changes in terms272

of their negative influence on the selected sectors. They273

were eventually used in the risk analysis. (Table 1–2).274

In terms of biodiversity, species characteristic for275

different ecological areas (such as mountains, swamps,276

forests, etc.) as well as environment protection zones277

are noticed in the region. As almost 40 % of plants and278

2/3 of animal species in the region are directly connected279

with water ecosystems or swamps (Kowalczak et al.,280

2009), the appropriate state of biodiversity in the region281

mainly depends on water resources. Considering this282

aspect, variables of mean air temperature (Tmean) and283

annual precipitation totals (RR) were taken into account284

as the most important factors affecting ecological state.285

The studies concerning future forest conditions in286

Poland and Germany showed a high importance of cli-287

Table 2: Probability categories depending on criteria mentioned inTable 1.

Rank Probability Criteria fulfilled

1 Very low none2 Low one3 Medium two4 High three5 Very high four

mate factors, especially precipitations (Lasy Panst- 288

wowe, 2015; SMUL, 2014). The negative impacts of 289

climate changes, resulting from the changes in air tem- 290

perature, precipitations and their indirect consequences 291

can be crucial for coniferous forest (Durło, 2012; IOS, 292

2013; Kundzewicz, 2013). Furthermore, forest areas 293

are vulnerable to material losses due to strong wind and 294

storms (Spathelf et al., 2014). Therefore, the indices of 295

mean air temperature (Tmean) as well as the frequency of 296

storms (observation period) and annual precipitation to- 297

tals (RR; projected data) were taken into the analysis. 298

In the case of agriculture, the economic efficiency is 299

mainly related to water availability which depends on air 300

temperature (and consequently evaporation) and precip- 301

itations. The analysis based on climate projections con- 302

firmed progressing water deficit (Szwed et al., 2010). 303

As a result, for the purposes of probability evaluation, 304

changes in mean air temperature (Tmean) and annual pre- 305

cipitation totals (RR) were considered for both observa- 306

tion and projection data. 307

Tourism and public health sectors are mainly vulner- 308

able to heat stress (i.e., Amelung et al., 2007; Hajat 309

et al., 2010; Di Napoli et al., 2018). Therefore, the fre- 310

quency of heat days (Tmax > 30 °C) was considered in 311

the analysis. In the case of public health, the observed 312

frequency of stormy days and the projections of max- 313

imum air temperature (Tmax) were also used. Regard- 314

ing tourism issues, snow cover is also the crucial fac- 315

tor. Thus, the frequency of snow cover depth of more 316

than 10 cm (SN > 10 cm), suitable for skiing, was taken 317

into account. In the context of the future period, the in- 318

dex of mean air temperature (Tmean) was used as the fac- 319

tor determining the occurrence of snow cover. 320

The sector of transport is also affected by high air 321

temperature. This has influence on road and railway in- 322

frastructure, rolling stock as well as social comfort of 323

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 5

Table 3: Criteria of evaluation of selected sectors in terms of their sensitivity to climate changes.

Biodiversity Forestry Agriculture Transport Tourism Public health

Environmentprotection area

% of forest areas inthe region

% of agricultureareas in the region

Roads density Mean altitude Population(persons/km2)

Water andwater-related

habitats

% of coniferousforests in overall

forest area

% of arable lands % of roads leadingthrough forest areas

No. of beds inhotels/apartments

Persons > 65 yearsold (persons/km2)

Mean altitude % of orchards andplantations in the

region

Railways density No. of touristsvisiting the region

Children < 6 yearsold (persons/km2)

% of meadows andpastures in the

region

Density of roadswith high local

significance

Tourism movementintensity

No. of physicians(per 1000

inhabitants)

Health resorts

passengers and employees. Therefore, the variable of324

maximum air temperature (Tmax) was used for the eval-325

uation of probability for both observation and projected326

data. Considering safety and economic issues, analy-327

sis on frequency of storms (observation data) and daily328

precipitations exceeding 10 mm (RR > 10 mm; projected329

data) was carried out. The selection of such indices330

was indicated in the studies concerning this problem331

(i.e., Einchhorst, 2009; Nemry and Demirel, 2012;332

Christodoulou and Demirel, 2018).333

2.4 Probability of climate changes334

In terms of the observation data, the basis for probabil-335

ity assessment are the results of the trends. If a trend for336

a given index is characterized by unfavorable direction337

in the context of a selected sector and is characterized338

by statistical significance, the criterion for probability is339

considered as fulfilled. In the case of the climate pro-340

jections for RCP8.5, the crucial aspect is the difference341

between the far future (2071–2100) and the reference342

period (1971–2000). If the difference of a given index343

is unfavorable for a selected sector, the criterion is met.344

The higher number of fulfilled criteria for both obser-345

vation period and climate projections, the higher is the346

value of probability (Table 2). The probability of climate347

changes was evaluated for four hypsometric zones.348

2.5 Sensitivity to climate changes349

Sensitivity to climate changes of the selected sectors was350

classified into four different classes (low, medium, high,351

very high). For each of the sectors, several aspects were352

taken into consideration (Table 3). The evaluation was353

carried out for each of the communes of the region. Bas-354

ing on the criteria for each sector, the particular com-355

munes were assessed in the context of the comparison356

of the results to the entire Polish-Saxon border region.357

Each of the criteria was given the rank of sensitivity.358

Consequently, the general sensitivity was evaluated for359

a selected sector.360

The evaluation of biodiversity sensitivity was based 361

on a principle that small areas of environment protec- 362

tion regions and habitats are the most sensitive to cli- 363

mate changes (Reid, 2006). The sensitivity of biodiver- 364

sity sector was assessed basing on two criteria: envi- 365

ronment protection area in each commune and infor- 366

mation on water and water-related habitats, listed in 367

the standard data forms (SDF) of the special habitat 368

protection areas (SOO) of Natura 2000 regions. In the 369

case of forestry, large areas are covered by monocul- 370

tural coniferous forests that are very sensitive to influ- 371

ence of various factors (severe weather conditions, pol- 372

lution, etc.). Furthermore, studies devoted to this prob- 373

lem showed that mountain ecosystems are affected by 374

climate changes the most. As much as 60 % of species of 375

these ecosystems can vanish (Sadowski, 2013). There- 376

fore, the analysis on forestry considered the percentage 377

of the total forest area and coniferous forests as well as 378

the factor of altitude (mean value for each commune). 379

Agriculture terrains in the Polish-Saxon region cover 380

vast areas. The problem of weather affection is signif- 381

icant in the context of all types of cultivation. Thus, bas- 382

ing on information on land use, the analysis was car- 383

ried out for four criteria: agriculture areas, arable lands, 384

orchards and plantations, meadows and pastures. The 385

sensitivity of public health sector is mainly related to 386

population issues. This concerns especially the social 387

groups vulnerable to weather factors, like elders or chil- 388

dren, who are susceptible especially to heat stress oc- 389

currence. A very important aspect is also health care in- 390

frastructure. Therefore, the population indices and the 391

number of physicians were taken into consideration. 392

The significance of tourism sector depends on the num- 393

ber of tourists and developed tourist infrastructure. In 394

addition, altitude is also an important factor as moun- 395

tain regions are the most attractive for tourists. Thus, 396

the sensitivity analysis included the number of beds in 397

hotels/apartments, annual number of tourists, tourism 398

movement intensity (based of density of walking trails 399

and bicycle paths), mean altitude a.s.l. and the presence 400

of health resorts. In the case of transport, the most im- 401

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Table 4: Risk matrix presenting probability of changes of the meteorological indices and the sensitivity of selected sectors to climate changes(red: very high risk, orange: high risk, yellow: moderate risk, green: low risk) (based on: Smolarkiewicz et al., 2011; modified).

RISK PROBABILITY

1 2 3 4 5

SEN

SIT

IVIT

Y 4 4 8 12 16 20

3 3 6 9 12 15

2 2 4 6 8 10

1 1 2 3 4 5

portant factor is the density of roads and railways, in-402

cluding the importance of local roads managed directly403

by local authorities. Because of storm impact, the per-404

centage of roads leading through forests was also con-405

sidered.406

The data used in the sensitivity analysis were derived407

from various sources, including environmental websites408

(http://crfop.gdos.gov.pl, https://natura2000.gdos.gov.409

pl; https://www.natura2000.sachsen.de), Statistics410

Poland (GUS; https://stat.gov.pl) Corine Land Cover411

database (https://land.copernicus.eu/pan-european/412

corine-land-cover/clc2018), State Statistical Office413

of Saxony (Statistisches Landesamt des Freistaates414

Sachsen), Eurostat database (https://ec.europa.eu/415

eurostat/data/database), tourism websites (https://416

www.kreis-goerlitz.de/city_info; http://www.b-tourist.417

eu; https://cardomap.idu.de), Provincial Centre for418

Geodesic and Cartographic Documentation (BDOT;419

https://www.geoportal.gov.pl) and DIVA GIS (https://420

www.diva-gis.org/Data).421

2.6 Risk evaluation422

Considering the results of probability and sensitiveness,423

risk levels for the selected sectors in the discussed region424

were evaluated. The risk assessment was carried out us-425

ing risk matrix (Table 4). Risk strictly depends on sen-426

sitiveness and the rate of probability of climate changes.427

Maximum risk level (‘very high’) is noticed under high428

rates of probability (4 or 5) and sensitivity (3 or 4). Us-429

ing risk matrix, risk evaluation for all of the considered430

sectors under climate change conditions in the discussed431

region was carried out.432

The sectors considered in this paper are affected by433

a vast spectrum of factors related to climate, social-434

economic and environmental conditions. Numerous435

studies were reviewed in order to evaluate the most im-436

portant indices for the evaluation of probability, sensitiv-437

ity and risk. Based on these assessments, the most cru-438

cial criteria were selected for the analysis. The impact439

of other factors on probability and sensitivity to climate440

changes cannot be denied because of a complex and dy- 441

namic structure of the considered sectors and uncertain 442

climate and social-economic conditions in the future. 443

Nevertheless, a relatively high number of the considered 444

criteria can approximate a possible influence of climate 445

changes on the selected sectors in the following decades 446

in the region. 447

3 Results 448

3.1 Probability of climate changes 449

In 1971–2015, one of the most important climate 450

features was increasing, statistically significant trend 451

of both mean and maximum annual air temperature 452

in the entire hypsometric profile. The most dynamic 453

changes were observed in the lowlands and moun- 454

tains, where mean air temperature increased at the rate 455

of 0.33–0.37 °C per decade. The growth for the an- 456

nual maximum values reached 0.39 °C per decade (Ta- 457

ble 5). In the uplands and mountain foreland, the rates 458

for mean and maximum air temperature amounted to 459

0.29–0.32 °C and 0.22–0.33 °C per decade, respectively. 460

Rising air temperature resulted in the positive trend of 461

heat stress conditions. The annual number of heat days 462

(Tmax > 30 °C) in the lowlands, uplands and mountain 463

foreland rose with the intensity of 1–2 days per decade. 464

In the mountains, such days usually appear sporadi- 465

cally or are not observed at all. In terms of precipita- 466

tions, neither of the considered stations was character- 467

ized by a statistically significant trend. It should be em- 468

phasized, that taking into account other numerous pre- 469

cipitation stations in the region, statistical significance 470

for 1971–2015 was noticed only for two of therm. In 471

both cases, the positive trends were observed which did 472

not have a negative impact on the selected sectors. In 473

the case of snow cover for skiing, a statistically signifi- 474

cant, negative trend was observed in the mountains. Re- 475

garding storms, a noticeable positive tendency was no- 476

ticed for the lowlands and uplands. In 1971–2015, the 477

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 7

Table 5: Rate of changes (per decade) in mean annual air temperature (Tmean), maximum air temperature (Tmax), annual number of heat days(Tmax > 30), annual precipitation totals (RR), frequency of snow cover depth exceeding 10 cm (SN > 10) and annual frequency of storms atthe stations representing various hypsometric zones in the Polish-Saxon border area for 1971–2015 (statistically significant trends markedin bold).

Station Zone Tmean

[°C]Tmax

[°C]Tmax > 30

[days]RR

[mm]SN > 10[days]

Storms[days]

Legnica Lowlands 0.33 0.39 2.04 1.69 −0.10 1.31Görlitz Uplands 0.32 0.22 1.21 1.26 0.01 1.79*Jelenia Góra Mountain foreland 0.29 0.33 1.33 13.59 0.62 0.27Siezka Mountains 0.37 0.39 – −47.81 −5.38 −0.45

* – data for storms frequency derived from the station of Zielona Góra.

Table 6: Range of changes (bandwidth between particular realizations) in selected climate factors between 2071–2100 and 1971–2000,according to RCP8.5 scenario (negative changes for considered sectors marked in bold).

Station Tmean [°C] Tmax [°C] Tmax > 30 [number of days] RR [%] RR > 10 [number of days]

Legnica 3.4 to 3.7 3.9 to 4.3 26 to 31 −9 to −2 −0.7 to 1.0Görlitz 3.4 to 3.8 3.7 to 4.1 22 to 25 −11 to −3 −2.2 to −1.1Jelenia Góra 3.4 to 3.7 3.9 to 4.3 20 to 24 −12 to −3 −2.0 to −1.0Siezka 3.6 to 4.0 3.8 to 4.2 – −17 to −10 −5.0 to −2.5

annual frequency of storms in these hypsometric zones478

increased by almost 6 (lowlands) and 8 days (uplands).479

In the context of climate projections based on480

RCP8.5 scenario, further intensive increase in air tem-481

perature, reaching as much as 4.0 °C, was simulated for482

the last three decades of the century (Table 6). A no-483

ticeable growth (3.7 °C to 4.3 °C) was also projected for484

maximum air temperature and modeled for the heat days485

frequency (for the stations located lower down). Thus,486

most of the projected thermal changes can be consid-487

ered as unfavorable in the context of their influence on488

the selected sectors.489

The projections related to precipitations simulate re-490

duction in the annual precipitation totals (RR) in the en-491

tire region. Their values can decrease more intensively492

in the regions located at higher altitudes. This nega-493

tive trend was also confirmed by the projected precip-494

itation totals for the warm half-year and the summer495

season. In this case, the rate of decrease can amount496

to 5?26 % for the warm half-year and 13?31 % for the497

summer months. Simultaneously, the negative tenden-498

cies are usually observed for daily precipitations exceed-499

ing 10 mm (RR > 10 mm), except the lowlands, where500

the number of such days can rise or fall by about 1 day,501

depending on run of the scenario. Thus, according to the502

WETTREG simulations, the further decrease in precip-503

itation totals can have a negative impact on biodiversity,504

agriculture, and forestry, while no significant increase505

in strong precipitations should not negatively affect the506

sector of transport. The results based on WETTREG507

model outputs can be different from those carried out508

using dynamical downscaling (such as those considered509

in Euro-Cordex project). Some of the projections for510

RCP8.5 scenario simulate the increase in precipitation511

totals and the intensification of heavy precipitations in512

the future. If such conditions were applied in the study,513

the rates of probability for biodiversity, agriculture and 514

forestry would be reduced by 1, potentially contributing 515

to the mitigation of risk levels. In the case of transport, 516

more frequent heavy precipitation events could increase 517

the rate of probability by 1 and also affect risk ranks. 518

Taking into consideration the probability of the neg- 519

ative impact of climate changes on the selected sec- 520

tors, the highest influence (and consequently the highest 521

probability rate) was observed for the sector of forestry 522

(Table 7). The observed or projected changes in thermal, 523

precipitation and storm conditions resulted in high (4) 524

or very high (5) probability value. The highest rank of 525

probability at the stations located lower down was no- 526

ticed for the sector of public health which was a result 527

of the changes in thermal conditions, heat stress fre- 528

quency and the number of storms. In the mountains, the 529

probability was ranked as low (2), because of no heat 530

stress occurrence and no significant trends for the in- 531

dex of storms. In the case of transport, the observed or 532

projected changes in thermal and storm conditions in 533

the lowlands and uplands contributed to the high prob- 534

ability level (4), whereas the rank of 3 was assessed for 535

the mountain foreland and mountains. According to the 536

analysis based on WETTREG model, the value of prob- 537

ability for transport was diminished in the entire region 538

due to no noticeable changes in the projected strong pre- 539

cipitations. In the case of biodiversity and agriculture, 540

the probability for the entire area reached the value of 4 541

due to increasing mean air temperature and projected 542

decrease in precipitation totals. The tourism sector is af- 543

fected by the increasing number of heat days in the lower 544

hypsometric zones, a negative trend of snow cover fre- 545

quency in the mountains and projected increase in mean 546

air temperature. As a result, this sector was given the 547

rank of 4 in the areas located lower down (lowlands, up- 548

lands, and mountain foreland) and 3 in the mountains. 549

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8 B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region Meteorol. Z. (Contrib. Atm. Sci.)PrePub Article, 2021

Table 7: Levels of probability of the negative impact of climate changes (based on the selected climate indices) on the considered sectors inparticular hypsometric zones.

Region Biodiversity Forestry Agriculture Transport Tourism Public health

Lowlands 4 5 4 4 4 5Uplands 4 5 4 4 4 5Mountain foreland 4 4 4 3 4 4Mountains 4 4 4 3 3 2

3.2 Sensitivity and risk analysis550

Basing on the considered criteria, the general sensitiv-551

ity level for the sector of biodiversity varied from low to552

very high (Fig. 2). The communes with the lowest sensi-553

tivity mainly concerned the regions used for agriculture554

purposes with a low percentage of forests and without555

environment protection areas. The highest rank of sensi-556

tivity was usually given to the regions with large forest557

areas located in the north as well as to the communes558

where natural environment contributes to the develop-559

ment of water and water-related habitats.560

As the probability of the negative climate changes in561

the entire regions was the same (4), the risk map was562

a deflection of spatial distribution of sensitivity classes.563

Thus, the highest risk was noticed in forests areas and564

in the regions characterized by a high value of natural565

environment conditions, such as Karkonoski National566

Park in the Sudetes Mountains.567

The highest level of sensitivity for forestry was ob-568

served for the areas located in the Sudetes Mountains569

where coniferous forests are predominant (Fig. 3). In the570

German region, the communes of Jonsdorf and Oybin,571

located above 450 m a.s.l., were also very sensitive. In572

the lower hypsometric zones, such a level was usually573

noticed in the north, where the percentage of forest is574

very high.575

In terms of risk map, the highest risk level was pro-576

jected for the northern part of the region, especially in577

the communes where coniferous forests are predom-578

inant. Such a risk rank was also observed for sev-579

eral communes representing the mountain foreland and580

mountains. The lowest risk was mainly observed in the581

regions where forests cover less than 5 % of the area.582

In the case of agriculture, most of the communes583

(57 %) were characterized by medium sensitivity. The584

lowest ranks were the most frequent in the communes585

with a high percentage of forest areas and/or in the586

mountains (Fig. 4). The highest sensitivity was noticed587

for the communes of Sulików and Zgorzelec (Poland)588

and the communes located in the middle part of the Ger-589

man region. This resulted from a very high percentage590

of arable lands and other agriculture areas.591

The probability of climate changes in the whole re-592

gion was characterized by the same level (4). Therefore,593

similarly to the forestry sector, the risk map was a di-594

rect deflection of the sensitiveness analysis. Thus, the595

highest risk level was observed for the communes with596

a high percentage of homogeneous agriculture areas, es-597

pecially in Sulików and Zgorzelec and in the German598

areas located in in the middle and southern part of the 599

region. 600

Considering the sector of public health, the highest 601

number of communes were characterized by medium or 602

low sensitivity (Fig. 5). The lowest level mainly con- 603

cerned the forested northern areas, while a very high 604

sensitivity was noticed for the densely populated re- 605

gions with a high number of children and elders. Such 606

a level was mainly characteristic for well-developed set- 607

tlements, including single municipalities in the central 608

and northern part of the region. 609

Therefore, a very high risk level was observed in the 610

highly populated communes, where the negative climate 611

changes can affect the public health the most. In the 612

mountains, the risk ranks were usually lower because of 613

no heat stress occurrence. The lowest level was noticed 614

in several Polish communes located in the mountains 615

and mountain foreland where the population density is 616

very low (≤ 50 persons/km2). 617

The southern areas, especially the Sudetes Moun- 618

tains and a part of the Zittauer Mountains, were given 619

a very high rank of sensitivity for tourism (Fig. 6). They 620

are characterized by a high tourism attraction, including 621

skiing. On the other hand, the lowest values were ob- 622

served in the lowlands where medium or low sensitivity 623

was noticed. The differences in the sensitivity between 624

the Polish and the German lower parts of the region were 625

caused by a higher density of tourism infrastructure in 626

Germany (such as bicycle paths and accommodations). 627

As a result, the highest risk was noticed in the moun- 628

tainous part of the region, especially in the communes 629

with health resorts – Jonsdorf, Swieradów-Zdrój and the 630

Municipality of Jelenia. Most of the remaining parts of 631

the Sudetes and Zittauer Mountains was characterized 632

by the class of high risk. This included the highest part of 633

the mountains, where (despite very high sensitivity) the 634

probability of the negative climate changes was lower 635

because of no heat stress problems. 636

The sensitivity of the sector of transport mainly de- 637

pends on the density of transport facilities (roads, rail- 638

ways). The highest number of communes in the region 639

was regarded as highly sensitive (almost 45 % of com- 640

munes), while a very high rate was usually observed for 641

the municipal areas and the communes located in the 642

mountains or mountain foreland. Higher ranks were also 643

observed in the areas with a high number of populations 644

which contributes to a high density of roads and railways 645

(Fig. 7). 646

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 9

Figure 2: Sensitivity to climate changes and risk assessment for the sector of biodiversity in the Polish-Saxon region (considering RCP8.5scenario).

Figure 3: Sensitivity to climate changes and risk assessment for the sector of forestry in the Polish-Saxon region (considering RCP8.5scenario).

Figure 4: Sensitivity to climate changes and risk assessment for the sector of agriculture in the Polish-Saxon region (considering RCP8.5scenario).

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10 B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region Meteorol. Z. (Contrib. Atm. Sci.)PrePub Article, 2021

Figure 5: Sensitivity to climate changes and risk assessment for the sector of public health in the Polish-Saxon region (considering RCP8.5scenario).

Figure 6: Sensitivity to climate changes and risk assessment for the sector of tourism in the Polish-Saxon region (considering RCP8.5scenario).

Figure 7: Sensitivity to climate changes and risk assessment for the sector of transport in the Polish-Saxon region (considering RCP8.5scenario).

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 11

As a result, the moderate risk class was predominant647

and was ranked for almost half of the communes. A648

very high risk category was given to the communes649

representing the municipal areas with a high density of650

roads and railways. Despite high or very high sensitivity,651

the mountains were usually characterized by moderate652

risk, because of lower values of probability.653

4 Discussion654

4.1 Climate changes655

The analysis of thermal conditions presented in the pa-656

per confirmed the results carried out in the previous657

studies that both mean and maximum air temperature658

as well as the frequency of heat days have significantly659

risen over the last decades in this area (Mehler et al.,660

2014; Pluntke et al., 2016; Miszuk, 2021). On the661

other hand, the observed changes in annual precipitation662

totals do not show homogeneous and statistically signifi-663

cant trends, which was also noticed in the papers devoted664

to this problem in Germany (Zebisch et al., 2005; DWD,665

2020) and Poland (Marosz et al., 2011; Łupikasza and666

Małarzewski, 2021). In the case of snow cover fre-667

quency, the rate of decrease was slightly more inten-668

sive than in the Eastern Sudetes Mountains (Urban,669

2015). Statistically significant negative trends of snow670

cover duration were also noticed in Germany and the671

coastal regions of Poland (Kreyling and Henry, 2011;672

Tomczyk et al., 2021). In the case of storms, the pos-673

itive tendency in the lower hypsometric zones corre-674

sponds to the rising trends observed for north-western675

and eastern Poland (Kirschenstein and Chlost, 2018;676

Bielec-Bakowska et al., 2021).677

Regarding the projected climate changes under678

RCP8.5 scenario, the increase in air temperature and679

decrease in precipitation totals are projected, con-680

firming the results obtained for Poland and Germany681

(Buth et al., 2015; Jagiełło et al., 2019; Szwed,682

2021), including the considered region (Lünich et al.,683

2014; Schwarzak et al., 2014; Pluntke et al., 2016;684

Adynkiewicz-Piragas and Miszuk, 2020). However,685

it is worth mentioning that some projections based686

on dynamic downscaling (i.e., in Euro-Cordex project)687

show a positive tendency of the annual precipitation to-688

tals, according to this scenario in the discussed region689

(Kahlenborn et al., 2021; Pinskwar and Chorynski,690

2021). The analysis on heat days occurrence showed a691

possible intensification of heat stress in the future, which692

was also indicated in the projections carried out for both693

states (Schwarzak et al., 2014; Miszuk et al., 2016;694

Brecht et al., 2020). The simulations of heavy precipi-695

tations frequency, carried out using WETTREG model,696

do not show a significant increase in the future, which697

is a favourable factor, especially for transport. How-698

ever, similarly to the projected precipitation totals, the699

direction of changes in the region can be different de-700

pending on applied models (Kahlenborn et al., 2021;701

Pinskwar and Chorynski, 2021). If to consider the702

simulations presenting the rising tendency, the impact 703

of heavy precipitations for the sector of transport would 704

be more intensive. 705

4.2 Forestry and biodiversity 706

The results of sensitivity to climate changes and risk 707

assessment for the selected sectors, supported by the 708

analysis of probability of climate changes, showed that 709

large forest areas in the northern part of the region con- 710

tributed to the very high level of sensitivity and risk 711

for biodiversity and forestry. In this context, the moun- 712

tains are also significantly impacted because of a high 713

percentage of coniferous forests and unique habitats. A 714

similar situation was observed in the Alpine regions, be- 715

cause of abundance of endemic species (Zebisch et al., 716

2005; Buth et al., 2015). The negative influence of 717

warming and diminished precipitations can cause seri- 718

ous problems for biodiversity and forestry (Reid, 2006; 719

Schröter et al., 2006; Kundzewicz and Matczak, 720

2012; IOS, 2013; Spathelf et al., 2014; Kahlenborn 721

et al., 2021). If such climate changes continue, a poten- 722

tial loss of species in the German part of region can 723

amount to 50 % (Zebisch et al., 2005). The studies on 724

vulnerability to climate changes showed that eastern and 725

southeastern Germany are one of the most vulnerable re- 726

gions (Schröter et al., 2006; Buth et al., 2015). A spe- 727

cial attention should be paid to water and water-related 728

habitats that are very sensitive to climate changes (IOS, 729

2013; Schliep et al., 2018; Kahlenborn et al., 2021) 730

and which structure can noticeably change, especially 731

under RCP8.5 conditions (Kundzewicz et al., 2018; 732

Kahlenborn et al., 2021). 733

4.3 Tourism 734

The mountain regions are also exposed to the high im- 735

pact of climate changes on tourism. Although the neg- 736

ative influence of heat stress is rarely observed in this 737

area, the decreasing frequency of snow cover can se- 738

riously limit the winter tourism. This concerns espe- 739

cially the lower mountain zones where skiing condi- 740

tions can deteriorate the most (Zebisch et al., 2005, Hoy 741

et al., 2011; Kundzewicz and Matczak, 2012; IPCC, 742

2014; Kahlenborn et al., 2021). High levels of sen- 743

sitivity and risk in the mountains of the Polish-Saxon 744

region confirm the analysis caried out for Germany. 745

They showed that all the mountain regions are very vul- 746

nerable to climate changes in terms of winter tourism 747

(Schröter et al., 2006; Kahlenborn et al., 2021). The 748

presence of health resorts additionally increases the sen- 749

sitivity to climate changes. In the regions located lower 750

down, the main factor contributing to the moderate risk 751

level is the increasing frequency of heat stress occur- 752

rence (Zebisch et al., 2005; Amelung et al. 2007; IOS, 753

2013). In the entire region of Germany, the moderate 754

levels of vulnerability and risk were mainly assessed 755

for the tourism forms not related to winter activities 756

(Schröter et al., 2006; Kahlenborn et al., 2021). 757

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12 B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region Meteorol. Z. (Contrib. Atm. Sci.)PrePub Article, 2021

4.4 Agriculture758

The main factors contributing to the very high ranks of759

sensitivity and risk for agriculture are the changes in760

thermal and precipitation conditions as well as a high761

percentage of arable lands and other agriculture areas.762

High temperatures and insufficient water supply can po-763

tentially cause economic losses (Zebisch et al., 2005;764

Schröter et al., 2006; Ministerswo Srodowiska,765

2013; Buth et al., 2015; Kundzewicz et al., 2018;766

EEA Report, 2019; Kahlenborn et al., 2021). Ac-767

cording to the analysis carried out for Germany, the768

sector of agriculture in the eastern regions is character-769

ized by a high vulnerability to climate changes (Zebisch770

et al., 2005; Schröter et al., 2006). The minor levels771

of sensitivity and risk were evaluated for the mountains772

of the Polish-Saxon region, which is in accordance to773

the analysis of vulnerability for Germany (Schröter774

et al., 2006). The further climate changes, contributing775

to the prolongation of growing season and affecting wa-776

ter balance, can result in the necessity of modification777

of crop structure in this part of Europe (Kundzewicz778

and Matczak, 2012; IOS, 2013; Buth et al., 2015;779

EEA Report, 2019; Graczyk et al., 2021). The analy-780

sis on potential climate changes showed, that crop yields781

in Poland and Germany, including the Polish-Saxon re-782

gion, can be significantly diminished due to limited783

water supply in the following decades of the century784

(Szwed et al., 2010; IOS, 2013; IPCC, 2014).785

4.5 Public health786

In terms of public health, high levels of sensitivity787

and risk confirmed the problems of depopulation and788

the increasing percentage of elders among the habi-789

tants of the region. The sensitivity to weather impact790

is also dependent on access to health and welfare ser-791

vices (O’Neill et al., 2009; IPCC, 2014). In the dis-792

cussed region, the main factor affecting sensitivity in793

the German part was a high number of people aged794

over 65 years, while in the Polish area, a relatively795

low number of physicians was the important issue. The796

larger cities of the region are also characterized by urban797

heat islands occurrence (IOS, 2018, REGKLAM, 2013).798

Its influence is noticeable especially under heat stress799

conditions which are the main weather factor affecting800

the human health (Zebisch et al., 2005; Szwed et al.,801

2010; Kundzewicz and Matczak, 2012; IOS, 2013;802

Buth et al., 2015; Ministerstwo Srodowiska, 2015;803

Kundzewicz et al., 2018; Mücke and Litvinovitch,804

2020; Kahlenborn et al., 2021). Regarding the vulner-805

ability of this sector, the southeastern regions of Ger-806

many was classified as highly vulnerable (Schröter807

et al., 2006). Further increase in air temperature can sig-808

nificantly increase the mortality in the future, especially809

under such pessimistic scenarios like RCP8.5 (Mücke810

and Litvinovitch, 2020). Simultaneously, the human811

health in the following years can be also affected by a812

rising tendency of extreme weather events, like storms813

or strong wind (Kundzewicz and Matczak, 2012; 814

Ministerstwo Srodowiska, 2013; Buth et al., 2015; 815

Kahlenborn et al., 2021). 816

4.6 Transport 817

In the case of transport, the maximum ranks of sensitiv- 818

ity and risk resulted from the highest number of trans- 819

port facilities in these areas. A high probability level 820

for the changes in thermal conditions and the frequency 821

heavy precipitations and storms were also the noticeable 822

factors. These variables significantly affect the trans- 823

port sector (Zebisch et al., 2005; IOS, 2013; Buth et al., 824

2015; Christodoulou and Demirel, 2018; IOS, 2018; 825

Kahlenborn et al., 2021). High values of air temper- 826

ature and heavy precipitations have an impact on road 827

accidents and erosion of slopes in the mountains, while 828

storms can negatively affect rail traffic through damag- 829

ing contact lines, breaking trees and flooding parts of 830

railway tracks (Zebisch et al., 2005; IOS, 2013; Min- 831

isterstwo Srodowiska, 2013; Christodoulou and 832

Demirel, 2018; UNECE, 2020). Considering a pes- 833

simistic scenario, the risk level for the transport sector 834

in Germany can be assessed as medium (Kahlenborn 835

et al., 2021). The medium level of vulnerability was also 836

assessed for the land of Saxony (Schröter et al., 2006). 837

4.7 Limitations 838

The results carried out in this study based on the selected 839

methods of evaluation of probability, sensitivity and risk 840

related to climate changes. The selection of meteoro- 841

logical variables for the purposes of probability eval- 842

uation was conducted on the basis of various research 843

outcomes concerning this problem. As a result, the most 844

important meteorological indices were selected. Never- 845

theless, the impact of other variables (and consequently 846

their influence on probability results) cannot be ruled out 847

because of a complex nature of the considered sectors. 848

Similar conditions can be defined in terms of sensitivity 849

and risk analysis. Although the selected criteria repre- 850

sented very important features and needs of particular 851

sectors, there may be a lot of different factors that can 852

affect the sensitivity to climate changes. The other lim- 853

itations concern climate scenarios and projections. The 854

selection of the most pessimistic scenario (RCP8.5) was 855

determined by the current trends of some variables and 856

the necessity for the development of the maximum level 857

of climate adaptation measures. However, it should be 858

emphasized that new global and national legal regula- 859

tions related to greenhouse emission may contribute to 860

the mitigation of climate changes intensity in the fu- 861

ture. Uncertainties can be also noticed in the case of the 862

selected climate model. The specific settings of WET- 863

TREG model in terms of precipitations can modify the 864

results of probability for the sectors dependent on this 865

variable. If to consider an increase in precipitation to- 866

tals projected by other models, the levels of probability 867

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B. Miszuk et al.: Climate changes and their impact on sectors of the Polish-Saxon border region 13

of climate changes for biodiversity, forestry and agricul-868

ture would be lower. Nevertheless, these sectors were869

originally given high or very high ranks of probability870

of climate changes. If they were decreased by 1, the risk871

levels in the communes with significant sensitivity val-872

ues would still be high or very high. On the other hand,873

the projected intensification of strong precipitations by874

the models related to dynamic downscaling can increase875

the probability and consequently risk ranks for the sector876

of transport. Taking into account all the limitations, the877

final results of risk analysis can slightly vary depending878

on selected criteria, climate models and scenarios. How-879

ever, the results carried out in this study are in many880

cases similar to the outcomes for the other regions of881

Germany and Poland obtained using different methods.882

5 Conclusions883

The results presented above indicate that in terms of sen-884

sitivity and risk related to climate changes, the discussed885

region is varied depending on sectors. Based on the ob-886

tained results, the following conclusions can be formu-887

lated:888

• Observed and projected changes in selected meteoro-889

logical variables mostly confirm the previous results890

on climate changes in this part of Europe. If such891

a trend continues, this can contribute to the further892

intensification of climate stress on various social-893

economic and environmental sectors.894

• Significant parts of the region are characterized by895

the highest values of sensitivity which along with896

noticeable climate changes result in very high risk897

levels for some of the areas.898

• Sensitivity and risk related to climate changes are899

characterized by a high spatial variability, resulting900

from the geographical factors and land use. This901

shows that the impact of climate conditions and con-902

sequently the need for adaptation measures can sig-903

nificantly vary even in a relatively small region.904

• A vast range of risk levels for each of the sectors in-905

dicates that potential adaptation measures should be906

tailored carefully depending on the most character-907

istic features of a given area (i.e., a special attention908

should be paid for public health and transport in ur-909

ban areas).910

• The results on sensitivity and risk usually confirm the911

previous outcomes on the impact of climate changes912

on social-economic and environmental sectors in913

Germany and Poland. This indicates that these sec-914

tors are affected by the climate factor in the region,915

regardless which scientific methods are used in the916

evaluation. However, it should be emphasized that917

results of the research, especially those based on cli-918

mate projections, are strongly dependent on applied919

models and simulations.920

• The outcomes of this study can be a basis for fur- 921

ther analysis also considering adaptation potential of 922

the region. Consequently, this will contribute to the 923

development of adaptation measures which should 924

minimize the negative impact of climate changes on 925

the selected sectors. 926

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