Scenario-based impact assessment of global and regional change on the semi-natural flow regime

Contents

List of Figures

List of Tables

1 Introduction
1.1 Background
1.2 Objective
1.3 Scope

2 Study Area
2.1 Physiography
2.2 Population and economy

3 Materials and Methods
3.1 SWAT model
3.1.1 Model features
3.1.2 Model set-up of the Narew River Basin
3.1.3 Auto-calibration tools
3.1.4 Calibration and validation approach
3.1.5 Calibration parameters
3.2 Global change scenarios
3.2.1 The emissions scenario and General Circulation Models
3.2.2 Downscaling of GCM output
3.2.3 Parameter adjustments due to elevated CO2
3.2.4 Projected changes in temperature for the 2050s
3.2.5 Projected changes in precipitation for the 2050s
3.3 Regional change scenarios
3.3.1 Background: SCENES project
3.3.2 Driving forces of regional scenarios
3.3.3 Qualitative changes in driving forces
3.3.4 Translation key
3.3.5 Computation of trends and modified SWAT parameters
3.4 Environmental flow requirements
3.4.1 Selection of biota
3.4.2 Building blocks of the environmental flow regime
3.4.3 Bankfull flow estimation
3.5 Development of indicators
3.5.1 State indicators: reliability and resilience
3.5.2 Environmental flow impact indicators
3.5.3 Experimental design

4 Results and Discussion
4.1 SWAT calibration and validation
4.1.1 Uncertainty analysis with SUFI-2
4.1.2 Calibration and validation with PSO
4.1.3 Spatial extent of the analysis
4.1.4 Validation of indicators
4.1.5 Discussion
4.2 Impact on catchment water balance
4.2.1 Annual and seasonal results
4.2.2 Scenario decomposition
4.3 Environmental Flow Impact Indicators
4.3.1 Hands-off flow
4.3.2 Spawning and nursery habitats for pike
4.3.3 Floodplain vegetation communities
4.3.4 Summary of impacts
4.4 Discussion
4.4.1 Global change impacts
4.4.2 Land use change impacts
4.4.3 Combined impacts
4.5 Implications for water management in semi-natural river basins

5 Conclusions

References

A Materials related to the fourth PA workshop
A.1 Agenda for the fourth PA workshop
A.2 Questionnaires for group work (qualitative changes)
A.3 Individual questionnaires (translation key)
A. 4 Computed trends in driving forces

B Maps of the EFIIs
B.1 Hands-off flow reliability indicator HOFrei
B.2 Hands-off flow resilience indicator HOFres
B.3 Pike spawning reliability indicator PIKErel
B.4 Pike spawning resilience indicator PIKEres
B.5 Floodplain vegetation reliability indicator FVCrel
B.6 Floodplain vegetation resilience indicator FVCres

Acknowledgements

List of Figures

1.1 Conceptual schematic overview of this thesis.
1.2 Schematic illustration of the scope of this thesis.

2.1 Map of the study area.
2.2 Map of geological units and glaciation extent in the NRB (source: Geologic Map of Poland from the Polish Geological Institute).
2.3 Topography and hydrographic network of the NRB (hydrographic source: Digital Map of Hydrological Division of Poland from the Institute of Meteorology and Water Management).
2.4 Generalised soil types in the NRB (after Gieiczewski, 2003).
2.5 Land cover map of the NRB (source: CORINE Land Cover 2000 from the Chief Inspectorate of Environment Protection).
2.6 Map of the interpolated mean January (A.) and July (B.) temperatures for 1989-2008.
2.7 Map of interpolated mean annual precipitation for 1989-2008.
2.8 Observed daily discharge hydrographs at the basin outlet (Zambski Koscielne station on the Narew) during a wet year (1994) and a dry year (2003).
2.9 Administrative subdivision in the NRB: NUTS 2 (provinces) and NUTS

3 (sub-regions) levels.
3.1 Visualisation of HRU delineation from an overlay of land use and soil maps within sub-basins.
3.2 Schematic pathways of water movement in SWAT (Neitsch et al., 2011).
3.3 Preliminary conguration of SWAT for the NRB as in Piniewski and Okruszko (2011).
3.4 SWAT input soil map of the NRB and benchmark soil profiles used to create the map.
3.5 New SWAT model set-up for the NRB (cf. Table 3.3 for flow gauges codes).
3.6 Daily outflow from the Siemianówka reservoir measured at the Bond­ary gauging station on the Narew for the time period 1990-2008.
3.7 Spatial calibration and validation approach.
3.8 Initial calibration and uncertainty analysis using SUFI-2 in 10 calib­ration areas.
3.9 Basin-averaged intra-annual variability in mean temperature for the baseline period and under two GCMs for the 2050s.
3.10 Spatial and seasonal variability in GCM projections of temperature change for the 2050s according to IPSL-CM4 (A, C, E, G) and MIROC3.2 (B, D, F, H); used notations: DJF: December - February; MAM: March - May; JJA: June - August; SON: September - November).
3.11 Basin-averaged inter-annual variability in mean precipitation for the baseline period and under two GCMs for the 2050s.
3.12 Spatial and seasonal variability in GCM projections of precipitation change for the 2050s according to IPSL-CM4 (A, C, E, G) and MIROC3.2 (B, D, F, H); used notations: DJF: December - February; MAM: March - May; JJA: June - August; SON: September - November).
3.13 Schematic representation of the scenario development process in the NRB Pilot Area (PA; after Gielczewski et al., 2011).
3.14 Division of the NRB into sub-regions.
3.15 Example fuzzy membership functions for descriptive terms associated with question 1A.
3.16 Groups of wetland vegetation communities in the NRB, according to the Spatial Information System of Polish Wetlands (http://www.gis- mokradla.info/). Typical alliances for each community are given in parenthesis.
3.17 Building blocks of the flow regime and dependent elements of the river ecosystem (after Acreman et al., 2009)
3.18 Building blocks of the flow regime and dependent elements of the river ecosystem - the NRB case.
3.19 Optimal duration of inundation for NRB reaches (FVC categories from Tab. 3.15) assigned by ITP (2010).
3.20 Bankfull flows estimated at selected gauging stations and interpolated across river reaches.
3.21 An example trapezoidal membership function.

4.1 Observed and SUFI-2 simulated (best solution and 95PPU band) daily flows at ten Phase 1 calibration areas for the time period 1995-2008.
4.2 Relationship between upstream catchment area and goodness-of-fit measures for best simulations obtained in SUFI-2 for ten analysed sites.
4.3 Nash-Sutcliffe Efficiency of best simulations, obtained using the PSO technique in different phases of spatial calibration and validation for calibration period (A) and temporal validation period (B)
4.4 Relationship between the model’s performance measures and the area upstream of the gauge in the calibration and temporal validation peri­ods.
4.5 Observed and PSO-simulated daily flows at six example sites (one for each calibration phase) for the joint time period of calibration and validation (1989-2008).
4.6 Final parameter values in all calibration areas.
4.7 Scatter plot of Nash-Sutcliffe Efficiencies of the “old” model set-up (NSEi) vs. the “new” set-up (NSE2) calculated for analysed gauges for calibration and temporal validation periods.
4.8 SWAT reaches selected for further analysis.
4.9 Spatial diversity in RLhof (A) and RShof (B), calculated using SWAT for 52 reaches of interest. Numbers next to the names of the gauging stations show bias, i.e. the difference between SWAT-based indicators and indicators calculated using the observed discharge data.
4.10 Spatial diversity in RLpike (A) and RSpike (B), calculated using SWAT for 52 reaches of interest. Numbers next to the names of the gauging stations show bias, i.e. the difference between SWAT-based indicators and indicators calculated using the observed discharge data.
4.11 Spatial diversity in RLfvc (A) and RSfvc (B), calculated using SWAT for 52 reaches of interest. Numbers next to the names of the gauging stations show bias, i.e. the difference between SWAT-based indicators and indicators calculated using the observed discharge data.
4.12 Seasonal distribution of mean annual precipitation and snowfall under two global change scenarios.
4.13 Seasonal distribution of runoff under single (A) and combined (B) scen­arios.
4.14 Differences in mean monthly runoff between the decomposed scenarios and the original scenarios.
4.15 Pie charts of different classes of impacts on the hands-off flow reliability indicator HOFrel under all analysed scenarios in NRB sub-regions (pie diameter is proportional to the number of reaches in a sub-region).
4.16 Pie charts of different classes of impacts on the hands-off flow resilience indicator HOFres under all analysed scenarios in NRB sub-regions (pie diameter is proportional to the number of reaches in a sub-region).
4.17 Pie charts of different classes of impacts on the pike spawning reliability indicator PIKErei under all analysed scenarios in NRB sub-regions (pie diameter is proportional to the number of reaches in a sub-region).
4.18 Pie charts of different classes of impacts on the pike spawning resilience indicator PIKEres under all analysed scenarios in NRB sub-regions (pie diameter is proportional to the number of reaches in a sub-region).
4.19 Pie charts of different classes of impacts on the floodplain vegetation reliability indicator FVCrel under all analysed scenarios in NRB sub­regions (pie diameter is proportional to the number of reaches in a sub-region).
4.20 Pie charts of different classes of impacts on the floodplain vegetation resilience indicator FVCres under all analysed scenarios in NRB sub­regions (pie diameter is proportional to the number of reaches in a sub-region).
4.21 Pie charts of different classes of impacts across all analysed reaches and EFIIs for eight model experiments.

List of Tables

2.1 Basic socio-economic statistics for the NRB sub-regions in 2009 (GUS, 2011).
2.2 Gross Value Added (GVA) by type of activity and sub-regions as a percentage of total GVA in 2009 (GUS, 2011).

3.1 GIS and monitoring datasets used to build the SWAT project in Piniewski and Okruszko (2011).
3.2 Land use/land cover classes used as SWAT input.
3.3 Flow gauges used in calibration and validation.
3.4 Two example crop calendars applied in SWAT for spring wheat and meadows.
3.5 Selected calibration parameters and their ranges.
3.6 Default and modified values and correction coefficients of maximum stomatal conductance GSI and maximum leaf area index BLAI for generic land cover types corresponding to the assumed CO2 increase by 1.48 for SRES A2 scenario.
3.7 Summary of storylines developed during the PA workshops.
3.8 Driving forces selected for converting storylines into model scenarios.
3.9 Stakeholder types participating in the fourth PA workshop.
3.10 Seven-level scale used for assigning qualitative changes to driving forces.
3.11 Summary of qualitative changes in driving forces for two scenarios.
3.12 Translation key developed using the MOM method.
3.13 Percentage of types of wetland plant communities in the NRB.
3.14 Mean values of the coefficient k used in the parametric version of the Kostrzewa method of determining hands-off flows.
3.15 Categories of floodplain vegetation communities (FVC) determined with respect to the optimal duration of inundation.
3.16 Characteristic values of trapezoidal membership functions for reaches with different inundation categories.
3.17 Summary of proposed reliability and resilience indicators.
3.18 Summary of proposed EFIIs.
3.19 Definition of thresholds for colour coding of the EFIIs representing reliability: HOFrei, PIKErei and FVCrei.
3.20 Definition of thresholds for colour coding of the EFIIs representing resilience: HOFres, PIKEres and FVCres.
3.21 Experimental design for running scenario simulations in SWAT.

4.1 Summary of SUFI-2 results (cf. Table 3.3 and Figure 3.5 for gauge codes and locations).
4.2 Summary of goodness-of-fit measures obtained, using the PSO tech­ nique in different calibration and validation areas during the calibra­tion and temporal validation phases. Cases with NSE < 0.4, R2 < 0.5 or \PBIAS\ > 25 are marked in red.
4.3 Technical comparison of calibration processes conducted with the “old” model set-up and with the “new” set-up.
4.4 Mean bias (i.e. difference between modelled and observed) and mean absolute error of estimation of reliability and resilience indicators based on validation with observed data from 18 gauging stations (cf. Tab. 3.17 for indicator description).
4.5 Mean annual water balance components as differences from the baseline for all model experiments.
4.6 Decomposition of the model experiments.
4.7 Percentage of main land use types in the regional scenarios (as a per­centage of total NRB area).

Chapter 1 Introduction

1.1 Background

Globally, freshwater ecosystems are considered to be under severe threat from human pressure and climate change (Vörösmarty et al., 2010). Malmqvist and Rundle (2002) suggest that running water is the most impacted upon ecosystem on Earth due to being surrounded by dense human settlements and exploited for domestic and indus­trial water supply, irrigation, electricity generation and waste disposal. For example, the progressive over-exploitation of surface water resources for irrigation and urban uses in the Colorado River Basin has resulted most years in no runoff reaching the river’s delta (Gleick, 2003).

Many aquatic ecologists perceive the flow regime to be the key driver of river and floodplain wetland ecosystems (Bunn and Arthington, 2002). Poetically, flow has been praised as “the maestro that orchestrates pattern and process in rivers” (Walker et al., 1995). Even if there is some exaggeration in this statement, there is indeed enough evidence to support the role played by the natural flow regime in maintaining river-dependent (i.e. aquatic and riparian) ecosystems in good health (Junk et al., 1989; Poff et al., 1997; Richter et al., 1997), as well as for the overwhelmingly negative responses of these ecosystems to flow alterations (Bunn and Arthington, 2002; Poff and Zimmerman, 2010).

The causes of flow alterations can be manifold. A comprehensive review of Palmer et al. (2009) reported multiple sources of stress for rivers and river-dependent ecosystems. Stressors are commonly divided into natural (i.e. climatic) and human- induced (anthropogenic). Global warming, driven by increased greenhouse gas emis­sions, has been observed for decades and reported in various global (IPCC, 2001, 2007) and Polish (Zmudzka, 2009; Marszelewski and Skowron, 2006; Maksymiuk et al., 2008) studies. Palmer et al. (2009) underlined that anticipating future condi­tions of a river and a river-floodplain ecosystem in the face of climate change depends largely on geographical location, as natural flow regimes vary by river size and by differences in climate, geology, topography and vegetative cover (Poff et al., 1997), as well as on the types of human pressures present in the river basin. In particular, these studies feature the following human activities: water withdrawals for multiple uses, damming rivers and land use changes. Furthermore, Blöschl et al. (2007) un­derlined the importance of spatial scale: any impact of land cover change is likely to decrease with catchment scale, whereas impacts of climate change are supposed to be scale-invariant and consistent in a region.

Multiple examples showing global and regional environmental change nat­urally raise concern about the future. A significant amount of effort from the sci­entific community in recent years has been devoted to modelling the future. In water resources, conducting this research has been partly enhanced by various stra­tegic water policy documents. For example, the Water Framework Directive of the European Union (EU, 2000) imposed a timetable with specific requirements to be met by European water bodies. Three six-year long cycles for river basin planning have been implemented, and 2027 is the final year for meeting these objectives. As it was noted by van Griensven et al. (2006a) that using catchment-scale hydrological models is the most appropriate reference framework for WFD-oriented integrated modelling, as watersheds form the physical borders for river basin management. These models always have some semi-physical or physical representation of runoff generation pro­cesses. They are applied in hydrology for different purposes, but one commonly used model application type involves employing them to study the hydrological effects of changes in various driving forces. Models are applied at all spatial scales: from hill­slopes (Ambroise et al., 1996), small catchments (Zehe et al., 2001) and large river basins (Barthel et al., 2005) through to continental and global applications (Alcamo et al., 2003). The larger the scale, the more important the spatial heterogeneity of the study area. Hence, models are nowadays often coupled with Geographical Informa­tion Systems (GIS), which facilitate their use to a large extent. Distributed models vary with respect to discretisation strategy: from fully-distributed, grid-element- based models, such as Système Hydrologique Europèen (Abbott et al., 1986) and its successors such as SHETRAN (Bathurst et al., 1995) and MIKE SHE (Refsgaard and Storm, 1995), to semi-distributed models built on the concept of hydrological similarity, such as TOPMODEL (Beven, 2002) or SWAT (Arnold et al., 1998).

As far as modelling used to quantify future impacts is concerned, a point has to be made about assumptions regarding changes in the driving forces and time horizons of anticipated changes. In this thesis the focus is only on long-term effects, even reaching beyond the WFD time-line, namely the 2050s. Hence, it would be naive to expect that extrapolating recently observed trends, be they in climate or land use, would provide a meaningful way of unfolding such a distant future. In the case of modelling the impacts of climate change, a well-established quantitative method for estimating them involves employing projections from General or Regional Circulation Models as the input for hydrological models. How to do it precisely is a non-obvious question, though (Fowler et al., 2007). In the case of modelling an­thropogenic change, there are undoubtedly more options, depending on the type of driving force considered: projections of population growth (Sun et al., 2008), land use change models (Verburg et al., 2002) and approaches involving stakeholders (Alcamo, 2008). The latter seem to be particularly tempting, given the role that stakeholder involvement has been given in European water resources planning by the WFD. In­stead of trying to predict the future, it is better to sketch out “alternative futures”, the likely results of different choices (Kundzewicz and Kindler, 1995). Such altern­ative water futures have recently been developed for the Western Bug River Basin (Schanze et al., 2012) and for the Narew River Basin (Gielczewski et al., 2011).

The relative significance of different driving forces for the flow regime is spatially- and scale-dependent. Climate has appeared to be a stronger driver for hydrological change than land use in several case studies in the United States (Tu, 2009; Sun et al., 2008; Choi, 2008; Chang, 2003), South Korea (Park et al., 2011) and Switzerland (Wolf et al., 2012). Only in the paper of Tong et al. (2012) was the magnitude of impacts (in this case on mean annual runoff) comparable between these two stressors; however, under the driest and the wettest climate change scen­arios the impacts were higher than under the land use change scenario. Undoubtedly, the results in all modelling efforts depend on scenario assumptions and, as mentioned previously, on geographical setting. For this reason it is not recommended to gener­alise findings from other studies. To the author’s knowledge, no Polish publications have attempted to weigh the projected long-term effects of natural and anthropogenic driving forces on the flow regime. This thesis therefore seeks to bridge this gap.

Hereafter, natural and anthropogenic driving forces will be referred to as global and regional driving forces, respectively. The future effects of these forces up to the 2050s will be assessed in quantitative scenarios implemented in a hydrological model. It is believed that using this nomenclature (i.e. global and regional instead of natural and anthropogenic) better reflects considered environmental stressors, since global-scale driving forces will include not only climatic change but also changes in CO2, atmospheric carbon dioxide and plant physiological parameters, whereas regional-scale driving forces will include changes in land use, agriculture development and agricultural water management. Hence, the difference is that the first group of driving forces acts globally and independently on the study area, whereas the second group includes factors that are specific to the study area. Furthermore, in order to expand on the title of this thesis, impacts in the present study will be assessed not only on the flow regime as such, but also on its ecological functions, i.e. on the environmental flow regime. This is motivated mainly by the the semi-natural character of the study area, that is unique in Poland and in Europe, but it also underlines the novelty of this thesis, as going beyond the pure impacts on the flow regime in a scenario-modelling framework is rare in hydrological science, if achieved at all.

1.2 Objective

The general aim of this thesis is to assess the impact of a multiple set of scenarios, describing changes of global- and regional-scale driving forces on the ecological func­tions of the flow regime. This general aim translates into three more specific research objectives:

1. A comprehensive spatial validation of a large-scale, semi-distributed hydrolo­gical model, to test its ability to assess spatial patterns in impacts on flow regime indicators.
2. A broad description of the water requirements of selected river-dependent biota and their parametrisation into indicators derivable using hydrological model output.
3. A spatially-distributed analysis of future impacts on environmental flow indic­ators under various model scenarios, derived from key local stakeholders’ input, as well as based on downscaled projections of global change.

The research undertaken to fulfil these objectives was carried out as part of a case study of the Narew River Basin, NE Poland. There were two main reasons for choos­ing this basin. Firstly, it is a semi-natural (near-pristine) river basin, where river water use for domestic supply, industry and agriculture is minor compared to other river basins of similar size in Poland. Secondly, this river basin was selected as the pilot area catchment in the EU FP6 research project SCENES (Water Scenarios for Europe and Neighbouring Countries), which was one of the motivations for this re­search. Although it is believed that the findings of this thesis are important, especially for the water management of the Narew River Basin, various approaches developed and adapted in this thesis are not limited to this area only but are applicable else­where.

illustration not visible in this excerpt

Figure 1.1: Conceptual schematic overview of this thesis.

Figure 1.1 illustrates a schematic conceptual overview of this thesis. Driving forces acting at both global and regional level influence catchment water balance, so scenarios of changes in driving forces can be considered either separately (single arrows in Figure 1.1) or as combined (joining arrows). Changes in catchment water balance imply flow alterations, which have environmental consequences for ecosystems that depend on river water (i.e. aquatic/riparian biota). The parametrisation of the water demand for particular biota enables one to develop a number of impact indicators that can be quantified using altered flow time series. In this way future impacts on the environmental water requirements of river-dependent biota are studied in this thesis.

1.3 Scope

With respect to Figure 1.1, illustrating the general concept of this thesis, it is clear that the application of a distributed, physically-based, catchment-scale hydrological model suitable for addressing water resource problems is, although probably not the only one, certainly a good method for fulfilling the main objective. Nowadays numer­ous hydrological models meeting the above-mentioned requirements exist, many of which would be adequate tools in this thesis; consequently, at the preliminary stage one tool had to be chosen. The Soil & Water Assessment Tool (SWAT) was selected due to several key features:

- Global popularity expressed by numerous published worldwide applications in hydrological impact assessment studies;
- Its full integration with Geographical Information Systems (GIS);
- Being a public domain model;
- Using readily available input data.

An additional stimulus for applying SWAT was its relatively low popularity in Po­land.

Figure 1.2 is an expansion of Figure 1.1, schematically illustrating the scope of this thesis. Five main zones are featured in different colours in this diagram, each related to a specific part of the thesis. The diagram has four starting points (numbered from 1 to 4 in Figure 1.2) expanding into chains of boxes representing different steps/processes/data and arrows representing conceptual flow. The scope of this thesis is discussed below with reference to the diagram in Figure 1.2.

The first (blue) chain begins with the set-up of the SWAT model, followed by model calibration and validation. This chain is a prerequisite because only a model which has passed calibration and validation criteria can be applied in scenario analyses. Furthermore, due to the basin size, preparation the set-up of the model is time-consuming, as the spatial calibration and validation approach is relatively complex. A general description of SWAT, its set-up for the Narew River Basin and the tools, approaches and parameters used for model calibration constitute section 3.1, whereas the calibration and validation results belong to section 4.1.

The second (red) chain illustrates the process involved in developing global scenarios. In this thesis the term “global scenarios” refers to projected changes in atmospheric CO2 levels and climate. Very often only climate variables are altered in climate change impact modelling studies focused on water resources, whereas, in reality, climate change projections of General Circulation Models (GCMs) are forced by elevated CO2 concentrations according to different greenhouse gas (GHG) emission scenarios. Furthermore, increased CO2 is known to alter plant physiology, which can also be considered in SWAT. Downscaling and bias correction of GCM output (precipitation and temperature projections) are necessary steps preceding the application of climate change data in a catchment model. Section 3.2 describes all the data and methods used in developing global scenarios in this thesis.

The points of departure in the third (green) chain are SCENES scenarios, namely Sustainability Eventually (SuE) and Economy First (EcF), which were de- veloped using mainly qualitative and semi-quantitative scenario development meth­ods during a series of stakeholder workshops carried out within SCENES. The green chain illustrates the development of regional model scenarios. In this thesis the term “regional scenarios” is understood as scenarios describing future changes in selected driving forces specific to the Narew River Basin: land use, agricultural development and the development of agricultural water management (drainage and irrigation). The process of transferring qualitative knowledge (SCENES scenarios) into SWAT model scenarios (modified parameters) was conducted with active stakeholder in­volvement. In the first stage a qualitative description of driving forces for both scenarios was produced by stakeholders in groups, which was followed by filling in individual questionnaires asking about the quantitative meanings of previously de­scribed changes. Post-processing of these data enabled the computation of trends in driving forces, and finally, the modification of adequate SWAT input parameters. The process involved in developing regional scenarios is described in section 3.3, with several supporting materials placed in the appendices.

illustration not visible in this excerpt

Figure 1.2: Schematic illustration of the scope of this thesis.

The first three chains in Figure 1.2 end up in the “Validated SWAT” box. In contrast, the fourth (purple) chain does not depend on SWAT. It originates with a broad, mainly literature-based description of the water requirements of selected representatives of the aquatic and riparian environment, namely fish and floodplain vegetation communities. The process of quantifying these requirements is carried out using the adapted building block approach, whereby three blocks are distinguished: one for maintaining minimum (in-stream) flows, one for providing a spawning and nursery habitat for pike and one for maintaining floodplain vegetation communities in good health. Environmental flow requirements of selected biota and the applied building block approach are described in section 3.4.

The last (black) chain illustrates how the final results of this thesis are achieved. A juxtaposition of water supply (modelled flows) with water demand (building blocks) enables the design of a set of indicators. Two types of indicat­ors are distinguished: state indicators and impact indicators. State indicators show the current state of a phenomenon under study, while impact indicators measure changes to a phenomenon under study caused by a certain impact. The main fo­cus of this thesis is on impact indicators, which are calculated by combining both altered (under a given scenario) and natural (baseline) modelled flow time series with the building blocks. Definitions of developed state and impact indicators belong to section 3.5, whereas the final results following the calculation of the Environmental Flow Impact Indicators are given in section 4.3. These indicators are spatially ex­plicit, biota-specific and scenario-specific. In particular, they are quantified for all possible combinations of global and regional scenarios.

Chapter 2 Study Area

The River Narew, situated in the north-east of Poland (Fig. 2.1), is the right tributary of the River Vistula, and the total drainage area upstream from its mouth is ca. 75,000 km2. However, in this thesis a part of the basin situated upstream of the Zambski Koscielne gauging station is analysed. This part of the Narew River Basin (which hereafter will be referred to as the NRB) is situated between the meridians of longitude 20°21’E and 24°27’E and between the parallels of latitude 52°35’N and 54°16’N and occupies ca. 28,000 km2. The motivation for selecting Zambski Koscielne as the main outlet is that it is the most downstream gauging station beyond the reach of the artificial Lake Zegrzyήskie’s backwater effects. Approximately 4% of the drainage area, in its upstream part, lies in western Belarus, i.e. outside of the territory of the Republic of Poland.

River basin physiography (geology, topography, hydrography, soils, land cover, climate and hydrology) is discussed in section 2.1, while socio-economic characterist­ics are given in section 2.2.

2.1 Physiography

The NRB lies in the western edge of the East European Plain, the largest mountain­free part of the European landscape. Present lowland relief of the NRB was shaped by two last glaciation periods: Riss (Oder) and Würm (Vistulian), using Alpine glaciation nomenclature. As shown in the map of geological units of the NRB (Fig. 2.2) three classes occupy over 76% of the Polish part of the NRB. Tills, weathered tills, glacial sands and gravels occupy ca. 31% of this area, covering most of the part north of last glacial maximum line. The second largest class (ca. 29%) is outwash sands and gravels, predominating especially in the extent of the Pomeranian phase of Vistulian glaciation limit. Finally, fluvial sands, gravels, muds, peats and organic silts are the third largest class covering ca. 17% of the NRB in Poland, filling the river valley bottoms, in particular the Biebrza ice-marginal valley.

The NRB can be characterised by a mean altitude of 137 m.a.s.l. and a flat topography (Fig. 2.3) spatially correlated with geological features. The highest elev­ated areas in the north correspond to till- and kame sand-covered end moraine hills, such as the Szeskie Hills, with an elevation reaching 307 m.a.s.l. and a topographic prominence surpassing 100 m. However, 99% of the drainage area lies below 200 m.a.s.l., while the mean basin slope calculated based on the contour DEM shown in Figure 2.3 is equal to 2.1%.

The basin’s present hydrographic network reflects its geology and relief, which is fully developed and rich in tributaries, many of which are right-bank tribu­taries draining post-glacial lakes located in the northern part of the basin (Fig. 2.3). The drainage area of these right bank tributaries is four times larger than that of their left bank counterparts, which is as a result of the direction of ice sheet reces­sion in the late Pleistocene. In the Lake District region there are more than 500 lakes greater than 1 ha, with the largest, Lake Sniardwy, occupying 102.4 km2. The mean channel slope along the 363 km-long stretch of the River Narew, downstream of the Siemianówka dam and upstream of Zambski Koscielne, is equal to 0.16 m/km, which is a low value even for lowland rivers. Other important landscape features of the NRB valley bottoms are large floodplains, very often still in connection with the main channel.

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Figure 2.2: Map of geological units and glaciation extent in the NRB (source: Geo­logic Map of Poland from the Polish Geological Institute).

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Figure 2.3: Topography and hydrographic network of the NRB (hydrographic source: Digital Map of Hydrological Division of Poland from the Institute of Met­eorology and Water Management).

Sandy soils of various types, but usually pure and loamy sands, dominate in the NRB (Fig. 2.4). Loamy soils that also exist are typically sandy loams; very heavy impermeable soils (clay, clay loam, silt loam) are rare in the landscape. The main valley bottoms were filled with peat deposits from the Holocene, and they are still partly undrained at present.

Agriculture dominates land use in this area: 46% of land is used as arable land and 17% as grassland, whereas 33% is occupied by forests (Fig. 2.5). The remaining 4% of land is covered by wetlands, lakes and urban areas. The upland of the basin area is mainly used for growing crops, and the valley bottoms are used as permanent meadows and pastures. The largest and most compact forest complexes are located in the northern, western and eastern parts of the basin, while wetland areas are concentrated mainly in the Biebrza valley bottom.

The NRB is the core part of the region known as the “Green Lungs of Poland” (GLP), an initiative that was launched as an inter-province agreement aiming to support eco-development in this exceptional region. There are three national parks (ca. 750 km2), protecting wetland and forest ecosystems, and a number of other protected areas, in particular Special Areas of Conservation (SACs, protected under the EC Habitats Directive) and Special Protection Areas (SPAs, protected under the

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Figure 2.5: Land cover map of the NRB (source: CORINE Land Cover 2000 from the Chief Inspectorate of Environment Protection).

EC Birds Directive) of the NATURA 2000 network (Fig. 2.1).

The NRB is located in a temperate climatic zone in which marine and contin­ental air masses collide. The latter shape the area’s climate to a larger extent than in central and western Poland. This region has moderately warm summers (July’s mean temperature equal to 18°C) and cool winters (January’s mean temperature equal to -2°C). Figure 2.6 illustrates spatial variability in mean annual January (A.) and July (B.) temperatures, which was interpolated using the Local Polynomial method in ArcGIS (only for demonstration purposes), based on data from 14 stations belonging to the monitoring network of the Polish Institute of Meteorology and Water Man­agement (IMGW). The rise in temperature can be observed in both cases from NE to SW, in general. However, the spatial pattern in winter is more subject to cold continental air and to the warming effect of lakes in the north, whereas the pattern in summer is correlated more with latitude. Spatial variability in temperature has a clear impact on the duration of the growing season, which will be discussed in subsection 3.1.2.

The annual basin-averaged precipitation yields ca. 600 mm, of which 35% falls between June and August, and only 17% between January and March. Figure 2.7 illustrates spatial variability in mean annual precipitation, which was interpolated using Radial Basis Functions method in ArcGIS (only for demonstration purpose), based on data from 78 stations from the IMGW network. The lowest amounts of annual precipitation are observed in the southern and central part of the basin. Two directions of spatial variability can be observed: from south to north and from centre to east. The highest precipitation is generally measured in the lake district in the north. Apart from general spatial trends, several smaller scale features of mean annual precipitation variability can be found, which proves the importance of maintaining dense network of precipitation gauging stations. Visual comparison of Figure 2.7 with DEM in Figure 2.3 demonstrates that mean annual precipitation is correlated to elevation in the NRB and elevation may sometimes explain small-scale variations.

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Figure 2.6: Map of the interpolated mean January (A.) and July (B.) temperatures for 1989-2008.

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Figure 2.7: Map of interpolated mean annual precipitation for 1989-2008.

Figure 2.8 shows discharge hydrographs at the Zambski Koscielne gauging station for two example hydrological years, specifically a wet year in 1994 and a dry year in 2003. In both cases hydrograph peaks are associated with snow-melt that usually occurs in early spring or during warmer spells of winter. The magnitude of floods can vary to a large extent between dry and wet years. Since evapotranspiration is the dominant process in summer, floods occur very rarely in this season, even after heavy rainfall events. Hence, the period between July and September is typically the low flow period. The hydrographs shown in Figure 2.8 represent the whole basin, and even though the flow regime of this area is rather uniform, some local variations certainly exist. One of the main differentiating features is intra-annual flow variability (or flashiness of the flow regime), which can be measured by the coefficient of daily flow variation (CV, i.e. standard deviation divided by mean flow). For example, CV for the River Pisa is equal to 0.37 at the Pisz gauge, whence CV for the upstream

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Figure 2.8: Observed daily discharge hydrographs at the basin outlet (Zambski Kos- cielne station on the Narew) during a wet year (1994) and a dry year (2003). part of the River Biebrza is equal to 1.31.

2.2 Population and economy

The Polish part of the NRB lies within three different provinces - Podlasie, Warmia- Masuria and Mazovia (Fig. 2.9). The largest part of ca. 52% of the drainage basin lies within Podlasie Province, followed by Warmia-Masuria Province and Mazovia Province occupying ca. 28% and 20% of the area, respectively. A further subdivision is provided by EUROSTAT for statistical purposes via NUTS (The Nomenclature of Territorial Units for Statistics) at level 3. Six NUTS 3 sub-regions belong to the Polish part of the NRB (actually there are eight of them, but two occupy a negligible por­tion): Bialostocki, L·omžyήski, Suwalski, Elcki, Olsztyήski and Ostrolçcko-Siedlecki. These six sub-regions cover a total area of 49,000 km2, which is larger than the ca. 27,000 km2 of the Polish part of the NRB; however, they reasonably approximate its present socio-economic conditions in terms of socio-economic statistics.

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Table 2.1 specifies several socio-economic indicators for the NRB sub-regions and combines them with similar statistics for Poland as for 2009 (GUS, 2011). The NRB area belongs to the most poorly populated Polish regions. Around 1.6 million inhabitants are estimated to live in this region, at an average of 59.2 people per km2, with three sub-regions below 50 people per km2, while in the rest of the country average population density is more than twice as high at up to 122 people per km2. More than half of the population (56.9%) live in urban areas, which is approximately 4.1% less than in the whole country. However, this statistic is over-estimated due to the inclusion of the city of Olsztyn, which is outside the NRB. The largest city of the NRB is Bialystok, the capital of Podlaskie province, with 285,000 inhabitants. Other cities are definitely smaller and none of them exceeds 70,000 inhabitants (cf. Fig. 2.1), which means that ca. 69% of the urban population live in towns and cities smaller than 70,000 inhabitants. All cities have sanitary sewerage systems, transporting effluent to wastewater treatment plants, and storm drainage systems that drain off precipitation water to the nearest receiving water source. In most of the cities the sewerage network is distributive. Enterprises situated in rural areas usually have their own effluent treatment plants (Okruszko et al., 2012).

The unemployment rate as of February 2012 in the NRB was around 4% higher than the average for Poland at 13.5%. It was particularly high in the Elcki sub-region, yielding 25.6%. GDP per capita for 2009 was also considerably (by 28.4%) lower than the Polish average. This indicator is clearly correlated with the percentage of the urban population. It is worth noting that the sub-regions Elcki, L·omžyήski

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Table 2.1: Basic socio-economic statistics for the NRB sub-regions in 2009 (GUS, 2011).

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a weight is defined here as the ratio of the area of the sub-region part overlapping with the NRB to the area of the NRB overlapping with all-subregions; this serves to calculate the “NRB approximation”.

b 1 - Population density (people per km2); 2 - Urban population (%); 3 - Unemployment rate (%) as of February 2012; 4 - GDP per capita (thous. PLN). and Suwalski are ranked 66th, 57th and 54-th, respectively, with respect to GDP per capita across 66 Polish sub-regions. In contrast, the Biaiostocki sub-region is ranked 24th.

Table 2.2 shows Gross Value Added (GVA) by type of activity in the NRB’s sub-regions. The most apparent feature of this region is its extraordinarily high share of agriculture, forestry and fishing (in short: agriculture) industries compared to country-wide data (11.7% in the NRB vs. 3.6% in Poland as a whole). This is compensated mainly by a lower share in non-manufacturing industries (e.g. mining, energy production and supply) and in a part by the services sector (trade, repair of motor vehicles, etc. cf. Tab. 2.2). The chief crops are cereals, potatoes and fodder, and cattle breeding is important. The region is not rich in mineral resources and there are no large heavy industry factories. Textiles, food processing and timber are the major manufacturing industries, and the food industry is probably the most dynamically developing branch in this region, with a growing number of factories producing milk, meat, poultry, cereals, vegetables, fruits and beer (creameries also being the most important at national level). Recently observed tourism development has taken place, due to extraordinary natural values rather than to the development of infrastructure (Gieiczewski et al., 2011).

The presented statistics prove that the NRB distinguishes itself from the rest of the Republic of Poland with respect to its socio-economic development, usu­ally in a rather negative way. Wójcik (2008) analysed sub-regional and temporal (1995-2005) variability of GDP per capita in Poland, demonstrating that the highest economic growth was observed in this period for metropolitan areas. The sub-regional diversification of relative GDP per capita grew between 1995 and 2005. The poorest sub-regions in the country (including L·omžyήski, Suwalski, Eicki and Ostroiçcko a 1 - Agriculture, forestry, fishing; 2 - Industry (total); 3 - Industry (manufacturing); 4 - Construction; 5 - Trade, repair of motor vehicles, transportation and storage, accommodation and catering, information and communication; 6 - Other services.

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Table 2.2: Gross Value Added (GVA) by type of activity and sub-regions as a per­centage of total GVA in 2009 (GUS, 2011).

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Siedlecki sub-regions) showed relative impoverishment due to the faster than normal growth of the richest sub-regions (mainly Warsaw, Poznaή and Kraków).

The advantage of lower economic development is generally lower pressure on water resources than in the rest of the country, and hence better chances for achieving a yet more sustainable status in the future. Koziowski (2006) stated that the European Union’s strategy of sustainable development is largely based on the creation of large spatial nature structures and Poland, most notably the GLP area, plays an important role in the implementation of this strategy.

Chapter 3 Materials and Methods

In this chapter the materials used and methods applied to reach the objectives out­lined previously are described. Section 3.1 presents basic features of the SWAT model, as well as the model’s set-up and calibration approaches. The following two sections describe global change scenarios (section 3.2) and regional changes (section 3.3). In section 3.4 an environmental flow method based on the building block approach is outlined, which is then followed in section 3.5 by an introduction to state and impact indicators measuring different aspects of providing sufficient amounts of water to the environment.

3.1 SWAT model

Piniewski and Okruszko (2011) undertook initial work to calibrate and validate the hydrological component of SWAT for the NRB, by using multiple gauges and evaluat­ing its ability to simulate flows on small spatial scales. In this thesis this preliminary work is further developed by making several essential changes to the previously con­structed model set-up, as well as the calibration and validation approaches. This section begins with a description of basic SWAT model features and modelling con­cepts in subsection 3.1.1, followed by a summary of the model set-up developed by Piniewski and Okruszko (2011) and a description of its newer features in subsection 3.1.2. Next, the tools applied for auto-calibration and uncertainty analysis are presen­ted in subsection 3.1.3, and the approach used for the calibration and validation of SWAT for the NRB is described in subsection 3.1.4. Finally, the parameters used in the calibration process are presented in subsection 3.1.5.

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