Multi-Faceted Environmental Analysis to Improve the Quality of Anthropogenic Water Reservoirs (Paprocany Reservoir Case Study)

Abstract

Maintaining good condition of dam reservoirs in urban areas seems increasingly important due to their valuable role in mitigating the effects of global warming. The aim of this study is to analyze possibilities to improve water quality and ecosystem condition of the Paprocany dam reservoir (highly urbanized area of southern Poland) using current data of the water parameters, historical sources, and DPSIR (Driver–Pressure–State–Impact–Response) and 3D modeling concerning human activity and the global warming effects. In its history Paprocany reservoir overcame numerous hydrotechnical changes influencing its present functioning. Also, its current state is significantly influenced by saline water from the coal mine (5 g L−1 of chlorides and sulphates) and biogenic elements in recreational area (about 70 mg L−1 of chlorate and to 1.9 mg L−1 Kjeldahl nitrogen) and in sediments (222.66 Mg of Kjeldahl nitrogen, 45.65 Mg of P, and 1.03 Mg of assimilable phosphorus). Concluding, the best solutions to improve the Paprocany reservoir water quality comprise: increasing alimentation with water and shortening the water exchange time, restoration of the 19th century water treatment plant, and wetlands and reed bed area revitalization. The study also proved the applicability of mathematical models in planning of the actions and anticipating their efficiency.

Full text

sensors
Article
Multi-Faceted Environmental Analysis to Improve the
Quality of Anthropogenic Water Reservoirs
(Paprocany Reservoir Case Study)
Damian Absalon 1, * , Magdalena Matysik 1, Andrzej Wo´znica 1, Bartosz Łozowski 1, *,
Wanda Jarosz 2, Rafał Ula´nczyk 3, Agnieszka Babczy ´nska 1and Andrzej Pasierbi´nski 1
1
Silesian Water Centre of University of Silesia in Katowice, Faculty of Natural Sciences, University of Silesia
in Katowice, 40-007 Katowice, Poland; magdalena.matysik@us.edu.pl (M.M.);
andrzej.woznica@us.edu.pl (A.W.); agnieszka.babczynska@us.edu.pl (A.B.);
andrzej.pasierbinski@us.edu.pl (A.P.)
2Institute for Ecology of Industrial Areas, 40-844 Katowice, Poland; w.jarosz@ietu.pl
3Institute of Environmental Protection—National Research Institute, 00-548 Warszawa, Poland;
rafal.ulanczyk@ios.edu.pl
*Correspondence: damian.absalon@us.edu.pl (D.A.); bartosz.lozowski@us.edu.pl (B.Ł.)
Received: 19 March 2020; Accepted: 30 April 2020; Published: 4 May 2020


Abstract:
Maintaining good condition of dam reservoirs in urban areas seems increasingly important
due to their valuable role in mitigating the effects of global warming. The aim of this study is
to analyze possibilities to improve water quality and ecosystem condition of the Paprocany dam
reservoir (highly urbanized area of southern Poland) using current data of the water parameters,
historical sources, and DPSIR (Driver–Pressure–State–Impact–Response) and 3D modeling concerning
human activity and the global warming effects. In its history Paprocany reservoir overcame numerous
hydrotechnical changes influencing its present functioning. Also, its current state is significantly
influenced by saline water from the coal mine (5 g L
−1
of chlorides and sulphates) and biogenic
elements in recreational area (about 70 mg L
−1
of chlorate and to 1.9 mg L
−1
Kjeldahl nitrogen) and in
sediments (222.66 Mg of Kjeldahl nitrogen, 45.65 Mg of P, and 1.03 Mg of assimilable phosphorus).
Concluding, the best solutions to improve the Paprocany reservoir water quality comprise: increasing
alimentation with water and shortening the water exchange time, restoration of the 19th century
water treatment plant, and wetlands and reed bed area revitalization. The study also proved the
applicability of mathematical models in planning of the actions and anticipating their efficiency.
Keywords:
3D modeling; global warming effects; algal blooming; chlorophyll a; water management;
water quality
1. Introduction
Global climate change causes increasingly severe weather effects throughout Europe. As a
result, changes in the environment are observed, which have an increasing impact on citizens’ lives,
environment, economy, and agriculture [
1
–
4
]. Such changes can have a great impact on aquatic
ecosystems that causes shrinking of water resources (droughts) or, more often, torrential rainfall
causing floods or urban flooding. These events result in the reduction of biodiversity by agriculture
damage, or loss of fish stock [
5
]. Residents of large agglomerations also appreciate the role of blue
and green infrastructure in mitigating climate change effects. Nature-based solutions use natural
capital to provide ecosystem services along with a number of other benefits that address key social,
environmental, and economic challenges [6].
Sensors 2020,20, 2626; doi:10.3390/s20092626 www.mdpi.com/journal/sensors

Sensors 2020,20, 2626 2 of 30
A systematic, statistically significant increase in the average annual air temperature is visible in
the Upper Silesian Conurbation. It has risen by more than 1
◦
C over the past 50 years. The number
and length of heat waves during which the maximum air temperature exceeds 30
◦
C also increases.
The so-called tropical nights during which the temperature does not fall below 20
◦
C also occur more
often. In the last decades, there has been an increase in the threat caused by several days of heavy
rainfall and short-term torrential rains causing floods and, locally, urban flooding [
7
]. The growing
trend for maximum daily rainfall is statistically significant, indicating the possible intensification of
heavy rains in the future [8].
Blue-green infrastructure is especially desirable within and around big cities. However, increasing
anthropopressure often limits the value of ecosystem services provided by the environments due to
the decrease of water quality in the rivers and other reservoirs [
9
]. Small watercourses and water
reservoirs located in urban areas and their immediate vicinity are most at risk. Water reservoirs
are often the areas of recreation (fishing, water sports, or walking, running, or cycling routes). This
is why the quality of water in this kind of reservoirs and care for their immediate vicinity are of
special concern of local authorities. Apart from the recreational function of the reservoirs, there are
also other important roles played by the reservoirs, such as water supply for consumption, food
production, agriculture, or industry, as well as flood protection. Also, there are other important
functions connected with the condition of aquatic systems and water quality such as: maintaining
biodiversity and supporting of ecosystems (often of unique character), binding and degradation of
pollutants, maintaining appropriate water balance, influencing local microclimatic conditions and
mitigating of climate change effects [
10
,
11
]. Because of this reason, water reservoirs deserve special
attention not only at the local (here, Paprocany reservoir) but also at global scale of the institutions
creating environmental protection policy [12,13].
Successful maintenance and protection actions depends inseparably on—and should be preceded
by—efficient and constant monitoring. The water quality control has been led to use various methods
that depend mainly on the aim and/or economic abilities as well as the time needed to obtain the results.
The methods and systems include classical bioindicator studies using sensitive plant or animal species
either directly (e.g., [
14
]) or in regular detection systems or as biosensors [
15
,
16
]. More advanced
molecular biology techniques are useful for the microbial quality of the water [
17
,
18
]. Another group
of methods includes the detection of specific chemicals in water, using numerous physicochemical
techniques of increasing novelty [
19
]. Finally, among the most promising monitoring techniques today
are those that incorporate nanotechnology, i.e., the use nanoparticles as sensors for pollutants [20,21].
Due to the great significance of the quality of water and water-dependent ecosystems, this issue is
the subject of many reports indicating various examples of actions in this area [
22
]. There are also many
methodological tools designed for efficient assessment of water quality [
23
–
27
] and the application
of this assessment for the systems of aquatic environment management. The assessment of the
possibilities to improve the quality of reservoir water resources is an inseparable element of commonly
used procedures for water resource management, such as Integrated Lake Basin Management (ILBM)
or Drivers, Pressures, State, Impact, and Response (DPSIR)—a method adapted by the European
Environment Agency for procedures of water resources management [
15
,
16
,
28
,
29
]. Increasingly, such
assessment is supported by mathematical models that allow for simulation of processes occurring in
reservoirs and their catchments. They also help assess potential effects of environmental changes that
determine water quality [
11
,
30
]. Thus, the models allow for the assessment of the reference state of the
environment in order to estimate the influence of analyzed factors (e.g., sources of pollution, duration
of water retention) on the water quality. They also enable the scientists to anticipate the efficiency of
decisions (catchment and reservoir management) and the consequences of the environmental (e.g.,
climate) changes on water quality.
The incorporation of biological data into the models (ecosystem models) requires a relatively large
amount of data describing the state of the reservoir. These models allow also for the prediction of
aquatic organisms’ behavior and interactions under different conditions. Among the most advanced

Sensors 2020,20, 2626 3 of 30
tools there are 3D models (e.g., AEM3D, ELCOM-CAEDYM, GEMSS, GETM) or systems in which
it is possible to choose the number of analyzed dimensions according to the purpose of the model
application (e.g., Delft3D, EFDC, WASP) [
18
]. It is currently emphasized that mathematical modeling
tools in both hydrology and water ecology should be routinely applied for water resource management
and planning [30].
The aim of the work is to indicate actions that may help to improve water quality of anthropogenic
urban reservoirs applying extended analyses of the environment of the reservoir and its catchment.
As the results of such analyses, proposals for changes in the management of the reservoir and its
catchment should be formulated to improve the quality of the water and reservoir functioning.
As a case study, the Paprocany reservoir (Tychy, Upper Silesia, Poland) was selected, which is
exploited and has been subjected to anthropopressure for almost 300 years. In the Paprocany, the
DPSIR method was used, including both hydrological and chemical pressures and those arising from
the basin management, also in historical terms. Moreover, archival and modern cartographic materials
(from 1737 to 2019) as well as hydrological and water quality data collected over the last 25 years were
included into the analyses.
2. Materials and Methods
2.1. Study Area
Due to its location, the Paprocany reservoir is an important object that may have an impact on
mitigating the effects of climate change in urbanized areas of the southern part of the Metropolitan
Association of Upper Silesia and D ˛abrowa Basin (GZM). The Paprocany reservoir is located in the city
of Tychy in the Gostynia River catchment (left-bank tributary of the Vistula River), in the southern
part of Poland, in the central part of the Silesia Voivodeship, which belongs to the most urbanized and
industrialized regions in Poland and Europe [
31
–
34
] (Figure 1). Despite structural changes, it is still
the area of coal mining and metallurgy industry. Tychy was one of the most dynamically developing
Polish cities in the second half of the 20th century—the city’s population in 1990 was over seven times
higher than in 1955. Currently, Tychy has almost 128 thousand residents [35].
Sensors2020,20,xFORPEERREVIEW3of30
thepredictionofaquaticorganisms’behaviorandinteractionsunderdifferentconditions.
Amongthemostadvancedtoolsthereare3Dmodels(e.g.,AEM3D,ELCOM‐CAEDYM,
GEMSS,GETM)orsystemsinwhichitispossibletochoosethenumberofanalyzed
dimensionsaccordingtothepurposeofthemodelapplication(e.g.,Delft3D,EFDC,WASP)
[18].Itiscurrentlyemphasizedthatmathematicalmodelingtoolsinbothhydrologyandwater
ecologyshouldberoutinelyappliedforwaterresourcemanagementandplanning[30].
Theaimoftheworkistoindicateactionsthatmayhelptoimprovewaterqualityof
anthropogenicurbanreservoirsapplyingextendedanalysesoftheenvironmentofthereservoir
anditscatchment.Astheresultsofsuchanalyses,proposalsforchangesinthemanagementof
thereservoiranditscatchmentshouldbeformulatedtoimprovethequalityofthewaterand
reservoirfunctioning.
Asacasestudy,thePaprocanyreservoir(Tychy,UpperSilesia,Poland)wasselected,
whichisexploitedandhasbeensubjectedtoanthropopressureforalmost300years.Inthe
Paprocany,theDPSIRmethodwasused,includingbothhydrologicalandchemicalpressures
andthosearisingfromthebasinmanagement,alsoinhistoricalterms.Moreover,archivaland
moderncartographicmaterials(from1737to2019)aswellashydrologicalandwaterquality
datacollectedoverthelast25yearswereincludedintotheanalyses.
2.MaterialsandMethods
2.1.StudyArea
Duetoitslocation,thePaprocanyreservoirisanimportantobjectthatmayhaveanimpact
onmitigatingtheeffectsofclimatechangeinurbanizedareasofthesouthernpartofthe
MetropolitanAssociationofUpperSilesiaandDąbrowaBasin(GZM).ThePaprocanyreservoir
islocatedinthecityofTychyintheGostyniaRivercatchment(left‐banktributaryoftheVistula
River),inthesouthernpartofPoland,inthecentralpartoftheSilesiaVoivodeship,which
belongstothemosturbanizedandindustrializedregionsinPolandandEurope[31–34]
(Figure1).Despitestructuralchanges,itisstilltheareaofcoalminingandmetallurgyindustry.
TychywasoneofthemostdynamicallydevelopingPolishcitiesinthesecondhalfofthe20th
century—thecityʹspopulationin1990wasoverseventimeshigherthanin1955.Currently,
Tychyhasalmost128thousandresidents[35].

Figure1.Studyarealocation.
Figure 1. Study area location.
Paprocany is one of 4773 water reservoirs in the Upper Silesian Anthropogenic Lake District
(70.54 water reservoirs per 100 km
2
). In certain parts, up to 200 water bodies are present per 100 km
2
, the
average lake density is 2.7% (ratio of water surface area to the total surface area). The water reservoirs

Sensors 2020,20, 2626 4 of 30
either served social and economic functions or were the unintended result of human activity [
31
,
36
].
The area of the Paprocany reservoir is 1.051 km
2
for the normal damming level (242 m a.s.l.), which at
present, does not change in time. As a consequence of the constant damming level, the fluctuation
of water level, reservoir area and volume of water is not significant (damming volume curve and
area curve can be found in “3.1. Cartographic Analysess” chapter). The reservoir was built in the
18th century for the needs of the metallurgical industry to power the water wheels of the “Huta
Paprocka” ironworks. The industrial revolution reduced the demand for water and in the second
half of the 19th century the reservoir began to perform recreation and fish farming functions that
dominate to this day. In addition, the reservoir has a water retention and flood protection function,
protecting the south-eastern districts of the city of Tychy. Nowadays, poor water quality and blooms
of cyanobacteria in the reservoir significantly limit the development of recreation, water sports, and
tourism at the reservoir.
The Gostynia River drains both the areas of industrial (e.g., Łaziska G
ó
rne, Tychy) and forest
and agricultural areas. The Gostynia catchment is clearly asymmetrical, i.e., left-bank tributaries that
flow down from the Silesian Upland predominate. The largest tributary of Gostynia is the Mleczna
River, but it flows into Gostynia below the Paprocany reservoir. The hydrographic network of the
Paprocany reservoir supply area is less abundant. The main watercourse in this part is Stara Gostynia
with small tributaries. Several small watercourses also supply the reservoir directly from the south
(Hydrographic Map of Poland, Table 1).
Originally, the supply area of the Paprocany reservoir covered the entire Gostynia drainage
basin, closed by the outlet section of the outflow from the reservoir, with an area of 130.6 km
2
.
However, its present supply area is limited to 17.94 km
2
, i.e., 13.7% of the original catchment area (see
“3.1. Cartographic Analysess” chapter).
While the normal damming level is maintained, the Paprocany reservoir average depth is 1.64 m.
Together with a small water supply, it results in a long period of stagnation of water in the reservoir
(over 180 days). In summer this leads to rapid heating of reservoir water, which in turn causes a
significant acceleration of the appearance of blooms.
Spatial management analysis of the catchment area was performed using Urban Atlas and Corrine
Land Cover 2012. Forests, meadows, and anthropogenic infrastructure contribute to the main forms
of land use in the reservoir subcatchment. Forests (64% of the area, including: 32%—coniferous,
27%—deciduous forests, 5%—clearcutting and young tree planting areas) are favorable to maintain
good water quality in the reservoir because they are natural filters limiting the reservoir’s exposure to
the pollution of atmospheric origin. Meadows are located in the valley of the former Gostynia riverbed.
Currently, only 26% of the area is utilized (mown or grazed), but not always properly. They should
be regularly mown and the swath removed to assure a significant reduction of nutrient runoffinto
the reservoir. Currently, the cattle grazing and leaving swaths as well as the stable with unprotected
leachate of the manure leads to an increased nutrient supply to the Paprocany reservoir.
Housing, road, agriculture and tourist infrastructure, which is potentially a source of nutrients
reaching the reservoir with surface runoff, is located in the eastern part of the direct catchment.
Forests that occupy over 48% of the catchment area are the dominant type of use in the upper
Gostynia catchment in which the Paprocany reservoir is located. Agricultural and urbanized areas
cover about 30% and 10% of the catchment, respectively. Despite the small percentage, industrial
areas, and waste dumps and heaps play a key role in determining water quality in the catchment. The
basin of the reservoir is occupied mainly by forests, which also dominate in the basin of the Potok
˙
Zwakowski Stream and Dopływ spod Chałup Stream.

Sensors 2020,20, 2626 5 of 30
Table 1. Cartographic data sources—historical and contemporary maps and digital data.
Date of
Development Type of Information Remarks
Topographic maps
1747–1753 Map No. 33 Tychy; Imielin; Mizerów;
O´swi˛ecim
Christian Friedrich von Wrede, scale 1:33,333
Krieges-Carte von Schlesien
1747–1753 Map No. 34 Palowice; ˙
Zwaków; Pawłowice;
Jankowice
1782 Mapa Hammer 1782 Hand-drawn map of the catchment
1794–1795
Situations Plan von einem Theile
Oberschlesiens an der Oestereich und
Neuschlesischen Grenze
Johannes Harnisch (copy of Fischer, 1801);
Scale—1:120,000
1800/1933 Map Furstenthums Ratibor Pleisner Creifes Hand-drawn map of the catchment
1806 Massenbach map 1806 Hand-drawn map of the catchment
1827 Kobier blat map 1827
No scale, Lieutenant von Sydow from the Border
Guard regiment
1856 Staffmap of Pszczyna Scale 1:100,000 Halemba; Szczakowa; Kobielice;
O´swi˛ecim
1881/1883 Staffmap of Kobier Scale 1:25,000 Paprocany Gosty´n, Radostowice
Jankowice
1906 Zone 5 Kol XX Myslovitz und O´swi˛ecim Map of Silesia including: Mysłowice, O´swi˛ecim,
Mikołów, Bieru ´n
1933 Polish staffmap Orzesze, Tychy, Gosty´n, Paprocany,
Scale 1:25,000
1944 German staffmap Scale 1:25,000 Zgo´n, Paprocka ironworks,
Kobielice, Jankowice
1995 Topographic Map of Poland Scale: 1:10,000; 1:50,000
Documentation and plans
1872
Repository of ducal files 1870—AKP XI 49,
Katowice State Archives, department of
Pszczyna
Sketches and technical drawings regarding the
development of the catchment, designs of
hydrotechnical devices in the Gostynia catchment
1895 Übersichtskarte des Tichauer Baches mit
seinem Niederschlagsgebiet im Kreis Pless
Hydrological documentation and river
regulation plans
1933
Repository of ducal files
1889–1933—Katowice State Archives,
Department of Pszczyna
Documentation of the renovation of
hydrotechnical equipment of the catchment
Thematic map
2015 Hydrographic Map of Poland Scale: 1:50,000
sheet: Tychy, Katowice, Chorzów, O´swi˛ecim
Digital data and metadata
2012 Corine Land Cover (CLC2012)
2012 Urban Atlas (LCLU 2012)
2015 Digital Elevation Model Scale: 1:5000
2018 Hydrographic division of Poland (MPHP) Scale: 1:10,000
2.2. Data Collection
The protection of the reservoir and its catchment area requires collecting and analyzing the broad
scope of data, usually not available at hand. Therefore, there were four main groups of activities
executed to achieve the objective: (1) collection and processing of spatial information including
historical documents dating back to the 18th century; (2) assessment of the status of surface waters

Sensors 2020,20, 2626 6 of 30
based on the state and local monitoring systems and a monitoring campaign dedicated to this study;
(3) detailed characterization of the reservoir using geo referenced sonar sounding and high-resolution
water quality probing; and (4) application of the three-dimensional, dynamic model of the reservoir
hydrodynamics and water quality in order to assess the impacts of planned measures and climate
changes, which may affect the efficiency of measures. The historical and contemporary cartographic
sources were shown in Table 1.
To assess the current state of the Paprocany reservoir and its catchment water (1) data from
the State Environmental Monitoring from 1995–2014; (2) results of water and sediment testing and
monitoring of the quality of the Paprocany reservoir water conducted at the request of the Council
City of Tychy in 2004–2006; (3) data of water quality from tributaries of the Gostynia River monitoring;
as well as (4) own research were used.
Water quality tests were also carried out at six sensitive points of the Paprocany reservoir and the
Gostynia catchment: inflow to the reservoir, pelagic zone, outflow from the reservoir, the mouth of the
Potok ˙
Zwakowski Stream to the Gostynia, Rów S1 (Ditch S1), which is the saline mine water deposit,
and the Gostynia above the mouth of Rów S1 (Ditch S1).
Sonarographic measurements of the reservoir depth were also taken using the bathymetric set, a
model of the reservoir bowl and a map of the location and the thickness of bottom sediments developed
based on sonarographic measurements of MaxiMapa Co Ltd. the University of Silesia in Katowice [
37
]
using Reffmaster and Surfer 18 software. The purpose of these measurements was to estimate the
reservoir capacity at different levels of damming, parameterization of the mathematical model of the
reservoir, and to design the measurement network with a multi-parameter probe.
The analysis of digital terrain model (DTM) of the water catchment area of the Paprocany reservoir
was obtained from the Central Geodetic and Cartographic Resource (Main Center for Geodesic and
Cartographic Documentation). Merge of DTM images, the water level analysis and terrain profiles and
3D imaging were carried out using the Surfer 18 Golden Software. The analysis of DTM was conducted.
In order to assess the spatial variability of the chemical parameters of water of the Paprocany reservoir,
a series of measurements of physicochemical parameters was taken using a multi-parameter Hydrolab
MS probe that was equipped with sensors for measuring: nitrates, chlorides, dissolved oxygen, water
temperature, conductivity, redox potential, and pH. These measurements were carried out at a depth
of 0.5 m and in the bottom zone, in a network of squares with a side of 150 m, which allowed obtaining
98 regularly spaced points with known geographical coordinates. Geostatistical methods were used to
create the maps of chemical variability [
38
]. Surfer 18.0 Golden Software was used for data interpolation.
Gridding was used with a standard semi-variogram and search neighborhood function.
2.3. Model Structure
The Aquatic Ecosystem 3D Model (AEM3D) developed by HydroNumerics was used for the
data analysis. The AEM3D is an example of three-dimensional integrated hydrodynamic and
ecosystems models. It is based on Estuary, Lake, and Coastal Ocean Model (ELCOM) and Computation
Aquatic Ecosystem DYnamics Model (CAEDYM) models developed earlier by the Center for Water
Research—the University of Western Australia [
39
–
42
]. The AEM3D allows the users to simulate,
among others water flow, water temperature and density, cycles of nitrogen, phosphorus, oxygen, silica,
carbon, sediments, metals and organic matter, biomass of bacteria, plankton, and macrophytes [
43
–
45
].
In this work, the AEM3D model was used to (1) recognize the variability of flow and water
quality over time, (2) assess the effects of increased water supply to the tank by transferring water
from catchments that do not belong to the current tank supply area, (3) assess the effects of a barrier
limiting the water supply to the bathing area in the eastern part of the reservoir, as well as (4) determine
the impact of climate change as a factor that may affect the effectiveness of the proposed remedial
measures for the reservoir and its catchment. Processes that were included in the model simulations
for the Paprocany reservoir are: velocity and directions of the water flow through the reservoir; water
retention time (reservoir); transport of a virtual marker, which allows tracking the spread of dissolved

Sensors 2020,20, 2626 7 of 30
substances in water, introduced along with the main tank inflow; water temperature changes; changes
in the nutrients and plankton concentration in the reservoir. The output variables that were analyzed
in this study include: water retention time, concentration of tracer, and concentration of chlorophyll a.
In the AEM3D model, the transport of mass and energy (hydrodynamics and thermodynamics)
are governed by more than a hundred equations and many of them are used to represent chemical
and biological processes. Therefore, this chapter describes briefly the main processes included in the
model and it is based on the technical documentation [
46
], while a detailed description of mathematical
formulas can be found in the mentioned documentation.
In each time step the model computes the heat exchange in the surface water layer, mixing
of scalar concentrations and momentum, wind energy as a momentum source in the wind-mixed
layer, the free-surface evolution and the velocity field, horizontal diffusion of momentum, and
advection and horizontal diffusion of scalars. The transport equations are the unsteady Reynolds
averaged Navier–Stokes and scalar transport equations using the Boussinesq approximation and
neglecting the non-hydrostatic pressure terms. The free surface evolution is governed by an
evolution equation developed by a vertical integration of the continuity equation applied to the
Reynolds-averaged kinematic boundary condition. The scalar (e.g., concentration) transport is based
on a conservative third-order method. The heat exchange through the water’s surface is governed
by standard bulk transfer models. The energy transfer across the free surface is separated into the
nonpenetrative components of long-wave radiation, the sensible heat transfer, and the evaporative heat
loss, complemented by penetrative shortwave radiation. The nonpenetrative effects are introduced as
sources of temperature in the surface-mixed layer, whereas the penetrative effects are introduced as
source terms in water layers on the basis of a decay and an extinction coefficient [46].
Phytoplankton dynamics includes six main processes—such as growth, mortality, respiration,
excretion, grazing by zooplankton, and vertical migration. The phytoplankton growth is limited by
light, temperature, and availability of C, N, P, and Si. For primary production, the shortwave intensity
at the surface is converted to the photosynthetically active component, which is assumed to penetrate
according to the Beer–Lambert Law with the light extinction coefficient adjusted to the concentrations
of algal, inorganic and detrital particulates, and dissolved organic carbon levels. The C, N, and P
cycles, which were mentioned as a limiting factor for the phytoplankton growth are modeled along the
degradation of the particulate organic matter to dissolved the organic and dissolved inorganic matter.
The nitrogen cycle includes the additional processes of denitrification, nitrification, and fixation. Silica is
represented in the model by two forms, i.e., dissolved and algal internal with the phytoplankton uptake
and mortality as main driving processes. Zooplankton was also simulated in case of the Paprocany
reservoir to balance (limit) the primary production. Main processes simulated for zooplankton are
grazing (parameterized primarily by food preferences for phytoplankton, zooplankton, and organic
matter), respiration (function of rate coefficient and temperature), losses (mortality, excretion, and
egestion), and predation by fish [45].
2.4. Specific Model Setting for the Paprocany Reservoir
The model structure is composed of 12 layers of cells with horizontal resolution of 10 m. Seven of
these layers are filled with water at the normal damming level. The thickness of layers ranges from
0.47 m in the deepest part to 0.1 m in the layer representing the normal damming level. The model
geometry was prepared with the use of above-mentioned sonar sounding data (for the area covered
with water at the normal damming level) and 1 by 1-m LIDAR-based digital elevation model (for
the area covered with water between normal and maximum damming levels). Meteorological inputs
were collected within one year (2016) and included precipitation, wind speed and gust, temperature,
pressure, humidity, and cloud cover. The precipitation data were based on six-hour observations,
while all of the remaining parameters had a temporal resolution of one hour. The surface inflows to
the reservoir were calculated based on the measured flow rate in five cross-sections in streams above
the reservoir. The flow rate was measured in a dry season, for which the total flow is considered as

Sensors 2020,20, 2626 8 of 30
a base flow, and in a wet season, for which the rainfall-flow rate functions were calculated. These
functions were used to calculate the rain-induced flow based on six-hour precipitation data. There
were no sufficient data available for the year of 2016 to parameterize the water quality in the inflows
and the initial status of the reservoir. Therefore, based on all of the available water quality observations
made in 2016 and before, monthly averaged values were calculated and used as inputs (Table 2).
Table 2.
Input data regarding the inflow and initial conditions in the Paprocany reservoir (units are
mg L−1unless otherwise specified).
Parameters Initial
Conditions
Inflow for Months (Monthly Averaged Observations
Available for the Reservoir)
6 7 8 9 10
Temperature (◦C) 18.53 17.867 22.167 21.167 11.917 8.300
Dissolved oxygen 8.59 8.083 7.397 6.700 6.760 6.980
pH 7.66 7.155 7.265 7.240 7.245 7.400
Total suspended solids 9.6 13.375 8.000 29.000 10.050 3.300
Dissolved org. C 6.81 6.217 *
Particulate org. C 0.88 1.613 *
Dissolved inorg. C 11.20 11.023 *
Dissolved org. N 0.89 0.815 0.905 0.880 0.805 0.800
Particulate org. N 0.36 0.36 0.38 0.46 0.53 0.37
Ammonia N 0.29 0.260 0.195 0.680 0.405 0.440
Nitrate N 0.21 0.360 0.200 0.530 0.330 0.820
Dissolved org. P 0.14 0.164 0.123 0.106 0.168 0.130
Particulate org. P 0.06 0.088 0.063 0.079 0.067 0.053
Phosphate P 0.080 0.082 0.076 0.035 0.033 0.054
Silica 1.28 1.368 *
Bacteria 0.02 0.047 *
Phyto-plankton
(µg Chl a L−1)
Mixotrophs 0.25 1.337 2.418 2.649 2.881 2.762
Cyanobacteria
0.14 0.616 1.115 1.221 1.328 1.273
Green algae 0.26 2.216 4.006 4.390 4.774 4.577
Diatoms 0.39 3.656 6.609 7.242 7.876 7.550
Zooplankton
(mg C L−1)
Predators 0.06 0.071 0.129 0.142 0.154 0.148
Filtrators 0.07 0.072 0.131 0.143 0.156 0.149
* No data available for the calculation of monthly averaged values.
The simulation covered a period of June–October 2016, which represents a warm season in which
the highest concentrations of chlorophyll can be observed. In the following table a set of model
configuration parameters is presented (Table 3).

Sensors 2020,20, 2626 9 of 30
Table 3. Configuration of the AEM3D model for the Paprocany reservoir.
Parameters Values
Time step (s) 120
Mean albedo of the water for shortwave radiation 0.08
Mean albedo of the water for long wave radiation 0.03
Wind drag coefficient 0.0013
Drag coefficient on bottom cells 0.005
Sediments reflectivity 0.9
Surface heat transfer coefficient 0.0015
Light extinction coefficients (m−1)
Photosynthetically
active radiation Near infrared Ultra violet A Ultra violet B
1.0 0.2 1.8 2.5
Phytoplankton mixotrophs Cyano-bacteria green algae Diatoms
Variable internal N and P store Yes
Vertical migration and settling type Motile Constant
Constant settling velocity (m s−1)- - - −0.12 ×10−6
Type of light limitation algorithm photo-inhibition no photoinhibition
Half saturation constant for density increase (uEm−2s−1)- 278 25 -
Rate coefficient for density increase (kgm−3min−1)- 0.9 -
Minimum rate of density decreases with time (kgm−3min−1)- 0.041 -
Rate for light dependent migration velocity (m h−1)0.6 0.3 0.3 0.85
Rate for nutrient dependent migration velocity (m h−1)0.27 0.30 0.30 0.65
Maximum N fixation rate(mg N mg Chl a 24 h−1)0 2 0 0
C:Chlorophyll a ratio 40
Light saturation for maximum production (µEm−2s−1)390 500 300 100

Sensors 2020,20, 2626 10 of 30
Table 3. Cont.
Parameters Values
Initial slope of photosynthesis-irradiance curve (µE m−2s−1)140 150 100 80
Maximum potential growth rate (d−1)1.3 1.0 1.5 3.2
Optimum temperature for growth (◦C) 20 20 24 18
Maximum temperature for growth (◦C) 28 35 30 30
Standard temperature for growth (◦C) 20 20 17 15
Half saturation constant for P 0.001
Half saturation constant for N 0.05 0.04 0.05 0.04
Maximum internal N concentration
(mg N mg Chla−1)12.5 5.0
Minimum internal N concentration
(mg N mg Chla−1)3.5 2.5 3.0 2.7
Maximum internal P concentration
(mg P mg Chla−1)0.76 1.50 1.00 0.64
Minimum internal P concentration
(mg P mg Chla−1)0.34 0.10 0.30 0.62
Specific attenuation coefficient
(µg chlaL−1m−1)0.02 0.02 0.04 0.04
Minimum density (kg m−3)
Temperature multiplier for respiration 1.04 1.03 1.08 1.07
Respiration rate coefficient (d−1)0.2

Sensors 2020,20, 2626 11 of 30
2.5. Scenarios Analyzed with the Use of Model
In the case of the Paprocany reservoir, information about the water supply to the reservoir is the
most important in terms of water quality and cyanobacterial blooms. It allows conducting variant
analysis, in which, apart from the current (real) inflow to the tank, scenarios of partial recreation of the
historical reservoir supply area were also analyzed, using the AEM3D model (Table 4).
Table 4. Summary of scenarios used in the AEM3D model.
Scenario/Description Inflows to the
Reservoir
Catchment
Area
(km2)
Inflow to the Reservoir in the
Simulated Period (m3s−1)
Average Minimum Maximum
S0 Current drainage area (reduced in
relation to the natural one)
Main inflow 8.45 0.075 0.055 0.423
3 small southern
streams 9.39 0.102 0.076 0.581
Scenario 0 17.84 0.177 0.131 1.004
S1 Is the scenario 0 and additional
transfer of water excess from the R
ó
w
S1 (Ditch S1) above the minimum
flow (baseflow 0.216 m3s−1)
Upper Gostynia
River 61.18 0.207 0 3.893
Scenario 1 77.84 0.384 0.131 4.897
S2 the scenario 1 and additional
transfer of water from the Potok
˙
Zwakowski stream
Potok ˙
Zwakowski
stream 18.83 0.134 0.123 0.483
Scenario 2 96.67 0.517 0.254 5.193
Climate scenario 1 Scenario 0 17.84 0.175 0.131 0.946
Climate scenario 2 Scenario 0 17.84 0.179 0.131 0.977
Map of catchment areas for scenarios
Sensors2020,20,xFORPEERREVIEW11of30
Inadditiontothreescenariosmentionedabove,twomore(climatescenario1and2)were
preparedinordertoassesstheimpactofclimatechangesonthewaterretentiontime,water
temperatureandconcentrationofchlorophyll(Table5).Scenario0describedaboveservedasa
reference(currentstatus)inrelationtowhichtheimpactofclimatechangeswasassessed.The
climatescenarioswerebasedontwoʺEuro‐CORDEXʺscenarios,i.e.,amoderateclimatechange
scenario—RCP4.5fortheyearof2030andintensechangesscenario—RCP8.5fortheyearof
2050.TheprojectionswerepreparedforthecityofTychywithintheprojectʺDevelopmentof
UrbanAdaptationPlansforcitieswithmorethan100,000inhabitantsinPolandʺ [2,35,36,49].
Inputdataregardingclimatechangesincludedmonthlychangesinthetemperatureand
precipitation(Table4).ThesedatawereusedtomodifymeteorologicalinputstotheAEM3D
modelandtomodifyrainfall‐basedinflowratetothereservoirandairtemperature‐based
temperatureofinflowscalculatedusingtheappropriateformula[37,50].
Table4.SummaryofscenariosusedintheAEM3Dmodel
Scenario/Description Inflowstothe
reservoir
Catchment
area(km2)
Inflowtothereservoirinthe
simulatedperiod(m3s‐1)
AverageMinimumMaximum
S0Currentdrainage
area(reducedinrelation
tothenaturalone)
Maininflow 8.450.0750.0550.423
3smallsouthern
streams 9.390.1020.0760.581
Scenario0 17.840.1770.1311.004
S1Isthescenario0and
additionaltransferof
waterexcessfromthe
RówS1(DitchS1)above
theminimumflow
(baseflow0.216m3s‐1)
UpperGostynia
River61.180.20703.893
Scenario1 77.840.3840.1314.897
S2thescenario1and
additionaltransferof
waterfromthePotok
Żwakowskistream
Potok
Żwakowski
stream
18.830.1340.1230.483
Scenario2 96.670.5170.2545.193
Climatescenario1Scenario0 17.840.1750.1310.946
Climatescenario2Scenario0 17.840.1790.1310.977
Mapofcatchmentareas
forscenarios

Table5.ClimatechangescenariosanalyzedforthePaprocanyreservoir
Climate
scenario 1212
In all of the scenarios, a constant weir located at the outflow from the reservoir is the section closing
the catchment. Water losses resulting from evaporation and connection with groundwater were taken
into account. Evaporation was calculated using the AEM3D model based on the evaporative heat flux
in accordance to [
47
] and taking into account the heat transfer coefficient, wind speed, vapor pressure,
and water surface temperature [
45
]. The groundwater contribution was calculated based on the
measured difference in the inflow and outflow from the reservoir reported in the “Water management
instructions for the Paprocany reservoir” [48], minus the evaporation calculated by the model.
In addition to three scenarios mentioned above, two more (climate scenario 1 and 2) were prepared
in order to assess the impact of climate changes on the water retention time, water temperature and
concentration of chlorophyll (Table 5). Scenario 0 described above served as a reference (current status)
in relation to which the impact of climate changes was assessed. The climate scenarios were based
on two “Euro-CORDEX” scenarios, i.e., a moderate climate change scenario—RCP4.5 for the year
of 2030 and intense changes scenario—RCP8.5 for the year of 2050. The projections were prepared
for the city of Tychy within the project “Development of Urban Adaptation Plans for cities with
more than 100,000 inhabitants in Poland” [
2
,
35
,
36
,
49
]. Input data regarding climate changes included
monthly changes in the temperature and precipitation (Table 4). These data were used to modify
meteorological inputs to the AEM3D model and to modify rainfall-based inflow rate to the reservoir
and air temperature-based temperature of inflows calculated using the appropriate formula [37,50].

Sensors 2020,20, 2626 12 of 30
Table 5. Climate change scenarios analyzed for the Paprocany reservoir.
Climate Scenario 1 2 1 2
Euro-CORDEX
Source Scenario RCP4.5 RCP8.5 RCP4.5 RCP8.5
Year 2030 2050 2030 2050
Month Change in the Average Monthly
Air Temperature (◦C)
Change in the Monthly Sum
of Precipitation (%)
1 0.8 1.6 −8.9 −7.1
2 0.8 2.2 13.7 36.1
3 0.4 1.4 −9.0 −5.4
4−1.1 −0.1 67.6 84.3
5−0.5 0.2 −12.2 2.9
6 0.0 0.7 −10.2 −10.0
7 0.2 0.9 −6.6 −3.1
8 0.6 1.4 −15.7 −3.3
9 1.4 1.8 5.0 24.0
10 1.5 2.3 35.1 49.4
11 0.0 0.8 −0.6 −2.5
12 0.5 1.0 56.2 53.8
Average 0.4 1.2 9.5 18.3
2.6. DPSIR Methodology
The solutions that lead to the elimination of poor water status require a comprehensive
approach to analyzing the causes of such a condition. The method of cause and effect analysis
recommended (among others by the European Environment Agency EEA), which comprehensively
characterizes the problems, indicates their causes and proposes corrective actions is the DPSIR
(Driver–Pressure–State–Impact–Response) analysis [
51
–
54
]. Such analysis consists of five elements:
Driving forces of environmental change; Pressures on the environment—environmental burdens
generated by activities in the catchment; State of the environment; an effect of pressure on the
environment and the economy, including the state of the environment (Impacts on population,
economy, ecosystems)—the ecological and economic effect of operations in the catchment and
reservoir; Response of the society—actions that are responses to observed phenomena, enabling
the maintenance/improvement of the state of the environment and, ultimately, the introduction of
appropriate environmental compensations in the studied area.
3. Results
3.1. Cartographic Analyses
The Paprocany reservoir appeared on the maps of Silesia as early as 1736 (Figure 2A). On the maps
made by Schubarth, Mattheus von Wieland in 1736 and by Frederick von Wrede (1748-49) (Figure 2B)
the reservoir was larger than today and was fed by the Gostynia River from the west, and by the Potok
˙
Zwakowski Stream from the northwest, the waters, which do not end up in the reservoir currently [
31
].
The analysis of the current digital terrain model (DTM) indicates that the reservoir damming level
must have been originally about 2 m higher (Figure 3).

Sensors 2020,20, 2626 13 of 30
Sensors2020,20,xFORPEERREVIEW13of30

Figure2.Paprocanyreservoiroverthecenturies—fragmentsofthemap:ChristianFriedrich
vonWrede1748(A);SituationsPlanvoneinemTheileOberschlesiensanderOestereich–und
NeuschlesischenGrenze,JohannesHarnisch,1794/1795r.(B);FriderizianischeSiedlungen
rechtsderOderbis1800(1933)(C);LieutenantvonSydowfromtheBorderGuardregiment
1827(D);TopographischeKarte1:25000(Meßtischblatt34225979)1944(E);topographicmap
(F).RedarrowshowsthePaprocanyreservoir,whitearrowshowsthePotokŻwakowski
StreamandbluearrowtheGostyniaRiver.

Figure3.HistoryoftheGostyniaRivercatchmentarea:DigitalTerrainModelofprimary
catchmentareaofthePaprocanyreservoir(A);partofthePaprocanycatchmentareawiththe
Paprocanyreservoir(B);3DmodelofthecatchmentareaofthePaprocanyreservoiratanormal
damminglevelof242.15ma.s.l.(C)and3DmodelofthecatchmentareaofthePaprocany
reservoirintheprimarydamminglevelof(244.15ma.s.l.);withvisibleseepagechannelsofthe
rootandplanttreatmentworks(E);needleweirfrom1873,originalrenovationplanfrom1931
(F);andoriginalplanofdrainageandcollectionchannelsfromrenovationperiodin1931(G).
3Dimagemodel(CandD)wascarriedoutonthebasisofDTMdatausingtheSurfer18
GoldenSoftware.
Inthe1820s,regulatoryworksoftheGostyniabegan,theeffectsofwhichcanbeseenon
vonSydowʹsmaps(1827;Figure2C).Atthattime,thereservoirwassmaller,andthePotok
Figure 2.
Paprocany reservoir over the centuries—fragments of the map: Christian Friedrich von Wrede
1748 (
A
); Situations Plan von einem Theile Oberschlesiens an der Oestereich – und Neuschlesischen
Grenze, Johannes Harnisch, 1794/1795 r. (
B
); Friderizianische Siedlungen rechts der Oder bis 1800
(1933) (
C
); Lieutenant von Sydow from the Border Guard regiment 1827 (
D
); Topographische Karte
1:25,000 (Meßtischblatt 3422 5979) 1944 (
E
); topographic map (
F
). Red arrow shows the Paprocany
reservoir, white arrow shows the Potok ˙
Zwakowski Stream and blue arrow the Gostynia River.
Sensors2020,20,xFORPEERREVIEW13of30

Figure2.Paprocanyreservoiroverthecenturies—fragmentsofthemap:ChristianFriedrich
vonWrede1748(A);SituationsPlanvoneinemTheileOberschlesiensanderOestereich–und
NeuschlesischenGrenze,JohannesHarnisch,1794/1795r.(B);FriderizianischeSiedlungen
rechtsderOderbis1800(1933)(C);LieutenantvonSydowfromtheBorderGuardregiment
1827(D);TopographischeKarte1:25000(Meßtischblatt34225979)1944(E);topographicmap
(F).RedarrowshowsthePaprocanyreservoir,whitearrowshowsthePotokŻwakowski
StreamandbluearrowtheGostyniaRiver.

Figure3.HistoryoftheGostyniaRivercatchmentarea:DigitalTerrainModelofprimary
catchmentareaofthePaprocanyreservoir(A);partofthePaprocanycatchmentareawiththe
Paprocanyreservoir(B);3DmodelofthecatchmentareaofthePaprocanyreservoiratanormal
damminglevelof242.15ma.s.l.(C)and3DmodelofthecatchmentareaofthePaprocany
reservoirintheprimarydamminglevelof(244.15ma.s.l.);withvisibleseepagechannelsofthe
rootandplanttreatmentworks(E);needleweirfrom1873,originalrenovationplanfrom1931
(F);andoriginalplanofdrainageandcollectionchannelsfromrenovationperiodin1931(G).
3Dimagemodel(CandD)wascarriedoutonthebasisofDTMdatausingtheSurfer18
GoldenSoftware.
Inthe1820s,regulatoryworksoftheGostyniabegan,theeffectsofwhichcanbeseenon
vonSydowʹsmaps(1827;Figure2C).Atthattime,thereservoirwassmaller,andthePotok
Figure 3.
History of the Gostynia River catchment area: Digital Terrain Model of primary catchment area
of the Paprocany reservoir (
A
); part of the Paprocany catchment area with the Paprocany reservoir (
B
);
3D model of the catchment area of the Paprocany reservoir at a normal damming level of 242.15 m a.s.l.
(
C
) and 3D model of the catchment area of the Paprocany reservoir in the primary damming level of
(244.15 m a.s.l.); with visible seepage channels of the root and plant treatment works (
E
); needle weir
from 1873, original renovation plan from 1931 (
F
); and original plan of drainage and collection channels
from renovation period in 1931 (
G
). 3D image model (
C
,
D
) was carried out on the basis of DTM data
using the Surfer 18 Golden Software.

Sensors 2020,20, 2626 14 of 30
In the 1820s, regulatory works of the Gostynia began, the effects of which can be seen on von
Sydow’s maps (1827; Figure 2C). At that time, the reservoir was smaller, and the Potok ˙
Zwakowski
Stream flowed into the Gostynia, not directly into the reservoir. The Gostynia was divided into two
branches: Nowa Gostynia and Stara Gostynia, which partly flowed in the old Gostynia riverbed. The
bed of Stara Gostynia has been regulated, and the meadows in the valley have been crossed by a
regular network of drainage ditches (irrigation and drainage). The Paprocany reservoir, however, has
not been completely cut offfrom the water resources of this part of the catchment (Figure 2D,E).
Problems with water quality must have appeared as early as the 19th century. In the 1860s, a soil
and plant water treatment system was constructed in the meadows above the reservoir, which purified
the waters of the Gostynia (Figure 3F). The wetlands with free water surface are the technology for an
effective effluent treatment of aquaculture flow-through systems. Compared with common treatment
facilities of flow-through systems, such as microsieves or settling basins, the removal performance of
free water surface systems was similar or even higher [
38
,
39
]. Thanks to these solutions, for about
120 years, until 1986, the Paprocany reservoir could have been fed, in controlled way, with the waters of
the Gostynia River through a channel separated by a weir, connecting New Gostynia (Figure 3) with the
reservoir and with the soil and plant treatment works that cleaned the Gostynia waters. According to
the designers’ concept, the waters of Nowa Gostynia were introduced to the treatment plant and after
filtrating through filtration fields, the water went to, among others, Stara Gostynia and further fed the
Paprocany reservoir (Figure 3). From the hydrological point of view, the liquidation of that connection
was the most significant change in the method of feeding the reservoir and its water balance, as it
caused the complete separation of the majority of the Gostynia River catchment from the reservoir. As
the consequence, water inflow to the reservoir got reduced and, in extreme cases, it did not compensate
the evaporation. In dry years, the water balance of the tank is negative and there is low, or even interim
lack of water exchange rate in the reservoir. The degradation of hydrotechnical devices, and their
liquidation, finally caused the separation of the Gostynia catchment into two areas: the south-eastern
catchment with an area of about 17.94 km
2
(Table 6), whose waters feed the Stara Gostynia riverbed
and the Paprocany reservoir and the north-west catchment with an area of 83.84 km
2
, supplying Nowa
Gostynia, whose waters are currently bypassing the reservoir (Figure 4A).
Table 6. Basic parameters of catchments computational.
Catchment Name
Catchment
Area
(A in km2)
Average Annual
Precipitation
(P in mm)
Average Annual Unit
Runoff
(SSq in dm3s−1km−2)
Average Annual
Runoff
(SSQ in m3s−1)
Current catchment area of
the Paprocany reservoir 17.94 730 7.2 0.130
Upper Gostynia River 61.18 758 8.3 0.510
Potok ˙
Zwakowski Stream 18.83 750 8.8 0.167
Dopływ spod Chałup
Stream 3.83 740 8.5 0.032

Sensors 2020,20, 2626 15 of 30
Sensors2020,20,xFORPEERREVIEW15of30

Figure4.TheGostyniariverbasinclosedwiththecross‐sectionofthePaprocanyreservoir;the
currentreservoircatchmentandcatchmentareaswerealsoseparated,whichwereconsidered
formaintenanceof(A)thereservoirthroughcontrolledwatermetastasis,(B)Bathymetric
modeloffernreservoirwithreservoircross‐sections;(C)dammingcurvesofthePaprocany
reservoirvolume;and(D)dammingcurvesofthePaprocanyreservoirarea.
3.2.ImpactontheWaterQuality
ThepoorqualityofthewateroftheGostyniariverisclearlydemonstratedbythehigh
concentrationofsulphatesandchlorides,andthussignificantvaluesofelectrolytic
conductivity.Theseionscaninfiltratethewateralongtheembankmentintothereservoir.The
sourceofthesalinityisRówS1(DitchS1),wherethechloridesandsulphatescontent
periodicallyexceeded5gL−1(Table7).Thisistheeffectofdischargesofsalineminewaters
fromtheʺBolesławŚmiałyʺcoalmine,whichislocatedinthenorthernpartoftheGostynia
catchment.ThehighsalinityoftheRówS1(DitchS1)affectsthewaterintheGostynia
throughouttheentirelowersection.Asaresult,thewateroftheGostyniaonthesectionfrom
RówS1(DitchS1)tothemouthisnotsuitableforuseasawatersourcetosupplythePaprocany
reservoir(Figure5).
Figure 4.
The Gostynia river basin closed with the cross-section of the Paprocany reservoir; the current
reservoir catchment and catchment areas were also separated, which were considered for maintenance
of (
A
) the reservoir through controlled water metastasis, (
B
) Bathymetric model of fern reservoir with
reservoir cross-sections; (
C
) damming curves of the Paprocany reservoir volume; and (
D
) damming
curves of the Paprocany reservoir area.
3.2. Impact on the Water Quality
The poor quality of the water of the Gostynia river is clearly demonstrated by the high concentration
of sulphates and chlorides, and thus significant values of electrolytic conductivity. These ions can
infiltrate the water along the embankment into the reservoir. The source of the salinity is R
ó
w S1
(Ditch S1), where the chlorides and sulphates content periodically exceeded 5 g L
−1
(Table 7). This is
the effect of discharges of saline mine waters from the “Bolesław ´
Smiały” coal mine, which is located
in the northern part of the Gostynia catchment. The high salinity of the R
ó
w S1 (Ditch S1) affects the
water in the Gostynia throughout the entire lower section. As a result, the water of the Gostynia on the
section from R
ó
w S1 (Ditch S1) to the mouth is not suitable for use as a water source to supply the
Paprocany reservoir (Figure 5).

Sensors 2020,20, 2626 16 of 30
Table 7.
Classification of water quality in the Paprocany reservoir and the catchment of the Gostynia
River, markings of water purity class: blue—class I; green—class II; red—out-of-class water (analyses
based on the data provided by the City of Tychy, the Regional Inspectorate of Environmental Protection
in Katowice and by authors’ analyses).
Indicator Unit Inflow Pelagial Outflow
Potok
˙
Zwakowski
Stream Mouth
to Gostynia
Rów S1
(Ditch S1)
Gostynia River
above the
Mouth of the
Ditch S1
No. according
to Figure 51 2 3 4 5 6
Nitrogen
N-NH4mg L−10.135 0.108 0.110 0.526 0.401 0.557
Nitrogen
N-NO3mg L−1<0.01 <0.01 <0.01 0.042 0.310 0.070
Nitrogen
N-NO2mg L−10.210 0.193 0.021 2.090 5.820 1.590
Nitrogen
Kjeldahl’s mg L−10.92 1.19 1.05 3.22 6.59 2.73
Chlorate mg L−179.4 70.9 65.9 29.8 4750.7 97.9
Phosphate
P-PO4mg L−1<0.01 <0.01 0.021 0.022 0.315 0.030
Magnesium Mg mg L−17.18 6.86 6.69 10.63 89.58 12.23
pH 6.97 7.83 7.54 8.16 8.23 7.83
Conductivity µS cm−1468 430 432 484 12261 1162
Sulphate SO4mg L−1- - 72.6 322.1 337.5 308.6
Total Dissolved
Solids mg L−1- - 304.4 330.0 8252.0 622.0
Calcium Ca mg L−140.14 37.77 37.36 66.34 146.00 85.56
Total Organic
Carbon mg L−17.83 9.22 9.06 6.63 3.69 5.34
Sensors2020,20,xFORPEERREVIEW16of30

Figure5.LandcoverofthePaprocanyreservoircatchmentareadevelopedontheUrbanAtlas
2012basis.Thenumberinthewhitecirclesshownlocalizationofpointsofwateranalysis.
Table7.ClassificationofwaterqualityinthePaprocanyreservoirandthecatchmentofthe
GostyniaRiver,markingsofwaterpurityclass:blue—classI;green—classII;red—out‐of‐class
water(analysesbasedonthedataprovidedbytheCityofTychy,theRegionalInspectorateof
EnvironmentalProtectioninKatowiceandbyauthors’analyses)
IndicatorUnitInflowPelagialOutflow
Potok
Żwakowski
Streammouth
toGostynia
Rów
S1
(Ditch
S1)
GostyniaRiver
abovethemouth
oftheDitchS1
No.according
toFig.5123456
Nitrogen
N‐NH
4
mgL
‐1
0.1350.1080.1100.5260.4010.557
Nitrogen
N‐NO
3
mgL
‐1
<0.01<0.01<0.010.0420.3100.070
Nitrogen
N‐NO
2
mgL
‐1
0.2100.1930.0212.0905.8201.590
Nitrogen
KjeldahlʹsmgL
‐1
0.921.191.053.226.592.73
ChloratemgL
‐1
79.470.965.929.84750.797.9
Phosphate
P‐PO
4
mgL
‐1
<0.01<0.010.0210.0220.3150.030
MagnesiumMgmgL
‐1
7.186.866.6910.6389.5812.23
pH 6.977.837.548.168.237.83
Conductivity μScm‐
1
468430432484122611162
SulphateSO
4
mgL
‐1
‐ ‐ 72.6322.1337.5308.6
TotalDissolved
SolidsmgL
‐1
‐ ‐ 304.4330.08252.0622.0
CalciumCamgL
‐1
40.1437.7737.3666.34146.0085.56
TotalOrganic
CarbonmgL
‐1
7.839.229.066.633.695.34
Figure 5.
Land cover of the Paprocany reservoir catchment area developed on the Urban Atlas 2012
basis. The number in the white circles shown localization of points of water analysis.
In the central part of the reservoir, there are areas with lower water temperature. This may
indicate a natural groundwater inflow in this area of the reservoir. However, the observed changes
are negligible.

Sensors 2020,20, 2626 17 of 30
In the region of intensive tourist and recreational use, increased levels of nitrates, chlorides, and
increased redox potential were observed (Figures 5and 6). This is probably also related to the inflow
located in the south-eastern part of the reservoir draining water from the forest pond. Surface runoffof
waters from regions heavily exploited for tourism cannot be excluded. This is indicated by the lack of
barriers preventing the entry of nutrients and other substances into the water.
Sensors2020,20,xFORPEERREVIEW17of30
Inthecentralpartofthereservoir,thereareareaswithlowerwatertemperature.Thismay
indicateanaturalgroundwaterinflowinthisareaofthereservoir.However,theobserved
changesarenegligible.
Intheregionofintensivetouristandrecreationaluse,increasedlevelsofnitrates,
chlorides,andincreasedredoxpotentialwereobserved(Figures5and6).Thisisprobablyalso
relatedtotheinflowlocatedinthesouth‐easternpartofthereservoirdrainingwaterfromthe
forestpond.Surfacerunoffofwatersfromregionsheavilyexploitedfortourismcannotbe
excluded.Thisisindicatedbythelackofbarrierspreventingtheentryofnutrientsandother
substancesintothewater.

Figure6.NitrogenspeciationinthePaprocanyreservoirwaterontheinflow,pelagial,and
outflowwater:(A)N‐NH
4+
;(B)N‐NO
2‐
;(C)N‐NO
3‐
;(D)Kjeldahlnitrogen.Point—median;
box—firstandthirdquartile;range—minandmaxvalues;asterisk—extremevalues.Colorin
thepictureshowsthewaterpurityclass:blue—classI;green—classII;red—out‐of‐classwater.
Theanalysesshowedtheisotropicnatureofthedistributionofthestudiedphenomena.
Theanalysisofthespatialdistributionoftemperature(Figure7)ofthewaterinthereservoir
indicateslowertemperatureinthewaterinflowzonefromtheoldGostyniariverbed.Thisis
theareawherecoldgroundwaterispumpedintothereservoirandthewatercomesfromthe
drainagebasinalongtheStaraGostyniaRiver.Thiscausesalargevariationinthemeasured
parametersinthispartofthetank.Inthisregion,loweroxygensaturationisalsoobserved,
whichmayindicatealowoxygencontentinthegroundwatersuppliedartificiallytothe
reservoir.
Figure 6.
Nitrogen speciation in the Paprocany reservoir water on the inflow, pelagial, and outflow
water: (
A
) N-NH
4+
; (
B
) N-NO
2−
; (
C
) N-NO
3−
; (
D
) Kjeldahl nitrogen. Point—median; box—first and
third quartile; range—min and max values; asterisk—extreme values. Color in the picture shows the
water purity class: blue—class I; green —class II; red—out-of-class water.
The analyses showed the isotropic nature of the distribution of the studied phenomena. The
analysis of the spatial distribution of temperature (Figure 7) of the water in the reservoir indicates
lower temperature in the water inflow zone from the old Gostynia riverbed. This is the area where
cold groundwater is pumped into the reservoir and the water comes from the drainage basin along the
Stara Gostynia River. This causes a large variation in the measured parameters in this part of the tank.
In this region, lower oxygen saturation is also observed, which may indicate a low oxygen content in
the groundwater supplied artificially to the reservoir.
In the southwestern part of the reservoir there are regions with an increased pH and dissolved
oxygen saturation of water. This indicates a higher photosynthesis activity in this region (Figure 7).
Environmental conditions, bioavailability of biogenic elements and temperature in this area stimulate
intensive development of algae and cyanobacteria.
The thickness of the sediments of the reservoirs ranges between 4 to 38 cm, and their total
estimated volume is about 253 thousand m
3
. This is a relatively small thickness of the sediments. The
Paprocany reservoir is characterized by a large bottom area in relation to the volume of the reservoir.

Sensors 2020,20, 2626 18 of 30
Sensors2020,20,xFORPEERREVIEW18of30

Figure7.SpatialvariabilityofphysicochemicalpropertiesofthePaprocanyreservoirwater.
InthesouthwesternpartofthereservoirthereareregionswithanincreasedpHand
dissolvedoxygensaturationofwater.Thisindicatesahigherphotosynthesisactivityinthis
region(Figure7).Environmentalconditions,bioavailabilityofbiogenicelementsand
temperatureinthisareastimulateintensivedevelopmentofalgaeandcyanobacteria.
Thethicknessofthesedimentsofthereservoirsrangesbetween4to38cm,andtheirtotal
estimatedvolumeisabout253thousandm
3
.Thisisarelativelysmallthicknessofthe
sediments.ThePaprocanyreservoirischaracterizedbyalargebottomareainrelationtothe
volumeofthereservoir.
Depositsatthebottomofthereservoircontainalargenumberofnutrients,theremovalof
whichcansignificantlyimprovethequalityofthewater.Itisestimatedthatthesediments
contain222.66MgofKjeldahlnitrogen(sumofammoniumandorganic,non‐nitratenitrogen),
45.65Mgofphosphorus(P)and1.03MgofassimilablephosphorusintheformofP
2
O
5
.Inthe
situationofproperthermal–oxygenrelations,limnicecosystemsaccumulatebiomass,
containingorganicmatter,aswellasnitrogenandphosphoruscompounds.Theamountof
phosphorusandorganicmatterstoredinthebottomsedimentsisveryhigh.Withthelossof
theabilitytodepositpollutioninsediments,theprocessofso‐calledinternalenrichment
begins.Itcausesarapidenrichmentofwatermasseswithmineralphosphoruscompounds,
whichresultsinaveryrapidincreaseinprimaryproduction(Table8).
Table8.EstimatedamountofsedimentsandnutrientsaccumulatedintheminthePaprocany
reservoir(owncalculations).
 Unit Value
Totalsedimentvolume(m
3
)253,028.0
Sedimentssurface(m
2
)1,550,911.0
Sedimentsthickness(m)0.23
Figure 7. Spatial variability of physicochemical properties of the Paprocany reservoir water.
Deposits at the bottom of the reservoir contain a large number of nutrients, the removal of
which can significantly improve the quality of the water. It is estimated that the sediments contain
222.66 Mg of Kjeldahl nitrogen (sum of ammonium and organic, non-nitrate nitrogen), 45.65 Mg of
phosphorus (P) and 1.03 Mg of assimilable phosphorus in the form of P
2
O
5
. In the situation of proper
thermal–oxygen relations, limnic ecosystems accumulate biomass, containing organic matter, as well
as nitrogen and phosphorus compounds. The amount of phosphorus and organic matter stored in the
bottom sediments is very high. With the loss of the ability to deposit pollution in sediments, the process
of so-called internal enrichment begins. It causes a rapid enrichment of water masses with mineral
phosphorus compounds, which results in a very rapid increase in primary production (Table 8).
Table 8.
Estimated amount of sediments and nutrients accumulated in them in the Paprocany reservoir
(own calculations).
Unit Value
Total sediment volume (m3)253,028.0
Sediments surface (m2)1,550,911.0
Sediments thickness (m) 0.23
Sediments wet weight (Mg) 27,833.08
Kjeldahl Nitrogen (Mg) 222.66
Fosfor (P) in the sediment (Mg) 45.65
Fosfor (P2O5) in the sediment (Mg) 1.03
3.3. Model-Based Analyses of Mitigation Options
The estimated time of water retention in the reservoir for the current state (scenario 0) is about
55 days in the western part of the tank and about 60 days in the eastern part, including the bathing area
and 77 days in its northeastern part. In the case of scenario 1, in which the reservoir is additionally
fed with water from the upper Gostynia catchment, the retention time is shortened to 26 days in the

Sensors 2020,20, 2626 19 of 30
western part and 36 days in the eastern part with a maximum value of 58 days in the north-eastern part
of the reservoir. In the bathing area, the water retention time is about 40 days. Another increase in the
reservoir water inflow, from the Potok ˙
Zwakowski Stream (scenario 2), shortens the water retention
time to 18, 25, and 47 days, respectively. The retention time in the bathing area is 25 days.
Based on the simulations, it can be stated that increasing the tank supply area in accordance with
scenario 1 (B) reduces the time of full water exchange in the tank by 25% of the current value, while
increasing the supply area following the scenario 2 (C) shortens the water exchange time by 39%. The
time of full water exchange is understood here as the time after which the water found in any part of
the reservoir flows out. As a consequence of increased inflows, the outflow increases also considerably
in analyzed scenarios. The average and maximum rate of outflow increases from 0.167 and 0.994 m
3
s
−1
in scenario 0 to 0.374 and 4.887 in scenario 1 and 0.507 and 5.183 in scenario 2. The minimum outflow
increases in the scenario 2 in relation to scenarios 0 and 1 from 0.121 to 0.244 m
3
s
−1
. The percentile of
outflows in analyzed scenarios are shown in Figure 8.
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Sedimentswetweight(Mg)27,833.08
KjeldahlNitrogen(Mg)222.66
Fosfor(P)inthesediment(Mg)45.65
Fosfor(P2O5)inthesediment(Mg)1.03
3.3.Model‐BasedAnalysesofMitigationOptions
Theestimatedtimeofwaterretentioninthereservoirforthecurrentstate(scenario0)is
about55daysinthewesternpartofthetankandabout60daysintheeasternpart,including
thebathingareaand77daysinitsnortheasternpart.Inthecaseofscenario1,inwhichthe
reservoirisadditionallyfedwithwaterfromtheupperGostyniacatchment,theretentiontime
isshortenedto26daysinthewesternpartand36daysintheeasternpartwithamaximum
valueof58daysinthenorth‐easternpartofthereservoir.Inthebathingarea,thewater
retentiontimeisabout40days.Anotherincreaseinthereservoirwaterinflow,fromthePotok
ŻwakowskiStream(scenario2),shortensthewaterretentiontimeto18,25,and47days,
respectively.Theretentiontimeinthebathingareais25days.
Basedonthesimulations,itcanbestatedthatincreasingthetanksupplyareain
accordancewithscenario1(B)reducesthetimeoffullwaterexchangeinthetankby25%ofthe
currentvalue,whileincreasingthesupplyareafollowingthescenario2(C)shortensthewater
exchangetimeby39%.Thetimeoffullwaterexchangeisunderstoodhereasthetimeafter
whichthewaterfoundinanypartofthereservoirflowsout.Asaconsequenceofincreased
inflows,theoutflowincreasesalsoconsiderablyinanalyzedscenarios.Theaverageand
maximumrateofoutflowincreasesfrom0.167and0.994m3s‐1inscenario0to0.374and4.887
inscenario1and0.507and5.183inscenario2.Theminimumoutflowincreasesinthescenario
2inrelationtoscenarios0and1from0.121to0.244m3s−1.Thepercentileofoutflowsin
analyzedscenariosareshowninFigure8.

Figure8.Outflowfromthereservoirinscenariosoftherestorationofthecatchmentarea
(percentile).
Forthethreescenariosdescribedabove,theimpactofthecurtainlimitingthewaterflow
intothebathingareahasalsobeenanalyzed(Figure9).Suchcurtainwasconsideredasa
solutionprotectingthebathingareafrominflowofnutrientsandphytoplankton.However,the
curtainissupposedtoincreasethewaterretentiontimeinthebathingareabyfourdaysinthe
scenarioofcurrentinflows(Figure9).Whilethelongerretentiontimeinthebathingareaposea
riskofthewaterdeterioration(warmerwater,increasedalgalgrowth,decreasedoxygen
concentrations),thewaterinnorthernpartofthereservoirwillbemoreintensivelymixeddue
Figure 8.
Outflow from the reservoir in scenarios of the restoration of the catchment area (percentile).
For the three scenarios described above, the impact of the curtain limiting the water flow into the
bathing area has also been analyzed (Figure 9). Such curtain was considered as a solution protecting
the bathing area from inflow of nutrients and phytoplankton. However, the curtain is supposed to
increase the water retention time in the bathing area by four days in the scenario of current inflows
(Figure 9). While the longer retention time in the bathing area pose a risk of the water deterioration
(warmer water, increased algal growth, decreased oxygen concentrations), the water in northern part
of the reservoir will be more intensively mixed due to the installation of the curtain. This positive effect
is more significant in combination with the increase in the catchment area (scenarios 1 and 2, Table 5).
In scenario 1 with the curtain the maximum water retention time in the reservoir decreases by 25% in
comparison to the same scenario without the curtain. In scenario 2, it decreases by 39%.
The impact of restoration of the reservoir’s catchment area (scenarios 1 and 2) on the water mixing
is also confirmed by the simulation of virtual tracer which can be considered as a dissolved conservative
substance. The tracer was introduced to the reservoir with its surface inflows at the concentration
of 1 mg L
−1
. The initial concentration in the reservoir was set to zero. In the current status scenario
(Scenario 0) the effect of inflow was barely noticeable in the eastern part of the reservoir in first months
of the simulated period. After one month, the concentration in the bathing area was close to zero and
after the second month it reached 0.2 mg L
−1
. The average concentration of tracer in the reservoir at
the end of five-month simulation was estimated at 0.5 mg L
−1
. In both scenarios which assume partial
restoration of the natural catchment area (scenarios 1 and 2) the final average concentration of the
tracer ranged from 0.8 to 0.85 mg L−1and was at such level already after 1.5 month.

Sensors 2020,20, 2626 20 of 30
Sensors2020,20,xFORPEERREVIEW20of30
totheinstallationofthecurtain.Thispositiveeffectismoresignificantincombinationwiththe
increaseinthecatchmentarea(scenarios1and2,Table5).Inscenario1withthecurtainthe
maximumwaterretentiontimeinthereservoirdecreasesby25%incomparisontothesame
scenariowithoutthecurtain.Inscenario2,itdecreasesby39%.

Figure9.ImpactofthecatchmentarearestorationonthewaterretentiontimeinthePaprocany
reservoir(A);impactofthecurtainonthewaterretentiontime(B);curtain(barrier)protecting
thebathingareafrominflowofpollutedwaters(C);chlorophyllconcentrationchangeinJuly
accordingtoscenarios0,1,and2(D).
Theimpactofrestorationofthereservoir’scatchmentarea(scenarios1and2)onthewater
mixingisalsoconfirmedbythesimulationofvirtualtracerwhichcanbeconsideredasa
dissolvedconservativesubstance.Thetracerwasintroducedtothereservoirwithitssurface
inflowsattheconcentrationof1mgL‐1.Theinitialconcentrationinthereservoirwassetto
zero.Inthecurrentstatusscenario(Scenario0)theeffectofinflowwasbarelynoticeableinthe
easternpartofthereservoirinfirstmonthsofthesimulatedperiod.Afteronemonth,the
concentrationinthebathingareawasclosetozeroandafterthesecondmonthitreached0.2
mgL−1.Theaverageconcentrationoftracerinthereservoirattheendoffive‐monthsimulation
wasestimatedat0.5mgL−1.Inbothscenarioswhichassumepartialrestorationofthenatural
catchmentarea(scenarios1and2)thefinalaverageconcentrationofthetracerrangedfrom0.8
to0.85mgL‐1andwasatsuchlevelalreadyafter1.5month.
Thesimulationoftracerindicatedthatthecurtainmayefficientlypreventtheinflowof
pollutantstothebathingarea,however,inaspecificconditiononly.Whenthequalityof
inflowsissimilartothewaterqualityinthereservoir,thecurtainprimarilyisolatesthebathing
areaincreasingthewaterretentiontime(especiallyinthecurrentstatusscenario).However,
whentheinflowbringsincreasedconcentrationsofpollutants(tracer)orincreasedvolumeof
cleanwatercausingthedissolutionofpollutantsinthereservoir,thecurtaincan,respectively,
protectthebathingareafrompollutionuntilthewind‐drivenmixingwillresultintheuniform
distributionofpollutantoritwilltrappollutantsinthebathingarea.
Toassessthealgaebloomrisk,themodelincludinginflowscenarios0,1,and2werealso
parameterizedinawayenablingthesimulationofphytoplanktonproduction.Thisprocess
wassimulatedtakingintoaccounttheimpactofthenutrientsavailability,watertemperature,
lightpenetrationandgrazing(accordingto[39]).ThesimulationcoveredtheperiodofJune
andJuly2016,theperiodinwhichtheincreasedconcentrationofchlorophyllawasobserved.
Calculationsallowedtoidentifylocationswiththealgalblooms.Theselocationsinclude
usuallytheshallowerpartsofthereservoiralongthebanks.Theirdistributiondependsonthe
inflowrateand,mostimportantly,onthewindgustandspeed.Regardless,thehydrological
andmeteorologicalconditions,higherchlorophyllaconcentrationwerepresentalongthe
wateredgesinthenorth‐eastern,south‐eastern(bathingarea),andsouth‐centralpartsofthe
reservoir.Lowerconcentrationswereestimatedforthedeeperpartsofthereservoirandfor
Figure 9.
Impact of the catchment area restoration on the water retention time in the Paprocany
reservoir (
A
); impact of the curtain on the water retention time (
B
); curtain (barrier) protecting the
bathing area from inflow of polluted waters (
C
); chlorophyll concentration change in July according to
scenarios 0, 1, and 2 (D).
The simulation of tracer indicated that the curtain may efficiently prevent the inflow of pollutants
to the bathing area, however, in a specific condition only. When the quality of inflows is similar to
the water quality in the reservoir, the curtain primarily isolates the bathing area increasing the water
retention time (especially in the current status scenario). However, when the inflow brings increased
concentrations of pollutants (tracer) or increased volume of clean water causing the dissolution of
pollutants in the reservoir, the curtain can, respectively, protect the bathing area from pollution until
the wind-driven mixing will result in the uniform distribution of pollutant or it will trap pollutants in
the bathing area.
To assess the algae bloom risk, the model including inflow scenarios 0, 1, and 2 were also
parameterized in a way enabling the simulation of phytoplankton production. This process was
simulated taking into account the impact of the nutrients availability, water temperature, light
penetration and grazing (according to [
39
]). The simulation covered the period of June and July 2016,
the period in which the increased concentration of chlorophyll a was observed. Calculations allowed
to identify locations with the algal blooms. These locations include usually the shallower parts of the
reservoir along the banks. Their distribution depends on the inflow rate and, most importantly, on the
wind gust and speed. Regardless, the hydrological and meteorological conditions, higher chlorophyll
a concentration were present along the water edges in the north-eastern, south-eastern (bathing area),
and south-central parts of the reservoir. Lower concentrations were estimated for the deeper parts of
the reservoir and for areas close to streams’ inflows—especially in scenarios of the larger catchment
area (scenarios 1 and 2) (Figure 9).
Apart from human activity, also other factors, such as climate change, affecting the efficiency
of planned actions should be taken into account. Therefore, two scenarios of change: a moderate
(scenario 1) and intense (scenario 2) were analyzed in relation to the current status (scenario 0). In
the simulated period (June–October) an average change in the precipitation was estimated at
−
3.5%
and 3.4% for scenarios 1 and 2, respectively. In case of the air temperature, the changes were 0.7
and 1.4
◦
C. For precipitation, the average inflow to the reservoir in analyzed period was 0.177, 0.175,
and 0.179 m
3
s
−1
for scenarios 0, 1, and 2, respectively. Similarly, the average outflow from reservoir
does not change by more than 1% in relation to the scenario 1, and the maximum outflow is even
smaller in scenarios 1 and 2 (by 6 and 3% respectively) due to the decrease in rainfall in scenario 1
and increase in evaporation calculated with the model. Even though the inflow to the reservoir was
not expected to change greatly, the temperature of inflows and the heat transfer through the water’s
surface affect significantly the water temperature in the reservoir. The average temperature of water

Sensors 2020,20, 2626 21 of 30
in the warm season is expected to increase by 0.6 and 1.3
◦
C in scenario 1 and 2, respectively. These
changes are not uniformly distributed in time and space (Figure 10). The difference of temperature
at the outflow ranges from 0 to over 2
◦
C in scenarios 0 and 2. In various parts of the reservoir, the
increase in temperature can exceed 2.5
◦
C (Figure 10). Expected increase in water temperature due
to climate changes can be even greater than presented here, because the simulation did not take into
consideration the projected change in the solar radiation.
Sensors2020,20,xFORPEERREVIEW21of30
areasclosetostreams’inflows—especiallyinscenariosofthelargercatchmentarea
(scenarios1and2)(Figure9).
Apartfromhumanactivity,alsootherfactors,suchasclimatechange,affectingthe
efficiencyofplannedactionsshouldbetakenintoaccount.Therefore,twoscenariosofchange:
amoderate(scenario1)andintense(scenario2)wereanalyzedinrelationtothecurrentstatus
(scenario0).Inthesimulatedperiod(June–October)anaveragechangeintheprecipitationwas
estimatedat−3.5%and3.4%forscenarios1and2,respectively.Incaseoftheairtemperature,
thechangeswere0.7and1.4°C.Forprecipitation,theaverageinflowtothereservoirin
analyzedperiodwas0.177,0.175,and0.179m
3
s
−1
forscenarios0,1,and2,respectively.
Similarly,theaverageoutflowfromreservoirdoesnotchangebymorethan1%inrelationto
thescenario1,andthemaximumoutflowisevensmallerinscenarios1and2(by6and3%
respectively)duetothedecreaseinrainfallinscenario1andincreaseinevaporationcalculated
withthemodel.Eventhoughtheinflowtothereservoirwasnotexpectedtochangegreatly,the
temperatureofinflowsandtheheattransferthroughthewaterʹssurfaceaffectsignificantlythe
watertemperatureinthereservoir.Theaveragetemperatureofwaterinthewarmseasonis
expectedtoincreaseby0.6and1.3°Cinscenario1and2,respectively.Thesechangesarenot
uniformlydistributedintimeandspace(Figure10).Thedifferenceoftemperatureatthe
outflowrangesfrom0toover2°Cinscenarios0and2.Invariouspartsofthereservoir,the
increaseintemperaturecanexceed2.5°C(Figure10).Expectedincreaseinwatertemperature
duetoclimatechangescanbeevengreaterthanpresentedhere,becausethesimulationdidnot
takeintoconsiderationtheprojectedchangeinthesolarradiation.

Figure10.MapsofdepthaveragedwatertemperatureinSeptemberandOctober2016andin
twoclimatechangescenarios.
Atemperatureriseofoneortwodegreesshouldbeconsideredasanimportantthreat
makingthereservoirmorepronetotheeutrophication,algalblooms,anoxia,andotheradverse
effects.Itisespeciallyimportantinthecaseofshallowreservoirswithconsiderablyhigh
concentrationsofnutrients—asthePaprocanyreservoir.Theriskisevenhigherbecauseofthe
climate‐drivenalterationofthewaterretentiontime.Theaveragewaterretentiontimeis
expectedtodecreaseslightlyinthescenario2,however,thebeneficialchangeisobservedin
modeloutputsforsouth‐westernpartofthereservoironly.Inthenorth‐easttheretentiontime
wasestimatedtoincrease.
Asaconsequenceofthefactorsabove‐mentioned,theclimatechangeshouldbeseenasa
factorlimitingtheefficiencyofcorrectivemeasuresandposingariskofmuchworseecological
statusofthereservoirifthecorrectivemeasureswillnotbeimplemented.Itisconfirmedbythe
simulationofphytoplanktoncycleinthePaprocanyreservoir.Theaverageincreaseinthe
chlorophyllaconcentrationattheoutflowwasestimatedat7.2and3.2%forscenarios1and2,
respectively.However,periodicallytheincreaseinchlorophyllconcentrationinpartsof
reservoirmayriseseveraltimes(Figure9).
3.4.DPSIRAnalysis
Figure 10.
Maps of depth averaged water temperature in September and October 2016 and in two
climate change scenarios.
A temperature rise of one or two degrees should be considered as an important threat making
the reservoir more prone to the eutrophication, algal blooms, anoxia, and other adverse effects. It
is especially important in the case of shallow reservoirs with considerably high concentrations of
nutrients—as the Paprocany reservoir. The risk is even higher because of the climate-driven alteration
of the water retention time. The average water retention time is expected to decrease slightly in the
scenario 2, however, the beneficial change is observed in model outputs for south-western part of the
reservoir only. In the north-east the retention time was estimated to increase.
As a consequence of the factors above-mentioned, the climate change should be seen as a factor
limiting the efficiency of corrective measures and posing a risk of much worse ecological status of
the reservoir if the corrective measures will not be implemented. It is confirmed by the simulation of
phytoplankton cycle in the Paprocany reservoir. The average increase in the chlorophyll a concentration
at the outflow was estimated at 7.2% and 3.2% for scenarios 1 and 2, respectively. However, periodically
the increase in chlorophyll concentration in parts of reservoir may rise several times (Figure 9).
3.4. DPSIR Analysis
It should be noted, however, that the estimated effect of implementing of the recommended
methods to improve or maintain water status will be reduced in the longer term due to the predicted
climate change. Therefore, the actions should be planned so that they are efficient also in the case
of, e.g., increased water temperature, increased evaporation, more intense precipitation and surface
runoff, higher (seasonal) plankton production, and other changes resulting from the phenomena
above-mentioned. The severity of potential impacts is confirmed by a lot of reviews, and therefore, the
water and the catchments management need to take these drivers into account [
55
–
57
]. These reviews
highlight uncertainties related to climate projections and consequently to aquatic ecology modeling,
confirming at the same time that the simulation of hydrological and ecological responses to the climate
change is the most efficient way to predict upcoming risks and to assess the mitigation measures.

Sensors 2020,20, 2626 22 of 30
As the main source of the problem, the DPSIR analysis indicates the large trophy of the reservoir
and its consequences, however, the causes of this condition are varied and have different sources.
The disturbed and unbalanced water relations in the Paprocany catchment should be considered as
the main source of problems. They cause that the tank is not supplied with sufficient quality water.
The recipe for this condition is undoubtedly the alimentation of water into the reservoir and at least
partial restoration of old water relations in the catchment. However, the DPSIR analysis shows that
enhancement of the positive effect can also be achieved by proper management in the catchment area
(e.g., collecting swaths from meadows, reducing grazing intensity in the immediate vicinity of the
reservoir, leachate control) and on the reservoir itself (e.g., sustainable fishing management, care for
reed bed in sensitive areas of the reservoir).
The analysis of the causative factors also indicates a potential source of pressure for the reservoir
in a situation of increasing its tourist and recreational attractiveness (which will probably take place
when the quality of bathing water improves). The influx of tourists will result in increased car traffic
and intensified use of recreational and gastronomic infrastructure in the immediate vicinity of the lake.
Presently, it is difficult to assess how intense these pressures can be, it should be assumed that the
reservoir used this way must be properly monitored in terms of physicochemical parameters of water,
and the use of infrastructure in its vicinity should be subjected to a detailed analysis for its adaptation
to the growing number of tourists to ensure environmental safety (Table 9).
Solutions that lead to the elimination of poor water status require a comprehensive
approach to analyzing the causes of such condition. The method of cause and effect analysis
recommended (among others by the European Environment Agency EEA), which comprehensively
characterizes the problems, indicates their causes and proposes corrective actions is the DPSIR
(Driver–Pressure–State–Impact–Response) analysis. Summary of the research materials, and analyses
utilized in this study, and their connection to the DPSIR-framework was presented in the Table 10.

Sensors 2020,20, 2626 23 of 30
Table 9. DPSIR analysis of the Paprocany reservoir.
Driver Pressure State Impact Response
Climate changes
Temperature rise;
Increased duration of drought period in
the catchment area;
Change in the nature of
precipitation—extreme rainfall events
Increase of water temperature in
the reservoir;
Increasing retention time and reducing
inflows in dry periods due to the change in
the precipitation characteristics and
increased evaporation;
Increased load of sediments and pollutants
due to the more intensive rainfall events
and runoff
Induction of phytoplankton blooms,
and biomass accumulation in
the sediments;
Periodic increase in nutrient
concentration in the water;
The ecosystem services
value decreased
Increased supply of good quality water
to the reservoir
Historical factors
Limiting the surface of the tank
catchment area causes too low water
inflow to the reservoir
Water stagnates in the reservoir, the supply
of nutrients after precipitation and
evaporation causes an increase in the
concentration of nutrients
Increased nutrient content in the
reservoir resulting in
phytoplankton blooms
Feeding good quality water into the
reservoir, increasing the reservoir basin,
restoring (at least partially) the former
water relations in the basin
Agricultural