A New Approach in Assessing Recharge of Highly Karstified Terrains-Montenegro Case Studies

7/29/2019 A New Approach in Assessing Recharge of Highly Karstified Terrains-Montenegro Case Studies

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A new approach in assessing recharge of highly karstified terrains Montenegro case studies

Milan Radulovic, Zoran Stevanovic, Micko Radulovic

Introduction

Montenegro is situated in the southeastern part of Europe (Fig. 1). Its entire territory belongs to the

External Dinarides branch of the Alpine geo structure. The largest part of its territory is highland.

Carbonate rocks (limestone and dolomite) cover over 60% of the region.

The geology of Montenegro is complex. In the central and southern part where the majority of studyareas are situated, the oldest stratigraphical unit is of PermianTriassic age. The prevailing Mesozoic

carbonate facies consist of limestone, dolomite-limestone, and rarely of dolomite. The flysch facies are

also present but mostly along the Adriatic coast.

Limestone and dolomite are highly fractured and karstified. Those are terrains with many different

features, phenomena, and processes typical of holokarst. Widely extended dolines, uvalas, dry hanging

and blind valleys, potholes, and caves in the area were studied by Cvijic (1926) and many other

researchers who named some of the most developed karstic zones stone seas (such as the one above

Bokakotorska Bay and the city of Risan).

The catchment area of Bokakotorska Bay (covering an area of 900 km2) has the highest annual

precipitation rate in the Mediterranean region (approximately 2,600 mm on average), but has no

permanent surface streams. Due to the high degree of karstification, lack of soil cover and poorvegetation, and intensive rainfall most of the precipitation (over 70% on average) is infiltrated deep

into the ground (Radulovic 2000).

Fig.1 Geographical position of Montenegro and the four tested catchment areas ofa Podgor springs; b

Crnojevia springs; c Karu springs; d Slatina springs

The rich karst aquifers in southern Montengro discharge through many periodic and perennial karst

springs as well as subsurface (or sublacustrine and submarine) springs along Lake Skadar and the

Adriatic Sea coast, which represent the main regional discharge zones (Radulovic et al. 2005).

Despite the abundance of water, it is well known that the population of this area paradoxically suffers

from an inadequate supply of fresh water during the summer months, and this directly affects the

economy and tourism of the region. This condition is a common feature of karst regions everywhere.


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Groundwater recharge is an issue that has been systematically addressed in numerous publications,

especially since the mid-1980s (Simmers 1988; Sharma 1989; Lerner et al. 1990; Healy 2010). A

significant contribution to the study of carbonate aquifer recharge has been provided through the

modeling of karst hydrodynamics (Mangin 1984; Sauter 1992; Jeannin and Sauter 1998; Teutsch and

Sauter 1998; Jones et al. 2000; Petric 2002; Jukic and Denic-Jukic 2004, 2008; Bauer et al. 2005;

Portoghese et al. 2005; Kovacs and Sauter 2007; Geyer et al. 2008; Hughes et al. 2008; Janza 2010;

Martinez-Santos and Andreu 2010).

Some important contributions for GIS-based aquifer vulnerability and recharge, which were

considered for this study, were written by De Vries and Simmers (2002), Scanlon et al. (2002), and

Goldscheider (2002). Among the recently developed methods for groundwater recharge assessment in

karst regions it is important to mention the APLIS method (Andreo et al. 2008) and the regional

method developed by Shaban et al. (2005).

Methodology

Assessing the spatial distribution of recharge in these karstified terrains is very important for water

management and protection, but it is a complex issue. The application of conventional methods, such

as direct measuring at experimental sites, estimation of evapotranspiration by empirical equations, and

Darcys law-based methods, has not provided satisfactory results in these terrains. Currently, the mostrealistic recharge assessments are obtained by using the black box water budget method applied to

the catchment areas, with rainfall and discharge being the main input and output components.

However, using this approach, it is not possible to evaluate the spatial distribution or seasonal

variations.

The research described in this paper concentrated on the development of a new method (KARSTLOP),

with the essential element being the creation of a map showing the spatial distribution of recharge in

highly karstified terrains. Because of the complexity of natural conditions in karst, it is not possible to

define recharge in absolute terms. Therefore, the research has addressed the eight most important

natural factors, which influence recharge and enable the most reliable assessment.

As is very common for this type of analysis, the KARSTLOP method is based on Geographic

information system (GIS) and its tools. An aim of the new GIS multilayer method is to provide themost accurate categorization based on selected parameters. An objective is to reduce subjectivity to the

lowest possible level. However, a personal footprint is always present, at least in selecting the

analytical parameters or in defining evaluation criteria and algorithm.

KARSTLOP has been developed as a regional method for assessing autogenic recharge solely in karst

terrains. It is most convenient for generating maps on the scale of 1:25,000 to 1:100,000. The method

has been tested at catchments of four karst springs in the territory of Montenegro (catchment areas of

Podgor, Crnojevia, Karu, and Slatina springs, Fig. 1). Collection of data included a field survey and

tests supported by remote sensing.

The acronym KARSTLOP is derived from the initial letters of selected factors: KarstificationK,

Atmospheric conditionsA, RunoffR, SlopeS, TectonicsT, LithologyL, Overlying layers

O, and PlantsP (Radulovic 2009).

Karstification (K)

The karstification map (Fig. 2; Table 1) is obtained by analyzing surface (Ksf) and subsurface

karstification (Kss).

Surface karstification (Ksf) is assessed on the basis of the area of karst features and landforms per

surface unit (subfactors Ksf1 and Ksf2 ). Karren fields, as degraded zones of karst surface, are most


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often the only karst features that appear on karst slopes. While dolines and other karst depressions

(uvalas, dry valleys, poljes etc.) are morphological forms related mostly to horizontal or slightly

inclined terrains. For mapping the degree of surface karstification, criteria concerning the density of

the features are applied: the area of degraded zonekarren fields per 1 km2

(Ksf1), or karst

depressions also per 1 km2

(Ksf2 ). By overlapping these two maps, a new map could be obtained

showing the degree of surface karstification (Ksf).

The two surfaces are distinguished on the map of subsurface karstification (Kss). The area with the

highest degree of subsurface karstification (Kss = 5) is represented by the zone 200 m on each side of

speleologically explored objects (caves, pits and swallow holes). Subsurface karstification for the rest

of the catchment area is estimated based on the following three indirect indicators: the amplitude of the

fluctuation of discharge of a karst spring or group of springs whose catchment area is analyzed

(Qmax/Qmin Kss1 ), the average fictive velocity obtained by the tracing tests (v Kss2 ), and the

calcite (or dolomite) saturation index of spring water (SI) which depends on the average length of the

groundwater pathway (d) in the catchment area (SI Kss3 ). Therefore, the surface that represents the

rest of the catchment area has values Kss = 1-4, an estimation based on the above-mentioned indirect

indicators (the average Kss1 , Kss2 and Kss3 values). The final map of the degree of karstification is

arrived at by overlapping the map of surface (Ksf) and subsurface (Kss) karstification.

Therefore, the factor K represents the average value of Ksf and Kss subfactors (Fig. 2; Table 1). The

map of K factor distinguishes five categories with K values from 1 to 5.

Atmospheric conditions (A)

Atmospheric conditions (A) also influence recharge magnitude primarily through variable

evapotranspiration.

Temperature and precipitation conditions can be indirectly mapped as a function of altitude (A 1).

Higher altitudes decrease air temperature which in turn causes evapotranspiration also to decrease. An

increase in altitude also leads to an increase in the rainfall and a more intense recharge of aquifers, i.e.

the increase in the amount of water that goes to effective infiltration and percolates into the aquifer

system.

The intensity of solar radiation (A2) also influences the amount of evapotranspiration and recharge.

Shadowed sides of mountains are characterized by lower intensity of solar radiation, especially during

the winter months. By modeling the shadow using the Digital Elevation Model (DEM) with a given

mean annual sun position, it is possible to identify surfaces which receive lower annual solar radiation

(surfaces with relative reflection lower than 0.5).

The overlapping of A1 and A2 maps results in a new map of atmospheric conditions A where five

categories (A = 1-5) can also be distinguished (Table 1).

Runoff (R)

Runoff (R) in karst depends on the permeability of carbonate rocks, the slope, and the rainfall rate.

Perennial streams may show that wider zones along riverbeds contain more impervious packages oflimestone and dolomite and may indicate lower hydraulic conductivities of pedologic or geologic

cover as well. However, the conditions in which the stream is initiated within the catchment have to be

considered, since big quantities of water in certain cases keep sustained stream flow regardless of the

permeability of the stream bed. Therefore, based on the presence of surface runoff, karst terrains are

divided into three categories as shown in Table 1.


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Fig. 2 Flow chart of procedure for derivation K map (the case example: catchment area of Karu

springs) a Map of karren fields; b Map of karst depressions; c Ksf1 matrix map; d Ksf2 matrix map; e

Ksf matrix map; fKsf contour map; g Kss map; h K map


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Table 1 Matrix to obtain parameters of KARSTLOP method and calculation of recharge


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Slope (S)

As stated above, the slope (S) of the terrain is one of the primary factors that triggers runoff and

determines the rate of precipitation water to flow at the surface. The APLIS method (Andreo et al.

2008) confirms that terrain with lower slopes are more favorable for recharge, i.e. the greater the slope,

the lower the aquifer recharge. This relationship is incorporated in the KARSTLOP method (Table 1).

The slope map (S) is relatively easily obtained based on DEM.

Tectonics (T)

Factor T includes two subfactors: Tfdensity of faults and Tddip of strata.

The value of recharge is significantly influenced by the degree of fissurization of rock masses which is

usually greater in recognized and major fault zones, i.e. in the areas with more prominent tectonic

degradation. The map of Tf reflects these areas in terms of the length of faults (km) per unit of surface

(km2).

Td is one of the factors that may have influence on the process of groundwater recharge. Interbedded

fissures (diastrome) sometimes have a significant role in transferring atmospheric water deeper into theunderground. Considering only bedding planes, their horizontal position can be considered less

favorable for infiltration of atmospheric water than the more inclined planes. In this case, massive

carbonate rocks are evaluated as rocks with horizontal bedding planes.

Aerial and satellite images, geological maps, together with data obtained by field survey and

measurements, are used as the basis for producing the T f and Td map. Overlapping of Tf and Td maps

produces the Tectonics map (T), with five categories distinguished (T = 1-5) (Table 1).

Lithology (L)

The lithological composition of rocks directly influences their permeability and therefore the rate of

recharge. Since only carbonate rocks are being considered, and in the case of Montenegro, only

limestones and dolomites, the following subfactors have been treated: type of carbonate rocks(subfactor a), bedding (subfactor b) and mineralogicpetrographic ingredients of carbonate rocks

(subfactor c).

Dolomites are usually considered to be less permeable than limestones. Dolomite is less soluble than

limestone and the fractures in the subsurface area are less expanded than fractures in limestone.

Therefore, the relative value of subfactor a is equal to 1 for limestone, while the relative value of the

same subfactor for dolomite is 0.7 (Table 1).

Strata of various thicknesses have various pressure resistances, i.e. they are characterized by different

degrees of fissurization. Laminated and thinly laminated limestone is less brittle than thickly bedded

and massive limestone, so the fissurization there is less present. Table 1 shows the classification and

relative valuation of carbonate rocks in reference to bedding (subfactor b).

Limestone and dolomite rarely occur in a pure state. They often contain various mineralogic

petrographic ingredients, so they often appear as limestone and dolomite with lumps or intercalations

of chert (silificied), as sandy limestone, marly or bituminous limestone and dolomite. Permeability of

carbonate rocks is usually lower if it contains more impure components. Therefore, the relative

valuation in reference to mineralogicpetrographic ingredients (c) has been made in reference to the

influence of such ingredients on the permeability of carbonate rocks, as presented in Table 1.


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A geologic map should be used as a basis for obtaining referred subfactors. Factor L is calculated by

the multiplication of previously described factors (L = a x b x c). There are five categories in the map

of factor L.

Overlying layers (O)

Overlying layers (O) can play a significant role in decreasing the potential of some terrain for recharge,mostly because it occurs as an additional obstacle to the infiltration of atmospheric water underground.

Overlying layers of carbonate rocks can represent geological cover and soil as typical in epikarst.

If the soil is thicker and has poorer hydraulic properties, the infiltration will be slower and

evapotranspiration greater. The ranking of soil (O1), which can occur over carbonate rocks or clastic

layers, has been done after statistical processing of results of 1213 texture analyses of soil samples

collected from the Montenegrin study areas (Fustic 2004). Therefore, the relative valuation is made in

reference to the type of soil and its thickness as shown in Table 1. Pedology maps with thickness of

soil are used as a basis for obtaining map O1.

In this case, the geological cover (O2) includes permeable drift sediments over carbonate rocks, which

do not contain perched aquifers. Geological cover over bedrock can be represented by scree (rock fall

mass), alluvial, colluvial, moraine, glacialfluvial, glaciallimnic, and other sediments (Table 1). Themap of subfactor O2 is obtained based on the geologic map. The final map of overlying layers is

generated by overlapping the two previously obtained maps and by classifying the obtained areas into

five categories (O = 1-5).

Plants (P)

Plants (P) also have an influence on recharge rate. A greater presence of vegetation directly increases

the mean annual evapotranspiration, thus reducing the infiltrated water quantity. The coverage of

vegetation and some of the basic characteristics, primarily the development of the root system and

ramification of branches, are considered for this layer. Table 1 shows the categorization of terrain in

accordance with vegetation. The five categories have been indicated, with P factors ranging between 1

and 5.

Recharge

A final recharge map showing the spatial distribution of mean annual recharge (Rch) expressed in

percentages (%) is created from overlapping the maps of selected factors (layers) according to the

established and hereunder presented algorithm. Data on the amount of recharge obtained from previous

investigations have been used for calibrating the method and for finding the most suitable algorithm.

From numerous tested solutions, the best match with results obtained in previous research and

measurements has been chosen (Radulovic 2009):

Rch = 4 x K + A + 4 x R + 2 x S + T + 4 x L + 3 x O + P (1)

It is clear that the algorithm is created by giving advantage to karstification, runoff and lithology as the

most influenced factors.

Results and discussion

The catchments of karst springs (Podgor, Crnojevia, Karu, and Slatina springs, Fig. 3) were selected

for method calibration in light of the availability of qualitative meteorological, hydrological,

hydrogeological, hydrochemical and other data (Zivaljevic and Boskovic 1984; Zivaljevic 1991;

Zogovic 1992; Radulovic 1994, 2000, 2009; IJC 2001).


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Fig. 3 a The Podgor spring; b The Crnojevia River downstream of the Crnojevia springs; c The

Karu springs; d The Slatina spring

The mean annual precipitation for the studied region ranges from 2,000 to over 3,000 mm. From

previous studies (Radulovic 2000; IJC 2001), it is well known that the Rch rate should regularly rank

between 60 and 80% of precipitation in these terrains of south Montenegro. Of the selected springs,

Karu and Crnojevia have the highest yield, followed by Podgor springs, while Slatina springs have

much lower yield (Table 2).

Table 2 Comparison of values of recharge Rch obtained by water budget method and values of

recharge RKARSTLOP assessed using the KARSTLOP method in catchment areas of Podgor springs,

Crnojevia springs, Karu springs and Slatina springs

Catchment Aa

(km2)

Qb

(m3/s)

Pc

(mm)

Rchd

(%)

RKARSTLOPe

(%)

Variationf

(%)

Podgor springs 23.8 1.64 2853 76.2 71.5 4.7Crnojevia springs 79.3 6.15 3214 76.1 71.0 5.1

Karu springs 116.0 7.0 2700 70.5 70.4 0.1

Slatina springs 1.6 0.076 2150 69.7 66.3 3.4aCatchment area

bMean annual discharge

cMean annual precipitation

dRecharge obtained by water budget method (and other methods)eRecharge assessed by KARSTLOP method

fVariation between Rch and RKARSTLOP

A better match between values obtained by water budget method and values assessed by KARSTLOP

method has been derived for the catchment area of Karu springs, than for Slatina springs, with the

largest deviations being for the catchment areas of Crnojevia and Podgor springs (Table 2).Therefore, deviations for selected catchment areas range from 0.1 to 5.1%. Figure 4 shows final maps

of spatial distribution of recharge obtained by the KARSTLOP method.


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Fig. 4 Maps of spatial distribution of recharge obtained by KARSTLOP method for catchments of a

Podgor springs; b Crnojevia springs; c Karu springs; d Slatina springs

The general value of the recharge rate for a whole catchment area can be obtained by some of standard

methods such as estimation of evapotranspiration by empirical equations, Cl-

mass balance, and water

budget (runoff/rainfall ratio). They all need precise and long-term measurements of water balance

elements. In contrast, digital recharge maps obtained by the KARSTLOP method display the spatialdistribution of recharge and enable getting statistical mean value of the recharge not only for a whole

catchment area, but also for a smaller area of particular interest. Moreover, most of standard methods

such as direct measurements (lysimeter, seepage meter) and other soil mass balance methods do not

provide representative values or cannot be applied due to lack of soil cover or non-accessible sites in

high karst. Generally, such methods do not account for the particular hydrogeological characteristics of

karst aquifers, while the KARSTLOP method is especially designed for the analysis of the variables

which correspond with larger values of recharge rate in highly karstified terrains.

Common for this type of GIS applications, the values obtained from the KARSTLOP method represent

approximations. To improve their accuracy, a large amount of field data need to be collected and

evaluated, which can be difficult task in high mountains and in non-accessible karst.

The two main limits of KARSTLOP are the following:1. It cannot be applied to the evaluation of allogenic recharge. Therefore, to assess the waterthat flows from non-karstic terrains it is necessary to use hydrological methods.

2. It is not possible to obtain temporal variations of the recharge rate, but only the mean annualvalue.

In addition, watersheds should be precisely delineated and known as much as possible, which is a

standard problem in karst regions.


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Using the KARSTLOP experience and applying this method in other karstic terrains should be done

with caution. Taking into account the eight factors that influence groundwater recharge, it is not

possible to simply copy complex natural conditions. Therefore, adaptation of the method to local

conditions requires the testing the algorithm as an obligatory step.

Application of the method can be useful when it is not possible to measure karst spring discharge

(submarine and sublacustrine springs), or when measurements are missing for any reason. In such a

way, one can roughly assess a total aquifer discharge rate. Similarly, application of the method could

be useful in identifying prospective areas for further aquifer research. This is a reverse approach, and

of course in this case the mechanism for results verification is limited. Similarly, with the obtained

recharge map, additional delineation between the catchment areas of springs is possible and could be

finely tuned.

Last, GIS techniques and data manipulation enable an assessment to be made relatively quickly. The

application of this method results in a qualitative assessment, which should be validated in the field.

Conclusions

The KARSTLOP method has been developed for highly developed karst, which differs in many

respects from other types of terrain. Application of the KARSTLOP method allows the spatialdistribution of groundwater recharge to be obtained, which is not possible using conventional methods

such as direct measuring on experimental sites, estimation of evapotranspiration by empirical

equations, Darcys law based methods, and isotopic and chemical techniques.

The KARSTLOP method simultaneously enables the synthesis of available data and complements

findings of the studied karst aquifer. Available historical data and remote sensing combined with field

survey and measurements (topographic maps, DEM, geological maps, pedological maps, vegetation

maps, aerial and satellite images, speleological data, discharge regime, tracer tests results,

hydrochemical data) are sufficient for the application of this method.

The systematic study of karst aquifers is of great importance for the region, since water from karst

contributes over 90% to the total drinking water supply of Montenegro and is also used for other

purposes (irrigation, industry and energy). The final recharge map, as well as layers obtained by theKARSTLOP method, can be used as groundwater modeling inputs and for addressing numerous

practical karst water management issues such as: water supply (discharge assessment, spatial

distribution of watersheds), transboundary water management, construction of underground objects,

and preliminary assessment of groundwater vulnerability.

By comparing the methods outcomes with previously obtained results by conventional methods and

discharge measurements, it has been concluded that possible error in the assessment of mean annual

recharge at the four tested catchment areas is approximately 5%, which is more than successful as an

initial result.

Despite some limitations the method offers a good prospect. It is necessary in the future to conduct

further testing at several other catchment areas to highlight the methods possible shortcomings and

take corrective actions thereof.


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A New Approach in Assessing Recharge of Highly Karstified Terrains-Montenegro Case Studies

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