227Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.DOI: 10.15201/hungeobull.69.3.1 Hungarian Geographical Bulletin 69 2020 (3) 227–243. Introduction Conventional land evaluation approaches are either land capability or land suitability sur- veys. Land capability assessment is meant to measure the overall agroecological potential of a region to produce common cultivated crops and pasture plants (or forestry) with- out deterioration over a long period of time (Beek, K.J. and Bennema, J. 1972; FAO 1976; Davidson, D.A. 1992). ’Land with the high- est capability is expected to be versatile and 1 Institute of Geography and Earth Sciences, University of Pécs, H-7624 Pécs, Ifjúság útja 6. Hungary. E-mails: loczyd@gamma.ttk.pte.hu, gnagy@gamma.ttk.pte.hu, dejozsi@gamma.ttk.pte.hu, gyenizse@gamma.ttk.pte.hu 2 Georgikon Faculty, University of Pannonia, H-8360 Keszthely, Deák Ferenc u. 16. Hungary. E-mails: toth.gergely@georgikon.hu, hermann.tamas@georgikon.hu 3 Doctoral School of Earth Sciences, University of Pécs, H-7624 Pécs, Ifjúság útja 6. Hungary. E-mails: rezsekma@gamma.ttk.pte.hu, alisalem@gamma.ttk.pte.hu 4 Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt. 5 Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium; Department of Earth and Environmental Sciences, Faculty of BioScience Engineering, University of Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium. E-mail: anne.gobin@vito.be 6 Department of Chemical and Geological Sciences, University of Cagliari, Cittadella Universitaria (Axis D), I-09042 Monserrato, Italy. E-mail: avacca@unica.it Perspectives of land evaluation of floodplains under conditions of aridification based on the assessment of ecosystem services Dénes L ÓC Z Y 1, Gergely T ÓT H 2, Tamás H E R M A N N 2, Marietta R E Z S E K 3, Gábor N A G Y 1, József D E Z S Ő1, Ali S A L E M 3,4, Péter G Y E N I Z S E 1, Anne G O B I N 5 and Andrea VA C C A 6 Abstract Global climate change has discernible impacts on the quality of the landscapes of Hungary. Only a dynamic and spatially differentiated land evaluation methodology can properly reflect these changes. The provision level, rate of transformation and spatial distribution of ecosystem services (ESs) are fundamental properties of landscapes and have to be integral parts of an up-to-date land evaluation. For agricultural land capability assessment soil fertility is a major supporting ES, directly associated with climate change through greenhouse gas emissions and carbon sequestration as regulationg services. Since for Hungary aridification is the most severe consequence of climate change, water-related ESs, such as water retention and storage on and below the surface as well as control of floods, water pollution and soil erosion, are of increasing importance. The productivity of agricultural crops is enhanced by more atmospheric CO2 but restricted by higher drought susceptibility. The value of floodplain landscapes, i.e. their agroecological, nature conservation, tourism (aesthetic) and other potentials, however, will be increasingly controlled by their water supply, which is characterized by hydrometeorological parameters. Case studies are presented for the estimation of the value of two water-related regulating ESs (water retention and groundwater recharge capacities) in the floodplains of the Kapos and Drava rivers, Southwest Hungary. It is predictable that in the future land evaluation techniques based on the FAO framework will be more dynamic and integrated with the monetary valuation of ESs. The latter task, however, still involves numerous methodological problems to solve. Keywords: land evaluation, climate change, aridification, ecosystem services, floodwater retention, groundwater recharge, flood reservoirs, floodplains Received March 2020; Accepted August 2020. Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.228 allow intensive use for a reasonably large range of enterprises’ (McRae, S.G. and Burn- ham, C.P. 1981, p. 67). Developed into inte- gral land evaluation (Smit, B. et al. 1984), land capability surveys help identify the processes of land degradation (Kertész, Á. and Křeček, J. 2019) and contribute to the foundation of regional development policies. Land suit- ability assessment is contrived to measure the adaptability of a given area for a specific kind of land use at a given date (FAO 1976). In Hungary the more than 140-year old ’Goldkrone’ system of land evaluation of the Austro-Hungarian Monarchy is still in use – although it will be hopefully soon replaced by the D-e-meter system under development (Dér, F. et al. 2007; Tóth, G. 2009). The D-e-meter is a scientifically based system, which equally con- siders topography, the water and nutrient avail- ability and complex properties of soils of high spatial but low temporal variability (Pásztor, L. et al. 2013) as well as the type of cultivation according to their true significance in land qual- ity. On the scale from 1 to 100, floodplain soils are placed between 30–70 scores in the variety for intensive land use and 20–50 in the exten- sive variety. In addition, climate (and thus en- vironmental dynamics) is also considered: for the 75 agrometeorological subdivisions of the country by the yields of agricultural crops three kinds of years are distinguished: – optimal years (when maximum production is achieved), – ’expected’ years (when production is at av- erage level), and – poor years (when yields are considerable lower). The system is designed to allow regular updates based on time series information, thus able to incorporate the effect of climate change and the change in agrotechnology as well. Apart from the scientifically sound conceptualisation of soil productivity model, including the effect of climate, soil and man- agement factors (Tóth, G. 2009) other build- ing blocks, such as time series yield and bio- mass data, validation datasets (case studies, long term field experimental data) and de- tailed soil maps for the country (Pásztor, L. et al. 2017; Tóth, G. et al. 2018) enable to de- velop a productivity map for all agricultural land of the country. However, the evaluation system is only capable of predicting future changes in productivity to a limited extent. Since the seminal (and much criticized) pa- per by Costanza, R. et al. (1997) on the value of global natural capital and the Millenium Ecosystem Assessment (MEA 2005), ecosys- tem services (ESs), with emphasis on regu- lating services, have become a central topic of environmental research. As this concept places the welfare of human society in the focus, this is a novel approach to the assess- ment of environmental quality. In the most simple definition of the often debated term, ESs are a set of ecosystem functions which are useful to humans (Kremen, C. 2005). According to Costanza, R. and Folke, C. (1997) ESs are ’the benefits human popu- lations derive, directly or indirectly, from ecosystem functions’. Fischer, B. et al. (2009) claim that the so-called intermediate servic- es interact to produce final services, which include floodwater retention and freshwa- ter provision. Other pioneers of the ES ap- proach (Potschin-Young, M. et al. 2017) question the applicability of the concept of intermediate services. The multiple functions of the landscape (i.e. ESs) are jointly evalu- ated (Schindler, S. et al. 2013, 2014), particu- larly often for landscapes (like floodplains) where water is the decisive component (e.g. Martin-Ortega, J. et al. 2015). However, value judgements on individual services are made difficult by the trade-offs between them (Sanon, S. et al. 2012). The assessment of anticipated changes in the level of provision of ESs is increasingly incorporated in planning (Albert, C. et al. 2014, 2016), found essential for achieving landscape sustainability (Wu, J. 2013) and assumed to serve as a measure of effective- ness for rehabilitation works (Alexander, S. et al. 2016). Concerning ESs, the elabora- tion of precise and objective indicators is the most important task in the opinion of many authors (Haines-Young, R. et al. 2012). Appropriate methods have to be developed 229Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. to translate the provision of ESs to a set of parameters. This task can only be accom- plished in close cooperation between experts in (landscape) ecology and environmental economics (Thurston, H.W. et al. 2009). The valuation procedure, however, should be as simple as possible (Simpson, R.D. 2017). The EU Biodiversity Strategy to 2020 fore- sees that Member States map and assess the state of ecosystems and their services on their territories (Zulian, G. et al. 2013; Erhard, M. et al. 2017; Maes, J. et al. 2018; Rendon, P. et al. 2019). The starting point was the list of ESs compiled as CICES 4.3 (Common International Classification of Ecosystem Services – Haines-Young, R. and Potschin, M. 2018). In Hungary the National Mapping and Assessment of Ecosystem Services (NÖSZTÉP) was launched in 2017 (Tanács, E. et al. 2019). In the first step the research budget only allowed the identification of in- dicators for a limited number of ESs. Numerous techniques have been pro- posed for the assessment and valuation of water-related ESs (Grizzetti, B. et al. 2016; Talbot, C.J. et al. 2018; Hornung, L.K. et al. 2019). Water availability is among the tem- porally most variable land qualities, which is of crucial significance for agriculture (Lóczy, D. 2000; Falkenmark, M. 2013). Although often evaluated globally (e.g. Gerten, D. et al. 2011), it is basically a regional property which cannot simply be described by point- like data (e.g. from soil survey), but the in- dicators of water availability have to reflect the landscape context, cascading and neigh- bourhood effects (Xu, H. and Wu, M. 2017; Duarte, G. et al. 2018). The water footprint is a concept employed by ecologists in the assessment of sustainability and efficiency of water use in a catchment (Lovarelli, D. et al. 2016; Roux, B. et al. 2017). Naturally, flood mitigation is a high- profile ES (Barth, N.-C. and Döll, P. 2016; Opperman, J.J. et al. 2017). Permeable flood- plain deposits allow floodwater storage and the ability to mitigate floods (Lü, S.B. et al. 2012). The substitute cost approach (com- parison with alternatives such as man-made reservoirs) is readily applicable to estimate the value of the flood mitigation service pro- vided by wetlands. In the priority list set up on the basis of willingness to pay, flood con- trol is also the most valuable ES of wetlands (Brower, R. et al. 1999). In an American case study (Mud Lake, South Dakota) the flood control service of wetlands was valued (based on monetary damages prevented) much higher (at ca. USD 440 per acre, i.e. ca. EUR 1016 ha-1 y-1) than water supply (public utility revenues, at USD 94 per acre, i.e. ca. EUR 217 ha-1 y-1) and other services (Roberts, L.A. and Leitch, J.A. 1997). In a more recent and more detailed investigation (Cui, L.J. et al. 2016) climate regulation makes up 62.0 per cent of the gross value of ultimate ESs provided by the Zhalong wetland (along the Wuyuer River, Heilongjiang province, China), while the value of flood regulation amounts to 33.3 per cent. It was found that German ripar- ian forests avoid damage from a 10-year flood in the value of EUR 4,300 ha-1 y-1 (Barth, N.-C. and Döll, P. 2016). The flood control service is closely related to another important service, groundwater re- plenishment, since with the storage of flood- water in soils and reservoirs promotes its deep percolation (Foster, T. et al. 2017). Localized recharge can be more efficient than diffuse recharge (Scanlon, B.R. et al. 2002). Key sites of surface water/groundwater interactions (Griebler, C. and Avramov, M. 2015; Salem, A. et al. 2020) are Groundwater Dependent Ecosystems (GDEs) like swamps and other wetlands (Eamus, D. et al. 2016). The undrained surfaces of the Hungarian Drava Plain mapped within the framework of the Old Drava land- scape rehabilitation programme (Trinity Enviro 2018) can be regarded as GDEs (Figure 1). Actual recharge demonstrably reaches the water table, while potential recharge feeds the moisture content of the unsaturated zone but it could potentially also contribute to groundwater in the aquifer (Bergkamp, G. and Cross, K. 2006; Walker, D. et al. 2018). Shallow (1 to 1.5 m deep) ponds with infiltration capacities ranging from 1 m d-1 to 5 m d-1 were found to be suitable for ar- Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.230 tificial recharge (Jódar-Abellán, A. et al. 2017). A study revealed that in the semiarid New Mexico about half the floodwater re- tained in an experiment infiltrated and re- charged groundwater (Valett, H.M. et al. 2005). Similar examples are cited from the Mediterranean (Opperman, J.J. et al. 2010; Chang, H. and Bonnette, M.R. 2016) and semiarid African regions (Acharya, G. 2000; Acharya, G., and Barbier, E.B. 2000). The economic value of the groundwater re- charge service can also be estimated through the contingent valuation (or willingness to pay) method (Damigos, D. et al. 2017) in most cases. In Hungary, however, the general pub- lic is not aware of the importance of this ser- vice, therefore, a questionnaire survey of this kind probably would not bring reliable results. The evaluation of nutrient availability is also central in land evaluation systems. The Fig. 1. The study areas in Hungary (1A) and in Southern Transdanubia (1B). 1C = Closed depressions (undrained surfaces), key areas of groundwater recharge, in the landscape rehabilitation area of the Hungarian Drava Plain (drawn by Gyenizse, P. after Trinity Enviro 2018). Pink dots are settlements. Numbers indicate groundwater observation wells in Table 1. Red line marks the boundary of the planning area of the Old Drava Programme. 231Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. problems in this field can be enlightened with the case of nitrogen. Although its actual quantification is still debated, the nitrogen cycle is one of the critical planetary bounda- ries (Rockström, J. et al. 2009) as it threatens the safe operation of human society. Nitrogen loss takes place to the atmosphere (ammo- nia and nitrous oxide emissions) and surface and groundwater (nitrate) (van Grinsven, H.J.M. et al. 2015). Improper fertilizer and manure application is identified as the most important source of nitrate contamination of groundwater in agricultural regions (see e.g. Diadin, D. et al. 2018) and can be reduced by integrating livestock and crop production. The needed planetary N fixation can be de- rived from demographic trends of the global population, the recommended dietary nitro- gen consumption per capita and the efficien- cy of nitrogen use (de Vries, W. et al. 2013). Global climate change has an impact on the quality of ecosystems and landscapes (Figure 2). Higher atmospheric CO2 concentra- tions may enhance agroecological potential and improve crop performance, but increased temperature and water scarcity (greater sus- ceptibility to drought) may severely restrict their impact (Gobin, A. 2010; Garofalo, P. et al. 2019; Szabó, Sz. et al. 2019). Research shows that climate change will particularly negatively affect the yields of crops like ce- reals (Monaco, E. et al. 2014; Bonfante, A. et al. 2015; Saab, M.T.A. et al. 2019), sugar beet and potatoes (Frutos Cachorro, J. et al. 2018). Adaptation can involve modified cropping systems, for instance, sowing winter wheat instead of crops with higher water demand (Debaeke, P. et al. 2017). To this extent, crop water requirements and productivity can help make informed decisions across different regions (Gobin, A. et al. 2017). The ongoing climate change in Hungary impacts on local water resources (Jankó, F. et al. 2018; Jakab, G. et al. 2019), particularly drops in groundwa- ter depth, and indirectly on soil and vegeta- tion changes (Farkas, J.Zs. et al. 2017; Fehér, Z.Zs. and Rakonczai, J. 2019). In the most se- verely affected region, on the Danube–Tisza Interfluve, a huge, more than 1,000 mm, mois- ture deficit accumulated between 1971 and 1985 (Major, P. 1994). Floodplains also show groundwater deficit (for the Drava Plain see e.g. Dezső, J., Lóczy, D. et al. 2019). The carbon sequestration capacity of soils demonstrates the impact in the opposite di- rection: that of soils on climate. European soils (particularly peatlands on floodplains) store huge amounts of carbon (73–79 billion tonnes) (Gobin, A. et al. 2011). Organic matter content also influences water-holding capaci- ty, thus, soil productivity and environmental quality, and can mitigate the damage caused by droughts and floods. Agricultural land evaluation has to ac- count for the changeability of input data caused by changing climate (Bonfante, A. et al. 2015; Makovníková, J. et al. 2019). The productivity of the landscapes varies with the changing circumstances and this will be even more typical in the future (FAO 2017). Although previous systems were primarily based on constant variables, there are several arguments for applying more dynamic tech- niques in land evaluation (Bonfante, A. et al. Fig. 2. Cascading effects of climate change on water related ecosystem services (ESs). Source: Modified after Chang, H. and Bonnette, M.R. 2016. Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.232 2018). Climate change is expected to lead to short-term modifications in the yield poten- tials of the main economic crops in Europe – although to geographically variable degrees (Supit, I. et al. 2010; EEA 2019). While in some regions of northern Europe yield potentials tend to increase (Burkhard, B. et al. 2009), in the Mediterranean region major changes in water availability, temperature and radia- tion significantly reduce potential crop yields (Schils, R. et al. 2018) and quality (Bonfante, A. et al. 2015, 2017). Along with natural fac- tors, independent from climate change im- pacts, the vital significance of socio-economic aspects of land system development and land policy in general are often emphasized. It is claimed that social and policy factors can cause a drop of up to 56 per cent in food pro- duction (Brown, C. et al. 2019). Methodological approaches: valuation of ESs Environmental economists have proposed several alternatives for the valuation of ESs (Pascual, U. and Muradian, R. 2010): – hedonic pricing: if ESs directly influence market prices; – contingent valuation or willingness to pay: questionnaire survey of people’s value per- ceptions; – benefit transfer: to infer economic values from the study of similar areas under simi- lar market conditions; – damage cost avoidance, replacement/sub- stitute cost: damage from lost services, pro- viding substitute(s) for services. Both benefit transfer and substitute cost seem to be more feasible solutions for the studied water-related ESs in Hungary than the first two which would require a higher level of environmental awareness from the public. Acharya, G. (2000), and Acharya, G. and Barbier, E.B. (2000) investigated the costs and benefits of development projects, both direct, and indirect, which divert some wa- ter away from the floodplain for irrigation in northern Nigeria. The value of replenishing and maintaining the shallow groundwater aq- uifer was calculated as USD 413 ha-1, the value of groundwater discharge as USD 32.5 per farmer per dry season or USD 62 ha-1 and for the entire wetland: USD 13,029 d-1. Since the environmental conditions are starkly differ- ent, the transfer of these values to Hungary (see below) is not possible. In the present paper experimentary mon- etary evaluations of two interrelated basic ESs are presented for two catchments in Southwest-Hungary: the Drava Plain and the Kapos Valley (see Figure 1). Both have to be regarded first approximations. As yet, the reliability of the procedure is equally made doubtful on the grounds of deficiencies in methodology and the inaccuracy of input data. Examples for the pricing of ecosystem services Floodwater retention in floodplains The Water Retention Index (WRI) is a useful tool to estimate potential water retention com- prehensively (Vandecasteele, I. et al. 2018). The WRI is calculated from the equation WRI = (wvRv + wgwRgw + wsRs + wslRsl + wwbRwb) · (1 – Rss ),(1) 100 where ws are the weights to be assigned to each parameter, and R are the parameter scores given for retention in vegetation (Rv), ground- water bodies (Rgw), soil (Rs), slope (Rsl), surface water bodies (Rwb), and for soil sealing (Rss). In the study areas slope inclinations are less than 1.00 per cent and floodplain soils are only sealed in built-up areas. Therefore, the components Rsl and Rss could be left out of consideration in the calculations. Moreover, increased water use of forests and grazing lands after floods cannot influence floodwa- ter storage significantly. Thus, in floodwater retention the vegetation effect (Rv) can also be ignored. In contrast, for drought mitiga- tion moisture storage in the vegetation (green water) is an important factor. 233Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. In the Drava Plain long-term precipitation is 682 mm y-1, out of which groundwater re- charge is 307 mm, actual evapotranspiration (ET) is 190 mm and surface run-off is 185 mm (Salem, A. et al. 2019). Actual daily evapotran- spiration (ET) in the growing season (April to September) only averaged 1.85 mm d-1 for the Drava Plain over the period 2000–2018 (Salem, A. et al. 2019). For the Kapos flood- plain, however, precipitation (Kaposvár) is 651 mm y-1 and yearly ET ranged from 464 to 660 mm in the (slightly overlapping) pe- riod 1981–2003 (Bakken, T.H. et al. 2006). Consequently, maximum actual ET amount- ed to ca. 2 mm d-1 in the Kapos Valley for the growing season (Lóczy, D. 2013). It follows from the above that floodwater retention as an ES primarily depends on the amounts of water retained in the soils/depos- its and in surface water bodies. The equation that is expected to provide its value is ESwr = w1Cs + w2Cwb, where ESwr is the value of the water reten- tion service (HUF ha-1), Cs is the value (sub- stituted cost) of specific water storage in the soil and alluvial deposits (HUF ha-1), Cwb is the value of water storage in surface water bodies (HUF ha-1), w1 and w2 are weights. Using nonlinear regression for a sen- sitivity analysis (Paruolo, P. et al. 2013), Vandecasteele, I. et al. (2018) established a weighting to both types of water retention, where surface water bodies received exactly double optimized weight (0.24) compared to soils and deposits (0.14). We followed this weighting and arrived at an equation which points to the relative importance of these components: ESwr = (Cs + 2Cwb)/3 Water retention in soils and deposits The capacity of soils for water storage is not apparent but can be very high. It depends on the depth of the vadose zone (to the ground- water table) and soil texture or sediment ma- croporosity. Groundwater table depth shows strong but fairly regular seasonal dynamics (Figures 3 and 4, Table 1). This fact supplies a good argument for elaborating a dynamic evaluation of ESs that includes water reten- tion. Extreme yearly ranges (up to > 4 m) oc- cur in some wells, but an average depth of 2.5 m can be accepted for the Drava Plain. The heterogeneous sequences of floodplain deposits present a great variety of grain sizes from heavy clay and silty fine sand to grav- elly coarse sand in the Drava Plain (Dezső, J., Czigány, Sz. et al. 2019), while in the Kapos catchment few massive rocks occur and the floodplain is built up of deposits ranging from silt to coarse sand (Lóczy, D. 2013). The macroporosity of alluvial sediments above mean groundwater table depth in the Drava Plain typically ranges from 40 to 50 per cent (sands) between paleochannels and from 55 to 70 per cent in paleochannel clayey depos- its (DDVÍZIG 2015; Terraexpert Kft. 2018). Geomorphological mapping in selected rep- resentative areas revealed that surfaces with deposits finer than silt make up less than 25 per cent of the total area. Therefore, 50 per cent as an average void ratio was used in the calculations. For the calculation of below-ground water retention the following equation was used: Rs = (VR · Dgw)/A, where Rs is water retention in soil and sedi- ment, VR is mean void ratio of prevailing de- posit (fraction), Dgw is depth to mean ground- water table (m), A is total catchment area (ha). The calculated mean storage is 12,600 m3 ha-1 for the Drava Plain. In the Kapos Valley the Regöly embayment had been selected for detailed investigations. Soil profiles were analyzed for maximum (saturated) water ca- pacity and storage capacity (water released gravitationally). The results are the following: 4,039 m3 ha-1 for the areas with chernozem meadow soils, 1,369 m3 ha-1 for the sand areas, 15,916 m3 ha-1 for the meadow soil areas and 2,189 m3 ha-1 for the wetlands (unpublished (2) (3) (4) Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.234 Fig. 3. Long-term monthly groundwater levels at an observation well in the Drava Plain with typical re- gime, Kemse, 1955–2018. Source: DDVÍZIG 2015. Fig. 4. Scheme of vertical zones considered for water-related ES calculations (by Lóczy, D.). A = Water retention below ground (1), on the surface (2); B = Groundwater recharge. For explanation see text. Table 1. Groundwater levels in the observation wells of the Drava Plain* Well Distance from Drava, km Observation period Groundwater level, m average maximum minimum range Cún-2 Darány Drávafok Drávaiványi Kákics Kemse Kétújfalu Lakócsa Potony Sellye Vajszló Vejti 2.7 4.4 5.3 3.9 10.7 3.1 9.2 3.9 2.9 6.3 6.5 1.8 2015–2017 1979–2016 1955–2016 1975–2016 1975–2016 1955–2016 1955–2016 1975–2016 1955–2016 1975–2016 1951–2016 1975–2016 90.60 118.64 98.68 97.52 97.71 95.28 104.49 99.02 100.66 98.09 94.59 94.24 91.69 120.49 100.10 99.36 98.92 96.57 106.59 100.32 103.17 99.24 96.46 96.67 90.01 116.37 97.57 96.11 99.66 91.87 102.01 97.75 99.17 96.83 93.63 93.14 1.68 4.12 2.53 3.25 2.26 4.70 4.58 2.57 4.00 2.41 2.83 3.53 *Compiled by Lóczy, D. 2019. Data source: Terraexpert Kft. 2018. For location of observation wells see Figure 1. data by Dezső, J.). The estimated average for the Kapos Valley is 12,800 m3 ha-1. Consequently, specific underground flood- water storage potential is roughly equal as regards the Drava and the Kapos floodplains. As a matter of course, the dynamic potential depends on the actual depth of the ground- water table. Surface water storage For the catchment of the Upper Kapos (122,000 ha; ca. 5,500 ha of which is flood- 235Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. plain) floodwater reservoir planning in the 1970s calculated with 3,700,000 m3 retention capacity, but the reservoirs were envisaged to be built mainly along the left-bank tribu- taries not on the trunk river (Szappanos, F. et al. 1976). At Dombóvár (65 river km) the 10 per cent probability flood discharge could be reduced with the help of reservoir storage from 63 m3 s-1 to 47 m3 s-1. On the trunk river a flood retention reservoir of 3,500,000 m3 capacity was planned for this purpose but not built. Unfortunately, the financial calculations (HUF 41,200,000, at the present value: ca. HUF 3,500,000,000, based on estimated purchase power parity) are completely outdated now as in the new political and economic system the investment environment is different. For the Drava Plain, total floodwater stor- age capacity in the project area of the Old Drava Programme (57,214 ha floodplain) in the surface depressions (see Figure 1) is re- cently estimated at 12 million m3 (DDVÍZIG 2015). This figure can be accepted as a rough estimate of maximum water retention in sur- face water bodies. (Although it is doubted to what percentage such depressions can be connected to the Drava River to receive floodwater discharge.) The application of the substitute cost method was made possible by the fact that repeated in- undations of agricultural areas in many valleys of Transdanubia called for the establishment of temporary floodwater-retaining reservoirs (Szappanos, F. et al. 1976). The approximate val- ue of natural water retention service is assumed to equal the cost of retention per unit floodplain area achieved by engineering structures (con- struction expenses of a dam, embankments, a feeder canal and related infrastructure). From the officially published figures (usually ob- tained from the South Transdanubian Water Management Directorate – DDVÍZIG) of their capacity and investment costs, the approximate expense of retaining 1 m3 of floodwater can be estimated (Table 2). Assuming that each reservoir collects run- off from the entire catchment above the site of impoundment, the specific cost of water retention is calculated from the equation: Cswr = Ctotal/Afp, where Cswr is the cost of surface water reten- tion (HUF), Ctotal is total investment cost of the engineering structure (HUF), Afp is floodplain area where floodwater is stored, above the site of river impoundment (ha). The specific cost derived from this calcula- tion can be regarded equal to a rough estimate of the ES ’flood mitigation through surface re- tention’ in the floodplain. Using equation (3) for the calculation of total floodwater retention potential, and taking irrigation water price at HUF 8 m-3 (Kemény, G. et al. 2018), the follow- ing results are achieved for the Kapos Valley: ESwr = (12,800 · 8 + 2 · 20,000 · 8)/3 = HUF 140,800 ha-1. The similar results for the Drava Plain: ESwr = (12,600 · 8 + 2 · 16,000 · 8)/3 = HUF 118,900 ha-1. The ES values for the two floodplains of similar character are fairly close to each other. Groundwater recharge in the Drava floodplain The pricing of groundwater replenishment service cannot be solved by the substitution cost approach since no technology is known that could supply sufficient amounts of sur- face water to fill up groundwater reserves. The aquifer under the Hungarian Drava Plain can be regarded a conditionally independent unit – although it is linked to the right-bank unit in Croatia. The focal areas of groundwater recharge are the closed depression represented in Figure 1. Table 3 summarizes the (sparse) data available to describe the groundwater situation in the Hungarian Drava Plain. Extracted unconfined groundwater is primarily used for irrigation (92% in arable farming, 7% in horticulture, 1% in other branches) since its quality is not suitable for drinking water (because of nitrate contami- nation). Therefore, the price of irrigation wa- ter (as a main component of the operation cost of irrigation systems) can be used in the calculation of the ES values. (5) Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.236 Fi g. 5 . T he fl oo d re se rv oi r o n th e K ap os a t K ap os vá r ( by L óc zy , D .). 1 = K ap os R iv er a nd tr ib ut ar y st re am s; 2 = a re a in un da te d du ri ng e m er ge nc y; 3 = d am ; 4 = b ui lt- up a re a; 5 = ra ilw ay ; 6 = m ai n pu bl ic ro ad Ta bl e 2. P ar am et er s of c om pl et ed a nd p la nn ed fl oo d re se rv oi rs in S ou th er n T ra ns da nu bi a* Pa ra m et er K ap os vá r (K ap os )1 M ag ya rs zé k (B ar an ya C an al )2 P ot on y (K or cs in a) Te kl af al u (K or cs in a) T üs ké s- pu sz ta Fe ke te -v íz St re am T ot al c at ch m en t, ha In au gu ra ti on d at e 31 2, 84 0 18 .0 5. 20 14 46 ,2 00 12 .0 7. 20 19 13 ,0 40 13 ,0 40 13 ,4 00 12 ,5 00 pl an ne d Pe rm an en t w at er s ta ge , m Pe rm an en t w at er s ur fa ce a re a, h a W at er s ta ge a t ( 10 0- ye ar ) d es ig n fl oo d , m Pe rm an en t s to ra ge v ol um e, 1 ,0 00 m 3 M ax im um w at er s ur fa ce a re a d ur in g fl oo d , h a M ax im um w at er v ol um e, 1 ,0 00 m 3 Fr ee c ap ac it y, 1 ,0 00 m 3 M ea n w at er d ep th w he n fi lle d , m M ax im um w at er d ep th w he n fi lle d , m T ot al c os t o f i m pl em en ta ti on , 1 ,0 00 H U F C os t o f 1 m 3 fl oo d w at er r et en ti on , H U F Fl oo d pl ai n ar ea a bo ve d am , h a Sp ec ifi c re te nt io n ca pa ci ty , m 3 h a- 1 Sp ec ifi c co st , 1 ,0 00 H U F ha -1 – 0 13 2. 8 0 10 4 1, 68 9 1, 68 9 1. 62 3. 40 55 0, 00 0 3, 25 6 34 7. 5 16 ,2 40 1, 58 3 15 1. 0 28 15 3. 0 41 0 54 1, 28 0 87 0 n. a. n. a. 1, 90 7, 00 0 2, 19 2 80 .0 23 ,7 04 23 ,8 38 n. a. n. a. 10 2. 5 n. a. 39 /1 0 40 0/ 37 0 n. a. n. a. n. a. n. a. n. a. 65 .0 n. a. n. a. 10 1. 5 52 10 1. 5 n. a. 52 56 0 0 n. a. n. a. n. a. n. a. 75 .0 n. a. n. a. n. a. n. a. 96 .0 n. a. 11 3 1, 20 0 1, 20 0 n. a. n. a. 1, 24 9, 50 0 1, 04 1 60 .0 20 ,0 00 20 ,8 25 n. a. n. a. 89 .5 n. a. 54 68 0 68 0 n. a. n. a. 1, 15 2, 00 0 1, 69 4 38 .0 12 ,5 93 30 ,3 16 *C om pi le d b y L óc zy , D . a ft er D D V ÍZ IG 2 01 5 an d G sc he id t, I. 2 01 7. 1 S ee F ig ur e 5. 2 S ee F ig ur e 6. n .a . = n o d at a. Fi g. 6 . T he fl oo d re se rv oi r o n th e Ba ra ny a C an al a t M ag ya rs zé k (b y Ló cz y, D .). 1 = ca na l, st re am ; 2 = fe ed er ca na l; 3 = da m , e m ba nk m en t; 4 = pe rm an en t w at er su rf ac e; 5 = re se r- vo ir a re a in un da te d du ri ng e m er ge nc y; 6 = ra ilw ay ; 7 = p ub lic ro ad ; 8 = b ui lt- up a re a 237Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. As a very rough estimate, in the short term, the value of the ES of groundwater recharge approximately equals the total extraction cost since the recharge is assumed to compen- sate for the loss of reserves through human extraction (see Figure 4). The starting date of observation of groundwater levels for most of the wells (1955) can be taken as reference and compared to groundwater levels in 2018. The drop of levels between these years indicated in the figure is due to two kinds of human action: – the construction of hydropower plants and their reservoirs upstream in Croatia, and – groundwater extraction. If we calculate with actual groundwater re- charge ranging from 0 mm y-1 to 360 mm y-1, the average being 241 mm y-1 (Salem, A. et al. 2020), the annual specific recharge is 0 to 36,000 m3 ha-1, the average of which is 24,100 m3 ha-1. Modelling also revealed the spatial distribu- tion of recharge (Figure 7). The actual price of irrigation water as of 2017 was HUF 8 m-3 (Kemény, G. et al. 2018). Accordingly, the value of the ES ’groundwa- ter recharge’ can be estimated at HUF 192,800 ha-1 y-1 on the average and HUF 288,000 ha-1 y-1 at maximum. Calculating with the maximum predicted price of HUF 40 m-3, the ES is esti- mated at HUF 964,000 ha-1 y-1 as an average and 1,440,000 ha-1 y-1 as a maximum. The latter values, however, seem to be unrealistic. Discussion There are several factors, processes and com- plications that may affect the above assump- tions on the provision of ecosystem services and complicating their monetary evaluation: – With warming climate evaporation losses from the open water surfaces of shallow reservoirs and from soil surfaces would reduce surface water retention capacities and should also be considered. – Natural processes, like the gradual en- trenchment of the Drava River, also re- duce reserves through ”drawing down” the groundwater table. The groundwater table sank over 48 per cent of the area be- tween 2008–2013 (DDVÍZIG 2015). – Climate change results in aridification, increased water uptake by vegetation and dropping groundwater table. Summer half-year evapotranspiration is predicted to grow from the present-day maximum of 860 mm to 885–959 mm (Trinity Enviro 2018). Fig. 7. Spatial distribution of groundwater recharge in the Hungarian Drava Plain (after Salem, A. et al. 2020) Table 3. Groundwater reserves of the Hungarian Drava Plain*, their exploitation** and costs of water utilization*** Parameter Unit Value Total groundwater reserves Total affected area (planning area) Specific groundwater reserve Annual groundwater extraction (based on water rights) Irrigation cost of agricultural land**** Cost of unit extraction**** Worst scenario water price for irrigation water m3 ha m3 ha-1 m3 y-1 HUF (10 ha)-1 y-1 HUF m-3 HUF m-3 n.a. 54,026 10,000–15,000 2,767,262 200,000 300 40 *The area of the Old Drava Programme. **DDVIZIG 2015, Pécsi HYDROTERV 2015. ***Kemény, G. et al. 2018. ****Cost calculations refer to national maximum costs of rotating sprinkler irrigation (at 2017 prices) using subsurface water only. Source: Kemény, G. et al. 2018. n.a. = no data. Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243.238 – The groundwater budget shows yearly fluctuations (up to 2.5 m amplitude) with weather conditions. – The value of floodwater retention and groundwater recharge services cannot be added up, because there is a significant overlap between them. – Water prices play a decisive role in the cal- culations. All these uncertainties also underline the need for a dynamic evaluation. However, at present it is not possible for Hungary because of data shortage. A monitoring network would allow for a dynamic approach to be realised. How could a land evaluation scheme in- corporate ecosystem services valuation? The aims of land evaluation as given in the origi- nal Framework (FAO 1976) remain wholly valid; where these refer to the identification of adverse effects and benefits of land uses, there is now greater emphasis on environ- mental consequences and on wider environ- mental benefits of ESs (FAO 2007). This way land evaluation could also be made more dy- namic, adjusted to changing societal needs. The incorporation of ESs assessment into the FAO land evaluation system is envi- sioned in the following way (Figure 8): As a matter of course, it will be possible only if the methodology for the economic valuation of all ecosystem services is elabo- rated and validated. Conclusions The main goal of land evaluation schemes is to assess the efficiency of landscape function- ing at present and under different environ- mental conditions of the future. The ongoing intensive research directed at ESs provides a new opportunity for the further development of land evaluation systems. In lack of appro- priate information and limited knowledge on ecosystem structures and processes the assessment of ecosystem condition is often difficult. With global climate change water-re- lated ESs (including water retention) increas- ingly come to the foreground. The presently used land evaluation systems are primarily based on static soil parameters which are easy to map and store in a GIS and could be extended to incorporate more dynamic vari- ables that are in tune with the new societal demands. Dynamic and holistic land evalu- ation is needed, particularly for floodplains where water availability directly or indirectly defines the value of the land to a large extent. The incorporation of ESs into the FAO eval- uation framework seems to be an inevitable task for the future, such as advocated in FAO (2007). We show a clear and practical example of the incorporation of ESs into a LE frame- work for the Hungarian Drava Plain and the Kapos Valley. At present, however, a wide range of necessary conditions are missing. The price of water is the single decisive factor contributing to the value of water-related ESs. The integrated assessment will only be possible if most of the important ESs are bro- ken down to indicators by ecologists and ex- pressed in monetary terms by environmental economists. The present study is only meant to be a first step in this direction. Acknowledgements: Authors are grateful for financial support to the European Commission in the frame of the H2020 Diverfarming project (contract no 728003), to the GINOP-2.3.2.15-2016-00055 research fund and to the National Office for Research, Development and Innovation (NKFIH) within the Programme Excellence in Higher Education Institutions 2019 Topic II. 3. (”Innovation for sustainable life and environment”). Fig. 8. A possibility of integrating ecosystem services val- uation into conventional land evaluation (by Lóczy, D.) 239Lóczy, D. et al. Hungarian Geographical Bulletin 69 (2020) (3) 227–243. R E F E R E N C E S Acharya, G. 2000. Approaches to valuing the hid- den hydrological services of wetland ecosystems. 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