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Inverse determination of groundwater inflow using water balance simulations

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Abstract

The IWAS project Ukraine deals among others with the water balance in the headwaters of the river Western Bug. During investigations in the sub-catchment Sasiv, a gap in the water balance was detected by Pluntke et al. (Environ Earth Sci, this issue, 2014). The runoff of this sub-catchment is significantly higher than that of any other catchment in the region. Unexpectedly, in 2011 the runoff was observed to be higher than precipitation. We hypothesize additional subsurface flow in the Sasiv catchment from adjacent aquifers. The hydrological model BROOK90 was used to model water flows. In addition to publicly available meteorological input data from the NOAA and ECA&C databases, own precipitation measurements in Sasiv were used. The mean seasonal leaf area index (LAI)—cycle of forest, grass, and agricultural land was determined using satellite data. To improve the output quality, the model was adapted for the superior catchment Kamianka-Buzka/Western Bug (2,500 km2). It was calibrated for the period 2001–2006 and validated for 2007–2011. Infiltration and drainage parameters were unknown, and were therefore calibrated applying Monte Carlo simulations. It was not possible to calibrate the sub-catchment Sasiv (107 km2) due to high deviations of the natural water balance. Thus, the model was calibrated for the superior catchment and afterwards applied to the Sasiv catchment. The monthly estimated inflow was calculated based on, first, a constant term, which is a fixed monthly inflow with its minimum in October (22 mm) and its maximum in May (41 mm). Secondly, a dynamic term was added. Here, different dependencies were tested, e.g., precipitation, soil moisture, groundwater level, and modelled runoff. The best coefficient of determination was achieved using a monthly modelled runoff from the 4 months before the current time. The model showed a good performance in the validation period (R 2 = 0.88). On average, the runoff observed in Sasiv consists of 60 percent groundwater inflow. At the gauge Kamianka-Buzka, this fraction is still about 5 %. The determined LAI cycles and the usage of an additional precipitation station near Sasiv significantly improved the quality of the water balance modelling in the Sasiv sub-catchment. The presented hydrological model approach was extended by hydrogeological simulations to enhance the understanding of processes in groundwater and surface water interactions. Therefore, it was a special challenge to model with scarce data the heterogeneous hydrogeological character of the catchment. The multi-level calibration enables a comprehensible estimation of annual groundwater recharge rates (30–700 mm) of the shallow quaternary aquifer, which is drained by the river Bug system.

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References

  • Armbruster M, Seegert J, Feger K-H (2004) Effects of changes in tree species composition on water flow dynamics—model applications and their limitations. Plant Soil 264:13–24. doi:10.1023/B:PLSO.0000047716.45245.23

    Article  Google Scholar 

  • Blumensaat F, Wolfram M, Krebs P (2012) Sewer model development under minimum data requirements. Environ Earth Sci 65:1427–1437. doi:10.1007/s12665-011-1146-1

    Article  Google Scholar 

  • Bogdanova EG, Ilyin BM, Dragomilova IV (2002) Application of a comprehensive bias-correction model to precipitation measured at Russian North Pole drifting stations. J Hydrometeorol 3:700–713. doi:10.1175/1525-7541(2002)003<0700:AOACBC>2.0.CO;2

    Article  Google Scholar 

  • Borevsky (1976) Бopeвcкий Б. B., Xopдикaйнeн M. A., Язвин Л. C. “Paзвeдкa и oцeнкa экcплyaтaциoнныx зaпacoв мecтopoждeний пoдзeмныx вoд в тpeщиннo-кapcтoвыx плacтax.” Heдpa, Mocквa 1976

  • Chaly P (2011) Hydrogeological problems of the Carpathian region in the Liviv province. In: Nalecz T (ed) Groundwater management in the East of the European Union, NATO Science for peace and security series C: Environmental Security. doi:10.1007/978-90-481-9534-3_8

  • Cohen WB, Maiersperger TK, Yang Z et al (2003) Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sens Environ 88:233–255. doi:10.1016/j.rse.2003.06.006

    Article  Google Scholar 

  • Combalicer EA, Cruz RVO, Lee SH, Im S (2010) Modelling hydrologic processes distribution in a tropical forest watershed in the Philippines. J Trop For Sci 22:155–169

    Google Scholar 

  • Doherty J, Hunt RJ (2010) Approaches to highly parameterized inversion: a guide to using PEST for groundwater-model calibration. US Geol Surv Sci Investig Rep 5169:60

    Google Scholar 

  • Ertel A-M, Lupo A, Scheifhacken N et al (2012) Heavy load and high potential: anthropogenic pressures and their impacts on the water quality along a lowland river (Western Bug, Ukraine). Environ Earth Sci 65:1459–1473. doi:10.1007/s12665-011-1289-0

    Article  Google Scholar 

  • Federer C (2002) BROOK90-A simulation model for evaporation, soil water, and streamflow. Documentation for versions 4 and 3.2/3/4. Compass Brook, Durham, New Hampshire

  • Fensholt R, Sandholt I, Rasmussen MS (2004) Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sens Environ 91:490–507. doi:10.1016/j.rse.2004.04.009

    Article  Google Scholar 

  • Groisman PY, Koknaeva VV, Belokrylova TA, Karl TR (1991) Overcoming biases of precipitation measurement: a history of the USSR experience. B Am Meterol Soc 72:1725–1733. doi:10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2

    Article  Google Scholar 

  • Hinterding A (2002) Entwicklung hybrider Interpolationsverfahren für den automatisierten Betrieb am Beispiel meteorologischer Grössen. IfGi, Institur für Geoinformatik Westfählische Wilhelms-Universität Münster

  • IEG et al (2013) Institute of Ecology and Geography, Academy of Science of Moldova (Moldova), Ukrainian Center of Environmental and Water Projects, Academy of Sciences (Ukraine) (2013) River Basin Management Plan for Prut Pilot Basin in the territories of Ukraine and Moldova

  • Kim S-H, Park J-H, Woo C-S, Lee K-S (2005) Analysis of temporal variability of MODIS leaf area index (LAI) product over temperate forest in Korea. IEEE, pp 4343–4346

  • Klimchouk AB, Aksem SD (2005) Hydrochemistry and solution rates in gypsum karst: case study from the Western Ukraine. Environ Geol 48:307–319. doi:10.1007/s00254-005-1277-3

    Article  Google Scholar 

  • Klimchouk AB and Andrejchuk V (2003) Karst breakdown mechanism from observations in the gypsum caves of the western Ukraine: implications for subsidence hazard assessment. In: AB Klimchouk et al (eds) Speleogenesis: evolution of Karst Aquifers 1 (1), pp 1–20

  • Knyazikhin Y, Glassy J, Privette J et al (1999) MODIS leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) product (MOD15) algorithm theoretical basis document. Theoretical Basis Document, NASA Goddard Space Flight Center, Greenbelt, MD 20771

  • Lipinskyy VM, Dyachuk VA, Babichenko VM (2003) Climate of Ukraine. Rayevskyy Publishing, Kyiv

    Google Scholar 

  • Mao D, Cherkauer KA (2009) Impacts of land-use change on hydrologic responses in the Great Lakes region. J Hydrol 374:71–82. doi:10.1016/j.jhydrol.2009.06.016

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner MW, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

  • Moulin L, Gaume E, Obled C (2009) Uncertainties on mean areal precipitation: assessment and impact on streamflow simulations. Hydrol Earth Syst Sci 13:99–114

    Article  Google Scholar 

  • Nalecz, T (2011) Sustainable use and protection of groundwater resources—transboundary water management–Belarus, Poland, Ukraine project as a step for establishing integrated transboundary groundwater management east of EU. In: Nalecz T (ed) Groundwater Management in the East of the European Union, NATO science for peace and security series C: Environmental Security. doi:10.1007/978-90-481-9534-3_1

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. doi:10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • Ognianik N, Paramonova N (2001) Estimation of real and predicted infiltration recharge for the irrigated area in southern Ukraine. In: Gehrels H, Peters NE, Hoehn E, Jensen K, Leibundgut C, Griffioen J, Webb B, Zaadnoordijk WJ (eds) Impact of Human Activity on Groundwater Dynamics (Proceedings of 6th IAHS Scientific Assembly, Maastricht, The Netherlands, July 2001). IAHS Publication 269, pp 95–98

  • Paton BY, Veklych L, Gritsenko P, Yevropina I et al (2007) National Atlas of Ukraine. In: Paton B, SSPE K (eds) Nat Acad Sci Ukraine “Cartography”, p 440

  • Pisek J, Chen JM (2007) Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sens Environ 109:81–94. doi:10.1016/j.rse.2006.12.004

    Article  Google Scholar 

  • Pluntke T, Pavlik D, Bernhofer C (2014) Reducing uncertainty in hydrological modelling in a data sparse region. Environ Earth Sci (this issue). doi:10.1007/s12665-014-3252-3

  • Privette J, Myneni R, Knyazikhin Y et al (2002) Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. Remote Sens Environ 83:232–243. doi:10.1016/S0034-4257(02)00075-5

    Article  Google Scholar 

  • Ruelland D, Ardoin-Bardin S, Billen G, Servat E (2008) Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa. J Hydrol 361:96–117. doi:10.1016/j.jhydrol.2008.07.049

    Article  Google Scholar 

  • Schanze J, Trümper J, Burmeister C et al (2012) A methodology for dealing with regional change in integrated water resources management. Environ Earth Sci 65:1405–1414. doi:10.1007/s12665-011-1311-6

    Article  Google Scholar 

  • Scheifhacken N, Haase U, Gram-Radu L et al (2012) How to assess hydromorphology? A comparison of Ukrainian and German approaches. Environ Earth Sci 65:1483–1499. doi:10.1007/s12665-011-1218-2

    Article  Google Scholar 

  • Schwärzel K, Feger K-H, Häntzschel J et al (2009) A novel approach in model-based mapping of soil water conditions at forest sites. Forest Ecol Manag 258:2163–2174

    Article  Google Scholar 

  • Segond M-L, Wheater HS, Onof C (2007) The significance of spatial rainfall representation for flood runoff estimation: a numerical evaluation based on the Lee catchment, UK. J Hydrol 347:116–131. doi:10.1016/j.jhydrol.2007.09.040

    Article  Google Scholar 

  • Shestopalov (1981) Шecтoпaлoв B.M “Ecтecтвeнныe pecypcы пoдзeмныx вoд плaтфopмeнныx apтeзиaнcкиx бacceйнoв Укpaины.”, Hayкoвa Дyмкa, Киeв 1981

  • Shestopalov (1988) Шecтoпaлoв B.M и дp. “Boдooбмeн в гидpo-гeoлoгичecкиx cтpyктypax Укpaины: Meтoды изyчeния вoдooбмeнa.” Hayкoвa Дyмкa, Киeв 1988

  • Spanner MA, Pierce LL, Running SW, Peterson DL (1990) The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index. Remote Sens Environ 33:97–112. doi:10.1016/0034-4257(90)90036-L

    Article  Google Scholar 

  • Tavarez Wahren FT, Tarasiuk M, Mykhnovych A et al (2011) Estimation of spatially distributed soil information: dealing with data shortages in the Western Bug Basin, Ukraine. Environ Earth Sci 65:1501–1510. doi:10.1007/s12665-011-1197-3

    Article  Google Scholar 

  • Wahren A, Schwärzel K, Feger KH et al (2007) Identification and model based assessment of the potential water retention caused by land-use changes. Adv Geosci 11:49–56

    Article  Google Scholar 

  • Wang W, Kolditz O (2007) Object-oriented finite element analysis of thermo-hydro-mechanical (THM) problems in porous media. Int J Numer Methods Eng 69(1):162–201

    Article  Google Scholar 

  • Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79

    Article  Google Scholar 

  • Yakovlev EA, Sitnikow AB, Zarizkyi AI et al (1991) Water balance under anthropogenic conditions. In: Schestopalow WM (ed) Water exchange processes in hydrogeological structures in Ukraine. Nyukowa Dymka, Kiev, pp 182–209

    Google Scholar 

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Acknowledgments

This work was supported by funding from the German Federal Ministry for Education and Research (BMBF) in the framework of the project “IWAS-International Water Research Alliance Saxony” (Grant 02WM1028). The authors would like to thank the State Environment Agency Rheinland-Pfalz, Germany, for providing the software package INTERMET for this work. Furthermore, special thanks go to the Ivan Franko National University of Lviv for the research cooperation. Special thanks to OpenGeoSys developers at the Department of Environmental Informatics, Helmholtz Centre for Environmental Research Leipzig. Special thanks to the reviewers for their very helpful annotations.

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Körner, P., Pluntke, T., Sachse, A. et al. Inverse determination of groundwater inflow using water balance simulations. Environ Earth Sci 72, 4757–4769 (2014). https://doi.org/10.1007/s12665-014-3327-1

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