Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2561-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2561-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 May 2022
Research article |
| 17 May 2022
| 17 May 2022
Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör
Department of Hydrology and Water Resources Management, Institute for
Natural Resource Conservation, Kiel University, Olshausenstr. 75, 24118 Kiel, Germany
Paul D. Wagner
Department of Hydrology and Water Resources Management, Institute for
Natural Resource Conservation, Kiel University, Olshausenstr. 75, 24118 Kiel, Germany
Nicola Fohrer
Department of Hydrology and Water Resources Management, Institute for
Natural Resource Conservation, Kiel University, Olshausenstr. 75, 24118 Kiel, Germany
Related authors
No articles found.
Paul Daniel Wagner, Fatemeh Saba, Pedro Achanccaray, and Nicola Fohrer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3091, https://doi.org/10.5194/egusphere-2025-3091, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Recent drought conditions in Europe led to significant land use and land cover changes that affect the water balance. In the study area of the Harz mountains we found increasing areas of dead coniferous trees. The loss of trees potentially exacerbates floods and droughts. Therefore, afforestation with climate-resilient trees is needed to improve both flood and drought resilience in the future.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2025-1076, https://doi.org/10.5194/egusphere-2025-1076, 2025
Short summary
Short summary
Four process-based and four data-driven hydrological models are compared using different training data. We found process-based models to perform better with small data sets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Ayenew D. Ayalew, Paul D. Wagner, Dejene Sahlu, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 28, 1853–1872, https://doi.org/10.5194/hess-28-1853-2024, https://doi.org/10.5194/hess-28-1853-2024, 2024
Short summary
Short summary
The study presents a pioneering comprehensive integrated approach to unravel hydrological complexities in data-scarce regions. By integrating diverse data sources and advanced analytics, we offer a holistic understanding of water systems, unveiling hidden patterns and driving factors. This innovative method holds immense promise for informed decision-making and sustainable water resource management, addressing a critical need in hydrological science.
Nariman Mahmoodi, Jens Kiesel, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 25, 5065–5081, https://doi.org/10.5194/hess-25-5065-2021, https://doi.org/10.5194/hess-25-5065-2021, 2021
Short summary
Short summary
In this study, we assessed the sustainability of water resources in a wadi region with the help of a hydrologic model. Our assessment showed that the increases in groundwater demand and consumption exacerbate the negative impact of climate change on groundwater sustainability and hydrologic regime alteration. These alterations have severe consequences for a downstream wetland and its ecosystem. The approach may be applicable in other wadi regions with different climate and water use systems.
Cited articles
Abdi, H.: Partial least squares regression and projection on latent structure regression (PLS Regression), Wiley Interdiscip. Rev. Comput. Stat., 2, 97–106, https://doi.org/10.1002/wics.51, 2010.
Aghsaei, H., Dinan, N. M., Moridi, A., Asadolahi, Z., Delavar, M., Fohrer, N., and Wagner, P. D.: Effects of dynamic land use/land cover change on water resources and sediment yield in the Anzali wetland catchment, Gilan, Iran, Sci. Total Environ., 712, 136449, https://doi.org/10.1016/j.scitotenv.2019.136449, 2020.
Amin, M. M., Veith, T. L., Shortle, J. S., Karsten, H. D., and Kleinman, P. J.: Addressing the spatial disconnect between national-scale total maximum daily loads and localized land management decisions, J. Environ. Qual., 49,
613–627, https://doi.org/10.1002/jeq2.20051, 2020.
Amiri, B. J. and Nakane, K.: Modeling the linkage between river water quality and landscape metrics in the Chugoku district of Japan, Water. Resor. Manage., 23, 931–956, https://doi.org/10.1007/s11269-008-9307-z, 2009.
Anand, J., Gosain, A. K., and Khosa, R.: Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model, Sci. Total Environ., 644, 503–519, https://doi.org/10.1016/j.scitotenv.2018.07.017, 2018.
Antolini, F., Tate, E., Dalzell, B., Young, N., Johnson, K., and Hawthorne,
P. L.: Flood risk reduction from agricultural best management practices, J.
Am. Water Resour. Assoc., 56, 161–179, https://doi.org/10.1111/1752-1688.12812, 2020.
Aredo, M. R., Hatiye, S. D., and Pingale, S. M.: Impact of land use/land
cover change on stream flow in the Shaya catchment of Ethiopia using the
MIKE SHE model, Arab. J. Geosci., 14, 1–15, https://doi.org/10.1007/s12517-021-06447-2, 2021.
Arnold, J., Kiniry, J., Srinivasan, R., Williams, J., Haney, E., and Neitsch, S.: SWAT 2012 input/output documentation, Texas Water Resources Institute, https://swat.tamu.edu/media/69296/swat-io-documentation-2012.pdf (last access: 12 October 2019), 2013.
Arnold, J., Kiniry, J., Srinivasan, R., Williams, J., Haney, E., and
Neitsch, S.: SWAT2012 Executables file, https://swat.tamu.edu/software/swat-executables/, last access: 10 January 2019.
Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large
area hydrologic modeling and assessment part I: model development 1, J. Am.
Water Resour. Assoc., 34, 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x, 1998.
Ayivi, F. and Jha, M. K.: Estimation of water balance and water yield in the
Reedy Fork-Buffalo Creek Watershed in North Carolina using SWAT, Int. Soil
Water Conserv. Res., 6, 203–213, https://doi.org/10.1016/j.iswcr.2018.03.007, 2018.
Basuki, T. M., Nugrahanto, E. B., Pramono, I. B., and Wijaya, W. W.: Baseflow and lowflow of catchments covered by various old teak forest areas, J. Degrad. Min. Lands Manage, 6, 1609, https://doi.org/10.15243/JDMLM.2019.062.1609, 2019.
Bicknell, B., Imhoff, J., Kittle, J., Donigian, A., and Johanson, R. C.:
Hydrological simulation program–Fortran (HSPF): User's manual for release 12, US Environmental Protection Agency, National Exposure Research
Laboratory, Athens, GA, https://www.casqa.org/sites/default/files/hspf-12.pdf (last access: 4 April 2021), 2001.
Bieger, K., Hörmann, G., and Fohrer, N.: Simulation of streamflow and
sediment with the soil and water assessment tool in a data scarce catchment
in the three gorges region, China, J. Environ. Qual., 43, 37–45,
https://doi.org/10.2134/jeq2011.0383, 2014.
Boongaling, C. G. K., Faustino-Eslava, D. V., and Lansigan, F. P.: Modeling
land use change impacts on hydrology and the use of landscape metrics as tools for watershed management: The case of an ungauged catchment in the
Philippines, Land Use Policy, 72, 116–128, https://doi.org/10.1016/j.landusepol.2017.12.042, 2018.
Dickhaut, W.: Fließgewässerrenaturierung Heute–Forschung zu Effizienz und Umsetzungspraxis – Abschlussbericht, Hochschule für
angewandte Wissenschaften, Hamburg, http://www.fischartenatlas.de/cms/images/stories/09a_Lebensraeume/Fliessgewaesser/SCHWARK_etal_2005_Fliessgewaesserrenaturierung_heute_Effizienz_Umsetzungspraxis_BMBF-Abschlussbericht.pdf (last access: 6 March 2020), 2005.
Ding, J., Jiang, Y., Liu, Q., Hou, Z., Liao, J., Fu, L., and Peng, Q.:
Influences of the land use pattern on water quality in low-order streams of
the Dongjiang River basin, China: a multi-scale analysis, Sci. Total Environ., 551, 205–216, https://doi.org/10.1016/j.scitotenv.2016.01.162, 2016.
DWD – Deutscher Wetterdienst: Precipitation data 1990–2019, Precipitation
stations Haale, Padenstedt, Nettelsee and Itzehoe, Climate data 1990–2019,
Climate station Pony Padenstedt, DWD [data set],
https://opendata.dwd.de/climate_environment/CDC/ (last access: July 2020), 2020a.
DWD – Deutscher Wetterdienst: Climate data 1990–2019, Climate station Pony
Padenstedt, https://opendata.dwd.de/climate_environment/CDC/ (last access: July 2020), 2020b.
Deutsches Einheitsverfahren: Selected Methods of Water Analysis, Bd. I, II. VEB, Gustav Fisher, Jena, 1997.
Farjad, B., Pooyandeh, M., Gupta, A., Motamedi, M., and Marceau, D.: Modelling interactions between land use, climate, and hydrology along with
stakeholders' negotiation for water resources management, Sustainability, 9,
2022, https://doi.org/10.3390/su9112022, 2017.
Ferreira, A., Fernandes, L. S., Cortes, R., and Pacheco, F.: Assessing
anthropogenic impacts on riverine ecosystems using nested partial least squares regression, Sci. Total Environ., 583, 466–477,
https://doi.org/10.1016/j.scitotenv.2017.01.106, 2017.
Fiener, P., Auerswald, K., and Van Oost, K.: Spatio-temporal patterns in land use and management affecting surface runoff response of agricultural catchments – A review, Earth-Sci. Rev., 106, 92–104, https://doi.org/10.1016/j.earscirev.2011.01.004, 2011.
Finnern, J.: Böden und Leitbodengesellschaften des Störeinzugsgebietes in Schleswig-Holstein: Vergesellschaftung und
Stoffaustragsprognose (K, Ca, Mg) mittels GIS, Schriftenreihe des Instituts für Pflanzenernährung und Bodenkunde der Universität Kiel, Kiel, 1997.
Forman, R. T.: Some general principles of landscape and regional ecology, Landsc. Ecol., 10, 133–142, https://doi.org/10.1007/BF00133027, 1995.
Gabriels, K., Willems, P., and Van Orshoven, J.: Performance evaluation of
spatially distributed, CN-based rainfall-runoff model configurations for
implementation in spatial land use optimization analyses, J. Hydrol., 602,
126872, https://doi.org/10.1016/j.jhydrol.2021.126872, 2021.
Gashaw, T., Tulu, T., Argaw, M., and Worqlul, A. W.: Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia, Sci. Total Environ., 619, 1394–1408,
https://doi.org/10.1016/j.scitotenv.2017.11.191, 2018.
Gémesi, Z., Downing, J. A., Cruse, R. M., and Anderson, P. F.: Effects
of watershed configuration and composition on downstream lake water quality,
J. Environ. Qual., 40, 517–527, https://doi.org/10.2134/jeq2010.0133, 2011.
Gessner, J., Spratte, S., and Kirschbaum, F.: Störe für die Stör – Wem hilft ein lebendes Fossil, Steinburger Jahrbuch, 54, 247–273, 2010.
Ghimire, C. P., Bruijnzeel, L. A., Lubczynski, M. W., Ravelona, M., Zwartendijk, B. W., and van Meerveld, H. I.: Measurement and modeling of
rainfall interception by two differently aged secondary forests in upland
eastern Madagascar, J. Hydrol., 545, 212–225,
https://doi.org/10.1016/j.jhydrol.2016.10.032, 2017.
Gleick, P. H.: A look at twenty-first century water resources development,
Water Int., 25, 127–138, https://doi.org/10.1080/02508060008686804, 2000.
Goldewijk, K. K. and Ramankutty, N.: Land cover change over the last three
centuries due to human activities: The availability of new global data sets,
Geo J., 61, 335–344, https://doi.org/10.1007/s10708-004-5050-z, 2004.
Gu, D., Zhang, Y., Fu, J., and Zhang, X.: The landscape pattern characteristics of coastal wetlands in Jiaozhou Bay under the impact of human activities, Environ. Monit. Assess., 124, 361–370, https://doi.org/10.1007/s10661-006-9232-7, 2007.
Guse, B., Reusser, D. E., and Fohrer, N.: How to improve the representation of hydrological processes in SWAT for a lowland catchment–temporal analysis of parameter sensitivity and model performance, Hydrol. Process., 28, 2651–2670, https://doi.org/10.1002/hyp.9777, 2014.
Guse, B., Kiesel, J., Pfannerstill, M., and Fohrer, N.: Assessing parameter identifiability for multiple performance criteria to constrain model parameters, Hydrolog. Sci. J., 65, 1158–1172, https://doi.org/10.1080/02626667.2020.1734204, 2020.
Haas, M. B., Guse, B., Pfannerstill, M., and Fohrer, N.: A joined multi-metric calibration of river discharge and nitrate loads with different
performance measures, J. Hydrol., 536, 534–545, https://doi.org/10.1016/j.jhydrol.2016.03.001, 2016.
Haas, M. B., Guse, B., and Fohrer, N.: Assessing the impacts of Best Management Practices on nitrate pollution in an agricultural dominated lowland catchment considering environmental protection versus economic
development, J. Environ. Manage., 196, 347–364, https://doi.org/10.1016/j.jenvman.2017.02.060, 2017.
Haidary, A., Amiri, B. J., Adamowski, J., Fohrer, N., and Nakane, K.: Assessing the impacts of four land use types on the water quality of wetlands in Japan, Water Resour. Manage., 27, 2217–2229, https://doi.org/10.1007/s11269-013-0284-5, 2013.
Hargis, C. D., Bissonette, J. A., and David, J. L.: The behavior of landscape metrics commonly used in the study of habitat fragmentation, Landsc. Ecol., 13, 167–186, 1998.
Hatano, R., Nagumo, T., Hata, H., and Kuramochi, K.: Impact of nitrogen cycling on stream water quality in a basin associated with forest, grassland, and animal husbandry, Hokkaido, Japan, Ecol. Eng., 24, 509–515,
https://doi.org/10.1016/j.ecoleng.2005.01.011, 2005.
Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., and Nowosad, J.:
landscapemetrics: An open-source R tool to calculate landscape metrics,
Ecography, 42, 1648–1657, https://doi.org/10.1111/ecog.04617, 2019.
Idrissou, M., Diekkrüger, B., Tischbein, B., Op de Hipt, F., Näschen, K., Poméon, T., Yira, Y., and Ibrahim, B.: Modeling the Impact of Climate and Land Use/Land Cover Change on Water Availability in an Inland Valley Catchment in Burkina Faso, Hydrology, 9, 12, https://doi.org/10.3390/hydrology9010012, 2022.
Jones, K. B., Neale, A. C., Nash, M. S., Van Remortel, R. D., Wickham, J. D., Riitters, K. H., and O'neill, R. V.: Predicting nutrient and sediment loadings to streams from landscape metrics: a multiple watershed study from the United States Mid-Atlantic Region, Landsc. Ecol., 16, 301–312,
https://doi.org/10.1023/A:1011175013278, 2001.
Kändler, M., Blechinger, K., Seidler, C., Pavlů, V., Šanda, M.,
Dostál, T., Krása, J., Vitvar, T., and Štich, M.: Impact of land
use on water quality in the upper Nisa catchment in the Czech Republic and
in Germany, Sci. Total Environ., 586, 1316–1325,
https://doi.org/10.1016/j.scitotenv.2016.10.221, 2017.
Kiesel, J., Schmalz, B., and Fohrer, N.: SEPAL – a simple GIS-based tool to
estimate sediment pathways in lowland catchments, Adv. Geosci., 21, 25–32,
https://doi.org/10.5194/adgeo-21-25-2009, 2009.
KTBL: Kuratorium für Technik und Bauwesen in der Landwirtschaft, Betriebsplanung Landwirtschaft 1995/1996 and 2008/2009, 14. and 21. Edn.,
KTBL, Darmstadt, ISBN 9783784319346, ISBN 9783939371663, 1995 and 2008.
Kucheryavskiy, S.: mdatools – R package for chemometrics, Chemom. Intel.
Lab. Syst., 198, 103937, https://doi.org/10.1016/j.chemolab.2020.103937, 2020.
Kühling, I.: Modellierung und räumliche Analyse der Phosphateintragspfade im Einzugsgebiet eines norddeutschen Tieflandbaches,
Master thesis, Christian-Albrechts-University Kiel, Kiel, Germany, 2011.
Kumar, S., Getirana, A., Libonati, R., Hain, C., Mahanama, S., and Andela, N.: Changes in land use enhance the sensitivity of tropical ecosystems to
fire-climate extremes, Sci. Rep., 12, 1–11, https://doi.org/10.1038/s41598-022-05130-0, 2022.
Lam, Q., Schmalz, B., and Fohrer, N.: Modelling point and diffuse source
pollution of nitrate in a rural lowland catchment using the SWAT model, Agr. Water Manage., 97, 317–325, https://doi.org/10.1016/j.agwat.2009.10.004, 2010.
Lam, Q., Schmalz, B., and Fohrer, N.: Assessing the spatial and temporal
variations of water quality in lowland areas, Northern Germany, J. Hydrol.,
438, 137–147, https://doi.org/10.1016/j.jhydrol.2012.03.011, 2012.
Lei, C., Wagner, P. D., and Fohrer, N.: Identifying the most important spatially distributed variables for explaining land use patterns in a rural
lowland catchment in Germany, J. Geogr. Sci., 29, 1788–1806,
https://doi.org/10.1007/s11442-019-1690-2, 2019.
Lei, C., Wagner, P. D., and Fohrer, N.: Effects of land cover, topography, and soil on stream water quality at multiple spatial and seasonal scales in a German lowland catchment, Ecol. Indic., 120, 106940, https://doi.org/10.1016/j.ecolind.2020.106940, 2021.
Li, G., Zhang, F., Jing, Y., Liu, Y., and Sun, G.: Response of evapotranspiration to changes in land use and land cover and climate in China during 2001–2013, Sci. Total Environ., 596, 256–265,
https://doi.org/10.1016/j.scitotenv.2017.04.080, 2017.
Li, S., Gu, S., Liu, W., Han, H., and Zhang, Q.: Water quality in relation
to land use and land cover in the upper Han River Basin, China, Catena, 75,
216–222, https://doi.org/10.1016/j.catena.2008.06.005, 2008.
LKN: Landesbetrieb für Küstenschutz, Nationalpark und Meeresschutz
Schleswig-Holstein, Discharge data from gauges Padenstedt (https://www.umweltdaten.landsh.de/pegel/jsp/pegel.jsp?gui=ganglinie&thema=q&mstnr=114200, last access: 3 April 2020), Sarlhusen (https://www.umweltdaten.landsh.de/pegel/jsp/pegel.jsp?gui=ganglinie&thema=q&mstnr=114131, last access: 25 March 2020) and
Willenscharen https://www.umweltdaten.landsh.de/pegel/jsp/pegel.jsp?gui=ganglinie&thema=q&mstnr=114135, last access: 25 March 2020), 2020a.
LKN: Landesbetrieb für Küstenschutz, Nationalpark und Meeresschutz Schleswig-Holstein, Discharge data, http://www.umweltdaten.landsh.de (last access: 3 April 2020), 2020b.
Lu, Y., Song, S., Wang, R., Liu, Z., Meng, J., Sweetman, A. J., Jenkins, A.,
Ferrier, R. C., Li, H., and Luo, W.: Impacts of soil and water pollution on
food safety and health risks in China, Environ. Int., 77, 5–15,
https://doi.org/10.1016/j.envint.2014.12.010, 2015.
LvermA: Digitales Geländenmodell (ATKIS-DGM LiDAR), Gitterweite 5×5 m, Land survey office, Schleswig-Holstein, Kiel, Germany, 2008.
LWK: Landwirtschaftskammer Schleswig-Holstein. Richtwerte für die
Düngung 1991 and 2011, 13. and 21 Edn., LWK, Rendsburg, 1991 and 2011.
Maranguit, D., Guillaume, T., and Kuzyakov, Y.: Land-use change affects phosphorus fractions in highly weathered tropical soils, Catena, 149, 385–393, https://doi.org/10.1016/j.catena.2016.10.010, 2017.
Mevik, B.-H., Wehrens, R., and Liland, K. H.: pls: Partial least squares and
principal component regression, R package version 2, http://CRAN.R-project.org/package=pls, last access: 20 December 2020.
Mirghaed, F. A., Souri, B., Mohammadzadeh, M., Salmanmahiny, A., and Mirkarimi, S. H.: Evaluation of the relationship between soil erosion and
landscape metrics across Gorgan Watershed in northern Iran, Environ. Monit.
Assess., 190, 1–14, https://doi.org/10.1007/s10661-018-7040-5, 2018.
Monaghan, R., Wilcock, R., Smith, L., Tikkisetty, B., Thorrold, B., and
Costall, D.: Linkages between land management activities and water quality
in an intensively farmed catchment in southern New Zealand, Agr. Ecosyst.
Environ., 118, 211–222, https://doi.org/10.1016/j.agee.2006.05.016, 2007.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Nafi'Shehab, Z., Jamil, N. R., Aris, A. Z., and Shafie, N. S.: Spatial variation impact of landscape patterns and land use on water quality across
an urbanized watershed in Bentong, Malaysia, Ecol. Indic., 122, 107254,
https://doi.org/10.1016/j.ecolind.2020.107254, 2021.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
water assessment tool theoretical documentation version 2009, Texas Water
Resources Institute, https://swat.tamu.edu/media/99192/swat2009-theory.pdf (last access: 1 January 2019), 2011.
Noe, G. B., Hupp, C. R., and Rybicki, N. B.: Hydrogeomorphology influences
soil nitrogen and phosphorus mineralization in floodplain wetlands, Ecosystems, 16, 75–94, https://doi.org/10.1007/s10021-012-9597-0, 2013.
Onderka, M., Wrede, S., Rodný, M., Pfister, L., Hoffmann, L., and Krein,
A.: Hydrogeologic and landscape controls of dissolved inorganic nitrogen (DIN) and dissolved silica (DSi) fluxes in heterogeneous catchments, J. Hydrol., 450, 36–47, https://doi.org/10.1016/j.jhydrol.2012.05.035, 2012.
Oppelt, N., Rathjens, H., and Dörnhöfer, K.: Integration of land cover data into the open source model SWAT, in: First Sentinel-2 Preparatory
Symposium, April 2012, Frascati, Italy, 23–27, ESA SP-707,
https://www.researchgate.net/publication/259632792 (last access: 2 June 2020), 2012.
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Vegetation impact on mean
annual evapotranspiration at a global catchment scale, Water Resour. Res., 46, W09508, https://doi.org/10.1029/2009WR008233, 2010.
Pfannerstill, M., Guse, B., and Fohrer, N.: A multi-storage groundwater
concept for the SWAT model to emphasize nonlinear groundwater dynamics in
lowland catchments, Hydrol. Process., 28, 5599–5612, https://doi.org/10.1002/hyp.10062, 2014.
Pott, C. A.: Integrated monitoring, assessment and modeling of nitrogen and
phosphorus pollution in a lowland catchment in Germany: a long-term study on
water quality, Christian-Albrechts Universität Kiel, Kiel, Germany, https://macau.uni-kiel.de/servlets/MCRFileNodeServlet/dissertation_derivate_00005297/Dissertation_Pott_AGRAR_29.1.14.pdf (last access: 7 July 2018), 2014.
Pott, C. A. and Fohrer, N.: Best management practices to reduce nitrate pollution in a rural watershed in Germany, Rev. Ambiente Agua, 12, 888–901,
https://doi.org/10.4136/ambi-agua.2099, 2017a.
Pott, C. A. and Fohrer, N.: Hydrological modeling in a rural catchment in
Germany, Appl. Res. Agrotech., 10, 7–16, 2017b.
Rathjens, H., Dörnhöfer, K., and Oppelt, N.: IRSeL – An approach to
enhance continuity and accuracy of remotely sensed land cover data, Int. J.
Appl. Earth Obs. Geoinf., 31, 1–12, https://doi.org/10.1016/j.jag.2014.02.010, 2014.
Riitters, K.: Pattern metrics for a transdisciplinary landscape ecology,
Landsc. Ecol., 34, 2057–2063, https://doi.org/10.1007/s10980-018-0755-4, 2019.
Ripl, W., Janssen, T., Hildmann, C., and Otto, I.: Entwicklung eines Land-Gewässer Bewirtschaftungskonzeptes zur Senkung von Stoffverlusten
an Gewässer (Stör-Projekt I und II), Forschungsbericht, TU Berlin,
Berlin, http://www.aquaterra-berlin.de/images/stories/stoer_endber01/Ripl-et-al_1996_Stoer-Endbericht_150dpi_mCmP_.pdf (last access: 2 May 2020), 1996.
Shawul, A. A., Chakma, S., and Melesse, A. M.: The response of water balance
components to land cover change based on hydrologic modeling and partial
least squares regression (PLSR) analysis in the Upper Awash Basin, J. Hydrol.: Reg. Stud., 26, 100640, https://doi.org/10.1016/j.ejrh.2019.100640, 2019.
Shi, P.-J., Yuan, Y., Zheng, J., Wang, J.-A., Ge, Y., and Qiu, G.-Y.: The
effect of land use/cover change on surface runoff in Shenzhen region, China,
Catena, 69, 31–35, https://doi.org/10.1016/j.catena.2006.04.015, 2007.
Shi, Z., Ai, L., Li, X., Huang, X., Wu, G., and Liao, W.: Partial least-squares regression for linking land-cover patterns to soil erosion and
sediment yield in watersheds, J. Hydrol., 498, 165–176,
https://doi.org/10.1016/j.jhydrol.2013.06.031, 2013.
Shrestha, S., Bhatta, B., Shrestha, M., and Shrestha, P. K.: Integrated
assessment of the climate and landuse change impact on hydrology and water
quality in the Songkhram River Basin, Thailand, Sci. Total Environ., 643,
1610–1622, https://doi.org/10.1016/j.scitotenv.2018.06.306, 2018.
Shuster, W. D., Bonta, J., Thurston, H., Warnemuende, E., and Smith, D.:
Impacts of impervious surface on watershed hydrology: A review, Urban Water
J., 2, 263–275, https://doi.org/10.1080/15730620500386529, 2005.
Singh, H., Singh, D., Singh, S. K., and Shukla, D.: Assessment of river water quality and ecological diversity through multivariate statistical techniques, and earth observation dataset of rivers Ghaghara and Gandak, India, Int. J. River Basin Manage., 15, 347–360, https://doi.org/10.1080/15715124.2017.1300159, 2017.
Soetaert, K. and Petzoldt, T.: Inverse modelling, sensitivity and Monte Carlo analysis in R using package FME, J. Stat. Softw, 33, 1–28, https://doi.org/10.18637/jss.v033.i03, 2010.
Song, S., Schmalz, B., and Fohrer, N.: Simulation, quantification and comparison of in-channel and floodplain sediment processes in a lowland
area – A case study of the Upper Stör catchment in northern Germany,
Ecol. Indic., 57, 118–127, https://doi.org/10.1016/j.ecolind.2015.03.030, 2015.
Sood, A., Ghosh, S. K., and Upadhyay, P.: Impact of land cover change on surface runoff, in: Advances in Remote Sensing for Natural Resource Monitoring, edited by: Pandey, P. C. and Sharma, L. K., Wiley, 150–169, https://doi.org/10.1002/9781119616016.ch10, 2021.
Srinivasan, J. T. and Reddy, V. R.: Impact of irrigation water quality on
human health: A case study in India, Ecol. Econ., 68, 2800–2807,
https://doi.org/10.1016/j.ecolecon.2009.04.019, 2009.
Statistical Office Schleswig-Holstein: Statistiches Jahrbuch Schleswig-Holstein, Statistiches Amt für Hamburg und Schleswig-Holstein, Kiel, https://www.statistischebibliothek.de/mir/receive/SHSerie_mods_00000001 (last access: 20 July 2013), 1992–2013.
Taka, M., Sillanpää, N., Niemi, T., Warsta, L., Kokkonen, T., and
Setälä, H.: Heavy metals from heavy land use? Spatio-temporal patterns of urban runoff metal loads, Sci. Total Environ., 817, 152855,
https://doi.org/10.1016/j.scitotenv.2021.152855, 2022.
Tan, M. L., Gassman, P. W., Liang, J., and Haywood, J. M.: A review of
alternative climate products for SWAT modelling: Sources, assessment and future directions, Sci. Total Environ., 795, 148915,
https://doi.org/10.1016/j.scitotenv.2021.148915, 2021.
Tigabu, T. B., Wagner, P. D., Hörmann, G., and Fohrer, N.: Modeling the
spatio-temporal flow dynamics of groundwater-surface water interactions of the Lake Tana Basin, Upper Blue Nile, Ethiopia, Hydrol. Res., 51, 1537–1559,
https://doi.org/10.2166/nh.2020.046, 2020.
Uuemaa, E., Antrop, M., Roosaare, J., Marja, R., and Mander, Ü.: Landscape metrics and indices: an overview of their use in landscape research, Living Rev. Landscape Res., 3, 1–28, 2009.
Venohr, M.: Einträge und Abbau von Nährstoffen in Fließgewässern der oberen Stör, Diploma thesis,
Christian-Albrechts-Universität Kiel, Kiel, Germany, 2000.
Wagner, P., Kumar, S., and Schneider, K.: An assessment of land use change
impacts on the water resources of the Mula and Mutha Rivers catchment
upstream of Pune, India, Hydrol. Earth Syst. Sci., 17, 2233–2246,
https://doi.org/10.5194/hess-17-2233-2013, 2013.
Wagner, P. D. and Fohrer, N.: Gaining prediction accuracy in land use modeling by integrating modeled hydrologic variables, Environ. Model. Softw.,
115, 155–163, https://doi.org/10.1016/j.envsoft.2019.02.011, 2019.
Wagner, P. D. and Waske, B.: Importance of spatially distributed hydrologic
variables for land use change modeling, Environ. Model. Softw., 83, 245–254,
https://doi.org/10.1016/j.envsoft.2016.06.005, 2016.
Wagner, P. D., Bhallamudi, S. M., Narasimhan, B., Kantakumar, L. N., Sudheer, K., Kumar, S., Schneider, K., and Fiener, P.: Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment, Sci. Total Environ., 539, 153–164, https://doi.org/10.1016/j.scitotenv.2015.08.148, 2016.
Wagner, P. D., Hoermann, G., Schmalz, B., and Fohrer, N.: Characterisation of the water and nutrient balance in the rural lowland catchment of the Kielstau, Hydrol. Wasserbewirtsch., 62, 145–158, 2018.
Wang, Q., Xu, Y., Xu, Y., Wu, L., Wang, Y., and Han, L.: Spatial hydrological responses to land use and land cover changes in a typical catchment of the Yangtze River Delta region, Catena, 170, 305–315, https://doi.org/10.1016/j.catena.2018.06.022, 2018.
Wang, W., Wu, X., Yin, C., and Xie, X.: Nutrition loss through surface
runoff from slope lands and its implications for agricultural management, Agr. Water Manage., 212, 226–231, https://doi.org/10.1016/j.agwat.2018.09.007, 2019.
Wei, W., Chen, L., Fu, B., Huang, Z., Wu, D., and Gui, L.: The effect of land uses and rainfall regimes on runoff and soil erosion in the semi-arid loess hilly area, China, J. Hydrol., 335, 247–258, https://doi.org/10.1016/j.jhydrol.2006.11.016, 2007.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, https://doi.org/10.1029/94WR00436, 1994.
Wijesekara, G., Gupta, A., Valeo, C., Hasbani, J.-G., Qiao, Y., Delaney, P.,
and Marceau, D.: Assessing the impact of future land-use changes on hydrological processes in the Elbow River watershed in southern Alberta,
Canada, J. Hydrol., 412, 220–232, https://doi.org/10.1016/j.jhydrol.2011.04.018, 2012.
Wold, S., Sjöström, M., and Eriksson, L.: PLS-regression: a basic tool of chemometrics, Chemom. Intel. Lab. Syst., 58, 109–130,
https://doi.org/10.1016/S0169-7439(01)00155-1, 2001.
Wu, J. and Lu, J.: Spatial scale effects of landscape metrics on stream water quality and their seasonal changes, Water Res., 191, 116811, https://doi.org/10.1016/j.watres.2021.116811, 2021.
Xu, S., Li, S.-L., Zhong, J., and Li, C.: Spatial scale effects of the variable relationships between landscape pattern and water quality: Example
from an agricultural karst river basin, Southwestern China, Agr. Ecosyst.
Environ., 300, 106999, https://doi.org/10.1016/j.agee.2020.106999, 2020.
Yan, B., Fang, N., Zhang, P., and Shi, Z.: Impacts of land use change on
watershed streamflow and sediment yield: An assessment using hydrologic
modelling and partial least squares regression, J. Hydrol., 484, 26–37,
https://doi.org/10.1016/j.jhydrol.2013.01.008, 2013.
Yu, D., Li, X., Cao, Q., Hao, R., and Qiao, J.: Impacts of climate variability and landscape pattern change on evapotranspiration in a grassland landscape mosaic, Hydrol. Process., 34, 1035–1051, https://doi.org/10.1002/hyp.13642, 2020.
Zeileis, A. and Grothendieck, G.: zoo: S3 infrastructure for regular and irregular time series, J. Stat. Softw., 14, 1–27, https://doi.org/10.18637/jss.v014.i06, 2005.
Zhang, W., Li, H., Hyndman, D. W., Diao, Y., Geng, J., and Pueppke, S. G.:
Water quality trends under rapid agricultural expansion and enhanced in-stream interception in a hilly watershed of Eastern China, Environ. Res.
Lett., 15, 084030, https://doi.org/10.1088/1748-9326/ab8981, 2020a.
Zhang, W., Li, H., Pueppke, S. G., Diao, Y., Nie, X., Geng, J., Chen, D.,
and Pang, J.: Nutrient loss is sensitive to land cover changes and slope
gradients of agricultural hillsides: evidence from four contrasting pond
systems in a hilly catchment, Agr. Water Manage., 237, 106165,
https://doi.org/10.1016/j.agwat.2020.106165, 2020b.
Zhang, Y.-K. and Schilling, K.: Increasing streamflow and baseflow in Mississippi River since the 1940s: Effect of land use change, J. Hydrol., 324, 412–422, https://doi.org/10.1016/j.jhydrol.2005.09.033, 2006.
Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series, R package version 0.4-0, https://github.com/hzambran/hydroGOF, last access: 8 July 2020.
Short summary
We presented an integrated approach to hydrologic modeling and partial least squares regression quantifying land use change impacts on water and nutrient balance over 3 decades. Results highlight that most variations (70 %–80 %) in water quantity and quality variables are explained by changes in land use class-specific areas and landscape metrics. Arable land influences water quantity and quality the most. The study provides insights on water resources management in rural lowland catchments.
We presented an integrated approach to hydrologic modeling and partial least squares regression...
