Modelling the effect of different agricultural practices on stream nitrogen load in central Germany

Energy, Sustainability and Society - Tập 6 - Trang 1-16 - 2016
Seifeddine Jomaa1,2, Sanyuan Jiang3, Daniela Thraen2,4, Michael Rode1
1Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research—UFZ, Magdeburg, Germany
2Department of Bioenergy, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
3Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
4Deutsches Biomasseforschugszentrum gGmbH, Leipzig, Germany

Tóm tắt

Understanding the response of nitrogen fluxes to changes in land use and agricultural practices is crucial for improving the instream water quality prediction. In central Germany, the expansion of bioenergy crops during the last decade led to an increase in fertiliser application rates. The purpose of this study is to investigate the effect of agricultural management changes on the stream nitrogen load of a drinking water reservoir catchment (Weida, 99.5 km2) using a hydrological water quality model. The semi-distributed hydrological water quality model—the HYdrological Predictions for the Environment (HYPE)—was calibrated and validated successfully for discharge and nitrate-N concentrations during the period 1997–2003 (the lowest discharge Nash-Sutcliffe efficiency (NSE) was 0.78). Subsequently, stream nitrogen load of six different land use scenarios and their associated agricultural practice changes were compared to the baseline simulations of the period 2006–2009. Some of these scenarios were designed considering the increased cultivation of bioenergy crops. Results revealed that an increase in mineral fertiliser by 20 % for all crops augmented an increase of monthly stream nitrogen loads in the range of 2–6 % compared to the baseline simulations. Also, it was found that stream nitrogen load increased in scenarios where all or some crop areas were converted to maize and rape, which are the established bioenergy crops in Germany. The increase of nitrogen load resulting from these scenarios differed in terms of magnitude and their temporal patterns, reflecting the importance of timing, the amount of fertiliser applications, and harvesting periods. However, results showed that nitrogen load was reduced in situations when only organic farming or summer barley was used and when rape and maize cropping areas were converted to winter wheat. In this intensively used agricultural catchment, the simulated stream nitrate-N loads quickly responded to fertiliser application changes (increase/decrease). This rapid response could be explained by short residence time of the interflow and baseflow runoff components because of the hardrock geological properties of the catchment.

Tài liệu tham khảo

Souza GM, Victoria RL, Joly RC, Verdade LM (2015) 72 Bioenergy & sustainability: bridging the gaps UNESCO. Scientific Committee on Problems of the Environment (SCOPE), Paris, in December 2013. http://rsb.org/pdfs/reports/Bioenergy%20and%20Sustainability%20-%20Bridging%20the%20Gaps.pdf Graham-Rowe D (2011) Agriculture: beyond food versus fuel. Nature 474:S6. doi:10.1038/474S06a Robbins M (2011) Policy: fuelling politics. Nature 474:S22. doi:10.1038/474S022a Fachagentur Nachwachsende Rohstoffe e.V. (FNR) (2013) Bioenergy: the multifaceted renewable energy2013. Available http://mediathek.fnr.de/media/downloadable/files/samples/f/n/fnr_brosch_re_bioenergie_2013_engl._web.pdf (last accessed 26 May 2015) Commission European for Renewable Energy, Road map renewable energies in the 21st century (2007), Building a more sustainable future Fräss-Ehrfeld C (2009) Renewable energy sources: a chance to combat climate change, Wolters Kluwer: law and business Jackson RB, Jobbágy EG, Avissar R, Roy SB, Barrett DJ, Cook CW, Farley KA, le Maitre DC, McCarl BA, Murray BC (2005) Trading water for carbon with biological carbon sequestration. Science 310:1944. doi:10.1126/science.1119282 Editorial N (2007) Kill king corn. Nature 449:637. doi:10.1038/449637a Fachagentur Nachwachsende Rohstoffe e. V. (FNR) (2012) Anbau nachwachsender Rohstoffe 2012 auf 2,5 Millionen Hektar 2012. Available http://www.fnr.de/presse/pressemitteilungen/aktuelle-mitteilungen/aktuelle-nachricht/?tx_ttnews%5Btt_news%5D=5713&cHash=9c910b70ba59c6d464ae217d5ca8e0da (last accessed 27 May 2015) Müller J, Kayser M, Benke M (2011) Nitrate leaching following the cultivation of silage maize. http://www.uni-goettingen.de/de/nitrate-leaching-following-the-cultivation-of-silage-maize-/55661.html (last accessed 30 July 2015) Offermann FHG, Kreins P, von Ledebur O, Pelikan J, Salamon P, Sanders J (2010) Baseline 2009 to 2019: agri-economic projections for Germany. Landbauforschung - vTI Agriculture and Forestry Research 3:157 Schreiber H, Behrendt H, Constantinescu LT, Cvitanic I, Drumea D, Jabucar D, Juran S, Pataki B, Snishko S, Zessner M (2005) Nutrient emissions from diffuse and point sources into the River Danube and its main tributaries for the period of 1998-2000-results and problems. Water Sci Technol 51:3–4 Hirt U, Venohr M, Kreins P, Behrendt H (2008) Modelling nutrient emissions and the impact of nutrient reduction measures in the Weser river basin, Germany. Water SciTechnol 58:11. doi:10.2166/wst.2008.833 Love BJ, Nejadhashemi AP (2011) Water quality impact assessment of large-scale biofuel crops expansion in agricultural regions of Michigan. Biomass Bioenergy 35:2200. doi:10.1016/j.biombioe.2011.02.041 Wu M, Demissie Y, Yan E (2012) Simulated impact of future biofuel production on water quality and water cycle dynamics in the Upper Mississippi river basin. Biomass Bioenergy 41:44. doi:10.1016/j.biombioe.2012.01.030 Pimentel D, Patzek T (2005) Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower. Nat Resour Res 14:65. doi:10.1007/s11053-005-4679-8 Maidl F-X, Sticksel E, Valta R (1999) Investigations for improved slurry utilization in maize. 1. Report: utilization of nitrogen, available in slurry by maize (silage and grain) using different application techniques. Ger J Agronomy 3:9 Dominguez-Faus R, Powers SE, Burken JG, Alvarez PJ (2009) The water footprint of biofuels: a drink or drive issue? Environ Sci Technol 43:3005. doi:10.1021/es802162x Demissie Y, Yan E, Wu M (2012) Assessing regional hydrology and water quality implications of large-scale biofuel feedstock production in the upper Mississippi River Basin. Environ Sci Technol 46:9174. doi:10.1021/es300769k Valipour M (2014) Land use policy and agricultural water management of the previous half of century in Africa. Appl Water Sci. doi:10.1007/s13201-014-0199-1 Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment. Part I: model development. J Am Water Resour Assoc 34:73. doi:10.1111/j.1752-1688.1998.tb05961.x Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19:563. doi:10.1002/hyp.5611 Rode M, Thiel E, Franko U, Wenk G, Hesser F (2009) Impact of selected agricultural management options on the reduction of nitrogen loads in three representative meso scale catchments in central Germany. Sci Total Environ 407:3459. doi:10.1016/j.scitotenv.2009.01.053 Vaché KB, Eilers JM, Santelmann MV (2002) Water quality modeling of alternative agricultural scenarios in the U.S. corn belt. J Am Water Resour Assoc 38:773. doi:10.1111/j.1752-1688.2002.tb00996.x Powers SE, Ascough JC II, Nelson RG, Larocque GR (2011) Modeling water and soil quality environmental impacts associated with bioenergy crop production and biomass removal in the Midwest USA. Ecol Model 222:2430. doi:10.1016/j.ecolmodel.2011.02.024 Lautenbach S, Volk M, Strauch M, Whittaker G, Seppelt R (2013) Optimization-based trade-off analysis of biodiesel crop production for managing an agricultural catchment. Environ Model Softw 48:98. doi:10.1016/j.envsoft.2013.06.006 Sarkar S, Miller SA (2014) Water quality impacts of converting intensively-managed agricultural lands to switchgrass. Biomass Bioenergy 68:32. doi:10.1016/j.biombioe.2014.05.026 Lindström G, Pers C, Rosberg J, Strömqvist J, Arheimer B (2010) Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrol Res 41:295. doi:10.2166/nh.2010.007 Jiang S, Jomaa S, Büttner O, Günter M, Michael R (2015) Multi-site identification of a distributed hydrological nitrogen model using Bayesian uncertainty analysis. J Hydrol 529:3. doi:10.1016/j.jhydrol.2015.09.009 Strömqvist J, Arheimer B, Dahne J, Donnelly C, Lindström G (2012) Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale. Hydrol Sci J 57:229. doi:10.1080/02626667.2011.637497 Jiang S, Jomaa S, Rode M (2014) Modelling inorganic nitrogen leaching in nested mesoscale catchments in central Germany. Ecohydrology 7:1345. doi:10.1002/eco.1462 van Griensven A, Bauwens W (2003) Multiobjective autocalibration for semidistributed water quality models. Water Resour Res 39:1348. doi:10.1029/2003WR002284 Rode M, Suhr U, Wriedt G (2007) Multi-objective calibration of a river water quality model-Information content of calibration data. Ecol Model 204:129. doi:10.1016/j.ecolmodel.2006.12.037 Sivapalan M, Takeuchi K, Franks SW, Gupta VK, Karambiri H, Lakshmi V, Liang X, McDonnell JJ, Mendiondo EM, O’Connell PE, OKI T, Pomeroy JW, Schertzer D, Uhlenbrook S, Zehe E (2003) IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48:857. doi:10.1623/hysj.48.6.857.51421 Fink M (2004) Regionale Modellierung der Wasser- und Stickstoff dynamik als Entscheidungsunterstützung für die Reduktion des N-Eintrags am Beispiel des Trinkwassertalsperrensystems Weida- Zeulenroda, Thüringen. PhD thesis (in German) Jena Friedrich- Schiller-Universität, Chemisch- Geowissenschaftliche Fakultät, Jena, p 206 Hesser FB, Franko U, Rode M (2010) Spatially distributed lateral nitrate transport at the catchment scale. J Environ Qual 39:193. doi:10.2134/jeq2009.0031 Kralisch S, Fink M, Flügel WA, Beckstein C (2003) A neural network approach for the optimisation of watershed management. Environ Model Softw 18:815. doi:10.1016/S1364-8152(03)00081-1 Andersson L, Rosberg J, Pers BC, Olsson J, Arheimer B (2005) Estimating catchment nutrient flow with the HBV-NP model: sensitivity to input data. Ambio 34:521. doi:10.1579/0044-7447-34.7.521 Arheimer B, Löwgren M, Pers BC, Rosberg J (2005) Integrated catchment modeling for nutrient reduction: scenarios showing impacts, potential, and cost of measures. Ambio 34:513. doi:10.1579/0044-7447-34.7.513 Moriasi DN, Arnold JG, van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, American Society of Agricultural and Biological Engineers 50:885–900 Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282. doi:10.1016/0022-1694(70)90255-6 Littlewood IG (1995) Hydrological regimes, sampling strategies, and assessment of errors in mass load estimates for United Kingdom rivers. Environ Int 21:211. doi:10.1016/0160-4120(95)00011-9 Doherty J (2005) PEST: Model independent parameter estimation, user manual, 5th edn. Watermark Numerical Computing, Brisbane Laloy E, Vrugt JA (2012) High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resour Res 48. doi: 10.1029/2011WR010608 Kayser M, Benke M, Isselstein J (2011) Little fertilizer response but high N loss risk of maize on a productive organic-sandy soil. Agron Sustain Dev 31:709. doi:10.1007/s13593-011-0046-9 Naumann K, Oehmichen K, Zeymer M, Meisel K (2014) DBFZ Report Nr. 11: Monitoring Biokraftstoffsektor (2. Auflage), https://www.dbfz.de/fileadmin/user_upload/DBFZ_Reports/DBFZ_Report11A_web.pdf. Accessed 20 Apr 2016 Müller U, Raissi F (2002) Arbeitshilfe für bodenkundliche Stellungnahmen und Gutachten im Rahmen der Grundwassernutzung. Niedersächsisches Landesamt für Bodenforschung, Hannover