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14.05.2020

Hydrological Impacts of Climate Change in a Well-preserved Upland Watershed

verfasst von: Carolina Natel de Moura, Sílvio Luís Rafaeli Neto, Claudia Guimarães Camargo Campos, Eder Alexandre Schatz Sá

Erschienen in: Water Resources Management | Ausgabe 8/2020

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Abstract

Much attention has been focused on investigating the effects of climate change on hydrological processes on a regional scale. However, the landscape approach, especially the case of well-preserved upland watersheds needs to be further studied. The Representative Concentration Pathway (RCP) or emission scenarios RCP 4.5 and RCP 8.5 were used to project the impacts of the climate change on the hydrological components of a well-preserved upland landscape with temperate climate, using the Upper Canoas watershed as a case study. The future hydrological projection indicated an increase in the monthly rainfall as well as a shift in the rainiest months from winter to spring. This change resulted in an increase in water balance components. The maximum discharges, as well as the modal discharge (Q50), may increase in the future, and the minimums corresponding to Q95 and Q98 may reduce for both emission scenarios. The results showed that a well-preserved upland watershed in a sub-tropical region might be capable of maintaining water availability at level that is enough for human activities in the future, even with the reduction of minimum permit discharge, which is supported by the increase of maximum and medium monthly discharges and a stable flow-duration curve.

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Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014 CrossRef

Abiodun OO, Guan H, Post VE, Batelaan O (2018) Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales. Hydrol Earth Syst Sci 22(5):2775–2794. https://doi.org/10.5194/hess-22-2775-2018 CrossRef

Amraoui N, Sbai MA, Stollsteiner P (2019) Assessment of climate change impacts on water resources in the Somme river basin (France). Water Resour Manag 33(6):2073–2092. https://doi.org/10.1007/s11269-019-02230-x CrossRef

Antunes TA (2015) Modelagem hidrológica da bacia hidrográfica do Alto Canoas através do modelo SWAT. Dissertation (Master’s in Forest Engineering) – Universidade do Estado de Santa Catarina. Lages. 130 p

Arnell N (2011) Uncertainty in the relationship between climate forcing and hydrological response in UK catchments. Hydrol Earth Syst Sci 15(3):897–912. https://doi.org/10.5194/hess-15-897-2011 CrossRef

Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R et al (2012) SWAT: model use, calibration, and validation. Trans ASABE 55(4):1491–1508. https://doi.org/10.13031/2013.42256 CrossRef

Bartiko D, Chaffe PLB, Bonumá NB (2017) Nonstationarity in maximum annual daily streamflow series from Southern Brazil. Revista Brasileira de Recursos Hidricos 22. https://doi.org/10.1590/2318-0331.0217170054

Chien H, Yeh PJF, Knouft JH (2013) Modeling the potential impacts of climate change on streamflow in agricultural watersheds of the Midwestern United States. J Hydrol 491:73–88. https://doi.org/10.1016/j.jhydrol.2013.03.026 CrossRef

Chou SC, Lyra A, Mourão C, Dereczynski C, Pilotto I, Gomes J, Campos D (2014) Evaluation of the Eta simulations nested in three global climate models. Am J Clim Chang 3(5):438–454. https://doi.org/10.4236/ajcc.2014.35039

Detzel DHM, Fernandes CVS, MRM M (2016) Nonstationarity in determining flow-duration curves aiming water resources permits. RBRH 21(1):80–87. https://doi.org/10.21168/rbrh.v21n1.p80-87 CrossRef

EMBRAPA – Empresa Brasileira de Pesquisa Agropecuária (2004) Mapa levantamento de reconhecimento dos solos do Estado de Santa Catarina [1:250.000 scale]. Embrapa Solos, Rio de Janeiro

EPAGRI – Empresa de Pesquisa Agropecuária e Extensão (2014) Mapas digitais de Santa Catarina. EPAGRI, Florianópolis Available in: http://ciram.epagri.sc.gov. Accessed 15 Mar 2019

Gharari S, Hrachowitz M, Fenicia F, Savenije HHG (2011) Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment. Hydrol Earth Syst Sci 15(11):3275–3291. https://doi.org/10.5194/hess-15-3275-2011 CrossRef

Gomi T, Sidle RC, Richardson JS (2002) Understanding processes and downstream linkages of headwater systems: headwaters differ from downstream reaches by their close coupling to hillslope processes, more temporal and spatial variation, and their need for different means of protection from land use. BioScience 52(10):905–916. https://doi.org/10.1641/0006-3568(2002)052[0905:UPADLO]2.0.CO;2 CrossRef

Grimm AM (2011) Interannual climate variability in South America: impacts on seasonal precipitation, extreme events, and possible effects of climate change. Stoch Environ Res Risk Assess 25(4):537–554. https://doi.org/10.1007/s00477-010-0420-1 CrossRef

Hakala K, Addor N, Seibert J (2018) Hydrological modeling to evaluate climate model simulations and their bias correction. J Hydrometeorol 19(8):1321–1337. https://doi.org/10.1175/JHM-D-17-0189.1 CrossRef

Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for monthly water quality data. Water Resour Res 18(1):107–121. https://doi.org/10.1029/WR018i001p00107 CrossRef

IPCC – Intergovernmental Panel on Climate Change (2013) Climate change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the Intergovernmental Panel on Climate Change (Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press, Cambridge/New York, 1535 p. http://www.ipcc.ch/report/ar5/wg1/. Accessed 26 Jan 2018

Karlsen RH, Grabs T, Bishop K, Buffam I, Laudon H, Seibert J (2016) Landscape controls on spatiotemporal discharge variability in a boreal catchment. Water Resour Res 52(8):6541–6556. https://doi.org/10.1002/2016WR019186 CrossRef

Kay AL, Davies HN, Bell VA, Jones RG (2009) Comparison of uncertainty sources for climate change impacts: flood frequency in England. Clim Chang 92(1–2):41–63. https://doi.org/10.1007/s10584-008-9471-4 CrossRef

Lenderink G, Buishand A, Deursen WV (2007) Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol Earth Syst Sci 11(3):1145–1159. https://doi.org/10.5194/hess-11-1145-2007 CrossRef

Li F, Zhang G, Xu YJ (2016) Assessing climate change impacts on water resources in the Songhua River basin. Water 8(10):420. https://doi.org/10.3390/w8100420 CrossRef

Li X, Sha J, Zhao Y, Wang ZL (2019) Estimating the responses of hydrological and sedimental processes to future climate change in watersheds with different landscapes in the Yellow River Basin, China. Int J Environ Res Public Health 16(20):4054CrossRef

Marengo JA, Camargo CC (2008) Surface air temperature trends in Southern Brazil for 1960–2002. Int J Climatol 28(7):893–904. https://doi.org/10.1002/joc.1584 CrossRef

Masood M, Yeh PF, Hanasaki N, Takeuchi K (2015) Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin. Hydrol Earth Syst Sci 19(2):747–770. https://doi.org/10.5194/hess-19-747-2015 CrossRef

Mauser W, Bach H (2009) PROMET–Large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds. J Hydrol 376(3–4):362–377. https://doi.org/10.1016/j.jhydrol.2009.07.046 CrossRef

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. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153 CrossRef

Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovix N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747. https://doi.org/10.1038/nature08823 CrossRef

Neto SLR, Sá EAS, Debastiani AB, Padilha VL, Antunes TA (2019) Efficacy of rainfall-runoff models in loose coupling spacial decision support systems modelbase. Water Resour Manag 33(3):889–904. https://doi.org/10.1007/s11269-018-2086-2 CrossRef

Nóbrega MT, Collischonn W, Tucci CEM, Paz AR (2011) Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil. Hydrol Earth Syst Sci 15(2):585–595. https://doi.org/10.5194/hess-15-585-2011 CrossRef

Papadaki C, Soulis K, Muñoz-Mas R, Martinez-Capel F, Zogaris S, Ntoanidis L, Dimitriou E (2016) Potential impacts of climate change on flow regime and fish habitat in mountain rivers of the south-western Balkans. Sci Total Environ 540:418–428. https://doi.org/10.1016/j.scitotenv.2015.06.134 CrossRef

Prudhomme C, Davies H (2009) Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: Future climate. Clim Chang 93(1–2):197–222. https://doi.org/10.1007/s10584-008-9461-6 CrossRef

Rostamian R, Jaleh A, Afyuni M, Mousavi SF, Heidarpour M, Jalalian A, Abbaspour KC (2008) Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrol Sci J 53(5):977–988. https://doi.org/10.1623/hysj.53.5.977 CrossRef

Sá EAS, Moura CN, Padilha VL, Campos CGC (2018) Trends in daily precipitation in highlands region of Santa Catarina, Southern Brazil. Revista Ambiente & Água 13(1). https://doi.org/10.4136/ambi-agua.21

Sampaio G, da Silva Dias PL (2014) Evolução dos Modelos Climáticos e de Previsão de Tempo e Clima. Revista USP (103):41–54

Shamir E, Megdal SB, Carrillo C, Castro CL, Chang HI, Chief K, Corkhill FE, Eden S, Georgakakos KP, Nelson KM, Prietto J (2015) Climate change and water resources management in the Upper Santa Cruz River, Arizona. J Hydrol 521:18–33. https://doi.org/10.1016/j.jhydrol.2014.11.062 CrossRef

SOSMA – SOS Mata Atlântica. Atlantic Forest. Accessed 6 March 2019

Teutschbein C, Grabs T, Laudon H, Karlsen RH, Bishop K (2018) Simulating streamflow in ungauged basins under a changing climate: the importance of landscape characteristics. J Hydrol 561:160–178. https://doi.org/10.1016/j.jhydrol.2018.03.060 CrossRef

Uniyal B, Jha MK, Verma AK (2015) Assessing climate change impact on water balance components of a river basin using SWAT model. Water Resour Manag 29(13):4767–4785. https://doi.org/10.1007/s11269-015-1089-5 CrossRef

Viola MR, De Mello CR, Chou SC, Yanagi SN, Gomes JL (2015) Assessing climate change impacts on Upper Grande River basin hydrology, southeast Brazil. Int J Climatol 35(6):1054–1068. https://doi.org/10.1002/joc.4038 CrossRef

Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resour Res 42(2). https://doi.org/10.1029/2005WR004065

Winter TC (2001) The concept of hydrologic landscapes. J Am Water Resour Assoc 37(2):335–349. https://doi.org/10.1111/j.1752-1688.2001.tb00973.x CrossRef

Xu CY (2000) Modelling the effects of climate change on water resources in central Sweden. Water Resour Manag 14(3):177–118. https://doi.org/10.1007/BF02918679 CrossRef

Xu ZX, Chen YN, Li JY (2004) Impact of climate change on water resources in the Tarim River basin. Water Resour Manag 18(5):439–458. https://doi.org/10.1023/B:WARM.0000049142.95583.98 CrossRef

Zhang Y, Su F, Hao Z, Xu C, Yu Z, Wang L, Tong K (2015) Impact of projected climate change on the hydrology in the headwaters of the Yellow River basin. Hydrol Process 29(20):4379–4397. https://doi.org/10.1002/hyp.10497 CrossRef

Titel: Hydrological Impacts of Climate Change in a Well-preserved Upland Watershed
verfasst von: Carolina Natel de Moura
Sílvio Luís Rafaeli Neto
Claudia Guimarães Camargo Campos
Eder Alexandre Schatz Sá
Publikationsdatum: 14.05.2020
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 8/2020
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-019-02450-1

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