Abstract
Land use and land cover have a direct influence on the hydrological regime. Urbanization and deforestation are accountable for reducing the water infiltration and consequent runoff’s increase. These modifications lead to high peak flows responsible for catastrophic events. Thus, this work analyzed the impact of land use and land cover’s change on the hydrological regime in a Brazilian southeastern urbanized watershed; considering current and future scenarios. The hydrological model HEC–HMS was used to estimate stream flow and aided in the search for suitable solutions. Using the Land Change Modeler tool made possible to design 2030’s land use and land cover in Grande river watershed. Hydrological simulations were carried out for 2030 using HEC–HMS. Some of the forested areas were replaced by agriculture and pasture. The agricultural areas were the ones that showed greater expansion. Such changes contributed to a mean increase of 52% in the stream flow hydrograph when compared to the watershed’s current scenario. After the use of best management practices, the stream flow hydrograph had a mean reduction of 44%. The structures considered were bio-retention cells, rain gardens, infiltration trenches, and permeable pavements for urban areas. For rural areas, the practices suggested were level terraces, no-till farming, and contour farming. All structures were applied in a distributed manner throughout the watershed. Therefore, it is concluded that land use and land cover’s change in Grande river watershed significantly influence the stream flow regime, and the use of best management practices proved efficient in reducing floods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adepoju MO, Millington AC, Tansey KT (2006) Land use/land cover change detection in metropolitan lagos (Nigeria): 1984–2002. ASPRS 2006 Annual Conference 7 Reno, Nevada May 2006
Alvares CA, Stape JL, Sentelhas PC et al (2013) Köppen’s climate classification map for Brazil. Meteorol Zeitschrift 22:711–728. https://doi.org/10.1127/0941-2948/2013/0507
ANA (2018) HIDROWEB—Sistema de Informações Hidrológicas. http://www.snirh.gov.br/hidroweb/publico/medicoes_historicas_abas.jsf#
Assis Dias MC, de Saito SM, dos Alvalá RC S et al (2018) Estimation of exposed population to landslides and floods risk areas in Brazil, on an intra-urban scale. Int J Disaster Risk Reduct 31:449–459. https://doi.org/10.1016/J.IJDRR.2018.06.002
Aydın M, Şen SG, Celik S (2018) Throughfall, stemflow, and interception characteristics of coniferous forest ecosystems in the western black sea region of Turkey (Daday example). Environ Monit Assess 190:316. https://doi.org/10.1007/s10661-018-6657-8
Azam M, Kim HS, Maeng SJ (2017) Development of flood alert application in Mushim stream watershed Korea. Int J Disaster Risk Reduct 21:11–26. https://doi.org/10.1016/J.IJDRR.2016.11.008
Baena LGN, Da Silva DD, Pruski FF, Calijuri ML (2004) Regionalização de vazões com base em modelo digital de elevação para a bacia do rio Paraíba do Sul. Eng Agrícola Jaboticabal 24:612–624
Bell CD, Mcmillan SK, Clinton SM, Jefferson AJ (2016) Hydrologic response to stormwater control measures in urban watersheds. J Hydrol 541:1488–1500. https://doi.org/10.1016/j.jhydrol.2016.08.049
Bhaskar AS, Hogan DM, Archfield SA (2016) Urban base flow with low impact development. Hydrol Process 30:3156–3171. https://doi.org/10.1002/hyp.10808
BRASIL (2012) Lei no 12.651/2012, de 25 de maio da Casa Civil. BRASIL. http://www.planalto.gov.br/ccivil_03/_Ato2011-2014/2012/Lei/L12651.htm
Calijuri ML, de Castro J S, Costa LS et al (2015) Impact of land use/land cover changes on water quality and hydrological behavior of an agricultural subwatershed. Environ Earth Sci 74:5373–5382. https://doi.org/10.1007/s12665-015-4550-0
Candela L, Tamoh K, Olivares G, Gómez M (2016) Climate and land use changes on streamflow and subsurface recharge in the Fluvià Basin, Spain. Water (Switzerland) 8:1–16. https://doi.org/10.3390/w8060228
Chiverton A, Hannaford J, Holman I et al (2015) Which catchment characteristics control the temporal dependence structure of daily river flows? Hydrol Process 29:1353–1369. https://doi.org/10.1002/hyp.10252
Correia EFG, Ribeiro GP, Baptista AC (2015) Modelagem hidrológica da bacia hidrográfica doo rio Bengalas, Nova Friburgo, RJ, utilizando o potencial de geotecnologias na definição de áreas de risco à inundação. Rev Bras Cartogr 67:1183–1202
Dagnew DC, Guzman CD, Zegeye AD et al (2015) Impact of conservation practices on runoff and soil loss in the sub-humid Ethiopian highlands: the Debre Mawi watershed. J Hydrol Hydromech 63:210–219. https://doi.org/10.1515/johh-2015-0021
Damodaram C, Giacomoni MH, Prakash Khedun C et al (2010) Simulation of combined best management practices and low impact development for sustainable stormwater management. J Am Water Resour Assoc 46:907–918. https://doi.org/10.1111/j.1752-1688.2010.00462.x
de Moraes TC, dos Santos VJ, Calijuri ML, Torres FTP (2018) Effects on runoff caused by changes in land cover in a Brazilian southeast basin: evaluation by HEC-HMS and HEC-GEOHMS. Environ Earth Sci 77:250. https://doi.org/10.1007/s12665-018-7430-6
Deng Z, Zhang X, Li D, Pan G (2015) Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin. Environ Earth Sci 73:1119–1132. https://doi.org/10.1007/s12665-014-3465-5
Deo RC, Şahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess 188:90. https://doi.org/10.1007/s10661-016-5094-9
Dwarakish GS, Ganasri BP (2015) Impact of land use change on hydrological systems: a review of current modeling approaches. Cogent Geosci 1:1–18. https://doi.org/10.1080/23312041.2015.1115691
EMBRAPA (2001) Mapa de Solos do Brasil. Escala 1:5.000.000. In: IBGE. https://www.dpi.inpe.br/Ambdata/mapa_solos.php. Accessed 26 Oct 2019
Fletcher TD, Andrieu H, Hamel P (2013) Understanding, management and modelling of urban hydrology and its consequences for receiving waters: a state of the art. Adv Water Resour 51:261–279. https://doi.org/10.1016/j.advwatres.2012.09.001
Garg V, Aggarwal SP, Gupta PK et al (2017) Assessment of land use land cover change impact on hydrological regime of a basin. Environ Earth Sci 76:635. https://doi.org/10.1007/s12665-017-6976-z
Gericke OJ, Smithers JC (2014) Review of methods used to estimate catchment response time for the purpose of peak discharge estimation. Sci J 59:1935–1971. https://doi.org/10.1080/02626667.2013.866712
Gessesse B, Bewket W, Bräuning A (2014) Model-based characterization and monitoring of runoff and soil erosion in response to land use/land cover changes in the Modjo watershed, Ethiopia. Land Degrad Dev 724:711–724
Governo do Rio de Janeiro (2018) Projetos e Programas Setoriais. In: Agric. pecuária, pesca e Abast. http://www.rj.gov.br/web/seapec/listaconteudo?generica&acaomenu=menufunc(%27ProjetoseProgramas%27);&forward=setoriais&label=setoriais&search-type=setoriais&secretaria=/seapec. Accessed 19 Nov 2018
IBGE (2010) Censo demográfico de 2010. https://censo2010.ibge.gov.br/resultados.html
IBGE (2018) Bases cartográficas contínuas—Estados. In: Bases Cart. contínuas—bc 25—RJ. https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc25/rj/versao2018. Accessed 5 Feb 2019
Idaho (2005) Catalog of stormwater best management practices for Idaho cities and counties. Boise, ID, USA
Jahanifar K, Amirnejad H, Mojaverian M, Azadi H (2018) Land change detection and effective factors on forest land use changes: application of land change modeler and multiple linear regression. J Appl Sci Environ Manag 22:1269–1275
Jain RK, Jain K, Ali SR (2017) Modeling urban land cover growth dynamics based on land change modeler (LCM) using remote sensing: a case study of Gurgaon, India. Adv Comput Sci Technol 10:2947–2961
Jian S, Zhao C, Fang S, Yu K (2015) Effects of different vegetation restoration on soil water storage and water balance in the Chinese Loess Plateau. Agric For Meteorol 206:85–96. https://doi.org/10.1016/J.AGRFORMET.2015.03.009
Jiang XJ, Liu W, Wu J et al (2017) Land degradation controlled and mitigated by rubber-based agroforestry systems through optimizing soil physical conditions and water supply mechanisms: a case study in Xishuangbanna, China. Land Degrad Dev 28:2277–2289. https://doi.org/10.1002/ldr.2757
Kavian A, Golshan M, Abdollahi Z (2017) Flow discharge simulation based on land use change predictions. Environ Earth Sci 76:588. https://doi.org/10.1007/s12665-017-6906-0
Kim N, Shin M-J, Kim NW, Shin M-J (2018) Estimation of peak flow in ungauged catchments using the relationship between runoff coefficient and curve number. Water 10:1669. https://doi.org/10.3390/w10111669
Kong F, Ban Y, Yin H et al (2017) Modeling stormwater management at the city district level in response to changes in land use and low impact development. Environ Model Softw 95:132–142. https://doi.org/10.1016/j.envsoft.2017.06.021
Lagadec L-R, Patrice P, Braud I et al (2016) Description and evaluation of a surface runoff susceptibility mapping method. J Hydrol 541:495–509. https://doi.org/10.1016/j.jhydrol.2016.05.049
Laouacheria F, Mansouri R (2015) Comparison of WBNM and HEC-HMS for runoff hydrograph prediction in a small urban catchment. Water Resour Manag 29:2485–2501. https://doi.org/10.1007/s11269-015-0953-7
Lee JG, Selvakumar A, Alvi K et al (2012) A watershed-scale design optimization model for stormwater best management practices. Environ Model Softw 37:6–18. https://doi.org/10.1016/j.envsoft.2012.04.011
Leite PAM, de Souza ES, dos Santos ES et al (2018) The influence of forest regrowth on soil hydraulic properties and erosion in a semiarid region of Brazil. Ecohydrology 11:e1910. https://doi.org/10.1002/eco.1910
Li C, Fletcher TD, Duncan HP, Burns MJ (2017) Can stormwater control measures restore altered urban flow regimes at the catchment scale? J Hydrol 549:631–653. https://doi.org/10.1016/J.JHYDROL.2017.03.037
Liu W, Chen W, Peng C (2015) Influences of setting sizes and combination of green infrastructures on community’s stormwater runoff reduction. Ecol Model 318:236–244. https://doi.org/10.1016/j.ecolmodel.2014.11.007
Liu Y, Engel BA, Flanagan DC et al (2017) A review on effectiveness of best management practices in improving hydrology and water quality: needs and opportunities. Sci Total Environ 601–602:580–593. https://doi.org/10.1016/j.scitotenv.2017.05.212
Livesley SJ, Baudinette B, Glover D (2014) Rainfall interception and stem flow by eucalypt street trees—The impacts of canopy density and bark type. Urban For Urban Green 13:192–197. https://doi.org/10.1016/J.UFUG.2013.09.001
Loperfido JV, Noe GB, Jarnagin ST, Hogan DM (2014) Effects of distributed and centralized stormwater best management practices and land cover on urban stream hydrology at the catchment scale. J Hydrol 519:2584–2595. https://doi.org/10.1016/J.JHYDROL.2014.07.007
Maithani S, Begum A, Kumar P, Kumar AS (2018) Simulation of peri-urban growth dynamics using weights of evidence approach. Geocarto Int 33:957–976. https://doi.org/10.1080/10106049.2017.1319425
MAPBIOMAS (2018) Projeto de Mapeamento Anual da Cobertura e Uso do Solo no Brasil. http://mapbiomas.org/. Accessed 2 Apr 2019
Mas JF, Kolb M, Paegelow M et al (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111. https://doi.org/10.1016/j.envsoft.2013.09.010
Miller JD, Kim H, Kjeldsen TR et al (2014) Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. J Hydrol 515:59–70. https://doi.org/10.1016/j.jhydrol.2014.04.011
Minnesota (2008) The Minnesota Stormwater manual, v. 2, Minnesota Pollution Control Agency, St. Paul, MN. ftp://ftp.odot.state.or.us/techserv/geo-environmental/Stormwater%20Team/Reference_Documents/Manuals/MN_Stormwater_Manual1.pdf
Mishra V, Rai P, Mohan K (2014) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Jovan Cvijic SASA 64:111–127. https://doi.org/10.2298/IJGI1401111M
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for sistematic quantification of accuracy in watershed simulations. Am Soc Agric Biol Eng 50:885–900
Oliveira PTS, Wendland E, Nearing MA et al (2015) The water balance components of undisturbed tropical woodlands in the Brazilian cerrado. Hydrol Earth Syst Sci 19:2899–2910. https://doi.org/10.5194/hess-19-2899-2015
Prosdocimi M, Tarolli P, Cerdà A (2016) Mulching practices for reducing soil water erosion: a review. Earth Sci Rev J 161:191–203. https://doi.org/10.1016/j.earscirev.2016.08.006
Pruski FF (ed) (2009) Conservação de solo e água: práticas mecânicas para o controle da erosão hídrica, 2nd edn. Universidade Federal de Viçosa, Viçosa
Pruski FF, Pruski FF, dos Santos Brandão V, da Silva DD (2003) Escoamento superficial, 2nd edn. UFV, Columbia
Rambaldi DM, Magnani A, Ilha A et al (2003) A Reserva da biosfera da Mata Atlântica no estado do Rio de Janeiro. Série Estados e Regiões da RBMA Cad da Reserv da Biosf da Mata Atlântica, February, Rio de Janeiro
Rezaei Zaman M, Morid S, Delavar M (2016) Evaluating climate adaptation strategies on agricultural production in the Siminehrud catchment and inflow into Lake Urmia, Iran using SWAT within an OECD framework. Agric Syst 147:98–110. https://doi.org/10.1016/J.AGSY.2016.06.001
Rosa DJ, Clausen JC, Dietz ME (2015) Calibration and verification of SWMM for low impact development. J Am Water Resour Assoc 51:746–757. https://doi.org/10.1111/jawr.12272
Salvador M (2014) Identificação e avaliação de eventos extremos na bacia hidrográfica do rio Piranga. Universidade Federal de Viçosa, Viçosa
Singh P, Gupta A, Singh M (2014) Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt J Remote Sens Space Sci 17:111–121. https://doi.org/10.1016/j.ejrs.2014.09.003
Sun X, Onda Y, Kato H et al (2015) Effect of strip thinning on rainfall interception in a Japanese cypress plantation. J Hydrol 525:607–618. https://doi.org/10.1016/J.JHYDROL.2015.04.023
Tavanti DR, Barbassa AP (2012) Análise dos Desenvolvimentos Urbanos de Baixo Impacto e Convencional. Rev Bras Recur Hídricos 17:17–28. https://doi.org/10.21168/rbrh.v17n4.p17-28
Tucci CEM (2012) Hidrologia: ciência e aplicação, 4th edn. UFRGS/ABRH, Porto Alegre
USACE (2000) Hydrologic modeling system HEC-HMS technical reference manual CPD-74B. https://www.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Technical%20Reference%20Manual_(CPD-74B).pdf
Vermont (2002) The vermont stormwater management manual volume II-technical guidance. Vermont Agency of Natural Resources, Vermont
Virginia (2013) Chapter 10—Uniform Stormwater BMP Sizing Criteria. Virginia stormwater management handbook. Virginia Departament of Environmental Quality, Virginia, p 19
Wang Y, Montas HJ, Brubaker KL et al (2017) A diagnostic decision support system for bmp selection in small urban watershed. Water Resour Manag 31:1649–1664. https://doi.org/10.1007/s11269-017-1605-x
Wei X, Liu W, Zhou P (2013) Quantifying the relative contributions of forest change and climatic variability to hydrology in large watersheds: a critical review of research methods. Water (Switzerland) 5:728–746. https://doi.org/10.3390/w5020728
Wijesiri B, Deilami K, Goonetilleke A (2018) Evaluating the relationship between temporal changes in land use and resulting water quality. Environ Pollut 234:480–486. https://doi.org/10.1016/J.ENVPOL.2017.11.096
Worland SC, Farmer WH, Kiang JE (2018) Improving predictions of hydrological low-flow indices in ungaged basins using machine learning. Environ Model Softw 101:169–182. https://doi.org/10.1016/J.ENVSOFT.2017.12.021
Yao L, Wei W, Chen L (2016) How does imperviousness impact the urban rainfall-runoff process under various storm cases? Ecol Indic 60:893–905. https://doi.org/10.1016/j.ecolind.2015.08.041
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), Finance Code 001. Also by the CNPq (Conselho Nacional de Desenvolvimento Ciêntífico e Tecnológico).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rodrigues, A.L.M., Reis, G.B., dos Santos, M.T. et al. Influence of land use and land cover’s change on the hydrological regime at a Brazilian southeast urbanized watershed. Environ Earth Sci 78, 595 (2019). https://doi.org/10.1007/s12665-019-8601-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-019-8601-9