Skip to main content
SpringerLink
Log in

Influence of forest proportion and configuration at the watershed and riparian zone scales on sediment yield: a simulation experiment

  • Research Article
  • Published:
Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Understanding how landscape pattern influences water quality is crucial for landscape planning and management aimed at providing hydrological services. Simulation modeling is a useful tool for exploring the effects of landscape heterogeneity on ecological processes.

Objectives

Evaluate how forest proportion and configuration at both the watershed and riparian zone scales influence sediment yield.

Methods

We selected a sub-basin in São Paulo state, Brazil, and created four scenarios with systematic and random allocation of forest cover in watershed replicates. For each landscape, sediment yield was estimated using a sediment delivery ratio model, while the pattern was quantified using landscape metrics. We compared the outcomes of different scenarios and assessed the influence of forest proportion and configuration on annual sediment loads.

Results

The proportion of forest cover in the watershed explained up to 55% and spatial autocorrelation explained up to 7% of the variation in sediment yield in random landscapes. Forest proportion in both the watershed and riparian zone was the stronger predictor of water quality, and the location of forest patches alongside the stream was the most important configuration aspect. In random landscapes without continuous forest riparian buffers and with intermediate amounts of forest cover, disaggregated forest patches were associated with better water quality.

Conclusions

Both watershed and riparian zone scales and composition and configuration should be considered in landscape planning and management to improve water quality. We recommend as priority management actions implementing a continuous forest riparian buffer, followed by increasing forest amount in areas close to the stream.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: MapBiomas (2021)

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The raw datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. The R and Python codes used to automate data generation and analysis are available in the GitHub repository (https://github.com/ligiambc/campanhao-and-ranieri-2023).

Notes

  1. We used data from 2006, 2008, 2009–2012, 2015, and 2017–2019.

  2. This constant is used for unit conversion and is proportional to the average number of seconds in a year, which is 31,557,600.

  3. Some metrics require a minimum number of patches in the landscape for their computation, which was not met for all replicates.

  4. kb values reported by these studies ranged from 1.6 to 3.5.

  5. Estimates from other studies using InVEST SDR ranged from 3.6 to 26% for sub-basins in Ethiopia (drainage areas from 48 to 192 km2 and 1426 km2) (Aneseyee et al. 2020; Degife et al. 2021), 7.7% for a small basin (75 km2) in Brazil (Rosário et al. 2019), and 6.1 to 10.5% using different parameterizations for a small basin (12 km2) in Brazil (Saad et al. 2018).

References

  • Agência Nacional de Águas (ANA) (2021) Portal Hidroweb v3.1.1. https://www.snirh.gov.br/hidroweb/mapa. Accessed 2 Apr 2021

  • Alewell C, Borrelli P, Meusburger K, Panagos P (2019) Using the USLE: chances, challenges and limitations of soil erosion modelling. Int Soil Water Conserv Res 7:203–225.

    Article  Google Scholar 

  • Alvares CA, Stape JL, Sentelhas PC et al (2013) Köppen’s climate classification map for Brazil. Meteorolog Z 22:711–728.

    Article  Google Scholar 

  • Amestoy PR, Azzalini A, Badics T, et al (2020) igraph: Network Analysis and Visualization. R package version 1.2.6. https://CRAN.R-project.org/package=igraph.

  • Aneseyee AB, Elias E, Soromessa T, Feyisa GL (2020) Land use/land cover change effect on soil erosion and sediment delivery in the Winike watershed, Omo Gibe Basin Ethiopia. Sci Total Environ 728:138776.

    Article  CAS  PubMed  Google Scholar 

  • Benavidez R, Jackson B, Maxwell D, Norton K (2018) A review of the (Revised) universal soil loss equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrol Earth Syst Sci 22:6059–6086.

    Article  Google Scholar 

  • Bertoni J, Lombardi Neto F (1990) Conservação do solo. Ícone, São Paulo.

  • Bivand R, Keitt T, Rowlingson B, et al (2021) rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5–23. https://CRAN.R-project.org/package=rgdal

  • Borselli L, Cassi P, Torri D (2008) Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment. CATENA 75:268–277.

    Article  Google Scholar 

  • Brasil (2012) Lei n 12.651, de 25 de maio de 2012. https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12651.htm

  • Instituto Brasileiro de Geografia e Estatística (IBGE) (2017) Censo Agropecuário 2017. https://sidra.ibge.gov.br/. Accessed 24 Mar 2021

  • Brauman KA (2015) Hydrologic ecosystem services: linking ecohydrologic processes to human well-being in water research and watershed management. Wires Water 2:345–358.

    Article  Google Scholar 

  • Brauman KA, Daily GC, Duarte TK, Mooney HA (2007) The nature and value of ecosystem services: an overview highlighting hydrologic services. Annu Rev Environ Resour 32:67–98.

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32.

    Article  Google Scholar 

  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees. Taylor & Francis

    Google Scholar 

  • Centro Integrado de Informações Agrometeorológicas (CIIAGRO) (2021) CIIAGRO Online. http://www.ciiagro.sp.gov.br/ciiagroonline/. Accessed 21 Jun 2021

  • Chang H, Psaris M (2013) Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA. Sci Total Environ 461–462:587–600.

    Article  PubMed  Google Scholar 

  • Chaplin-Kramer R, Hamel P, Sharp R et al (2016) Landscape configuration is the primary driver of impacts on water quality associated with agricultural expansion. Environ Res Lett 11:074012.

    Article  Google Scholar 

  • Clément F, Ruiz J, Rodríguez MA et al (2017) Landscape diversity and forest edge density regulate stream water quality in agricultural catchments. Ecol Indic 72:627–639.

    Article  Google Scholar 

  • ClimateSERV (2021) Data and tools for sustainable development. https://climateserv.servirglobal.net/. Accessed 25 Mar 2021

  • Cole LJ, Stockan J, Helliwell R (2020) Managing riparian buffer strips to optimise ecosystem services: a review. Agric Ecosyst Environ 296:106891.

    Article  CAS  Google Scholar 

  • Companhia Ambiental do estado de São Paulo (CETESB) (2021) Sistema Infoáguas. https://sistemainfoaguas.cetesb.sp.gov.br/Home. Accessed 2 Apr 2021

  • Cutler A, Cutler DR, Stevens JR (2012) Random Forests. In: Zhang C, Ma Y (eds) Ensemble Machine Learning: Methods and Applications. Springer, Boston, pp 157–175

    Chapter  Google Scholar 

  • Daily GC, Polasky S, Goldstein J et al (2009) Ecosystem services in decision making: time to deliver. Front Ecol Environ 7:21–28.

    Article  Google Scholar 

  • da Silva FDGB, Minotti RT, Lombardi Neto F et al (2010) Previsão da perda de solo na Fazenda Canchim—SP (EMBRAPA) utilizando geoprocessamento e o USLE 2D. Engenharia Sanitária e Ambiental 15:141–148.

    Article  Google Scholar 

  • De Groot RS, Alkemade R, Braat L et al (2010) Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol Complex 7:260–272.

    Article  Google Scholar 

  • De Mello K, Valente RA, Randhir TO et al (2018) Effects of land use and land cover on water quality of low-order streams in Southeastern Brazil: watershed versus riparian zone. CATENA 167:130–138.

    Article  Google Scholar 

  • De Mello K, Taniwaki RH, de Paula FR et al (2020) Multiscale land use impacts on water quality: assessment, planning, and future perspectives in Brazil. J Environ Manag 270:110879.

    Article  CAS  Google Scholar 

  • De Mello K, Valente RA, Ribeiro MP, Randhir T (2022) Effects of forest cover pattern on water quality of low-order streams in an agricultural landscape in the Pirapora river basin. Brazil Environ Monit Assess 194:189.

    Article  PubMed  Google Scholar 

  • De Oliveira LM, Maillard P, de Andrade Pinto EJ (2017) Application of a land cover pollution index to model non-point pollution sources in a Brazilian watershed. CATENA 150:124–132.

    Article  Google Scholar 

  • De Vente J, Poesen J, Verstraeten G et al (2013) Predicting soil erosion and sediment yield at regional scales: where do we stand? Earth-Sci Rev 127:16–29.

    Article  Google Scholar 

  • Degife A, Worku H, Gizaw S (2021) Environmental implications of soil erosion and sediment yield in Lake Hawassa watershed, south-central Ethiopia. Environ Syst Res 10:28.

    Article  Google Scholar 

  • Departamento de Águas e Energia Elétrica (DAEE) (2021) Banco de Dados Hidrológicos. http://www.hidrologia.daee.sp.gov.br/. Accessed 2 Apr 2021

  • Desmet PJJ, Govers G (1996) A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51:427–433

    Google Scholar 

  • di Raimo LADL, Amorim RSS, Torres GN et al (2019) Variabilidade espacial da erodibilidade no estado de Mato Grosso, Brasil. Revista de Ciências Agrárias 42:55–67.

    Google Scholar 

  • Ding J, Jiang Y, Liu Q et al (2016) 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 551–552:205–216.

    Article  PubMed  Google Scholar 

  • Dormann CF, Elith J, Bacher S et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27–46.

    Article  Google Scholar 

  • dos Santos HG, Jacomine PKT, dos Anjos LHC et al (2018) Sistema Brasileiro de Classificação de Solos, 5th edn. Embrapa, Brasília

    Google Scholar 

  • Dosskey MG, Vidon P, Gurwick NP et al (2010) The role of riparian vegetation in protecting and improving chemical water quality in streams1. JAWRA J Am Water Resour Assoc 46:261–277.

    Article  CAS  Google Scholar 

  • Fournier A, Fussell D, Carpenter L (1982) Computer rendering of stochastic models. Commun ACM 25:371–384.

    Article  Google Scholar 

  • Gergel SE (2005) Spatial and non-spatial factors: when do they affect landscape indicators of watershed loading? Landsc Ecol 20:177–189.

    Article  Google Scholar 

  • Guidotti V, de Ferraz SF, B, Pinto LFG, et al (2020) Changes in Brazil’s forest code can erode the potential of riparian buffers to supply watershed services. Land Use Policy 94:104511.

    Article  Google Scholar 

  • Gumiere SJ, Le Bissonnais Y, Raclot D, Cheviron B (2011) Vegetated filter effects on sedimentological connectivity of agricultural catchments in erosion modelling: a review. Earth Surf Process Landf 36:3–19.

    Article  Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4:135–143.

    Article  Google Scholar 

  • Haddad NM, Brudvig LA, Clobert J et al (2015) Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv 1:e1500052.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hamel P, Chaplin-Kramer R, Sim S, Mueller C (2015) A new approach to modeling the sediment retention service (InVEST 3.0): case study of the cape fear catchment, North Carolina, USA. Sci Total Environ 524–525:166–177.

    Article  PubMed  Google Scholar 

  • Hamel P, Bremer LL, Ponette-González AG et al (2020) The value of hydrologic information for watershed management programs: the case of Camboriú Brazil. Sci Total Environ 705:135871.

    Article  CAS  PubMed  Google Scholar 

  • Han H, Yang J, Ma G et al (2020) Effects of land-use and climate change on sediment and nutrient retention in Guizhou. China Ecosyst Health Sustain 6:1810592.

    Article  Google Scholar 

  • Harmel RD, Smith PK, Migliaccio KW et al (2014) Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations. Environ Model Softw 57:40–51.

    Article  Google Scholar 

  • Hijmans RJ, van Etten J, Summer M, et al (2021) raster: Geographic Data Analysis and Modeling. R package version 3.4–5. https://CRAN.R-project.org/package=raster

  • InVEST (2021) Tutorial: batch processing on Windows. https://invest.readthedocs.io/en/latest/scripting.html. Accessed 15 Feb 2021

  • Jiang Y, Xie Z, Zhang H et al (2017) Effects of land use types on dissolved trace metal concentrations in the Le’an River Basin. China Environ Monit Assess 189:633.

    Article  PubMed  Google Scholar 

  • Keeler BL, Polasky S, Brauman KA et al (2012) Linking water quality and well-being for improved assessment and valuation of ecosystem services. Proc Natl Acad Sci 109:18619–18624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lake PS (2007) Flowing Waters in the Landscape. In: Lindenmayer DB, Hobbs RJ (eds) Managing and Designing Landscapes for Conservation. Blackwell Publishing Ltd, Oxford, pp 445–457

    Chapter  Google Scholar 

  • Laurance WF, Camargo JLC, Luizão RCC et al (2011) The fate of Amazonian forest fragments: a 32-year investigation. Biol Conserv 144:56–67.

    Article  Google Scholar 

  • Lee S-W, Hwang S-J, Lee S-B et al (2009) Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics. Landsc Urban Plan 92:80–89.

    Article  Google Scholar 

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landsc Ecol 19:389–399.

    Article  Google Scholar 

  • Li H, Liu L, Ji X (2015) Modeling the relationship between landscape characteristics and water quality in a typical highly intensive agricultural small watershed, Dongting lake basin, south central China. Environ Monit Assess 187:1–12.

    Article  Google Scholar 

  • Li C, Zhang Y, Kharel G, Zou CB (2018) Impact of climate variability and landscape patterns on water budget and nutrient loads in a peri-urban watershed: a coupled analysis using process-based hydrological model and landscape Indices. Environ Manag 61:954–967.

    Article  Google Scholar 

  • Liaw A, Wiener M (2022) randomForest: Breiman and Cutler's Random Forests for Classification and Regression. R package version 4.7–1. https://CRAN.R-project.org/package=randomForest

  • Lind L, Hasselquist EM, Laudon H (2019) Towards ecologically functional riparian zones: a meta-analysis to develop guidelines for protecting ecosystem functions and biodiversity in agricultural landscapes. J Environ Manag 249:109391.

    Article  Google Scholar 

  • Liu J, Zhang X, Wu B et al (2017) Spatial scale and seasonal dependence of land use impacts on riverine water quality in the Huai River basin, China. Environ Sci Pollut Res 24:20995–21010.

    Article  CAS  Google Scholar 

  • Lombardi Neto F, Moldenhauer WC (1992) Erosividade da chuva: sua distribuição e relação com as perdas de solo em Campinas (SP). Bragantia 51:189–196.

    Article  Google Scholar 

  • Lopez RD, Nash MS, Heggem DT, Ebert DW (2008) Watershed vulnerability predictions for the ozarks using landscape models. J Environ Qual 37:1769–1780.

    Article  CAS  PubMed  Google Scholar 

  • Lovell ST, Sullivan WC (2006) Environmental benefits of conservation buffers in the United States: evidence, promise, and open questions. Agr Ecosyst Environ 112:249–260.

    Article  Google Scholar 

  • Luke SH, Slade EM, Gray CL et al (2019) Riparian buffers in tropical agriculture: scientific support, effectiveness and directions for policy. J Appl Ecol 56:85–92.

    Article  Google Scholar 

  • Mair P, Wilcox R (2020) Robust statistical methods in R using the WRS2 package. Behav Res 52:464–488.

    Article  Google Scholar 

  • Mair P, Wilcox R, Patil I (2022) WRS2: A Collection of Robust Statistical Methods. R package version 1.1–3. https://CRAN.R-project.org/package=WRS2

  • MapBiomas (2021) MapBiomas Project - Collection 5 of the Annual Series of Land Use and Land Cover Maps of Brazil. https://mapbiomas.org/. Accessed 23 May 2021

  • Mayer AL, Buma B, Davis A et al (2016) How landscape ecology informs global land-change science and policy. BioScience. https://doi.org/10.1093/biosci/biw035

    Article  Google Scholar 

  • McGarigal K (2014) Landscape Pattern Metrics. Wiley StatsRef: Statistics Reference Online, Hoboken. https://doi.org/10.1002/9781118445112.stat07723

    Book  Google Scholar 

  • McGarigal K (2015) Fragstats Help v4. Fragstats Help v4 https://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed 17 Oct 2020

  • McGarigal K, Cushman S, Ene E (2012) FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. V. 4.2.1. https://github.com/kmcgarigal/Fragstats

  • Milborrow S (2021) rpart.plot: Plot 'rpart' Models: An Enhanced Version of ‘plot.rpart’. R package version 3.1.0. https://cran.r-project.org/web/packages/rpart.plot/

  • Mitchell MGE, Bennett EM, Gonzalez A (2015) Strong and nonlinear effects of fragmentation on ecosystem service provision at multiple scales. Environ Res Lett 10:094014.

    Article  Google Scholar 

  • Moreno-Mateos D, Mander Ü, Comín FA et al (2008) Relationships between landscape pattern, wetland characteristics, and water quality in agricultural catchments. J Environ Qual 37:2170–2180.

    Article  CAS  PubMed  Google Scholar 

  • Morgan R (1995) Soil erosion and conservation. Longman Scientific and Technical, London

    Google Scholar 

  • Mori GB, De Paula FR, De Ferraz SFB et al (2015) Influence of landscape properties on stream water quality in agricultural catchments in Southeastern Brazil. Ann Limnol 51:11–21.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Moriasi DN, Gitau MW, Pai N et al (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58:1763–85.

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part I—A discussion of principles. J Hydrol 10:282–290.

    Article  Google Scholar 

  • Nassauer JI, Opdam P (2008) Design in science: extending the landscape ecology paradigm. Landsc Ecol 23:633–644.

    Article  Google Scholar 

  • Natural Capital Project (2021) Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST). V. 3.9.0. Natural Capital Project. https://naturalcapitalproject.stanford.edu/software/invest.

  • Oliveira PTS, Wendland E, Nearing MA (2012) Rainfall erosivity in Brazil: a review. CATENA 100:139–147.

    Article  Google Scholar 

  • Paula FRD, Gerhard P, Ferraz SFDB, Wenger SJ (2018) Multi-scale assessment of forest cover in an agricultural landscape of Southeastern Brazil: implications for management and conservation of stream habitat and water quality. Ecol Indic 85:1181–1191.

    Article  CAS  Google Scholar 

  • Pissarra TCT, Valera CA, Costa RCA et al (2019) A Regression model of stream water quality based on interactions between landscape composition and riparian buffer width in small catchments. Water 11:1757.

    Article  CAS  Google Scholar 

  • Poesen J, Nachtergaele J, Verstraeten G, Valentin C (2003) Gully erosion and environmental change: importance and research needs. CATENA 50:91–133.

    Article  Google Scholar 

  • QGIS.org (2021) QGIS Geographic Information System. V. 3.16.4. QGIS Association. http://www.qgis.org.

  • Qiu J (2019) Effects of landscape pattern on pollination, pest control, water quality, flood regulation, and cultural ecosystem services: a literature review and future research prospects. Curr Landsc Ecol Rep 4:113–124.

    Article  Google Scholar 

  • Qiu J, Turner MG (2015) Importance of landscape heterogeneity in sustaining hydrologic ecosystem services in an agricultural watershed. Ecosphere 6:1–19.

    Article  CAS  Google Scholar 

  • R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Redhead JW, May L, Oliver TH et al (2018) National scale evaluation of the InVEST nutrient retention model in the United Kingdom. Sci Total Environ 610–611:666–677.

    Article  PubMed  Google Scholar 

  • Renard KG, Foster GR, Weesies GA, Porter JP (1991) RUSLE: revised universal soil loss equation. J Soil Water Conserv 46:30–33

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, et al (coord) (1997) Predicting Soil Erosion by Water: A Guide to Conservation Planning with the revised soil loss equation. United States Department of Agriculture (USDA) Handbook no. 703.

  • Rieb JT, Bennett EM (2020) Landscape structure as a mediator of ecosystem service interactions. Landsc Ecol 35:2863–2880.

    Article  Google Scholar 

  • Rosário VAC, Guimarães JC, Viani RAG (2019) How changes in legally demanded forest restoration impact ecosystem services: a case study in the atlantic forest. Brazil Trop Conserv Sci 12:194008291988288. https://doi.org/10.1177/1940082919882885

    Article  Google Scholar 

  • Rossi M (2017) Mapa pedológico do Estado de São Paulo: revisado e ampliado. Instituto Florestal, São Paulo. https://www.infraestruturameioambiente.sp.gov.br/institutoflorestal/category/publicacoes-online/

  • Saad SI, Mota da Silva J, Silva MLN et al (2018) Analyzing ecological restoration strategies for water and soil conservation. PloS one 13:e0192325.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sánchez-Canales M, López-Benito A, Acuña V et al (2015) Sensitivity analysis of a sediment dynamics model applied in a Mediterranean river basin: global change and management implications. Sci Total Environ 502:602–610.

    Article  PubMed  Google Scholar 

  • São Paulo state (2013) Modelo Digital De Elevação (MDE) do estado de São Paulo. Scale 1:50,000. Secretaria de Infraestrutura e Meio Ambiente do estado de São Paulo, Brasil. https://datageo.ambiente.sp.gov.br/. Accessed 8 Feb 2021

  • São Paulo state (2018) Hidrografia do estado de São Paulo - UGRHI 5. Scale 1:50,000. Secretaria de Infraestrutura e Meio Ambiente do estado de São Paulo, Brasil. https://datageo.ambiente.sp.gov.br/. Accessed 8 Feb 2021

  • Sciaini M, Fritsch M, Hesselbarth MHK, et al (2020a) landscapetools: Landscape Utility Toolbox. R package version 0.6.2. https://CRAN.R-project.org/package=landscapetools

  • Sciaini M, Fritsch M, Scherer C, Simpkins CE (2020b) NLMR: Simulating Neutral Landscape Models. R package version 1.0. https://CRAN.R-project.org/package=NLMR

  • Sharp R, Douglass J, Wolny S, et al (2020) InVEST 3.9.0.post249+invest.gf12cf86d User’s Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund. https://storage.googleapis.com/releases.naturalcapitalproject.org/invest/3.9.0.post249+gf12cf86d/userguide/index.html. Accessed 12 Dec 2022

  • Shen ZY, Chen L, Liao Q et al (2013) A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling. Agric Water Manag 118:93–102.

    Article  Google Scholar 

  • Shi P, Zhang Y, Li Z et al (2017) Influence of land use and land cover patterns on seasonal water quality at multi-spatial scales. CATENA 151:182–190.

    Article  CAS  Google Scholar 

  • Spake R, Lasseur R, Crouzat E et al (2017) Unpacking ecosystem service bundles: towards predictive mapping of synergies and trade-offs between ecosystem services. Glob Environ Chang 47:37–50.

    Article  Google Scholar 

  • Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychol Methods 14:323–348.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sun Y, Guo Q, Liu J, Wang R (2014) Scale effects on spatially varying relationships between urban landscape patterns and water quality. Environ Manag 54:272–287.

    Article  Google Scholar 

  • Sweeney BW, Newbold JD (2014) Streamside forest buffer width needed to protect stream water quality, habitat, and organisms: a literature review. JAWRA J Am Water Resour Assoc 50:560–584.

    Article  Google Scholar 

  • Synes NW, Brown C, Watts K et al (2016) Emerging Opportunities for landscape ecological modelling. Curr Landsc Ecol Rep 1:146–167.

    Article  Google Scholar 

  • Termorshuizen JW, Opdam P (2009) Landscape services as a bridge between landscape ecology and sustainable development. Landsc Ecol 24:1037–1052.

    Article  Google Scholar 

  • Therneau T, Atkinson B, Ripley B (2022) rpart: Recursive Partitioning and Regression Trees. R package version 4.1.16. https://cran.r-project.org/web/packages/rpart/

  • Thomas A, Masante D, Jackson B et al (2020) Fragmentation and thresholds in hydrological flow-based ecosystem services. Ecol Appl 30:e02046.

    Article  PubMed  PubMed Central  Google Scholar 

  • Turner MG, Gardner RH (2015) Landscape Ecology in theory and practice, 2nd edn. Springer, New York

    Book  Google Scholar 

  • Turner MG, Donato DC, Romme WH (2013) Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research. Landsc Ecol 28:1081–1097.

    Article  Google Scholar 

  • Uriarte M, Yackulic CB, Lim Y, Arce-Nazario JA (2011) Influence of land use on water quality in a tropical landscape: a multi-scale analysis. Landsc Ecol 26:1151–1164.

    Article  PubMed  PubMed Central  Google Scholar 

  • Valera CA, Pissarra TCT, Filho MVM et al (2019) The buffer capacity of riparian vegetation to control water quality in anthropogenic catchments from a legally protected area: a critical view over the brazilian new forest code. Water 11:549.

    Article  CAS  Google Scholar 

  • Verhagen W, Van Teeffelen AJA, Compagnucci AB et al (2016) Effects of landscape configuration on mapping ecosystem service capacity: a review of evidence and a case study in Scotland. Landsc Ecol 31:1457–1479.

    Article  Google Scholar 

  • Vigiak O, Borselli L, Newham LTH et al (2012) Comparison of conceptual landscape metrics to define hillslope-scale sediment delivery ratio. Geomorphol 138:74–88.

    Article  Google Scholar 

  • Villard M-A, Metzger JP (2014) REVIEW: beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J Appl Ecol 51:309–318.

    Article  Google Scholar 

  • Walker WW (1999) Simplified procedures for eutrophication assessment and prediction: user manual. US Army Corps of Engineers. (Instruction report W–96–2)

  • Wang Q, Malanson GP (2008) Neutral landscapes: bases for exploration in landscape ecology. Geogr Compass 2:319–339.

    Article  Google Scholar 

  • Wang H, Wu Z, Hu C (2015) A comprehensive study of the effect of input data on hydrology and non-point source pollution modeling. Water Resour Manage 29:1505–1521.

    Article  Google Scholar 

  • Wilcox RR (2010) Fundamentals of modern statistical methods: substantially improving power and accuracy, 2nd edn. Springer, New York. https://doi.org/10.1007/978-1-4419-5525-8

    Book  Google Scholar 

  • Wilcox RR (2012) Introduction to robust estimation and hypothesis testing, 3rd edn. Elsevier, The Netherlands. https://doi.org/10.1016/C2010-0-67044-1

    Book  Google Scholar 

  • Wilcox RR, Tian TS (2011) Measuring effect size: a robust heteroscedastic approach for two or more groups. J Appl Stat 38:1359–1368.

    Article  Google Scholar 

  • Wu J (2013) Key concepts and research topics in landscape ecology revisited: 30 years after the Allerton Park workshop. Landsc Ecol 28:1–11.

    Article  CAS  Google Scholar 

  • Wu J (2021) Landscape sustainability science (II): core questions and key approaches. Landsc Ecol 36:2453–2485.

    Article  Google Scholar 

  • Ye Y, He X-Y, Chen W et al (2014) Seasonal water quality upstream of Dahuofang Reservoir, China—the effects of land use type at various spatial scales. Clean—Soil, Air, Water 42:1423–1432.

    Article  CAS  Google Scholar 

  • Zuur AF, Ieno EN, Smith GM (2007) Analysing Ecological Data. Springer, New York. https://doi.org/10.1007/978-0-387-45972-1

    Book  Google Scholar 

Download references

Funding

Ligia M. B. Campanhão was granted a Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq—Brazil) scholarship (Process 140337/2017–2).

Author information

Authors and Affiliations

  1. São Carlos School of Engineering, University of São Paulo (USP), Av. João Dagnone, 1100, São Carlos, SP, 13563-120, Brazil

    Ligia Maria Barrios Campanhão & Victor Eduardo Lima Ranieri

  2. Luiz de Queiroz College of Agriculture, University of São Paulo (USP), Av. Pádua Dias, 11, Piracicaba, SP, 13418-260, Brazil

    Ligia Maria Barrios Campanhão

Authors
  1. Ligia Maria Barrios Campanhão
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Eduardo Lima Ranieri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors conceived and designed the study. Data collection and analysis were performed by LMBC. All authors wrote the manuscript text and approved the final manuscript.

Corresponding author

Correspondence to Ligia Maria Barrios Campanhão.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 1109 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campanhão, L.M.B., Ranieri, V.E.L. Influence of forest proportion and configuration at the watershed and riparian zone scales on sediment yield: a simulation experiment. Landsc Ecol 38, 2839–2860 (2023). https://doi.org/10.1007/s10980-023-01751-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-023-01751-6

Keywords

Search

Navigation