Land cover change induced sediment transport behaviour in a large tropical Mexican catchment

References

• Aich, V., Zimmermann, A., and Elsenbeer, H., 2014. Quantification and interpretation of suspended-sediment discharge hysteresis patterns: how much data do we need? Catena, 122, 120–129. doi:10.1016/j.catena.2014.06.020
   Web of Science ®Google Scholar
• Appendini, C.M., et al., 2014. Wave climate and trends for the Gulf of Mexico: a 30-yr wave hindcast. Journal of Climate, 27 (4), 1619–1632. doi:10.1175/JCLI-D-13-00206.1
   Web of Science ®Google Scholar
• Arévalo, P., Olofsson, P., and Woodcock, C.E., 2019. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: a test methodology for REDD+ reporting. Remote Sensing of Environment, 238, 111051.
   Web of Science ®Google Scholar
• Bello, J., et al., 2009. Sitio piloto Río Papaloapan-Laguna de Alvarado. In: J. Buenfil Friedman, ed. Adaptación a los impactos del cambio climático en los humedales costeros del Golfo de México. Mexico: SEMARNAT-INE, 435–456.
   Google Scholar
• Blackman, A., Goff, L., and Rivera-Planter, M., 2018. Does eco-certification stem tropical deforestation? Forest stewardship council certification in Mexico. Journal of Environmental Economics and Management, 89 (C), 306–333. doi:10.1016/j.jeem.2018.04.005
   Web of Science ®Google Scholar
• Bocco, G., Mendoza, M., and Velázquez, A., 2001. Remote sensing and GIS-based regional geomorphological mapping—a tool for land use planning in developing countries. Geomorphology, 39 (3–4), 211–219. doi:10.1016/S0169-555X(01)00027-7
   Web of Science ®Google Scholar
• Bolaños-González, M.A., et al., 2016. Mapa de erosión de los suelos de México y posibles implicaciones en el almacenamiento de carbono orgánico del suelo. Terra Latinoamericana, 34 (3), 271–288.
   Google Scholar
• Bravo-Cabrera, J.L., et al., 2017. Effects of El Niño in Mexico during rainy and dry seasons: an extended treatment. Atmósfera, 30 (3), 221–232. doi:10.20937/ATM.2017.30.03.03
   Web of Science ®Google Scholar
• Bruijnzeel, L.A., 2004. Hydrological functions of tropical forests: not seeing the soil for the trees? Ecosystems and Environment, 104 (1), 185–228. doi:10.1016/j.agee.2004.01.015
   Web of Science ®Google Scholar
• Collins, A.L. and Walling, D.E., 2004. Documenting catchment suspended sediment sources: problems, approaches and prospects. Progress in Physical Geography: Earth and Environment, 28 (2), 159–196. doi:10.1191/0309133304pp409ra
   Google Scholar
• CONAGUA, 1989. Diagnóstico de la ganadería bovina en la región Papaloapan. Mexico: IMTA.
   Google Scholar
• CONAGUA, 2016. Banco Nacional de Datos de Aguas Superficiales (BANDAS) [online]. Available from: https://app.conagua.gob.mx/bandas/ [ Accessed 6 December 2016].
   Google Scholar
• Cuervo-Robayo, A.P., et al., 2014. Precipitación anual en México (1910-2009. Conjunto de datos vectoriales. Mexico: CONABIO, Escala 1: 1 000 000.
   Google Scholar
• Dineshkumar, C., Kumar, J.S., and Nitheshnirmal, S., 2019. Rice monitoring using sentinel-1 data in the google earth engine platform. Proceedings, 24 (1), 4. doi:10.3390/IECG2019-06206
   Google Scholar
• DOF, 1998. Tomo DXL No. 13, 18.09.1998. 8.
   Google Scholar
• Douglas, I. and Guyot, J.L., 2005. Erosion and sediment yield in the humid tropics. In: M. Bonell and Bruijnzeel, L.A., ed. Forests, water and people in the humid tropics: Past, present and future hydrological research for integrated land and water management. Amsterdam: Cambridge University Press, 407–421.
   Google Scholar
• Duvert, C., et al., 2010. Drivers of erosion and suspended sediment transport in three headwater catchments of the Mexican Central Highlands. Geomorphology, 123 (3–4), 243–256. doi:10.1016/j.geomorph.2010.07.016
   Web of Science ®Google Scholar
• FAO, 2011. State of the world’s forests. Rome: FAO.
   Google Scholar
• Fauvel, M., et al., 2015. Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (6), 2824–2831. doi:10.1109/JSTARS.2015.2441771
   Web of Science ®Google Scholar
• Finer, B.M., et al., 2018. Combating deforestation: from satellite to intervention. Science, 360 (6395), 1303–1305. doi:10.1126/science.aat1203
   PubMed Web of Science ®Google Scholar
• Funk, C., et al., 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Scientific Data, 2 (1), 150066. doi:10.1038/sdata.2015.66
   PubMed Web of Science ®Google Scholar
• García, E., 2004. Modificaciones al sistema de clasificación climática de Köppen. 5th ed. Mexico: Instituto de Geografía-UNAM.
   Google Scholar
• Gellis, A.C., 2013. Factors influencing storm-generated suspended-sediment concentrations and loads in four basins of contrasting land use, humid-tropical Puerto Rico. Catena, 104, 39–57. doi:10.1016/j.catena.2012.10.018
   Web of Science ®Google Scholar
• Hamed, K.H., 2016. The distribution of Spearman’s rho trend statistic for persistent hydrologic data. Hydrological Sciences Journal, 61 (1), 214–223. doi:10.1080/02626667.2014.968573
   Web of Science ®Google Scholar
• Hamed, K.H. and Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204 (1–4), 182–196. doi:10.1016/S0022-1694(97)00125-X
   Web of Science ®Google Scholar
• Hamshaw, S.D., et al., 2018. A new machine-learning approach for classifying hysteresis in suspended-sediment-discharge relationships using high-frequency monitoring data. Water Resources Research, 54 (6), 4040–4058. doi:10.1029/2017WR022238
   Web of Science ®Google Scholar
• INEGI, 1999. Conjunto de datos geológicos vectoriales serie I. Mexico: INEGI, Escala 1:250 000.
   Google Scholar
• INEGI, 2013. Erosión del suelo de México Serie I. Mexico: INEGI. Conjunto de datos vectoriales.
   Google Scholar
• Jansson, M.B., 2002. Determining sediment source areas in a tropical river basin, Costa Rica. Catena, 47 (1), 63–84. doi:10.1016/S0341-8162(01)00173-4
   Web of Science ®Google Scholar
• Karasiak, N., 2019. dzetsaka [online]. Available from: https://github.com/lennepkade/dzetsaka/ [Accessed 5 September 2019]
   Google Scholar
• Karasiak, N. and Perbet, P., 2018. Remote sensing of distinctive vegetation in Guiana Amazonian Park. In: N. Baghdadi, C. Mallet, and M. Zribi, eds. QGIS and applications in agriculture and forest. London: ISTE and Wiley, 215–246.
   Google Scholar
• Klein, M., 1984. Anti clockwise hysteresis in suspended sediment concentration during individual storms: holbeck catchment; Yorkshire, England. Catena, 11 (2–3), 251–257.
   Web of Science ®Google Scholar
• Konecny, G., 2014. Geoinformation : remote sensing, photogrammetry, and geographic information systems. India: CRC Press.
   Google Scholar
• Landholm, D.M., Pradhan, P., and Kropp, J.P., 2019. Diverging forest land use dynamics induced by armed conflict across the tropics. Global Environmental Change, 56, 86–94. doi:10.1016/j.gloenvcha.2019.03.006
   Web of Science ®Google Scholar
• Li, G., et al., 2011. Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery. International Journal of Remote Sensing, 32 (23), 8207–8230. doi:10.1080/01431161.2010.532831
   PubMed Web of Science ®Google Scholar
• Liu, J., et al., 2019. Evaluation and comparison of CHIRPS and MSWEP daily-precipitation products in the Qinghai-Tibet Plateau during the period of 1981–2015. Atmospheric Research, 230, 104634. doi:10.1016/j.atmosres.2019.104634
   Web of Science ®Google Scholar
• Lloyd, C.E.M., et al., 2016. Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments. Science of the Total Environment, 543, 388–404. doi:10.1016/j.scitotenv.2015.11.028
   PubMed Web of Science ®Google Scholar
• Lusardi, J., et al., 2020. Can process-based modelling and economic valuation of ecosystem services inform land management policy at a catchment scale? Land Use Policy, 96, 104636. doi:10.1016/j.landusepol.2020.104636
   Web of Science ®Google Scholar
• Machiwal, D. and Jha, M.K., 2012. Hydrologic time series analysis: theory and practice. New Delhi: Springer.
   Google Scholar
• Magaña, V., Pérez, J., and Conde, C., 1998. El fenómeno del El Niño y la oscilación del sur. Sus impactos en México. Ciencias, 51, 14–18.
   Google Scholar
• Magaña, V., et al., 2003. Impact of El Niño on precipitation in Mexico. Geofísica Internacional, 42 (3), 313–330.
   Google Scholar
• Manson, R.H., 2004. Los servicios hidrológicos y la conservación de los bosques de México. Madera y Bosques, 10 (1), 3–20. doi:10.21829/myb.2004.1011276
   Google Scholar
• Martínez, M.L., et al., 2009. Effects of land use change on biodiversity and ecosystem services in tropical montane cloud forests of Mexico. Forest Ecology and Management, 258 (9), 1856–1863. doi:10.1016/j.foreco.2009.02.023
   Web of Science ®Google Scholar
• Mas, J.F., et al., 2004. Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5 (4), 249–261. doi:10.1016/j.jag.2004.06.002
   Google Scholar
• Minella, J.P.G., et al., 2017. Long-term sediment yield from a small catchment in southern Brazil affected by land use and soil management changes. Hydrological Processes, 32 (2), 1–12.
   Web of Science ®Google Scholar
• Moragoda, N. and Cohen, S., 2020. Climate-induced trends in global riverine water discharge and suspended sediment dynamics in the 21st century. Global and Planetary Change, 191, 103199. doi:10.1016/j.gloplacha.2020.103199
   Web of Science ®Google Scholar
• Muñoz-Villers, L.E. and López-Blanco, J., 2008. Land use/cover changes using Landsat TM/ETM images in a tropical and biodiverse mountainous area of central-eastern Mexico. International Journal of Remote Sensing, 29 (1), 71–93. doi:10.1080/01431160701280967
   Web of Science ®Google Scholar
• Nampak, H., et al., 2018. Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT-5 satellite images and Revised Universal Soil Loss Equation model. Land Degradation and Development, 29 (10), 3440–3455. doi:10.1002/ldr.3112
   Web of Science ®Google Scholar
• Ojeda, E., Appendini, C., and Mendoza, E., 2017. Storm-wave trends in Mexican waters of the Gulf of Mexico and Caribbean Sea. Natural Hazards and Earth System Sciences, 17 (8), 1305–1317. doi:10.5194/nhess-17-1305-2017
   Web of Science ®Google Scholar
• Oviedo, G., 2002. The community protected natural areas in the state of Oaxaca, Mexico. Gland, Switzerland: WWF.
   Google Scholar
• Owens, P.N., 2020. Soil erosion and sediment dynamics in the Anthropocene: a review of human impacts during a period of rapid global environmental change. Journal of Soils and Sediments, 20, 4115–4143.684.
   Web of Science ®Google Scholar
• Paredes-Trejo, F.J., Barbosa, H.A., and Lakshmi Kumar, T.V., 2017. Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil. Journal of Arid Environments, 139, 26–40. doi:10.1016/j.jaridenv.2016.12.009
   Web of Science ®Google Scholar
• Peña-Arancibia, J.L., et al., 2019. Forests as ‘sponges’ and ‘pumps’: assessing the impact of deforestation on dry-season flows across the tropics. Journal of Hydrology, 574, 946–963. doi:10.1016/j.jhydrol.2019.04.064
   Web of Science ®Google Scholar
• Pérez-Vega, A. and Ortiz-Pérez, M., 2002. Cambio de la cubierta vegetal y vulnerabilidad a la inundación en el curso bajo del río Papaloapan, Veracruz. Investigaciones Geográficas, (48), 90–105.
   Google Scholar
• Persichillo, M.G., et al., 2018. The role of human activities on sediment connectivity of shallow landslides. Catena, 160, 261–274. doi:10.1016/j.catena.2017.09.025
   Web of Science ®Google Scholar
• Peters, J.J., 1998. Diseño de sistemas (métodos y procedimeintos) aplicables en México para la medición de sedimentos en suspensión y arrastre de fondo. Informe Final. Ginebra: Programa de Modernización del Manejo del Agua en México (PROMMA).
   Google Scholar
• Phiri, D., Morgenroth, J., and Xu, C., 2019. Four decades of land cover and forest connectivity study in Zambia—An object-based image analysis approach. International Journal of Applied Earth Observation and Geoinformation, 79, 97–109. doi:10.1016/j.jag.2019.03.001
   Web of Science ®Google Scholar
• Poeppl, R.E., et al., 2020. Managing sediment (dis) connectivity in fluvial systems. Science of the Total Environment, 736, 139627. doi:10.1016/j.scitotenv.2020.139627
   PubMed Web of Science ®Google Scholar
• QGIS Development Team, 2019. QGIS geographic information system. Open source.
   Google Scholar
• Regüés, D., et al., 2000. Relación entre las tendencias temporales de producción y transporte de sedimentos y las condiciones climáticas en una pequeña cuenca de montaña mediterránea (Vallcebre, Pirineos Orientales). Cuadernos de Investigación Geográfica, 26 (26), 41–65. doi:10.18172/cig.1062
   Google Scholar
• Reid, L.M., 1998. Cumulative watershed effects and watershed analysis. In: R.J. Naiman and R.E. Bilby, eds. River ecology and management: lessons from the Pacific coastal ecoregion. New York: Springer-Verlag, 476–501.
   Google Scholar
• Restrepo, J.D., Kettner, A.J., and Syvitski, J.P.M., 2015. Recent deforestation causes rapid increase in river sediment load in the Colombian Andes. Anthropocene, 10, 13–28. doi:10.1016/j.ancene.2015.09.001
   Web of Science ®Google Scholar
• Richards, K., 2002. Drainage basin structure, sediment delivery and the response to environmental change. Geological Society, London, Special Publications, 191 (1), 149–160. doi:10.1144/GSL.SP.2002.191.01.10
   Google Scholar
• Rivera, J.A., Marianetti, G., and Hinrichs, S., 2018. Validation of CHIRPS precipitation dataset along the Central Andes of Argentina. Atmospheric Research, 213, 437–449. doi:10.1016/j.atmosres.2018.06.023
   Web of Science ®Google Scholar
• Rose, L.A., Karwan, D.L., and Godsey, S.E., 2018. Concentration-discharge relationships describe solute and sediment mobilization, reaction, and transport at event and longer timescales. Hydrological Processes, 32 (18), 2829–2844. doi:10.1002/hyp.13235
   Web of Science ®Google Scholar
• Ruiz-Fernández, A.C., et al., 2014. Chronology of recent sedimentation and geochemical characteristics of sediments in Alvarado Lagoon, Veracruz (southwestern gulf of Mexico) | Cronología de la sedimentación reciente y caracterización geoquímica de los sedimentos de la laguna de Alvarado, Veracruz. Ciencias Marinas, 40 (4), 291–303.
   Web of Science ®Google Scholar
• Seeger, M., et al., 2004. Catchment soil moisture and rainfall characteristics as determinant factors for discharge/suspended sediment hysteretic loops in a small headwater catchment in the Spanish pyrenees. Journal of Hydrology, 288 (3–4), 299–311. doi:10.1016/j.jhydrol.2003.10.012
   Web of Science ®Google Scholar
• Sherriff, S.C., et al., 2016. Storm event suspended sediment-discharge hysteresis and controls in agricultural watersheds: implications for watershed scale sediment management. Environmental Science & Technology, 50 (4), 1769–1778. doi:10.1021/acs.est.5b04573
   PubMed Web of Science ®Google Scholar
• Sidhu, N., Pebesma, E., and Câmara, G., 2018. Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51 (1), 486–500. doi:10.1080/22797254.2018.1451782
   Web of Science ®Google Scholar
• Smith, H.G. and Dragovich, D., 2009. Interpreting sediment delivery processes using suspended sediment-discharge hysteresis patterns from nested upland catchments, south-eastern Australia. Hydrological Processes, 23 (17), 2415–2426. doi:10.1002/hyp.7357
   Web of Science ®Google Scholar
• Solano-Rivera, V., et al., 2019. Exploring extreme rainfall impacts on flow and turbidity dynamics in a steep, pristine and tropical volcanic catchment. Catena, 182, 104118. doi:10.1016/j.catena.2019.104118
   Web of Science ®Google Scholar
• Solís-Alvarado, Y., et al., 2015. Location and search via the web of the Mexican National Hydrometric Network. In: C.A. Brebbia, ed. Water and society III. Southampton: WIT Press, 79–88.
   Google Scholar
• Sun, L., et al., 2016. Suspended sediment dynamics at different time scales in the Loushui River, south-central China. Catena, 136, 152–161. doi:10.1016/j.catena.2015.02.014
   Web of Science ®Google Scholar
• Tobón, W., Martínez-Garza, C., and Campo, J., 2011. Soil responses to restoration of a tropical pasture in Veracruz, south-eastern Mexico. Journal of Tropical Forest Science, 23 (3), 1–8.
   Web of Science ®Google Scholar
• Torres-Rojo, J.M., Magaña-Torres, O.S., and Moreno-Sánchez, F., 2016. Prediction of land use change/forest cover in Mexico trough transition probabilities. Agrociencia, 60 (6), 769–785.
   Google Scholar
• Twisa, S. and Buchroithner, M.F., 2019. Land-use and land-cover (LULC) change detection in Wami river basin, Tanzania. Land, 8 (9), 9. doi:10.3390/land8090136
   Web of Science ®Google Scholar
• Velázquez, A., et al., 2002. Patrones y tasas de cambio de uso del suelo en México. Gaceta Ecológica, (62), 21–37.
   Google Scholar
• Venkatappa, M., et al., 2019. Determination of vegetation thresholds for assessing land use and land use changes in Cambodia using the Google Earth Engine cloud-computing platform. Remote Sensing, 11 (13), 1514. doi:10.3390/rs11131514
   Web of Science ®Google Scholar
• Verstraeten, G., Lang, A., and Houben, P., 2009. Human impact on sediment dynamics - quantification and timing. Catena, 77 (2), 77–80. doi:10.1016/j.catena.2009.01.005
   Web of Science ®Google Scholar
• Vogelmann, J.E., et al., 2016. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment, 185, 258/270. doi:10.1016/j.rse.2016.02.060
   Web of Science ®Google Scholar
• Warrick, J.A., 2014. Trend analyses with river sediment rating curves. Hydrological Processes, 29 (6), 936–949. doi:10.1002/hyp.10198
   Web of Science ®Google Scholar
• Williams, G.P., 1989. Sediment concentration versus water discharge during single hydrologic events in rivers. Journal of Hydrology, 111 (1–4), 89–106. doi:10.1016/0022-1694(89)90254-0
   Web of Science ®Google Scholar
• Wohl, E., et al., 2012. The hydrology of the humid tropics. Nature Climate Change, 2 (9), 655–662. doi:10.1038/nclimate1556
   Web of Science ®Google Scholar
• Wuthrich, D., 2006. Google Earth Pro. Geospatial Solutions, 16, 30–32.
   Google Scholar
• Xie, H., Li, D., and Xiong, L., 2014. Exploring the ability of the Pettitt method for detecting change point by Monte Carlo simulation. Stochastic Environmental Research and Risk Assessment, 28 (7), 1643–1655. doi:10.1007/s00477-013-0814-y
   Web of Science ®Google Scholar
• Yang, C.C. and Lee, K.T., 2018. Analysis of flow-sediment rating curve hysteresis based on flow and sediment travel time estimations. International Journal of Sediment Research, 33 (2), 171–182. doi:10.1016/j.ijsrc.2017.10.003
   Web of Science ®Google Scholar
• Young, N.E., et al., 2017. A survival guide to Landsat preprocessing. Ecology, 98 (4), 920–932. doi:10.1002/ecy.1730
   PubMed Web of Science ®Google Scholar
• Zhu, Z., 2017. Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384. doi:10.1016/j.isprsjprs.2017.06.013
   Web of Science ®Google Scholar
• Zhu, Z. and Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. doi:10.1016/j.rse.2014.01.011
   Web of Science ®Google Scholar
• Zuecco, G., et al., 2016. A versatile index to characterize hysteresis between hydrological variables at the runoff event timescale. Hydrological Processes, 30 (9), 1449–1466. doi:10.1002/hyp.10681
   Web of Science ®Google Scholar