Advertisement

Aquatic Sciences

, 81:26 | Cite as

Integrating catchment land cover data to remotely assess freshwater quality: a step forward in heterogeneity analysis of river networks

  • Ionuţ Şandric
  • Alina Satmari
  • Claudia Zaharia
  • Milca Petrovici
  • Mirela Cîmpean
  • Karina-Paula Battes
  • Dragomir-Cosmin David
  • Octavian Pacioglu
  • András Weiperth
  • Blanka Gál
  • Mălina Pîrvu
  • Hanelore Muntean
  • Marian Neagul
  • Adrian Spătaru
  • Claudiu G. Toma
  • Lucian PârvulescuEmail author
Research Article

Abstract

Attempts to obtain information from geospatial data in freshwater ecology is a highly challenging task requiring the development of new concepts and adequate tools. Conventionally, river networks are represented as collections of vectors, but they can also be thought of as a succession of raster cells corresponding to the digital elevation model of the landscape they traverse. Based on the principle that each cell in the river raster collects environmental influences from its upstream drainage basin, we defined a remote measure of the potential of pollution named RWQ (Remote Water Quality). We used the CORINE Land Cover categories found in the catchment area of each cell in the river network grouped by ecological relevance and weighted by their respective areas in the catchment. To refine the index to account for the proximity of potential pollution sources, we tested successive buffers of 1 km up to the full catchment of each investigated point, concluding that the RWQ calculated for the full catchment is the most suitable index. For implementation, we developed RIVERenhancer, a free Python-based ArcGIS tool making possible the enhancement of raster river networks with data extracted from various files. The reliability of RWQ was tested with the aid of in situ measurements of chemical and biological water quality obtained from several sources in Danube basin (Romania and Hungary). The strong correlation with field data shows that this index can be considered a surrogate to depict the quality of freshwater habitats and to analyse network heterogeneity. The strength of this concept comes from taking advantage of the dendritic nature of river networks, opening new directions of operations for large scale approaches concerning important issues in global ecology, biogeography and conservation.

Keywords

CORINE Land Cover Danube basin Freshwater ecology Habitat fragmentation Remote sensing Spatial ecology Water quality 

Notes

Acknowledgements

This work was partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation (UEFISCDI) project number PN-II-ID-PCE-2008-2-1458. We kindly thank the National Administration of Romanian Waters and Hungarian General Directorate of Water Management for providing water quality datasets used for validation, and all the volunteer students from the West University of Timisoara for their help in the field and laboratory work. We thank the Editors and anonymous reviewers whose valuable suggestions significantly improved the early version of the manuscript.

Authors’ contribution

LP, ASa and CZ conceived the idea. IȘ developed the tool software. ASa and IȘ applied and tested the tool. CZ performed statistical analyses. MPe, MC, KPB, DCD, AW, BG, MPî, HM and OP provided sample collection, and MN, ASp and CGT provided database management. LP led the writing of the manuscript. All authors contributed and approved publication.

References

  1. Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Kløve B (2015) A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752CrossRefGoogle Scholar
  2. Agresti A (2010) Analysis of ordinal categorical data (Vol. 656). Wiley, New YorkCrossRefGoogle Scholar
  3. Allan E, Manning P, Alt F, Binkenstein J, Blaser S, Blüthgen N, Böhm S, Grassein F, Hölzel N, Klaus VH, Kleinebecker T, Morris EK, Oelmann Y, Prati D, Renner SC, Rillig MC, Schaefer M, Schloter M, Schmitt B, Schöning I, Schrumpf M, Solly E, Sorkau E, Steckel J, Steffen-Dewenter I, Stempfhuber B, Tschapka M, Weiner CN, Weisser WW, Werner M, Westphal C, Wilcke W, Fische M (2015) Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol Lett 18:834–843CrossRefGoogle Scholar
  4. Amiri BJ, Nakane K (2009) Modeling the linkage between river water quality and landscape metrics in the Chugoku district of Japan. Water Resour Manage 23:931–956CrossRefGoogle Scholar
  5. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19:563–572CrossRefGoogle Scholar
  6. Booker DJ, Snelder TH, Greenwood MJ, Crow SK (2015) Relationships between invertebrate communities and both hydrological regime and other environmental factors across New Zealand’s rivers. Ecohydrology 8:13–32CrossRefGoogle Scholar
  7. Borja Á (2005) The European water framework directive: A challenge for nearshore, coastal and continental shelf research. Cont Shelf Res 25:1768–1783CrossRefGoogle Scholar
  8. Boskidis I, Gikas GD, Pisinaras V, Tsihrintzis VA (2011) Spatial and temporal changes of water quality, and SWAT modeling of Vosvozis River Basin, North Greece. J Environ Sci Health A 45:1421–1440CrossRefGoogle Scholar
  9. Burkhard B, Kroll F, Nedkov S, Müller F (2012) Mapping ecosystem service supply, demand and budgets. Ecol Ind 21:17–29CrossRefGoogle Scholar
  10. Cade BS, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Front Ecol Environ 1:412–420CrossRefGoogle Scholar
  11. Capinha C, Anastácio P (2011) Assessing the environmental requirements of invaders using ensembles of distribution models. Divers Distrib 17:13–24CrossRefGoogle Scholar
  12. CEN (2004) A guidance standard for assessing the hydromorphological features of rivers. Comité Européen de NormalisationGoogle Scholar
  13. Chen B, Chang SX, Lam SK, Erisman JW, Gu B (2017) Land use mediates riverine nitrogen export under the dominant influence of human activities. Environ Res Lett 12:094018CrossRefGoogle Scholar
  14. Cîmpean M (2011) Studiul taxonomic şi ecologic asupra comunităţilor de acarieni acvatici (Acari, Hydrachnidia) din bazinul de drenaj al râului Someşul Mic şi rolul acestor organisme ca indicatori ai calităţii apei. Presa Universitră Clujeană, pp. 1–190 (in Romanian) Google Scholar
  15. Clarke RT, Wright JF, Furse MT (2003) RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecol Model 160:219–233CrossRefGoogle Scholar
  16. De Mesnard L (2013) Pollution models and inverse distance weighting: Some critical remarks. Comput Geosci 52:459–469CrossRefGoogle Scholar
  17. Domisch S, Amatulli G, Jetz W (2015) Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Sci Data 2:150073CrossRefGoogle Scholar
  18. Dumnicka E, Jelonek M, Klich M, Kwandrans J, Wojtal A, Żurek R (2006) Ichtiofauna i status ekologiczny wód Wisły, Raby, Dunajca i Wisłoki (Ichthyofauna and Ecological Status of Vistula, Raba, Dunajec and Wisłoka Rivers). Instytut Ochrony Przyrody PAN, Kraków (in Polish) Google Scholar
  19. Eme D, Zagmajster M, Fišer C, Galassi D, Marmonier P, Stoch F, Cornu J-F, Oberdorff T, Malard F (2015) Multi-causality and spatial non-stationarity in the determinants of groundwater crustacean diversity in Europe. Ecography 38:531–540CrossRefGoogle Scholar
  20. Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The shuttle radar topography mission. Rev Geophys 45:RG2004CrossRefGoogle Scholar
  21. Ferreira J, Pádua J, Hughes SJ, Cortes RM, Varandas S, Holmes N, Raven P (2011) Adapting and adopting river habitat survey: Problems and solutions for fluvial hydromorphological assessment in Portugal. Limnetica 30:263–272Google Scholar
  22. Ficetola GF, Marziali L, Rossaro B, De Bernardi F, Padoa-Schioppa E (2011) Landscape–stream interactions and habitat conservation for amphibians. Ecol Appl 21:1272–1282CrossRefGoogle Scholar
  23. Gholizadeh MH, Melesse AM, Reddi L (2016) A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 16:1298CrossRefGoogle Scholar
  24. Gikas GD, Yiannakopoulou T, Tsihrintzis VA (2006) Water quality trends in a coastal lagoon impacted by non-point source pollution after implementation of protective measures. Hydrobiologia 563:385–406CrossRefGoogle Scholar
  25. Griebler C, Avramov M (2015) Groundwater ecosystem services: a review. Freshwater Science 34:355–367CrossRefGoogle Scholar
  26. Hansen GJ, Hein CL, Roth BM, Vander Zanden MJ, Gaeta JW, Latzka AW, Carpenter SR (2013) Food web consequences of long-term invasive crayfish control. Can J Fish Aquat Sci 70:1109–1122CrossRefGoogle Scholar
  27. Hanski I, Zurita GA, Bellocq MI, Rybicki J (2013) Species–fragmented area relationship. Proc Natl Acad Sci USA 110:12715–12720CrossRefGoogle Scholar
  28. Hawkes HA (1998) Origin and development of the Biological Monitoring Working Party score system. Water Res 32:964–968CrossRefGoogle Scholar
  29. Hawkins CP, Vinson MR (2000) Weak correspondence between landscape classifications and stream invertebrate assemblages: implications for bioassessment. J N Am Benthol Soc 19:501–517CrossRefGoogle Scholar
  30. Herbst DB, Feng AY, Gregorio DE (2001) The California Streamside Biosurvey. An introduction to using aquatic invertebrates as water quality indicators. State Water Resources Control Board Publication, SacramentoGoogle Scholar
  31. Hoekstra AY, Mekonnen MM (2012) The water footprint of humanity. Proc Natl Acad Sci USA 109:3232–3237CrossRefGoogle Scholar
  32. Ioniță C, Bărbulescu A (2015) Real-time video processing in web applications. 12th Romanian Human-Computer Interaction Conference, RoCHI 2015, Bucharest, Romania, pp. 129–132Google Scholar
  33. Irvine K (2004) Classifying ecological status under the European Water Framework Directive: the need for monitoring to account for natural variability. Aqua Conserv: Mar Freshw Ecosyst 14:107–112CrossRefGoogle Scholar
  34. Ivașcu C, Balaur C, Cîmpean M, Battes KP (2013) Water quality assessment using biotic indices based on benthic invertebrates in the Caraș catchment area. Studia Universitatis Babeş-Bolyai, Biologia 58, 55–68Google Scholar
  35. Jenson SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information system analysis. Photogr Eng Rem Sens 54:1593–1600Google Scholar
  36. Johnson S, Domínguez-García V, Donetti L, Muñoz MA (2014) Trophic coherence determines food-web stability. Proc Natl Acad Sci USA 111:17923–17928CrossRefGoogle Scholar
  37. Khan F, Hayat Z, Ahmad W, Ramzan M, Shah Z et al (2013) Effect of slope position on physico-chemical properties of eroded soil. Soil Environment 32:22–28Google Scholar
  38. Kløve B, Allan A, Bertrand G, Druzynska E, Ertürk A, Goldscheider N, Henry S, Karakaya N, Karjalainen TP, Koundouri P, Kupfersberger H, Kvœrner J, Lundberg A, Muotka T, Preda E, Pulido-Velazquez M, Schipper P (2011) Groundwater dependent ecosystems. Part II. Ecosystem services and management in Europe under risk of climate change and land use intensification. Environ Sci Policy 14:782–793CrossRefGoogle Scholar
  39. Knouft JH, Page LM (2011) Assessment of the relationships of geographic variation in species richness to climate and landscape variables within and among lineages of North American freshwater fishes. J Biogeogr 38:2259–2269CrossRefGoogle Scholar
  40. Koenker R (2005) Quantile regression. Econometric Society Monographs No. 38. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  41. Koenker R (2016) quantreg: quantile regression. R package version 5.29. https://CRAN.R-project.org/package=quantreg
  42. Kreps TA, Baldridge AK, Lodge DM (2012) The impact of an invasive predator (Orconectes rusticus) on freshwater snail communities: insights on habitat-specific effects from a multilake long-term study. Can J Fish Aquat Sci 69:1164–1173CrossRefGoogle Scholar
  43. McDonald RI, Weber KF, Padowski J, Boucher T, Shemie D (2016) Estimating watershed degradation over the last century and its impact on water-treatment costs for the world’s large cities. Proc Natl Acad Sci USA 113:9117–9122CrossRefGoogle Scholar
  44. Meitzen KM, Kupfer JA, Gao P (2018) Modeling hydrologic connectivity and virtual fish movement across a large Southeastern floodplain, USA. Aquat Sci 80:5CrossRefGoogle Scholar
  45. Momeu L, Battes K, Battes K, Stoica I, Avram A, Cîmpean M, Pricope F, Ureche D (2009) Algae, macroinvertebrate and fish communities from the Aries River catchment area (Transylvania, Romania). Transylvanian Rev Syst Ecol Res 7:149–181Google Scholar
  46. Momot WT (1995) Redefining the role of crayfish in aquatic ecosystems. Rev Fish Sci 3:33–63CrossRefGoogle Scholar
  47. Moore D, Cranston G, Reed A, Galli A (2012) Projecting future human demand on the Earth’s regenerative capacity. Ecol Ind 16:3–10CrossRefGoogle Scholar
  48. Naura M, Clark MJ, Sear DA, Atkinson PM, Hornby DD, Kemp P, England J, Peirson G, Bromley C, Carter MG (2016) Mapping habitat indices across river networks using spatial statistical modelling of River Habitat Survey data. Ecol Ind 66:20–29CrossRefGoogle Scholar
  49. Newbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T, Ingram DJ, Itescu Y, Kattge J, Kemp V, Kirkpatrick L, Kleyer M, Correia P, Martin DL, Meiri CD, Novosolov S, Pan M, Phillips Y, Purves HRP, Robinson DW, Simpson A, Tuck J, Weiher SL, White E, Ewers HJ, Mace RM, Scharlemann GM, Purvis JPW, A (2015) Global effects of land use on local terrestrial biodiversity. Nature 520:45–50CrossRefGoogle Scholar
  50. Nguyen P (2007) NonbinROC: software for evaluating diagnostic accuracies with non-binary gold standards. J Stat Softw 21:1–10CrossRefGoogle Scholar
  51. Nguyen P (2012) nonbinROC: ROC-type analysis for non-binary gold standards. R package version 1.0.1. https://cran.r-project.org/src/contrib/Archive/nonbinROC
  52. Obuchowski NA (2005) Estimating and comparing diagnostic tests’ accuracy when the gold standard is not binary. Acad Radiol 12:1198–1204CrossRefGoogle Scholar
  53. Ode PR, Hawkins CP, Mazor RD (2008) Comparability of biological assessments derived from predictive models and multimetric indices of increasing geographic scope. J N Am Benthol Soc 27:967–985CrossRefGoogle Scholar
  54. Palaoro AV, Dalosto MM, Costa GC, Santos S (2013) Niche conservatism and the potential for the crayfish Procambarus clarkii to invade South America. Freshw Biol 58:1379–1391CrossRefGoogle Scholar
  55. Parsons M, Thoms MC, Norris RH (2004) Development of a standardised approach to river habitat assessment in Australia. Environ Monit Assess 98:109–130CrossRefGoogle Scholar
  56. Pârvulescu L, Zaharia C, Groza MI, Csillik O, Satmari A, Drăguț L (2016) Flash-flood potential: a proxy for crayfish habitat stability. Ecohydrology 9:1507–1516CrossRefGoogle Scholar
  57. Peckarsky BL, McIntosh AR, Horn SC, McHugh K, Booker DJ, Wilcox AC, Brown W, Alvarez M (2014) Characterizing disturbance regimes of mountain streams. Freshw Sci 33:716–730CrossRefGoogle Scholar
  58. Pettorelli N, Owen HJF, Duncan C (2016) How do we want Satellite Remote Sensing to support biodiversity conservation globally? Methods Ecol Evol 7:656–665CrossRefGoogle Scholar
  59. Poquet JM, Alba-Tercedor J, Puntí T, del Mar Sánchez-Montoya M, Robles S, Alvarez M, Zamora-Muñoz C, Sáinz-Cantero CE, Vidal-Abarca MR, Suárez ML, Toro M, Pujante AM, Rieradevall M, Prat N (2009) The MEDiterranean Prediction And Classification System (MEDPACS): an implementation of the RIVPACS/AUSRIVAS predictive approach for assessing Mediterranean aquatic macroinvertebrate communities. Hydrobiologia 623:153–171CrossRefGoogle Scholar
  60. Raymond PA, Saiers JE, Sobczak WV (2016) Hydrological and biogeochemical controls on watershed dissolved organic matter transport: pulse-shunt concept. Ecology 97:5–16CrossRefGoogle Scholar
  61. Schneck F, Melo AS (2012) Hydrological disturbance overrides the effect of substratum roughness on the resistance and resilience of stream benthic algae. Freshw Biol 57:1678–1688CrossRefGoogle Scholar
  62. Seitz NE, Westbrook CJ, Noble BF (2011) Bringing science into river systems cumulative effects assessment practice. Environ Impact Assess Rev 31:172–179CrossRefGoogle Scholar
  63. Senay C, Macnaughton CJ, Lanthier G, Harvey-Lavoie S, Lapointe M, Boisclair D (2015) Identifying key environmental variables shaping within-river fish distribution patterns. Aquat Sci 77:709–721CrossRefGoogle Scholar
  64. Sliva L, Williams DD (2001) Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Resour 35:3462–3472Google Scholar
  65. Smith MJ, Kay WR, Edward DHD, Papas PJ, Richardson KSJ, Simpson JC, Pinder AM, Cale DJ, Horwitz PHJ, Davis JA, Yung FH, Norris RH, Halse SA (1999) AusRivAS: using macroinvertebrates to assess ecological condition of rivers in Western Australia. Freshw Biol 41:269–282CrossRefGoogle Scholar
  66. Soranno PA, Bissell EG, Cheruvelil KS, Christel ST, Collins SM, Fergus CE, Filstrup CT, Lapierre J-F, Lottig NR, Oliver SK, Scott CE, Smith NJ, Stopyak S, Yuan S, Bremigan MT, Downing JA, Gries C, Henry EN, Skaff NK, Stanley EH, Stow CA, Tan P-N, Wagner T, Webster KE (2015) Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. GigaScience 4, 1Google Scholar
  67. Steffen W, Broadgate W, Deutsch L, Gaffney O, Ludwig C (2015) The trajectory of the Anthropocene: the great acceleration. The Anthropocene Review 2:81–98CrossRefGoogle Scholar
  68. Tarboton DG, Bras RL, Rodriguez-Iturbe I (1991) On the extraction of channel networks from digital elevation data. Hydrol Process 5:81–100CrossRefGoogle Scholar
  69. Tong ST, Chen W (2002) Modeling the relationship between land use and surface water quality. J Environ Manage 66:377–393CrossRefGoogle Scholar
  70. Tudesque L, Tisseuil C, Lek S (2014) Scale-dependent effects of land cover on water physico-chemistry and diatom-based metrics in a major river system, the Adour-Garonne basin (South Western France). Sci Total Environ 466:47–55CrossRefGoogle Scholar
  71. Xia Y, Ti C, She D, Yan X (2016) Linking river nutrient concentrations to land use and rainfall in a paddy agriculture–urban area gradient watershed in southeast China. Sci Total Environ 566:1094–1105CrossRefGoogle Scholar
  72. Yee TW (2017) VGAM: vector generalized linear and additive models. R package version 1.0-3. https://CRAN.R-project.org/package=VGAM

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Regional and Environmental Geography, Faculty of GeographyUniversity of BucharestBucharestRomania
  2. 2.Department of Geography, Faculty of Chemistry, Biology, GeographyWest University of TimisoaraTimisoaraRomania
  3. 3.Department of Mathematics, Faculty of Mathematics and Computer ScienceWest University of TimisoaraTimisoaraRomania
  4. 4.Department of Biology-Chemistry, Faculty of Chemistry, Biology, GeographyWest University of TimisoaraTimisoaraRomania
  5. 5.Department of Taxonomy and Ecology, Faculty of Biology and GeologyBabeş-Bolyai UniversityCluj-NapocaRomania
  6. 6.MTA Centre for Ecological ResearchDanube Research InstituteBudapestHungary
  7. 7.Doctoral School of Environmental SciencesEötvös Loránd UniversityBudapestHungary
  8. 8.MTA Centre for Ecological Research, Balaton Limnological InstituteTihanyHungary
  9. 9.Banat River Basin Administration, National Administration of Romanian WatersTimisoaraRomania
  10. 10.Department of Computer Science, Faculty of Mathematics and Computer ScienceWest University of TimisoaraTimisoaraRomania
  11. 11.Arheo Vest AssociationTimisoaraRomania

Personalised recommendations