Open Access Peer-reviewed Review

Main Article Content

Safieh Javadinejad corresponding author

Abstract

Hydrologic classification is the method of scientifically arranging streams, rivers or catchments into groups with the most similarity of flow regime features and use it to recognize hydrologically homogenous areas. Previous homogeneous attempts were depended on  overabundance of hydrologic metrics that considers features of variability of flows that are supposed to be meaningful in modelling physical progressions in the basins. This research explains the techniques of hydrological homogeneity through comparing past and existing methods;  in addition it provides a practical framework for hydrological homogeneity that illustrates serious elements of the classification process.

Keywords
classification process, homogeneous, hydrologic classification, physical processes, modeling

Article Details

How to Cite
Javadinejad, S. (2021). A review on homogeneity across hydrological regions. Resources Environment and Information Engineering, 3(1), 124-137. https://doi.org/10.25082/REIE.2021.01.004

References

  1. Rodriguez RD, Singh VP, Pruski FF, et al. Using entropy theory to improve the definition of homogeneous regions in the semi-arid region of Brazil. Hydrological Sciences Journal, 2016, 61(11): 2096-2109. https://doi.org/10.1080/02626667.2015.1083651
  2. Olden JD, Kennard MJ and Pusey BJ. A framework for hydrologic classification with a review of methodologies and applications in ecohydrology. Ecohydrology, 2012, 5(4): 503-518. https://doi.org/10.1002/eco.251
  3. Latt ZZ, Wittenberg H and Urban B. Clustering hydrological homogeneous regions and neural network based index flood estimation for ungauged catchments: an example of the Chindwin River in Myanmar. Water Resources Management, 2015, 29(3): 913-928. https://doi.org/10.1007/s11269-014-0851-4
  4. Rolls RJ, Heino J and Chessman BC. Unravelling the joint effects of flow regime, climatic variability and dispersal mode on beta diversity of riverine communities. Freshwater Biology, 2016, 61(8): 1350-1364. https://doi.org/10.1111/fwb.12793
  5. Kim Z and Singh VP. Assessment of environmental flow requirements by entropy-based multi-criteria decision. Water Resources Management, 2014, 28(2): 459-474. https://doi.org/10.1007/s11269-013-0493-y
  6. Oueslati O, De Girolamo AM, Abouabdillah A, et al. Classifying the flow regimes of Mediterranean streams using multivariate analysis. Hydrological Processes, 2015, 29(22): 4666-4682. https://doi.org/10.1002/hyp.10530
  7. Huitema D and Meijerink S. The politics of river basin organizations: institutional design choices, coalitions, and consequences. Ecology and Society, 2017, 22(2): 1-22. https://doi.org/10.5751/ES-09409-220242
  8. Arikan BB and Kahya E. Homogeneity revisited: analysis of updated precipitation series in Turkey. Theoretical and Applied Climatology, 2017, 1(1): 1-10.
  9. Song X and Bai Y. A new empirical river pattern discriminant method based on flow resistance characteristics. Catena, 2015, 135(1): 163-172. https://doi.org/10.1016/j.catena.2015.07.026
  10. Novakova J, Melcakova I, Svehlakova H, et al. Hydro morphological assessment of the Porubka river. in IOP Conference Series: Earth and Environmental Science, IOP Publishing, October, 2017, 92(1): 012046. https://doi.org/10.1088/1755-1315/92/1/012046
  11. Booker DJ and Woods RA. Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments. Journal of Hydrology, 2014, 508(1): 227-239. https://doi.org/10.1016/j.jhydrol.2013.11.007
  12. Dollar ESJ, James CS, Rogers KH, et al. A framework for interdisciplinary understanding of rivers as ecosystems. Geomorphology, 2007, 89(1-2): 147-162. https://doi.org/10.1016/j.geomorph.2006.07.022
  13. Merwe J and Hellgren EC. Spatial variation in trophic ecology of small mammals in wetlands: support for hydrological drivers. Ecosphere, 2016, 7(11): 117-123. https://doi.org/10.1002/ecs2.1567
  14. Gao M, Chen X, Liu J, et al. Regionalization of annual runoff characteristics and its indication of co-dependence among hydro-climate - landscape factors in Jinghe River Basin, China. Stochastic Environmental Research and Risk Assessment, 2018, 32(6): 1613-1630. https://doi.org/10.1007/s00477-017-1494-9
  15. Stagnitta TJ, Kroll CN and Zhang Z. A comparison of methods for low streamflow estimation from spot measurements. Hydrological Processes, 2018, 32(4): 480-492. https://doi.org/10.1002/hyp.11426
  16. Kumari B, Paul PK, Singh R, et al. Regionalization study of satellite based hydrological model (SHM) in hydrologically homogeneous river basins of India. in EGU General Assembly Conference Abstracts, 2017, 19: 2538.
  17. Adams MD, Kanaroglou PS and Coulibaly P. Spatially constrained clustering of ecological units to facilitate the design of integrated water monitoring networks in the St. Lawrence Basin. International Journal of Geographical Information Science, 2016, 30(2): 390-404. https://doi.org/10.1080/13658816.2015.1089442
  18. Nepal S. Impacts of climate change on the hydrological regime of the Koshi river basin in the Himalayan region. Journal of Hydro-environment Research, 2016, 10(1): 76-89. https://doi.org/10.1016/j.jher.2015.12.001
  19. Poff NL, Olden JD, Merritt DM, et al. Homogenization of regional river dynamics by dams and global biodiversity implications. Proceedings of the National Academy of Sciences, USA, 2007, 104: 5732-5737. https://doi.org/10.1073/pnas.0609812104
  20. McCuen RH. Modeling Hydrologic Change: Statistical Methods, Springer publication, CRC Press. 2016. https://doi.org/10.1201/9781420032192
  21. Mediero L, Kjeldsen TR, Macdonald N, et al. Identification of coherent flood regions across Europe by using the longest streamflow records. Journal of Hydrology, 2015, 528(1): 341-360. https://doi.org/10.1016/j.jhydrol.2015.06.016
  22. Beskow S, de Mello CR, Vargas MM, et al. Artificial intelligence techniques coupled with seasonality measures for hydrological regionalization of Q90 under Brazilian conditions. Journal of Hydrology, 2016, 541(1): 1406-1419. https://doi.org/10.1016/j.jhydrol.2016.08.046
  23. Javadinejad S, Dara R and Jafary F. Impacts of Extreme Events on Water Availability. Annals of Geographical Studies, 2019, 2(3): 16-24.
  24. Rinaldi M, Gurnell AM, Del T´anago MG, et al. Classification of river morphology and hydrology to support management and restoration. Aquatic Sciences, 2016, 78(1): 17-33. https://doi.org/10.1007/s00027-015-0438-z
  25. Radović T, Grujić S, Petković A, et al. Determination of pharmaceuticals and pesticides in river sediments and corresponding surface and ground water in the Danube River and tributaries in Serbia. Environmental Monitoring and Assessment, 2015, 187(1): 4092. https://doi.org/10.1007/s10661-014-4092-z
  26. Foufoula E, Ebtehaj AM and Bras RL. A novel Bayesian algorithm for microwave retrieval of precipitation from space: applications in snow and coastal hydrology. in EGU General Assembly Conference Abstracts, April, 2015, 17.
  27. Wang B, Mezlini AM, Demir F, et al. Similarity network fusion for aggregating data types on a genomic scale. Nature Methods, 2014, 11(3): 333. https://doi.org/10.1038/nmeth.2810
  28. Razavi S, Elshorbagy A, Wheater H, et al. Toward understanding nonstationarity in climate and hydrology through tree ring proxy records. Water Resources Research, 2015, 51(3): 1813-1830. https://doi.org/10.1002/2014WR015696
  29. Chuan ZL, Ismail N, Shinyie WL, et al. The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments. in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2018, 342(1): 012070. https://doi.org/10.1088/1757-899X/342/1/012070
  30. Belletti B, Rinaldi M, Bussettini M, et al. Characterising physical habitats and fluvial hydromorphology: a new system for the survey and classification of river geomorphic units. Geomorphology, 2017, 283(17): 143-157. https://doi.org/10.1016/j.geomorph.2017.01.032
  31. Lin GF andWang CM. Performing cluster analysis and discrimination analysis of hydrological factors in one step. Advances in Water Resources, 2006, 29(11): 1573-1585. https://doi.org/10.1016/j.advwatres.2005.11.008
  32. Dronova I, Gong P,Wang L, et al. Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 2015, 158(1): 193-206. https://doi.org/10.1016/j.rse.2014.10.027
  33. Ley R, Hellebrand H, Casper MC, et al. Comparing classical performance measures with signature indices derived from flow duration curves to assess model structures as tools for catchment classification. Hydrology Research, 2016, 47(1): 1-14. https://doi.org/10.2166/nh.2015.221
  34. Koch J, Cornelissen T, Fang Z, et al. Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment. Journal of Hydrology, 2016, 533(1): 234-249. https://doi.org/10.1016/j.jhydrol.2015.12.002
  35. Mackay SJ, Arthington AH and James CS. Classification and comparison of natural and altered flow regimes to support an Australian trial of the ecological limits of hydrologic alteration framework¿ Ecohydrology, 2014, 7(6): 1485-1507. https://doi.org/10.1002/eco.1473
  36. Kuentz A, Arheimer B, Hundecha Y, et al. Understanding hydrologic variability across Europe through catchment classification. Hydrology and Earth System Sciences, 2017, 21(6): 2863-2879. https://doi.org/10.5194/hess-21-2863-2017
  37. West C, Wagener T and Rosolem R. A comparative hydrology approach to understand groundwater recharge variability across the African continent. in EGU General Assembly Conference Abstracts, 2018, 20: 9576.
  38. Fernandez R and Sayama T. Hydrological recurrence as a measure for large river basin classification and process understanding. Hydrology and Earth System Sciences, 2015, 19(4): 1919-1942. https://doi.org/10.5194/hess-19-1919-2015
  39. Tiner RW. Wetland Indicators: A Guide to Wetland Formation, Identification, Delineation, Classification, and Mapping, Springer publication, CRC Press. 2016.
  40. Gehlot S, Hagemann S and Brovkin V. Global distribution of riverine DOC concentration: coupling terrestrial carbon and lateral hydrology in MPI-ESM. in EGU General Assembly Conference Abstracts, 2018, 20: 13511.
  41. Jones JAA. Global Hydrology: Processes, Resources and Environmental Management, Taylor & Francis publication, Routledge. 2014.
  42. McMillan H, Montanari A, Cudennec C, et al. Panta Rhei 2013-2015: global perspectives on hydrology, society and change. Hydrological Sciences Journal, 2016, 61(7): 1174-1191. https://doi.org/10.1080/02626667.2016.1159308
  43. Almorox J, Quej VH and Mart´ı P. Global performance ranking of temperature-based approaches for evapotranspiration estimation considering K¨oppen climate classes. Journal of Hydrology, 2015, 528(1): 514-522. https://doi.org/10.1016/j.jhydrol.2015.06.057
  44. Goyal MK and Gupta V. Identification of homogeneous rainfall regimes in Northeast Region of India using fuzzy cluster analysis. Water Resources Management, 2014, 28(13): 4491-4511. https://doi.org/10.1007/s11269-014-0699-7
  45. Webb JA, Bond NR, Wealands SR, et al. Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification. Ecography, 2007, 30(4): 526-536. https://doi.org/10.1111/j.0906-7590.2007.05002.x
  46. Jarihani B, Sidle RC, Bartley R, et al. Characterisation of hydrological response to rainfall at multi spatio-temporal scales in savannas of semi-arid Australia. Water, 2017, 9(7): 540. https://doi.org/10.3390/w9070540
  47. Naghibi SA, Pourghasemi HR and Dixon B. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 2016, 188(1): 44. https://doi.org/10.1007/s10661-015-5049-6
  48. Braaten R and Gates G. Groundwater-surface water interaction in inland New South Wales: a scoping study. Water Science and Technology, 2003, 48(7): 215-224. https://doi.org/10.2166/wst.2003.0443
  49. Cohen TJ, Jansen JD, Gliganic LA, et al. Hydrological transformation coincided with megafaunal extinction in central Australia. Geology, 2015, 43(3): 195-198. https://doi.org/10.1130/G36346.1
  50. Lawson JR, Fryirs KA and Leishman MR. Hydrological conditions explain variation in wood density in riparian plants of South-eastern Australia. Journal of Ecology, 2015, 103(4): 945-956. https://doi.org/10.1111/1365-2745.12408
  51. Halliday BT, Matthews TG, Iervasi D, et al. Potential for water-resource infrastructure to act as refuge habitat. Ecological Engineering, 2015, 84(1): 136-148. https://doi.org/10.1016/j.ecoleng.2015.07.020
  52. Brown S. Assessing Spatio-temporal Hydrologic Variability: A Case-study in Western Victoria, PhD, Deakin Univeristy. 2015.
  53. Boscarello L, Ravazzani G, Cislaghi A, et al. Regionalization of flow-duration curves through catchment classification with streamflow signatures and physiographic-climate indices. Journal of Hydrologic Engineering, 2015, 21(3): 05015027. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001307
  54. Javadinejad S, Dara R and Jafary F. Potential impact of climate change on temperature and humidity related human health effects during extreme condition. Safety in Extreme Environments, 2020, 1-7. https://doi.org/10.1007/s42797-020-00021-x
  55. McManamay RA, Bevelhimer MS and Frimpong EA. Associations among hydrologic classifications and fish traits to support environmental flow standards. Ecohydrology, 2015, 8(3): 460-479. https://doi.org/10.1002/eco.1517
  56. Erskine W, Saynor M and Lowry J. Application of a new river classification scheme to Australia’s tropical rivers. Singapore Journal of Tropical Geography, 2017, 38(2): 167-184. https://doi.org/10.1111/sjtg.12196
  57. Alam J, Muzzammil M and Khan MK. Regional flood frequency analysis: comparison of L-moment and conventional approaches for an Indian catchment. ISH Journal of Hydraulic Engineering, 2016, 22(3): 247-253. https://doi.org/10.1080/09715010.2016.1177739
  58. Shein EV, Kiryushin VI, Korchagin AA, et al. Assessment of agronomic homogeneity and compatibility of soils in the Vladimir Opolie region. Eurasian Soil Science, 2017, 50(10): 1166-1172. https://doi.org/10.1134/S1064229317100118
  59. Pappad`a R, Durante F, Salvadori G, et al. Clustering of concurrent flood risks via hazard scenarios’, Spatial Statistics, 2018, 23(1): 124-142. https://doi.org/10.1016/j.spasta.2017.12.002
  60. Bharath R and Srinivas VV. Delineation of homogeneous hydrometeorological regions using waveletbased global fuzzy cluster analysis. International Journal of Climatology, 2015, 35(15): 4707-4727. https://doi.org/10.1002/joc.4318