Combining Hydrologic Simulations and Stream‐network Models to Reveal Flow‐ecology Relationships in a Large Alpine Catchment
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Abstract
Flow regimes profoundly influence river organisms and ecosystem functions, but regulatory approaches often lack the scientific basis to support sustainable water allocation. In part, this reflects the challenge of understanding the ecological effects of flow variability over different temporal and spatial domains. Here, we use a process‐based distributed hydrological model to simulate 23 years of natural flow regime in 100 bioassessment sites across the Adige River network (NE Italy), and to identify typical nivo‐glacial, nivo‐pluvial, and pluvial reaches. We then applied spatial stream‐network models (SSN) to investigate the relationships between hydrologic and macroinvertebrate metrics while accounting for network spatial autocorrelation and local habitat conditions. Macroinvertebrate metrics correlated most strongly with summer, winter, and temporal variation in streamflow, but effects varied across flow regime types. For example (i) taxon richness appeared limited by high summer flows and high winter flows in nivo‐glacial and pluvial streams, respectively; (ii) invertebrate grazers increased proportionally with the annual coefficient of flow variation in nivo‐glacial streams but tended to decline with flow variation in pluvial streams. Although local land‐use and water quality also affected benthic communities, most variation in macroinvertebrate metrics was associated with spatial autocorrelation. These findings highlight the importance of developing environmental flow management policies in ways that reflect specific hydro‐ecological and land use contexts. Our analyses also illustrate the importance of spatially‐explicit approaches that account for auto‐correlation when quantifying flow‐ecology relationships.
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“Subsurface return flow was modeled by a nonlinear reservoir (Majone et al., 2010).”
BackgroundSelf-citationDoretto, A., Bona, F., Falasco, E., Morandini, D., Piano, E., & Fenoglio, S. (2019) · Rivers Research and Applications: an international journal devoted to river research and management · doi:10.1002/rra.3563 · Cited by 41 · cited 1×
“The sensitivity of grazers to flow conditions, for example, can derive from their reliance on attached algae, which can be scoured during high flows or buried by fines during low flows (Buchanan et al., 2013; Doretto et al., 2020; Kennen et al., 2010).”
BackgroundQuadroni, S., Crosa, G., Gentili, G., & Espa, P. (2017) · Science of the Total Environment · doi:10.1016/j.scitotenv.2017.07.099 · Cited by 41 · cited 1×
“In agreement with recent investigations (Larsen et al., 2019; Quadroni et al., 2017), these results provide additional evidence of the limited sensitivity of WFD biological quality indicators, such as the Star_ICMi index, to stream hydrologic conditions.”
ResultNATURAL FLOW REGIME CLASSIFICATIONS ARE SENSITIVE TO DEFINITION PROCEDURES
Snelder, T., & Booker, D. (2013) · doi:10.1002/rra.2581 · Cited by 41
Flow–ecology relationships are spatially structured and differ among flow regimes
Bruckerhoff, L., Leasure, D., & Magoulick, D. D. (2018) · Journal of Applied Ecology · doi:10.1111/1365-2664.13297 · Cited by 35 · cited 3×
“These findings are in line with a recent study from the US where SSN models were used to examine variation in fish community traits (Bruckerhoff et al., 2019).”
BackgroundResultInfluentialThe Torgegram for Fluvial Variography: Characterizing Spatial Dependence on Stream Networks
Zimmerman, D., & Hoef, J. V. V. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2016.1247006 · Cited by 35 · cited 3× · see citing works
“…variography (i.e., spatial statistics applied to river networks) have provided the tools to model these spatial dependencies over the Euclidean and watercourse dimension, while also accounting for flow directionality (Carrara et al., 2012; Ver Hoef & Peterson, 2010; Zimmerman & Ver Hoef, 2017).”
Frieden, J., Peterson, E., Webb, J., & Negus, P. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.06.019 · Cited by 35 · cited 3× · see citing works
“The importance of using spatially explicit approaches to model empirical data in river networks is increasingly recognized (Frieden et al., 2014; Isaak et al., 2017; Larsen et al., 2019), and our results further support their application in flow-ecology research (Bruckerhoff et al.”
BackgroundMethodResultA dual-layer MPI continuous large-scale hydrological model including Human Systems
Avesani, D., Galletti, A., Piccolroaz, S., Bellin, A., & Majone, B. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105003 · Cited by 35 · cited 2×
“To achieve the above-mentioned goals, first, we used the process-based HYPERstreamHS hydrological model (Avesani et al., 2021) to simulate the natural streamflow series of one-hundred stream reaches throughout the Adige River basin where biological information was available.”
MethodSelf-citationMazor, R. D., May, J. T., Sengupta, A., McCune, K. S., Bledsoe, B. P., & Stein, E. D. (2018) · Freshwater Biology · doi:10.1111/fwb.13062 · Cited by 34 · cited 1× · see citing works
“25 (Belmar et al., 2011; Legendre & Legendre, 2012; Mazor et al., 2018), which provides an intermediate solution between chaining obtained via single linkage, and space dilation deriving from complete linkage.”
BackgroundFornaroli, R., Cabrini, R., Sartori, L., Marazzi, F., Vračević, D., Mezzanotte, V., Annala, M., & Canobbio, S. (2014) · Hydrobiologia · doi:10.1007/s10750-014-1974-6 · Cited by 33
Drift benthos relationships in the seasonal colonization dynamics of alpine streams
Robinson, C., Tockner, K., & Burgherr, P. (2004) · doi:10.1127/0003-9136/2004/0160-0447 · Cited by 30
Ilg, C., Castella, E., Lods-Crozet, B., & Marmonier, P. (2001) · doi:10.1127/archiv-hydrobiol/151/2001/335 · Cited by 26
HYPERstream: A multi-scale framework for streamflow routing in large-scale hydrological model
Piccolroaz, S., Lazzaro, M. D., Zarlenga, A., Majone, B., Bellin, A., & Fiori, A. (2015) · doi:10.5194/hess-20-2047-2016 · Cited by 22 · cited 11×
“In this paper, we develop and discuss a framework that addresses these challenges using the Adige River basin (northeastern Italy) as a case study.”
MethodInfluentialSelf-citationPatrick, C., & Yuan, L. L. (2017) · Ecological Applications · doi:10.1002/eap.1554 · Cited by 21 · cited 2×
“Statistical hydrological models aim to predict flow metrics at ungauged locations from the observed relation between available streamflow series and catchment characteristics (Booker et al., 2015; Patrick & Yuan, 2017), or by means of geostatistical interpolation (Skøien et al., 2006).”
BackgroundLarsen, S., Bruno, M., & Zolezzi, G. (2018) · bioRxiv · doi:10.1101/419804 · Cited by 14 · cited 1×
“In agreement with recent investigations (Larsen et al., 2019; Quadroni et al., 2017), these results provide additional evidence of the limited sensitivity of WFD biological quality indicators, such as the Star_ICMi index, to stream hydrologic conditions.”
ResultSelf-citationRock glaciers and paraglacial features influence stream invertebrates in a deglaciating Alpine area
Brighenti, S., Tolotti, M., Bertoldi, W., Wharton, G., & Bruno, M. (2020) · Freshwater Biology · doi:10.1111/fwb.13658 · Cited by 9 · cited 1× · see citing works
“Besides the range of anthropogenic influence, the three streamflow types analyzed here are characterized by different levels of environmental severity, which declines with decreasing glacial influence (Brighenti et al., 2021).”
BackgroundSelf-citationDisentangling the multiple stressors acting on stream ecosystems to support restoration priorities.
Azzellino, A., Canobbio, S., Cervigen, S., Marchesi, V., & Piana, A. (2015) · Water Science and Technology · doi:10.2166/wst.2015.177 · Cited by 8
When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species?
Kiesel, J., Kakouei, K., Guse, B., Fohrer, N., & Jähnig, S. (2020) · Ecohydrology · doi:10.1002/eco.2193 · Cited by 6
Flagged first, then by citations
12 of the 96 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Suitability of ERA5-Land reanalysis dataset for hydrological modelling in the Alpine region
Torre, D. D., Marco, N. D., Menapace, A., Avesani, D., Righetti, M., & Majone, B. (2024) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2024.101718 · Cited by 67 · see citing works
Self-citationMajone, B., Avesani, D., Zulian, P., Fiori, A., & Bellin, A. (2021) · Hydrology and Earth System Sciences · doi:10.5194/hess-2021-467 · Cited by 35
“Hydrological simulations were performed at a daily timescale with the HYPERstreamHS model (Avesani et al., 2021; Laiti et al., 2018; Larsen et al., 2021); HYPERstreamHS couples the HYPERstream routing scheme, recently proposed by Piccolroaz et al. (2016), with a continuous module for surface and…”
MethodSelf-citationDetailed simulation of storage hydropower systems in large Alpine watersheds
Galletti, A., Avesani, D., Bellin, A., & Majone, B. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.127125 · Cited by 29 · see citing works
Self-citationPompeu, C. R., Peñas, F. J., Goldenberg‐Vilar, A., Álvarez‐Cabria, M., & Barquín, J. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109662 · Cited by 32 · see citing works
Mainali, J., Chang, H., & Parajuli, R. (2022) · Annals of the American Association of Geographers · doi:10.1080/24694452.2022.2107478 · Cited by 15
“This feature not only allows for obtaining enhanced insights into the impacts of local landscape factors on water quality, but also takes care of any spatial inconsistencies brought about by the location of the sites along with the stream network (e.g., Larsen et al. 2021).”
BackgroundNapoli, A., Matiu, M., Laiti, L., Barbiero, R., Bellin, A., Zardi, D., & Majone, B. (2025) · Climatic Change · doi:10.1007/s10584-025-03890-y · Cited by 12 · see citing works
“Precipitation and contributions from underground water, along with evapotranspiration, play a significant role in determining flows in the val - leys, especially in the southern portion of the basin (Chiogna et al. 2016; Larsen et al. 2021 ).”
Self-citationExtreme low‐flow effects on riverine fauna: A perspective on methodological assessments
White, J. C., Aspin, T. W. H., Picken, J., Ledger, M., Wilby, R., & Wood, P. (2022) · Ecohydrology · doi:10.1002/eco.2422 · Cited by 10 · see citing works
“, 2021) and spatial autocorrelation effects (e.g. Bruckerhoff et al., 2019; Larsen et al., 2021).”
BackgroundDong, Z., Ji, X., & Ma, K. (2025) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2025.102228 · Cited by 1 · see citing works
Eddy, M. C., Lord, B., Perrot, D., Bower, L. M., & Peoples, B. K. (2021) · Ecohydrology · doi:10.1002/eco.2387 · Cited by 5
“…to simulation of relatively few sites and HMs, (Caldwell et al., 2015; Eddy, Moreda, et al., 2017; Shrestha et al., 2014; Teutschbein et al., 2018; Worthington et al., 2019) and have provided insight into several flow–ecology analyses (Drew et al., 2018; Larsen et al., 2021; Phelan et al., 2017).”
BackgroundSorensen, S. F., Fox, J., & Magoulick, D. D. (2026) · Science of the Total Environment · doi:10.1016/j.scitotenv.2026.181562 · Cited by 2
Effects of irrigation dams on riverine biota in mountain streams
Pompeu, C. R., Peñas, F. J., & Barquín, J. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2024.1332268 · Cited by 2
Zhang, J., & Xin, Z.-B. (2025) · Hydrological Processes · doi:10.1002/hyp.70319 · Cited by 1
“Furthermore, the drying up of the river caused a series of ecological impacts, including a reduction in aquatic habitats, a drop in the groundwater table and increased surface water infiltration (Larsen et al. 2021).”
Long-term changes of alpine stream macroinvertebrates in relation to glacial recession
Bruni, S., Wanner, L., Grolimund, A., & Robinson, C. (2025) · Aquatic Sciences · doi:10.1007/s00027-025-01232-9 · Cited by 1 · see citing works
“These environmental shifts, along with alterations in hydrologic regimes and glacial recession, exert substantial impacts on alpine ecosystems and the kind of habitats available for aquatic organisms over time (Fornaroli et al. 2019; Meißner et al. 2019; Larsen et al. 2021).”
Medina Madariaga, G., Nguyen, H. H., Kiesel, J., Peters, K., Feld, C. K., Jaehnig, S. C., & Cambas, Y. T. (2024) · doi:10.1101/2024.05.28.596146 · Cited by 1 · see citing works
Wu, X., Yu, J.-L., Chen, B., Lou, Y., He, M., Miao, P., Jiang, S., Yang, F., & Tang, T. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.104001
Quadroni, S., Erba, S., Cazzola, M., Cislaghi, S., & Buffagni, A. (2026) · Environmental Sciences Europe · doi:10.1186/s12302-026-01455-z
“Such as in most research producing simulations of habitats due to hydrological variations [64, 65], changes in substrate grain size due to observed variations in flow over time were not accounted for in this study.”
Wang, Q., Shang, J., & Wang, L.-H. (2026) · Ecological Engineering: The Journal of Ecotechnology · doi:10.1016/j.ecoleng.2026.108094
- Authors
- Larsen, S., Majone, B., Zulian, P., Stella, E., Bellin, A., Bruno, M. C., & Zolezzi, G.
- Affiliations
- Fondazione Edmund Mach (IT)
- University of Trento (IT)
- National Research Council (IT)
- Corresponding author
- Larsen, Stefano
- ORCID
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2021-04
- Volume
- 57
- Issue
- 4
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W3081392650
- Cited by
- 28
- References
- 96
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 2 · 2023: 3 · 2022: 7 · 2021: 5
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Water Resources Research (publishedVersion, cc-by)
- Iris (University of Trento) (submittedVersion, cc-by)
- CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- H2020 Marie Skłodowska-Curie Actions (748969)
- Ministero dell’Istruzione, dell’Università e della Ricerca (L.232/2016)
- Horizon 2020 Framework Programme (824158)
- Topics
- Physical Sciences › Environmental Science › Ecology › Freshwater macroinvertebrate diversity and ecology
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Fish Ecology and Management Studies
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- flow regimes, flow-ecology relationships, environmental flows, macroinvertebrates, benthic communities, alpine catchments, Adige River, spatial autocorrelation, water allocation, distributed hydrological model, land use effects
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