Non-perennial segments in river networks

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(12), P. 815 - 830

Published: Nov. 23, 2023

Language: Английский

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Stream Network Dynamics of Non‐Perennial Rivers: Insights From Integrated Surface‐Subsurface Hydrological Modeling of Two Virtual Catchments

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(2)

Published: Feb. 1, 2024

Abstract Understanding the spatio‐temporal dynamics of runoff generation in headwater catchments is challenging, due to intermittent and fragmented nature surface flows. The active stream network non‐perennial rivers contracts expands, with a dynamic behavior that depends on complex interplay among climate, topography, geology. In this work, CATchment HYdrology, an integrated surface–subsurface hydrological model (ISSHM), used simulate two virtual same, spatially homogeneous, subsurface characteristics (hydraulic conductivity, porosity, water retention curves) but different morphology. We run sets simulations reproduce sequence steady‐states at catchment wetness levels transient conditions analyze joint variations length ( L ) discharge outlet Q high resolutions. shape curves differs does not depend climate forcing, as it mainly controlled by underlying topography. then analyzed suitability topographic index contributing area identify spatial configuration maximum catchments. These morphometric parameters provided good estimate distribution flowing both study Our numerical indicate ISSHMs have potential accurately describe networks processes driving such that, overall, they can be useful tools gain insights into main physical drivers streams.

Language: Английский

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Streamflow Intermittence in Europe: Estimating High‐Resolution Monthly Time Series by Downscaling of Simulated Runoff and Random Forest Modeling

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(8)

Published: July 31, 2024

Abstract Knowing where and when rivers cease to flow provides an important basis for evaluating riverine biodiversity, biogeochemistry ecosystem services. We present a novel modeling approach estimate monthly time series of streamflow intermittence at high spatial resolution the continental scale. Streamflow is quantified more than 1.5 million river reaches in Europe as number no‐flow days grouped into five classes (0, 1–5, 6–15, 16–29, 30–31 days) each month from 1981 2019. Daily observed 3706 gauging stations were used train validate two‐step random forest approach. Important predictors derived 73 15 arc‐sec (∼500 m) grid cells that computed by downscaling 0.5 arc‐deg (∼55 km) output global hydrological model WaterGAP, which accounts human water use. Of perennial non‐perennial station‐months, 97.8% 86.4%, respectively, correctly predicted. Interannual variations months satisfactorily simulated, with median Pearson correlation 0.5. While prevalence underestimated, overestimated dry regions artificial storage abounds. Our estimates 3.8% all European reach‐months 17.2% during 1981–2019, predominantly days. Although estimation uncertainty high, our study provides, first time, information on continent‐wide dynamics streams.

Language: Английский

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Are temporary stream observations useful for calibrating a lumped hydrological model?

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130686 - 130686

Published: Feb. 3, 2024

Multi-criteria model calibration can lead to a better representation of hydrological processes and reduce parameter uncertainty compared on streamflow data alone. However, the additional may be difficult collect or aggregate into representative catchment average value that used calibrate lumped model. Temporary streams are highly dynamic, their flow state observed visually. temporary still uncommon rarely in modelling. In this study, we unique dataset with discrete observations for France evaluated how informative these calibrating lumped, bucket-type We calibrated HBV 92 catchments using discharge stream-level at different temporal resolutions (daily, one daily per month, season) as proxy groundwater storage. stream generally did not result overall simulation validation period. which performance based only was poor, it more likely an improvement performance. The use combination reduced uncertainties low-flow simulations up half catchments. This caused by better-constrained storage coefficient slowest reservoir elimination sets led substantial variations improvements due inclusion were related characteristics. Thus, remains unclear help improve uncertainty.

Language: Английский

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Improving calibration of groundwater flow models using headwater streamflow intermittence

Hydrological Processes, Journal Year: 2024, Volume and Issue: 38(6)

Published: June 1, 2024

Abstract Non‐perennial streams play a crucial role in ecological communities and the hydrological cycle. However, key parameters processes involved stream intermittency remain poorly understood. While climatic conditions, geology land use are well identified, assessment modelling of groundwater controls on streamflow intermittence challenge. In this study, we explore new opportunities to calibrate process‐based 3D flow models designed simulate hydrographic network dynamics groundwater‐fed headwaters. Streamflow measurements maps considered together constrain effective hydraulic properties aquifer hydrogeological models. The simulations were then validated using visual observations water presence/absence, provided by national monitoring France (ONDE). We tested methodology two pilot unconfined shallow crystalline catchments, Canut Nançon catchments (Brittany, France). found that both expansion/contraction required simultaneously estimate conductivity porosity with low uncertainties. calibration allowed good prediction intermittency, terms spatial extent. For studied, Nançon, is close reaching 1.5 × 10 −5 m/s 4.5 m/s, respectively. they differ more their storage capacity, estimated at 0.1% 2.2%, Lower capacity leads higher level fluctuations, shorter response times, an increase proportion intermittent reduction perennial flow. This framework for predicting headwater can be deployed improve our understanding different geomorphological, geological contexts. It will benefit from advances remote sensing crowdsourcing approaches generate observational data products high temporal resolution.

Language: Английский

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Predicting wastewater treatment plant influent in mixed, separate, and combined sewers using nearby surface water discharge for better wastewater-based epidemiology sampling design

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 906, P. 167375 - 167375

Published: Sept. 27, 2023

Language: Английский

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The importance of source data in river network connectivity modeling: A review

Limnology and Oceanography, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 7, 2024

Abstract River network connectivity (RC) describes the hydrologic exchange of water, nutrients, sediments, and pollutants between river channel other “sites” via heterogenous flowpaths along corridor. As water moves downstream it carries these constituents, creating a stream‐to‐ocean continuum that regulates global carbon, nutrient cycling. models have developed over many decades, culminating in recent years with network‐scale RC explicitly simulate transport elements from headwaters to coasts, sometimes requiring contain tens millions reaches. These advances provide transformative insights into aggregate effects on material across scales local global. Yet, reviews pointed several challenges need be overcome continue advancing modeling. In service goals, I summarize maps identify similarities differences large‐scale modeling landscape. Although our computational upscaling abilities significantly improved revealed new insights, current are still limited by quantity, quality, resolution, lack standardization available situ databases source data necessary for This suggests we can extend if keep improving datasets, while continuously revisiting physics theory explain those data. doing so, will expand role informing quality management future.

Language: Английский

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Drainage network dynamics in an agricultural headwater sub-basin

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 914, P. 169826 - 169826

Published: Jan. 5, 2024

Language: Английский

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Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Hydrological Processes, Journal Year: 2023, Volume and Issue: 37(12)

Published: Dec. 1, 2023

Abstract Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts density (DD) varies over time. Monitoring DD dynamics is essential to understand processes controlling it. However, our knowledge of event‐scale limited because high spatial temporal resolution data on remain sparse. Therefore, team monitored hydrologic variables in two 5‐ha headwater catchments Swiss pre‐Alps summer 2021, through mapping surveys flow state a wireless streamwater level sensor network. We combined sources calculate at event‐time scale. Our so‐called CEASE method assumes that channel reach occurs above set water thresholds, it determined DDs with accuracies >94%. responses events differed for catchments, despite their proximity similar size. ranged from 2.7 32.2 km −2 flatter catchment (average slope: 15°). For this catchment, discharge‐DD relationship became steeper when exceeded 20 increased substantially relatively small increases discharge. rainfall during dry conditions, showed counterclockwise hysteresis, likely due initially groundwater discharge area near outlet; once stopped, remained streamflow recession rising levels throughout catchment. wet responded synchronously. In 24°), varied only 7.8 14.6 there was no hysteresis or threshold behaviour relationship, multiple springs maintained monitoring period. These results highlight variability its across catchments.

Language: Английский

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Identifying invertebrate indicators for streamflow duration assessments in forested headwater streams

Freshwater Science, Journal Year: 2023, Volume and Issue: 42(3), P. 247 - 267

Published: June 5, 2023

Streamflow-duration assessment methods (SDAMs) are rapid, indicator-based tools for classifying streamflow duration (e.g., intermittent vs perennial flow) at the reach scale. Indicators easily assessed stream properties used as surrogates of flow duration, which is too resource intensive to measure directly many reaches. Invertebrates commonly SDAM indicators because not highly mobile, and different species have life stages that require durations times year. The objectives this study were 1) identify invertebrate taxa can be distinguish between reaches having flow, 2) compare indicator strength across taxonomic numeric resolutions, 3) assess relative importance season habitat type on ability invertebrates predict streamflow-duration class. We 2 methods, random forest models analysis, analyze aquatic terrestrial data (presence/absence, density, biomass) family genus levels from 370 samples collected both erosional depositional habitats during wet dry seasons. In total, 36 53 sampled along 31 forested headwater streams in 4 level II ecoregions United States. Random family- genus-level datasets had classification accuracy ranging 88.9 93.2%, with slightly higher density than presence/absence biomass datasets. Season (wet/dry) tended a stronger predictor class (erosional/depositional). Many (58.8%) (61.6%) reaches, most exclusive 1 rarely collected. However, 23 family-level or (20 3 terrestrial) 44 genera identified potential streams. utility varied part representation dataset but also variable ecological responses drying among species. Aquatic been an important field existing SDAMs, our findings highlight how including further maximize classifications.

Language: Английский

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