A high-resolution streamflow and hydrological metrics dataset for ecological modeling using a regression model

Irving, Katie; Kuemmerlen, Mathias; Kiesel, Jens; Kakouei, Karan; Domisch, Sami; Jähnig, Sonja C.

doi:10.1038/sdata.2018.224

Cited by 26 publications

(16 citation statements)

References 43 publications

(47 reference statements)

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“…Nonetheless, state and national governments often make policy decisions at regional or national scales (e.g., Alexander, 2015), and therefore models at these scales may provide critical information (e.g., Lin et al, 2019). Notably, while most of these models simulate processes at the reach-to watershed-scale, those processes are often not finely resolved enough to make local management decisions (Irving et al, 2018).…”

Section: Process-based Hydrologic Modeling To Provide Context For Interpretationmentioning

confidence: 99%

Zero or not? Causes and consequences of zero‐flow stream gage readings

et al. 2020

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Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed-scale processes.When stream gages read zero, this may indicate that the stream has dried at this location; however, zero-flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero-flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero-flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human-driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero-flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero-flow gage readings and implications for reach-and watershedscale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero-flows will only attain greater importance in a more variable and changing hydrologic climate.

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Section: Process-based Hydrologic Modeling To Provide Context For Interpretationmentioning

confidence: 99%

Zero or not? Causes and consequences of zero‐flow stream gage readings

et al. 2020

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“…The dataset from Irving et al (2018), includes 53 of the Indicators of Hydrological Alteration (IHA) metrics that describe the magnitude, frequency, timing, duration and rate of change of flow events (Olden & Poff, 2003). IHA metrics are commonly used in flowecology assessments (e.g.…”

Section: Hydrologymentioning

confidence: 99%

“…These data were purposefully created at the same spatiotemporal resolution as the bioclimate and hydroclimate data for explicit use in SDMs. The data have been found to be effective for use in predictive modelling (Irving et al, 2018) within the same study area (Irving et al, 2020). All 53 IHA metrics were computed for the time period 1985-2013 to capture enough variability to produce accurate metrics following Kennard et al ( 2010) who recommends at least 15 year's worth of data, and informs that there is negligible change in variability over 30 years.…”

Section: Hydrologymentioning

confidence: 99%

“…Recently, data describing stream ecosystems in greater detail are becoming available, e.g. stream specific climate and land use (Domisch et al, 2015a;Linke et al, 2019), hydrology (Barbarossa et al 2018;Irving et al 2018), river classification and characteristics (Vogt et al, 2007;Ouellet Dallaire et al, 2019) as well as dams and reservoirs (globaldamwatch.org; Lehner et al, 2011). Despite this increasing availability, most studies focus on climate (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Disentangling the effect of climatic and hydrological predictor variables on benthic macroinvertebrate distributions from predictive models

2021

Self Cite

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Lotic freshwater macroinvertebrate species distribution models (SDMs) have been shown to improve when hydrological variables are included. However, most studies to date only include data describing climate or stream flow-related surrogates. We assessed the relative influence of climatic and hydrological predictor variables on the modelled distribution of macroinvertebrates, expecting model performance to improve when hydrological variables are included. We calibrated five SDMs using combinations of bioclimatic (bC), hydrological (H) and hydroclimatic (hC) predictor datasets and compared model performance as well as variance partition of all combinations. We investigated the difference in trait composition of communities that responded better to either bC or H configurations. The dataset bC had the most influence in terms of proportional variance, however model performance was increased with the addition of hC or H. Trait composition demonstrated distinct patterns between associated model configurations, where species that prefer intermediate to slow-flowing current conditions in regions further downstream performed better with bC–H. Including hydrological variables in SDMs contributes to improved performance, it is however, species-specific and future studies would benefit from hydrology-related variables to link environmental conditions and diverse communities. Consequently, SDMs that include climatic and hydrological variables could more accurately guide sustainable river ecosystem management.

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“…River discharge is one of the most important variables in hydrology and constitutes fundamental information [1,2] in the fields of environmental monitoring and in water management operations. However, measurements of discharge are often absent as conventional gauging methods are expensive and impractical as well as hard to deploy and maintain [3,4].…”

Section: Introductionmentioning

confidence: 99%

Discharge Estimation with the Use of Unmanned Aerial Vehicles (UAVs) and Hydraulic Methods in Shallow Rivers

Lagogiannis

Dimitriou

2021

Water

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Although river discharge is essential hydrologic information, it is often absent, especially for small rivers and remote catchment areas. Practical difficulties frequently impede the installation and operation of gauging stations, while satellite-sensed data have proved to be relatively useful only for discharge estimation of large-scale rivers. In this study, we propose a new methodology based on UAV-sensed data and photogrammetry techniques combined with empirical hydraulic equations for discharge estimation. In addition, two different riverbed particle size distributions were incorporated, to study the effect of fine sediment inclusion (or exclusion) in the estimation process. Accordingly, 17 study sites were selected and six different approaches were applied in each. Results show that at 75% of sites at least one approach produced an accurate discharge estimation, while in 10 out the 17 sites (58.8%) all six approaches produced accurate estimations. A strong correlation between a threshold value for the hydraulic radius (Rh = 0.3 m) of cross-sections and high estimation errors for sites exceeding it was also observed. The fine sediment inclusion improved only the performance of certain approaches and did not have a consistently positive effect. Overall, the relatively high percentage of sites with satisfactory discharge estimates indicates that using UAV-derived data and simple hydraulic equations can be used for this purpose, with an acceptable level of accuracy.

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A high-resolution streamflow and hydrological metrics dataset for ecological modeling using a regression model

Cited by 26 publications

References 43 publications

Zero or not? Causes and consequences of zero‐flow stream gage readings

Zero or not? Causes and consequences of zero‐flow stream gage readings

Disentangling the effect of climatic and hydrological predictor variables on benthic macroinvertebrate distributions from predictive models

Discharge Estimation with the Use of Unmanned Aerial Vehicles (UAVs) and Hydraulic Methods in Shallow Rivers

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