Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989–2018

Sando, Roy; Jaeger, Kristin L.; Farmer, William; Barnhart, T. B.; McShane, Ryan R.; Welborn, Toby L.; Kaiser, Kendra E.; Hafen, Konrad; Blasch, Kyle W.; York, Benjamin N.; Shallcross, Alden

doi:10.1016/j.hydroa.2022.100138

Cited by 4 publications

(11 citation statements)

References 97 publications

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“…Many past studies depend on these databases to get and analyze Climate data (e.g. Rockett, P., 2021;Deshmane, M et al, 2020;Chandler, W et al, 2015;Zhang, T et al, 2010;Puri, P., Puri, V., 2022;Sando, R. et al, 2022;Marelign, M. A. et al, 2020). The current study used POWER to download temperature data °C, wind speed (km\h), and relative humidity (%) (https://power.larc.nasa.gov/data-accessviewer).…”

Section: 2-climatic Analysismentioning

confidence: 99%

Tectonic Geomorphology of Wadi Wasit in Sinai Peninsula (Egypt)

2023

The Egyptian Journal of Environmental Change

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Structural geomorphological landforms arise due to geological processes in tectonic regions. This study aims to identify the geomorphological landforms resulting from these processes in Wadi Wasit in the Sinai Peninsula in Egypt. The study depends on geological and topographic maps, aerial images, DEM (SRTM), fieldwork, and Global climate databases (POWER, ERA5). It used the GIS technique. The results showed that the surface rocks in the study area were deposited in the Upper Cretaceous, Paleocene, Eocene, Miocene, Pleistocene, and Holocene. The rocks consist of Limestone, Sandstone, Wadi deposits, Alluvial Hamadah deposits, and Fanglomerate. It was affected by 41 faults and a concave fold. These tectonic processes formed geomorphological landforms in the study area, which are: Fault scarps, Cuesta scarps, Structural Basins, Faulted Wadies, and Gorges.

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Section: 2-climatic Analysismentioning

confidence: 99%

Tectonic Geomorphology of Wadi Wasit in Sinai Peninsula (Egypt)

2023

The Egyptian Journal of Environmental Change

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“…Probabilities are useful for identifying which parts of the channel network have the highest and lowest likelihood of late summer flow that correspond, in this case, to probability values well above and below 0.5, respectively. Similarly, it is useful to identify those parts of the channel network that have less likelihood of belonging to a definitive class (e.g., probability values surrounding 0.5), which either may require additional data collection to better determine the streamflow permanence class (Jaeger et al, 2019;Messager et al, 2021;Sando et al, 2022) or which may represent locations that are near the threshold between classes and which may be more sensitive to shifts between streamflow permanence classes from climate change (Ward et al, 2020;Zipper et al, 2021;Botter et al, 2021).…”

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confidence: 99%

“…Empirical models have shown promise for providing predictions on streamflow permanence, collectively defined here as both the spatial extent of surface flow and persistence of surface flow in terms of timing and duration of flow. Models have been produced across a range of spatial and temporal scales from global, one-time predictions (Messager et al, 2021), to multi-state regional, annual (Jaeger et al, 2019;Sando et al, 2022) or daily predictions (Yu et al, 2019), to individual watershed and reach scales at varying time steps (Durighetto et al, 2020;Jensen et al, 2018;Kaplan et al, 2020;Moidu et al, 2021;Pate et al, 2020). The strength of these models is the ability to leverage readily available spatial data to provide predictions of streamflow permanence based on typically sparse response variable data (Biau & Scornet, 2016), in this case streamflow-permanence observations.…”

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confidence: 99%

“…The geographic extent of the Mount Rainier study area ($10 000 km 2 ) is substantially larger than the individual catchment and relied on less intensive crowd-sourced data collection efforts focused within the National Park boundary. Additionally, 85% of the observational data was collected during climatically similar years (2019 and 2020), likely limiting the variability of climate conditions that have been shown to be useful predictors of streamflow permanence(Jaeger et al, 2019;Moidu et al, 2021;Sando et al, 2022). More observational data that extends geographically beyond the targeted Mount Rainier National Park boundary and across climatically different years would be expected to better capture the variability of environmental covariates including climate conditions across years and consequently improve accuracy and confidence in predictions of streamflow permanence throughout the study area.…”

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confidence: 99%

“…The primary objective of this study was to develop an empirical model that provides predictions on the probability of streamflow permanence, here defined as the probability of the presence of late summer surface flow, with greater accuracy than the regional PROS-PER PNW model. The model does not provide predictions on timing or duration of surface flow conditions (e.g., Durighetto et al, 2020;Jensen et al, 2018;Kaplan et al, 2020), but is a probability of membership to a class of streams that have late summer flow (e.g., Jaeger et al, 2019;Messager et al, 2021;Sando et al, 2022).…”

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confidence: 99%

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Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region

Jaeger

Sando

Dunn

et al. 2023

Hydrological Processes

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Accurate mapping of streams that maintain surface flow during annual baseflow periods in mountain headwater streams is important for informing water availability for human consumption and is a fundamental determinant of in-channel conditions for stream-dwelling organisms. Yet accurate mapping that captures local spatial variability and associated local controls on surface flow presence is limited. An empirical random-forest model was developed to predict streamflow permanence (late summer surface-flow presence) for Mount Rainier National Park and the surrounding mountainous area in western Washington, USA. This model was developed to improve upon the existing multi-state, regional-scale probability of stream permanence developed for the greater Pacific Northwest Region (PROSPER PNW ). The model was trained on 544 wet/dry observations collected during the late summer, baseflow period from 2018 to 2020 using the crowd-source mobile application, FLOwPER.Final model accuracy was 0.74 with drainage area and covariates describing geology, topography, and land cover as top predictors of streamflow permanence compared to coarser resolution climatic covariates. The prevalence of static covariates over climatic covariates as top ranked important covariates highlights the importance of scale when evaluating controls on streamflow permanence. Cross validation of the model indicates that streamflow permanence probabilities from this model is an improvement over the regional-scale PROSPER PNW model demonstrating the utility of relatively simple, crowd-sourced data to address water resource needs, and that determination of important predictors of streamflow permanence is influenced by the spatial and temporal resolution of analysis.

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Predicting Streamflow Duration From Crowd‐Sourced Flow Observations

Peterson,

Kampf,

Puntenney‐Desmond

et al. 2024

Water Resources Research

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Streamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes.

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Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989–2018

Cited by 4 publications

References 97 publications

Tectonic Geomorphology of Wadi Wasit in Sinai Peninsula (Egypt)

Tectonic Geomorphology of Wadi Wasit in Sinai Peninsula (Egypt)

Predicting probabilities of late summer surface flow presence in a glaciated mountainous headwater region

Predicting Streamflow Duration From Crowd‐Sourced Flow Observations

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