Prediction of Transmission Losses in Ephemeral Streams, Western U.S.A

Cataldo, Joseph C.

doi:10.2174/1874378101004010019

Cited by 18 publications

(15 citation statements)

References 20 publications

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“…On Earth, transmission losses are determined by quantifying the water balance via stream gauges at different locations within a reach (e.g., Schoener, ). Complex regression equations and models can be constructed from such data (e.g., Cataldo et al, ). For the unknown conditions of early Mars, we employ equation :

R_{run} = P - T - E,

where R run is the runoff rate, T is the transmission loss into the regolith, and E is the evaporative losses.…”

Section: Methodsmentioning

confidence: 99%

Climate Simulations of Early Mars With Estimated Precipitation, Runoff, and Erosion Rates

2020

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The debate over the early Martian climate is among the most intriguing in planetary science. Although the geologic evidence generally supports a warmer and wetter climate, climate models have had difficulty simulating such a scenario, leading some to suggest that the observed fluvial geology (e.g., valley networks, modified landscapes) on the Martian surface could have formed in a cold climate instead. However, as we have originally predicted using a single‐column radiative‐convective climate model (Ramirez, Kopparapu, Zugger, et al., 2014, https://doi.org/10.1038/ngeo2000), warming from CO2‐H2 collision‐induced absorption on a volcanically active early Mars could have raised mean surface temperatures above the freezing point, with later calculations showing that this is achievable with hydrogen concentrations as low as ~1%. Nevertheless, these predictions should be tested against more complex models. Here we use an advanced energy balance model that includes a northern lowlands ocean to show that mean surface temperatures near or slightly above the freezing point of water were necessary to carve the valley networks. Our scenario is consistent with a relatively large ocean as has been suggested. Valley network distributions would have been global prior to subsequent removal processes. At lower mean surface temperatures and smaller ocean sizes, precipitation and surface erosion efficiency diminish. The warm period may have been approximately <107 years, perhaps suggesting that episodic warming mechanisms were not needed. Atmospheric collapse and permanently glaciated conditions occur once surface ice coverage exceeds a threshold depending on collision‐induced absorption assumptions. Our results support an early warm and semiarid climate consistent with many geologic observations.

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R_{run} = P - T - E,

where R run is the runoff rate, T is the transmission loss into the regolith, and E is the evaporative losses.…”

Section: Methodsmentioning

confidence: 99%

Climate Simulations of Early Mars With Estimated Precipitation, Runoff, and Erosion Rates

2020

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“…Streamflow is predominantly temporary in water limited regions (Newman et al, 2006;Larned et al, 2010;Datry et al, 2014) such as the Western United States (US), where ∼89% of streams flow intermittently (US Geological Survey, 2008). These brief streamflow episodes have a profound effect on ecohydrological processes (Stromberg et al, 2008;Blasch et al, 2010;Jaeger and Olden, 2012) and groundwater recharge (Constantz, 1982;Goodrich et al, 2004;Coes and Pool, 2005;Blasch et al, 2006;Baillie et al, 2007;Callegary et al, 2007;Cataldo et al, 2010;Tillman et al, 2011). Despite the importance of temporary surface waters to biological processes and water resources, the frequency and temporal distribution of streamflow in water limited regions remains poorly quantified.…”

Section: Introductionmentioning

confidence: 99%

Estimating Surface Water Presence and Infiltration in Ephemeral to Intermittent Streams in the Southwestern US

et al. 2020

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Streamflow in arid and semi-arid regions is predominantly temporary, an integral part of mountain block hydrology and of significant importance for groundwater recharge and biogeochemical processes. However, temporary streamflow regimes, especially ephemeral flow, remain poorly quantified. We use electrical resistance sensors and USGS stream gauge data in 15 southern Arizona streams spanning a climate gradient (mean annual precipitation from 160 to 750 mm) to quantify temporary streamflow as streamflow presence and water presence, which includes streamflow, ponding and soil moisture. We use stream channel sediment data to estimate saturated hydraulic conductivity and potential annual infiltration. Annual streamflow ranged 0.6–82.4% or 2–301 days; while water presence ranged from 2.6 to 82.4% or 10 to over 301 days, or 4–33 times longer than streamflow. We identified 5 statistically distinct flow regimes based on the annual percent streamflow and water presence: (1) dry-ephemeral, (2) wet-ephemeral, (3) dry-intermittent, (4) wet-intermittent, and (5) seasonally-intermittent. In contrast to our expectations, stream channel density was a better predictor of annual streamflow and water presence than annual rainfall alone. Whereas, the dry-ephemeral and wet-ephemeral flow regimes varied with seasonal precipitation, the dry-intermittent, wet intermittent and seasonally-intermittent flow regimes did not. These results coupled with the potential infiltration estimates indicate that streamflow at the driest sites occurs in response to rainfall and overland flow while groundwater discharge and vadose zone contributions enhance streamflow at the wetter sites. We suggest that on a short temporal scale, and with respect to water presence, wetter sites might be buffered better against shifts in the timing and distribution of precipitation in response to climate change. Flow regime classifications that include both stream flow and water presence, rather than on stream flow alone, may be important for predicting thresholds in ecological functions and refugia in these dryland systems.

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“…Estimating total transmission losses and/or individual components in dryland river systems has previously been undertaken using three main approaches (Cataldo et al, 2004;Cataldo et al, 2010): (i) small-scale field experiments (Dahan et al, 2008;Dunkerley and Brown, 1999;Dunkerley, 2008;Maurer, 2002;Parsons et al, 1999); (ii) interpolation of sparse streamflow networks using simple regression and/or differential equations (Arnott et al, 2009;Costelloe et al, 2006;Knighton and Nanson, 1994;Knighton and Nanson, 2001;McCallum et al, 2012;Schmadel et al, 2010); and (iii) water balance modelling to allow estimation of total and component transmission losses (Morin et al, 2009). Key papers for these approaches are summarised in Table 1, and includes examples where hydrodynamic modelling has incorporated remotely sensed data in order to: (i) provide input data; (ii) calibrate and validate such models; and (iii) estimate various components of transmission losses (Karim et al, 2011;Milewski et al, 2009;Sharma and Murthy, 1994).…”

Section: Introductionmentioning

confidence: 99%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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Prediction of Transmission Losses in Ephemeral Streams, Western U.S.A

Cited by 18 publications

References 20 publications

Climate Simulations of Early Mars With Estimated Precipitation, Runoff, and Erosion Rates

Climate Simulations of Early Mars With Estimated Precipitation, Runoff, and Erosion Rates

Estimating Surface Water Presence and Infiltration in Ephemeral to Intermittent Streams in the Southwestern US

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

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