Prediction of river temperature surges is dependent on precipitation method

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@article{57cbb9be060f4e5ea69de19624609329,
title = "Prediction of river temperature surges is dependent on precipitation method",
abstract = "Urban river systems are particularly sensitive to precipitation-driven water temperature surges and fluctuations. These result from rapid heat transfer from low-specific heat capacity surfaces to precipitation, which can cause thermally polluted surface run-off to enter urban streams. This can lead to additional ecological stress on these already precarious ecosystems. Although precipitation is a first-order driver of hydrological response, water temperature studies rarely characterize rain event dynamics and typically rely on single gauge data that yield only partial estimates of catchment precipitation. This paper examines three precipitation measuring methods (a statutory automatic weather station, citizen science gauges, and radar estimates) and investigates relationships between estimated rainfall inputs and subhourly surges and diurnal fluctuations in urban river water temperature. Water temperatures were monitored at 12 sites in summer 2016 in the River Rea, in Birmingham, UK. Generalized additive models were used to model the relationship between subhourly water temperature surges and precipitation intensity and subsequently the relationship between daily precipitation totals and standardized mean water temperature. The different precipitation measurement sources give highly variable precipitation estimates that relate differently to water temperature fluctuations. The radar catchment-averaged method produced the best model fit (generalized cross-validation score [GCV] = 0.30) and was the only model to show a significant relationship between water temperature surges and precipitation intensity (P < 0.001, R 2  = 0.69). With respect to daily metrics, catchment-averaged precipitation estimates from citizen science data yielded the best model fit (GCV score = 0.20). All precipitation measurement and calculation methods successfully modelled the relationship between standardized mean water temperature and daily precipitation (P < 0.001). This research highlights the potential for the use of alternative precipitation datasets to enhance understanding of event-based variability in water quality studies. We conclude by recommending the use of spatially distributed precipitation data operating at high spatial (<1 km 2 ) and temporal (<15 min) resolutions to improve the analysis of event-based water temperature and water quality studies. ",
keywords = "water temperature, water quality, precipitation, citizen science, radar, event analysis, urban hydrology",
author = "Danny Croghan and {Van Loon}, {Anne F.} and Sadler, {Jon P.} and Chris Bradley and Hannah, {David M.}",
year = "2019",
month = jan,
day = "1",
doi = "10.1002/hyp.13317",
language = "English",
volume = "33",
pages = "144--159",
journal = "Hydrological Processes",
issn = "0885-6087",
publisher = "Wiley",
number = "1",

}

RIS

TY - JOUR

T1 - Prediction of river temperature surges is dependent on precipitation method

AU - Croghan, Danny

AU - Van Loon, Anne F.

AU - Sadler, Jon P.

AU - Bradley, Chris

AU - Hannah, David M.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Urban river systems are particularly sensitive to precipitation-driven water temperature surges and fluctuations. These result from rapid heat transfer from low-specific heat capacity surfaces to precipitation, which can cause thermally polluted surface run-off to enter urban streams. This can lead to additional ecological stress on these already precarious ecosystems. Although precipitation is a first-order driver of hydrological response, water temperature studies rarely characterize rain event dynamics and typically rely on single gauge data that yield only partial estimates of catchment precipitation. This paper examines three precipitation measuring methods (a statutory automatic weather station, citizen science gauges, and radar estimates) and investigates relationships between estimated rainfall inputs and subhourly surges and diurnal fluctuations in urban river water temperature. Water temperatures were monitored at 12 sites in summer 2016 in the River Rea, in Birmingham, UK. Generalized additive models were used to model the relationship between subhourly water temperature surges and precipitation intensity and subsequently the relationship between daily precipitation totals and standardized mean water temperature. The different precipitation measurement sources give highly variable precipitation estimates that relate differently to water temperature fluctuations. The radar catchment-averaged method produced the best model fit (generalized cross-validation score [GCV] = 0.30) and was the only model to show a significant relationship between water temperature surges and precipitation intensity (P < 0.001, R 2  = 0.69). With respect to daily metrics, catchment-averaged precipitation estimates from citizen science data yielded the best model fit (GCV score = 0.20). All precipitation measurement and calculation methods successfully modelled the relationship between standardized mean water temperature and daily precipitation (P < 0.001). This research highlights the potential for the use of alternative precipitation datasets to enhance understanding of event-based variability in water quality studies. We conclude by recommending the use of spatially distributed precipitation data operating at high spatial (<1 km 2 ) and temporal (<15 min) resolutions to improve the analysis of event-based water temperature and water quality studies.

AB - Urban river systems are particularly sensitive to precipitation-driven water temperature surges and fluctuations. These result from rapid heat transfer from low-specific heat capacity surfaces to precipitation, which can cause thermally polluted surface run-off to enter urban streams. This can lead to additional ecological stress on these already precarious ecosystems. Although precipitation is a first-order driver of hydrological response, water temperature studies rarely characterize rain event dynamics and typically rely on single gauge data that yield only partial estimates of catchment precipitation. This paper examines three precipitation measuring methods (a statutory automatic weather station, citizen science gauges, and radar estimates) and investigates relationships between estimated rainfall inputs and subhourly surges and diurnal fluctuations in urban river water temperature. Water temperatures were monitored at 12 sites in summer 2016 in the River Rea, in Birmingham, UK. Generalized additive models were used to model the relationship between subhourly water temperature surges and precipitation intensity and subsequently the relationship between daily precipitation totals and standardized mean water temperature. The different precipitation measurement sources give highly variable precipitation estimates that relate differently to water temperature fluctuations. The radar catchment-averaged method produced the best model fit (generalized cross-validation score [GCV] = 0.30) and was the only model to show a significant relationship between water temperature surges and precipitation intensity (P < 0.001, R 2  = 0.69). With respect to daily metrics, catchment-averaged precipitation estimates from citizen science data yielded the best model fit (GCV score = 0.20). All precipitation measurement and calculation methods successfully modelled the relationship between standardized mean water temperature and daily precipitation (P < 0.001). This research highlights the potential for the use of alternative precipitation datasets to enhance understanding of event-based variability in water quality studies. We conclude by recommending the use of spatially distributed precipitation data operating at high spatial (<1 km 2 ) and temporal (<15 min) resolutions to improve the analysis of event-based water temperature and water quality studies.

KW - water temperature

KW - water quality

KW - precipitation

KW - citizen science

KW - radar

KW - event analysis

KW - urban hydrology

UR - http://www.scopus.com/inward/record.url?scp=85057093671&partnerID=8YFLogxK

U2 - 10.1002/hyp.13317

DO - 10.1002/hyp.13317

M3 - Article

VL - 33

SP - 144

EP - 159

JO - Hydrological Processes

JF - Hydrological Processes

SN - 0885-6087

IS - 1

ER -