One Hundred Years of Streamflow Measurements in Massachusetts and Rhode Island

Ashman, Mary S.; Socolow, R.S.; Zanca, J.L.; Bonito, Mark V.

doi:10.3133/fs20043056

Cited by 2 publications

(2 citation statements)

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“…Streamflow is one of the most commonly observed hydrologic variables. Its earliest measurements date back to the late 19th century (Ashman et al, 2004). In more recent decades, the assimilation of streamflow observations into hydrologic models has followed various DA approaches and covered the gamut of applications (e.g., Wood and Szöllösi-Nagy, 1978;Kitanidis and Bras, 1980;Moradkhani et al, 2005;Weerts and El Serafy, 2006;Clark et al, 2008;Pauwels and De Lannoy, 2009;Seo et al, 2009;DeChant and Moradkhani, 2012;Noh et al, 2013;McMillan et al, 2013;Rafieeinasab et al, 2014;Lee and Seo, 2014;Sun et al, 2015;Ercolani and Castelli, 2017;Abbaszadeh et al, 2018;Ziliani et al, 2019, and references therein).…”

Section: Introductionmentioning

confidence: 99%

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Gharamti¹,

McCreight

Noh

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short timescales, combined with multiple sources of model error, makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Instantaneous streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of 1 month. The data assimilation (DA) system developed in this paper explores two novel contributions, namely (1) along-the-stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases which often occur in flooding situations. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40 % more accurate (root mean square error) streamflow estimates than regular, Euclidean distance-based localization. An assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along the Pee Dee River in South Carolina, and many other locations in the domain, where inflation is able to avoid filter divergence and, thereby, assimilate significantly more observations.

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Section: Introductionmentioning

confidence: 99%

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Gharamti¹,

McCreight

Noh

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Streamflow is one of the most commonly observed hydrologic variables. Its earliest measurements date back to the late nineteenth century (Ashman et al, 2004). In more recent decades, the assimilation of streamflow observations into hydrologic models has followed various DA approaches and covered the gamut of applications (e.g., Wood and Szöllösi-Nagy, 1978;Kitanidis and Bras, 1980;Moradkhani et al, 2005;Weerts and El Serafy, 2006;Clark et al, 2008;Pauwels and De Lannoy, 2009;Seo et al, 2009;DeChant and Moradkhani, 2012;Noh et al, 2013;McMillan et al, 2013;Rafieeinasab et al, 2014;Lee and Seo, 2014;Sun et al, 2015;Ercolani and Castelli, 2017;Abbaszadeh et al, 2018;Ziliani et al, 2019, and references therein).…”

mentioning

confidence: 99%

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

Gharamti

McCreight

Noh

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Predicting major floods during extreme rainfall events remains an important challenge. Rapid changes in flows over short time-scales combined with multiple sources of model error makes it difficult to accurately simulate intense floods. This study presents a general data assimilation framework that aims to improve flood predictions in channel routing models. Hurricane Florence, which caused catastrophic flooding and damages in the Carolinas in September 2018, is used as a case study. The National Water Model (NWM) configuration of the WRF-Hydro modeling framework is interfaced with the Data Assimilation Research Testbed (DART) to produce ensemble streamflow forecasts and analyses. Hourly streamflow observations from 107 United States Geological Survey (USGS) gauges are assimilated for a period of one month. The data assimilation (DA) system developed in this paper explores two novel contributions: (1) Along-The-Stream (ATS) covariance localization and (2) spatially and temporally varying adaptive covariance inflation. ATS localization aims to mitigate not only spurious correlations, due to limited ensemble size, but also physically incorrect correlations between unconnected and indirectly connected state variables in the river network. We demonstrate that ATS localization provides improved information propagation during the model update. Adaptive prior inflation is used to tackle errors in the prior, including large model biases. Analysis errors incurred during the update are addressed using posterior inflation. Results show that ATS localization is a crucial ingredient of our hydrologic DA system, providing at least 40% more accurate (RMSE) streamflow estimates than regular, Euclidean distance-based localization. Assessment of hydrographs indicates that adaptive inflation is extremely useful and perhaps indispensable for improving the forecast skill during flooding events with significant model errors. We argue that adaptive prior inflation is able to serve as a vigorous bias correction scheme which varies both spatially and temporally. Major improvements over the model's severely underestimated streamflow estimates are suggested along Pee Dee River in South Carolina and many other locations in the domain, where inflation is able to avoid filter divergence and thereby assimilate significantly more observations.

show abstract

One Hundred Years of Streamflow Measurements in Massachusetts and Rhode Island

Abstract: Streamflow-gaging station Little River at Oxford, Massachusetts. This 1940 photo shows the gage house, cable car, and cableway.

Cited by 2 publications

References 1 publication

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

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