scholarly journals A Retrospective Streamflow Ensemble Forecast for an Extreme Hydrologic Event: a Case Study of Hurricane Irene and on the Hudson River basin

2016 ◽  
Author(s):  
F. Saleh ◽  
V. Ramaswamy ◽  
N. Georgas ◽  
A. Blumberg ◽  
J. Pullen
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Hudson River ◽  
The United States ◽  
Ensemble Forecast ◽  
Short Term ◽  
Hurricane Irene ◽  
Hydrologic Event ◽  
Extreme Hydrological Event

Abstract. This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River Basin, USA (~ 36,000 km2) using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation datasets reforecast by the 21 ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were used to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 hours pre-event utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modelling framework was implemented on the Hudson River basin, it is flexible and re-locatable in other parts of the world.

scholarly journals A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

2016 ◽  
Vol 20 (7) ◽  
pp. 2649-2667 ◽  
Author(s):  
Firas Saleh ◽  
Venkatsundar Ramaswamy ◽  
Nickitas Georgas ◽  
Alan F. Blumberg ◽  
Julie Pullen
Keyword(s):  
United States ◽  
River Basin ◽  
Hydrological Model ◽  
Hudson River ◽  
The United States ◽  
Ensemble Forecast ◽  
Short Term ◽  
Hurricane Irene ◽  
Hydrologic Event ◽  
Extreme Hydrological Event

Abstract. This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ∼  36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

scholarly journals Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas

2018 ◽  
Vol 19 (9) ◽  
pp. 1467-1483 ◽  
Author(s):  
Sunghee Kim ◽  
Hossein Sadeghi ◽  
Reza Ahmad Limon ◽  
Manabendra Saharia ◽  
Dong-Jun Seo ◽  
...  
Keyword(s):  
River Basin ◽  
Time Horizon ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Trinity River ◽  
Daily Streamflow ◽  
Emergency Managers ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
The Impact

Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.

scholarly journals Using Genomics to Link Populations of an Invasive Species to Its Potential Sources

2021 ◽  
Vol 9 ◽  
Author(s):  
Carlee A. Resh ◽  
Matthew P. Galaska ◽  
Kasey C. Benesh ◽  
Jonathan P. A. Gardner ◽  
Kai-Jian Wei ◽  
...  
Keyword(s):  
United States ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Hudson River ◽  
The United States ◽  
Eastern United States ◽  
Native Range ◽  
The Yangtze River Basin ◽  
The U.S

The introduction and subsequent range expansion of the Northern snakehead (Channa argus: Channidae, Anabantiformes) is one of a growing number of problematic biological invasions in the United States. This harmful aquatic invasive species is a predatory freshwater fish native to northeastern Asia that, following deliberate introduction, has established itself in multiple water basins in the eastern United States, as well as expanding its range into the Midwest. Previous work assessed the population structure and estimated the long-term effective population sizes of the populations present in the United States, but the source of the initial introduction(s) to the U.S. remains unidentified. Building on earlier work, we used whole genome scans (2b-RAD genomic sequencing) to analyze single nucleotide polymorphisms (SNPs) from C. argus to screen the genomes of these invasive fish from United States waters and from three sites in their native range in China. We recovered 2,822 SNP loci from genomic DNA extracted from 164 fish sampled from the eastern United States and Arkansas (Mississippi River basin), plus 30 fish sampled from three regions of the Yangtze River basin in China (n = 10 individuals per basin). Our results provide evidence supporting the Yangtze River basin in China, specifically the Bohu and/or Liangzi lakes, is a likely source of the C. argus introductions in multiple regions of the U.S., including the Lower Hudson River basin, Upper Hudson River basin and Philadelphia (Lower Delaware River basin). This information, in conjunction with additional sampling from the native range, will help to determine the source(s) of introduction for the other U.S. populations. Additionally, this work will provide valuable information for management to help prevent and manage future introductions into United States waterways, as well as aid in the development of more targeted strategies to regulate established populations and inhibit further spread.

scholarly journals A Dynamical-Statistical-Analog Ensemble Forecast Model: Theory and an Application to Heavy Rainfall Forecasts of Landfalling Tropical Cyclones

2020 ◽  
Vol 148 (4) ◽  
pp. 1503-1517 ◽  
Author(s):  
Fumin Ren ◽  
Chenchen Ding ◽  
Da-Lin Zhang ◽  
Deliang Chen ◽  
Hong-li Ren ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Forecast Model ◽  
The United States ◽  
Ensemble Forecast ◽  
Initial Value ◽  
Forecast Performance ◽  
Typhoon Track ◽  
Perfect Model ◽  
Accumulated Rainfall

Abstract Combining dynamical models with statistical algorithms is an important way to improve weather and climate prediction. In this study, a concept of a perfect model, whose solutions are from observations, is introduced, and a dynamical-statistical-analog ensemble forecast (DSAEF) model is developed as an initial-value problem of the perfect model. This new analog-based forecast model consists of the following three steps: (i) construct generalized initial value (GIV), (ii) identify analogs from historical observations, and (iii) produce an ensemble of predictands. The first step includes all appropriate variables, not only at an instant state but also during their temporal evolution, that play an important role in determining the accuracy of each predictand. An application of the DSAEF model is illustrated through the prediction of accumulated rainfall associated with 21 landfalling typhoons occurring over South China during the years of 2012–16. Assuming a reliable forecast of landfalling typhoon track, two different experiments are conducted, in which the GIV is constructed by including (i) typhoon track only; and (ii) both typhoon track and landfall season. Results show overall better performance of the second experiment than the first one in predicting heavy accumulated rainfall in the training sample tests. In addition, the forecast performance of both experiments is comparable to the operational numerical weather prediction models currently used in China, the United States, and Europe. Some limitations and future improvements as well as comparisons with some existing analog ensemble models are also discussed.

scholarly journals Genomic analyses of Northern snakehead (Channa argus) populations in North America

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4581 ◽  
Author(s):  
Carlee A. Resh ◽  
Matthew P. Galaska ◽  
Andrew R. Mahon
Keyword(s):  
United States ◽  
Invasive Species ◽  
River Basin ◽  
Hudson River ◽  
The United States ◽  
Aquatic Invasive Species ◽  
Potomac River ◽  
Potomac River Basin ◽  
Channa Argus

Background The introduction of northern snakehead (Channa argus; Anabantiformes: Channidae) and their subsequent expansion is one of many problematic biological invasions in the United States. This harmful aquatic invasive species has become established in various parts of the eastern United States, including the Potomac River basin, and has recently become established in the Mississippi River basin in Arkansas. Effective management of C. argus and prevention of its further spread depends upon knowledge of current population structure in the United States. Methods Novel methods for invasive species using whole genomic scans provide unprecedented levels of data, which are able to investigate fine scale differences between and within populations of organisms. In this study, we utilize 2b-RAD genomic sequencing to recover 1,007 single-nucleotide polymorphism (SNP) loci from genomic DNA extracted from 165 C. argus individuals: 147 individuals sampled along the East Coast of the United States and 18 individuals sampled throughout Arkansas. Results Analysis of those SNP loci help to resolve existing population structure and recover five genetically distinct populations of C. argus in the United States. Additionally, information from the SNP loci enable us to begin to calculate the long-term effective population size ranges of this harmful aquatic invasive species. We estimate long-term Ne to be 1,840,000–18,400,000 for the Upper Hudson River basin, 4,537,500–45,375,000 for the Lower Hudson River basin, 3,422,500–34,225,000 for the Potomac River basin, 2,715,000–7,150,000 for Philadelphia, and 2,580,000–25,800,000 for Arkansas populations. Discussion and Conclusions This work provides evidence for the presence of more genetic populations than previously estimated and estimates population size, showing the invasive potential of C. argus in the United States. The valuable information gained from this study will allow effective management of the existing populations to avoid expansion and possibly enable future eradication efforts.

scholarly journals Eastern U.S. Verification of Ensemble Precipitation Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 117-139 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Nicholas Balderas ◽  
Jose D. Fuentes ◽  
Seann Reed ◽  
...  
Keyword(s):  
United States ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Forecast ◽  
Spatial Aggregation ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Eastern United States ◽  
Skill Scores

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

Developing semi-distributed hydrological models for streamflow forecasting in upper Narmada River basin

2021 ◽  
Author(s):  
Ankit Singh ◽  
Soubhik Mondal ◽  
Sanjeev Kumar Jha
Keyword(s):  
River Basin ◽  
Stream Flow ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Streamflow Forecasting ◽  
Vic Model ◽  
Narmada River ◽  
Medium Range ◽  
Streamflow Forecasts

<p>Short-term streamflow forecast is important for various hydrological applications such as, estimating inflow to reservoirs, sending alarms in case of extreme events like flood and flash floods etc. Flooding events in last few years in the Indian subcontinent emphasized the importance of more accurate streamflow forecasts and the possible benefit of high-resolution Numerical Weather Prediction (NWP) models has been confirmed. In India, National Center for Medium Range Weather Forecasting (NCMRWF) provides rainfall forecasts from its UK Met office Unified Model based deterministic model (NCUM), and ensemble prediction system (NEPS). The comparison of NCMRWF with the forecast from other agencies such as Japan Metrological Agency (JMA)and European Center for Medium Range Forecast (ECMWF) have been addressed in this work. Global NWP models developed by different international agencies applydifferent algorithms, initial and boundaries conditions.The usefulness of several forecasts in streamflow forecasting is still being investigated in India. Recent studies on streamflow forecasting by using different NWP models shows that the performance of streamflow forecasts directly depends on the skill of NWP models. Hydrological model also plays a vital role in stream flow forecasting, because different hydrological model have different structure, parameters and algorithms to simulate the flow.</p><p>            In this study we use the Soil and Water Assessment Tool (SWAT) a Hydrological Response Unit (HRU’s) based hydrological model. HRU is the area that contains similar type of soil, land use and slope properties in a subbasin. For comparison, the streamflow generated from the forecasted rainfall by NWP, we select three different NWP models namely JMA, ECMWF and NCMRWF for streamflow forecasting. Manot watershed part of Narmada River basin in central India is selected as the study area for this study. Streamflow is examined for monsoon (June to September) period of 2018 at multiple lead times i.e. 1 to 5 days. Rain-gauge based gridded Indian Meteorological Department (IMD) rainfall product is used as observed data in SWAT. All rainfall products are at 0.25*0.25-degree spatial resolution. The preliminary comparison between the simulated streamflow and the observation shows that the stream flow patterns produced by various forecast products are in good comparison with high peaks. Our results also indicate that the forecast accuracy of NCMRWF is closely comparable with other forecast products for all lead time. In addition, the setup of Variable Infiltration Capacity (VIC), the hydrological model for Streamflow forecasting is in progress. The VIC model is a grid-based model with variable infiltration soil layers and each of this layer characterizes the soil hydrological responses and heterogeneity in land cover classes. For routing, VIC model divides the whole basin into grides and water balance is calculated at the outlet of each and every grid and the flow simulate according to the flow direction. This model considers both the baseflow and the surface flow. The detailed results of ongoing work will be presented at the conference.</p>

Cultural Awareness of Students in Communication Sciences and Disorders: A Mixed-Methods Study

2020 ◽  
Vol 5 (5) ◽  
pp. 1231-1242
Author(s):  
Celeste Domsch ◽  
Lori Stiritz ◽  
Jay Huff
Keyword(s):  
United States ◽  
Study Abroad ◽  
Cultural Awareness ◽  
Qualitative Data ◽  
The United States ◽  
Self Report ◽  
Global Awareness ◽  
Short Term ◽  
Term Study ◽  
Short Term Study

Purpose This study used a mixed-methods design to assess changes in students' cultural awareness during and following a short-term study abroad. Method Thirty-six undergraduate and graduate students participated in a 2-week study abroad to England during the summers of 2016 and 2017. Quantitative data were collected using standardized self-report measures administered prior to departure and after returning to the United States and were analyzed using paired-samples t tests. Qualitative data were collected in the form of daily journal reflections during the trip and interviews after returning to the United States and analyzed using phenomenological methods. Results No statistically significant changes were evident on any standardized self-report measures once corrections for multiple t tests were applied. In addition, a ceiling effect was found on one measure. On the qualitative measures, themes from student transcripts included increased global awareness and a sense of personal growth. Conclusions Measuring cultural awareness poses many challenges. One is that social desirability bias may influence responses. A second is that current measures of cultural competence may exhibit ceiling or floor effects. Analysis of qualitative data may be more useful in examining effects of participation in a short-term study abroad, which appears to result in decreased ethnocentrism and increased global awareness in communication sciences and disorders students. Future work may wish to consider the long-term effects of participation in a study abroad for emerging professionals in the field.

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