scholarly journals Comparing Trends in Modeled and Observed Streamflows at Minimally Altered Basins in the United States

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1728
Author(s):  
Glenn A. Hodgkins ◽  
Robert W. Dudley ◽  
Amy M. Russell ◽  
Jacob H. LaFontaine
Keyword(s):  
United States ◽  
Model Performance ◽  
The United States ◽  
Model Fit ◽  
Distributed Parameter ◽  
Mean Flow ◽  
Low Flows ◽  
High Flows ◽  
Found Model ◽  
Transfer Models

We compared modeled and observed streamflow trends from 1984 to 2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows, indicating the importance of including more extreme high flows in analyses of model performance.

How Universal Is a Construct of Loneliness? Measurement Invariance of the UCLA Loneliness Scale in Indonesia, Germany, and the United States

Assessment ◽  
2021 ◽  
pp. 107319112110345
Author(s):  
Joevarian Hudiyana ◽  
Tania M. Lincoln ◽  
Steffi Hartanto ◽  
Muhammad A. Shadiqi ◽  
Mirra N. Milla ◽  
...  
Keyword(s):  
United States ◽  
Measurement Invariance ◽  
Social Isolation ◽  
Total Sample ◽  
The United States ◽  
Cross Cultural ◽  
Model Fit ◽  
Short Version ◽  
Loneliness Scale ◽  
The One

The UCLA Loneliness Scale (ULS-20) and its short version (ULS-8) are widely used to measure loneliness. However, the question remains whether or not previous studies using the scale to measure loneliness are measuring the construct equally across countries. The present study examined the measurement invariance (MI) of both scales in Germany, Indonesia, and the United States ( N = 2350). The one-, two-, and three-factor structure of the ULS-20 did not meet the model fit cut-off criteria in the total sample. The ULS-8 met the model fit cut-off criteria and has configural, but not metric invariance because two items unrelated to social isolation were not MI. The final six items (ULS-6) exclusively related to social isolation had complete MI. Participants from the United States scored highest in the ULS-6, followed by participants from Germany and then Indonesia. We conclude that the ULS-6 is an appropriate measure for cross-cultural studies on loneliness.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

scholarly journals The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

2020 ◽  
Vol 12 (9) ◽  
pp. 1490 ◽  
Author(s):  
Calum Baugh ◽  
Patricia de Rosnay ◽  
Heather Lawrence ◽  
Toni Jurlina ◽  
Matthias Drusch ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
The United States ◽  
Streamflow Prediction ◽  
Soil Moisture Data ◽  
Skill Scores ◽  
High Flows ◽  
The Impact ◽  
Awareness System

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

scholarly journals Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

2015 ◽  
Vol 19 (1) ◽  
pp. 209-223 ◽  
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Data Set ◽  
Wide Range

Abstract. We present a community data set of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980–2010 was generated for three basin spatial configurations – basin mean, hydrologic response units (HRUs) and elevation bands – by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe efficiency scores ≥0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases.

Trends in floods and low flows in the United States: impact of spatial correlation

2000 ◽  
Vol 240 (1-2) ◽  
pp. 90-105 ◽  
Author(s):  
E.M. Douglas ◽  
R.M. Vogel ◽  
C.N. Kroll
Keyword(s):  
United States ◽  
Spatial Correlation ◽  
The United States ◽  
Low Flows

scholarly journals Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance

2014 ◽  
Vol 11 (5) ◽  
pp. 5599-5631
Author(s):  
A. J. Newman ◽  
M. P. Clark ◽  
K. Sampson ◽  
A. Wood ◽  
L. E. Hay ◽  
...  
Keyword(s):  
United States ◽  
Seasonal Cycle ◽  
Model Performance ◽  
The United States ◽  
Hydrologic Model ◽  
Low Flow ◽  
Reference Level ◽  
Hydrologic Response ◽  
Daily Streamflow ◽  
Wide Range

Abstract. We present a community dataset of daily forcing and hydrologic response data for 671 small- to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Areally averaged forcing data for the period 1980–2010 was generated for three basin delineations – basin mean, Hydrologic Response Units (HRUs) and elevation bands – by mapping the daily, 1 km gridded Daymet meteorological dataset to the sub-basin and basin polygons. Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the dataset for community use; and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting conceptual hydrologic model, calibrated using the Shuffled Complex Evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash–Sutcliffe Efficiency scores > 0.55 for the calibration period. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow, and, for a given aridity, fewer extreme error days are present as basin snow water equivalent increases.

scholarly journals Topography significantly influencing low flows in snow-dominated watersheds

2017 ◽  
Author(s):  
Qiang Li ◽  
Xiaohua Wei ◽  
Xin Yang ◽  
Krysta Giles-Hansen ◽  
Mingfang Zhang ◽  
...  
Keyword(s):  
Regression Models ◽  
Low Flow ◽  
Mean Flow ◽  
Slope Length ◽  
Low Flows ◽  
Slope Length Factor ◽  
High Flows ◽  
Flow Variables ◽  
Characterization Index

Abstract. Watershed topography plays an important role in determining the spatial heterogeneity of ecological, geomorphological, and hydrological processes. Few studies have quantified the role of topography on various flow variables. In this study, 28 watersheds with snow-dominated hydrological regimes were selected with daily flow records from 1989 to 1996. The watersheds are located in the Southern Interior of British Columbia, Canada and range in size from 2.6 to 1,780 km2. For each watershed, 22 topographic indices (TIs) were derived, including those commonly used in hydrology and other environmental fields. Flow variables include annual mean flow (Qmean), Q10%, Q25%, Q50%, Q75%, Q90%, and annual minimum flow (Qmin), where Qx% is defined as flows that at the percentage (x) occurred in any given year. Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, stepwise regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important role in low flows than high flows. However, the effects of TIs on flow variables are not consistent. Our analysis also determines five significant TIs including perimeter, surface area, openness, terrain characterization index, and slope length factor, which can be used to compare watersheds when low flow assessments are conducted, especially in snow-dominated regions.

scholarly journals COVID-19 scenarios for the United States

2020 ◽  
Author(s):  
◽  
Simon I Hay
Keyword(s):  
United States ◽  
Model Performance ◽  
State Level ◽  
The United States ◽  
Economic Damage ◽  
Reference Scenario ◽  
Level Model ◽  
Per Capita ◽  
Novel Coronavirus ◽  
The Impact

The United States (US) has not been spared in the ongoing pandemic of novel coronavirus disease. COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to cause death and disease in all 50 states, as well as significant economic damage wrought by the non-pharmaceutical interventions (NPI) adopted in attempts to control transmission. We use a deterministic, Susceptible, Exposed, Infectious, Recovered (SEIR) compartmental framework to model possible trajectories of SARS-CoV-2 infections and the impact of NPI at the state level. Model performance was tested against reported deaths from 01 February to 04 July 2020. Using this SEIR model and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates, and mask use per capita), we assessed some possible futures of the COVID-19 pandemic from 05 July through 31 December 2020. We explored future scenarios that included feasible assumptions about NPIs including social distancing mandates (SDMs) and levels of mask use. The range of infection, death, and hospital demand outcomes revealed by these scenarios show that action taken during the summer of 2020 will have profound public health impacts through to the year end. Encouragingly, we find that an emphasis on universal mask use may be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Masks may save as many as 102,795 (55,898-183,374) lives, when compared to a plausible reference scenario in December. In addition, widespread mask use may markedly reduce the need for more socially and economically deleterious SDMs.

scholarly journals Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aixia Guo ◽  
Rahmatollah Beheshti ◽  
Yosef M. Khan ◽  
James R. Langabeer ◽  
Randi E. Foraker
Keyword(s):  
United States ◽  
Cardiovascular Health ◽  
Short Term Memory ◽  
Health Management ◽  
Smoking Status ◽  
Low Density Lipoprotein ◽  
Model Performance ◽  
Density Lipoprotein ◽  
The United States ◽  
Electronic Health

Abstract Background Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients’ CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data. Methods The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks – to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric. Results The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures. Conclusions We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients’ future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.

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