scholarly journals The Probability Distribution of Daily Precipitation at the Point and Catchment Scales in the United States

2018 ◽  
Author(s):  
Lei Ye ◽  
Lars S. Hanson ◽  
Pengqi Ding ◽  
Dingbao Wang ◽  
Richard M. Vogel
Keyword(s):  
United States ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Correlation Coefficients ◽  
The United States ◽  
Catchment Scale ◽  
Precipitation Frequency ◽  
Climate Trend ◽  
Pearson Type

Abstract. Choosing a probability distribution to represent daily precipitation depths is important for precipitation frequency analysis, stochastic precipitation modeling and in climate trend assessments. Early studies identified the 2-parameter Gamma (G2) distribution as a suitable distribution for wet-day precipitation based on traditional goodness of fit tests. Here, probability plot correlation coefficients and L-moment diagrams are used to examine distributional alternatives for the full-record and wet-day series of daily precipitation at the point and catchment scales in the United States. Importantly, the G2 distribution performs poorly in comparison to either the Pearson Type-III (P3) or Kappa (KAP) distributions. The analysis indicates that the P3 distribution fits the full record of daily precipitation at both the point and catchment scales remarkably well; while the KAP distribution best describes the distribution of wet-day precipitation at the point scale, and the performance of KAP and P3 distributions is comparable for wet-day precipitation at the catchment scale.

scholarly journals The probability distribution of daily precipitation at the point and catchment scales in the United States

2018 ◽  
Vol 22 (12) ◽  
pp. 6519-6531 ◽  
Author(s):  
Lei Ye ◽  
Lars S. Hanson ◽  
Pengqi Ding ◽  
Dingbao Wang ◽  
Richard M. Vogel
Keyword(s):  
United States ◽  
Probability Distribution ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Correlation Coefficients ◽  
The United States ◽  
Catchment Scale ◽  
Precipitation Frequency ◽  
Climate Trend ◽  
Pearson Type

Abstract. Choosing a probability distribution to represent daily precipitation depths is important for precipitation frequency analysis, stochastic precipitation modeling and in climate trend assessments. Early studies identified the two-parameter gamma (G2) distribution as a suitable distribution for wet-day precipitation based on the traditional goodness-of-fit tests. Here, probability plot correlation coefficients and L-moment diagrams are used to examine distributional alternatives for the wet-day series of daily precipitation for hundreds of stations at the point and catchment scales in the United States. Importantly, both Pearson Type-III (P3) and kappa (KAP) distributions perform very well, particularly for point rainfall. Our analysis indicates that the KAP distribution best describes the distribution of wet-day precipitation at the point scale, whereas the performance of G2 and P3 distributions are comparable for wet-day precipitation at the catchment scale, with P3 generally providing the improved goodness of fit over G2. Since the G2 distribution is currently the most widely used probability density function, our findings could be considerably important, especially within the context of climate change investigations.

scholarly journals A Changing Climatology of Precipitation Persistence across the United States Using Information-Based Measures

2019 ◽  
Vol 20 (8) ◽  
pp. 1649-1666 ◽  
Author(s):  
Allison E. Goodwell ◽  
Praveen Kumar
Keyword(s):  
United States ◽  
Time Scales ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
The United States ◽  
Binary Sequences ◽  
Precipitation Frequency ◽  
Climate Trend ◽  
Information Measures ◽  
Time Lagged

Abstract The sequencing, or persistence, of daily precipitation influences variability in streamflow, soil moisture, and vegetation states. As these factors influence water availability and ecosystem health, it is important to identify spatial and temporal trends in precipitation persistence and predictability. We take an information theoretic perspective to address regional and temporal trends in daily patterns, based on the Climate Prediction Center (CPC) gridded gauge-based dataset of daily precipitation over the continental United States from 1948 to 2018. We apply information measures to binary sequences of precipitation occurrence to quantify uncertainty, predictability in the form of lagged mutual information between the current state and two time-lagged histories, and associated dominant time scales. We find that this information-based predictability is highest in the western United States, but the relative influence of longer lagged histories in comparison to a 1-day history is highest in the east. Information characteristics and time scales vary seasonally and regionally and constitute an information climatology that can be compared with traditional indices of precipitation and climate. Trend analyses over the 70-yr time period also show varying regional characteristics that differ between seasons. In addition to increasing precipitation frequency over most of the country, we detect increasing and decreasing predictability in western and eastern regions, respectively, with average trend magnitudes corresponding to shifts in predictability ranging from −50% to 110%. This new perspective on precipitation persistence has broad potential to link shifts in climate and weather to patterns and predictability of related environmental factors.

scholarly journals Mathematical pattern of Kessler psychological distress distribution in the general population of the U.S. and Japan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shinichiro Tomitaka ◽  
Toshiaki A. Furukawa
Keyword(s):  
United States ◽  
General Population ◽  
National Survey ◽  
Goodness Of Fit ◽  
Quadratic Function ◽  
The United States ◽  
Depression Screening ◽  
Distribution Model ◽  
Epidemiological Surveys ◽  
Screening Scale

Abstract Background Although the 6-item Kessler psychological scale (K6) is a useful depression screening scale in clinical settings and epidemiological surveys, little is known about the distribution model of the K6 score in the general population. Using four major national survey datasets from the United States and Japan, we explored the mathematical pattern of the K6 distributions in the general population. Methods We analyzed four datasets from the National Health Interview Survey, the National Survey on Drug Use and Health, and the Behavioral Risk Factor Surveillance System in the United States, and the Comprehensive Survey of Living Conditions in Japan. We compared the goodness of fit between three models: exponential, power law, and quadratic function models. Graphical and regression analyses were employed to investigate the mathematical patterns of the K6 distributions. Results The exponential function had the best fit among the three models. The K6 distributions exhibited an exponential pattern, except for the lower end of the distribution across the four surveys. The rate parameter of the K6 distributions was similar across all surveys. Conclusions Our results suggest that, regardless of different sample populations and methodologies, the K6 scores exhibit a common mathematical distribution in the general population. Our findings will contribute to the development of the distribution model for such a depression screening scale.

scholarly journals A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan

2016 ◽  
Vol 11 (1) ◽  
pp. 432-440 ◽  
Author(s):  
M. T. Amin ◽  
M. Rizwan ◽  
A. A. Alazba
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Future Research ◽  
Maximum Rainfall ◽  
Type Iii ◽  
Goodness Of Fit Tests ◽  
Pearson Type ◽  
Northern Regions ◽  
Best Fit

AbstractThis study was designed to find the best-fit probability distribution of annual maximum rainfall based on a twenty-four-hour sample in the northern regions of Pakistan using four probability distributions: normal, log-normal, log-Pearson type-III and Gumbel max. Based on the scores of goodness of fit tests, the normal distribution was found to be the best-fit probability distribution at the Mardan rainfall gauging station. The log-Pearson type-III distribution was found to be the best-fit probability distribution at the rest of the rainfall gauging stations. The maximum values of expected rainfall were calculated using the best-fit probability distributions and can be used by design engineers in future research.

scholarly journals On the probability distribution of daily streamflow in the United States

2017 ◽  
Vol 21 (6) ◽  
pp. 3093-3103 ◽  
Author(s):  
Annalise G. Blum ◽  
Stacey A. Archfield ◽  
Richard M. Vogel
Keyword(s):  
United States ◽  
Probability Distribution ◽  
The United States ◽  
Good Representation ◽  
Lognormal Distributions ◽  
Daily Streamflow ◽  
The Us ◽  
Flow Duration Curves ◽  
A Site ◽  
Physiographic Regions

Abstract. Daily streamflows are often represented by flow duration curves (FDCs), which illustrate the frequency with which flows are equaled or exceeded. FDCs have had broad applications across both operational and research hydrology for decades; however, modeling FDCs has proven elusive. Daily streamflow is a complex time series with flow values ranging over many orders of magnitude. The identification of a probability distribution that can approximate daily streamflow would improve understanding of the behavior of daily flows and the ability to estimate FDCs at ungaged river locations. Comparisons of modeled and empirical FDCs at nearly 400 unregulated, perennial streams illustrate that the four-parameter kappa distribution provides a very good representation of daily streamflow across the majority of physiographic regions in the conterminous United States (US). Further, for some regions of the US, the three-parameter generalized Pareto and lognormal distributions also provide a good approximation to FDCs. Similar results are found for the period of record FDCs, representing the long-term hydrologic regime at a site, and median annual FDCs, representing the behavior of flows in a typical year.

Changes in Observed Daily Precipitation over the United States between 1950–79 and 1980–2009

2013 ◽  
Vol 14 (1) ◽  
pp. 105-121 ◽  
Author(s):  
R. W. Higgins ◽  
V. E. Kousky
Keyword(s):  
United States ◽  
Great Plains ◽  
El Niño ◽  
Daily Precipitation ◽  
El Nino ◽  
Southern Oscillation ◽  
Heavy Precipitation ◽  
The United States ◽  
Enso Cycle ◽  
Precipitation Events

Abstract Changes in observed daily precipitation over the conterminous United States between two 30-yr periods (1950–79 and 1980–2009) are examined using a 60-yr daily precipitation analysis obtained from the Climate Prediction Center (CPC) Unified Raingauge Database. Several simple measures are used to characterize the changes, including mean, frequency, intensity, and return period. Seasonality is accounted for by examining each measure for four nonoverlapping seasons. The possible role of the El Niño–Southern Oscillation (ENSO) cycle as an explanation for differences between the two periods is also examined. There have been more light (1 mm ≤ P < 10 mm), moderate (10 mm ≤ P < 25 mm), and heavy (P ≥ 25 mm) daily precipitation events (P) in many regions of the country during the more recent 30-yr period with some of the largest and most spatially coherent increases over the Great Plains and lower Mississippi Valley during autumn and winter. Some regions, such as portions of the Southeast and the Pacific Northwest, have seen decreases, especially during the winter. Increases in multiday heavy precipitation events have been observed in the more recent period, especially over portions of the Great Plains, Great Lakes, and Northeast. These changes are associated with changes in the mean and frequency of daily precipitation during the more recent 30-yr period. Difference patterns are strongly related to the ENSO cycle and are consistent with the stronger El Niño events during the more recent 30-yr period. Return periods for both heavy and light daily precipitation events during 1950–79 are shorter during 1980–2009 at most locations, with some notable regional exceptions.

scholarly journals Simulation of Daily Extreme Precipitation over the United States in the CMIP5 30-Yr Decadal Prediction Experiment

2019 ◽  
Vol 58 (4) ◽  
pp. 875-886 ◽  
Author(s):  
Steve T. Stegall ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
The United States ◽  
Natural Variability ◽  
Return Level ◽  
Historical Period ◽  
Regional Variability ◽  
Anthropogenic Forcing ◽  
Near Term

AbstractThe CMIP5 decadal hindcast (“Hindcast”) and prediction (“Predict”) experiment simulations from 11 models were analyzed for the United States with respect to two metrics of extreme precipitation: the 10-yr return level of daily precipitation, derived from the annual maximum series of daily precipitation, and the total precipitation exceeding the 99.5th percentile of daily precipitation. Both Hindcast simulations and observations generally show increases for the 1981–2010 historical period. The multimodel-mean Hindcast trends are statistically significant for all regions while the observed trends are statistically significant for the Northeast, Southeast, and Midwest regions. An analysis of CMIP5 simulations driven by historical natural (“HistoricalNat”) forcings shows that the Hindcast trends are generally within the 5th–95th-percentile range of HistoricalNat trends, but those outside that range are heavily skewed toward exceedances of the 95th-percentile threshold. Future projections for 2006–35 indicate increases in all regions with respect to 1981–2010. While there is good qualitative agreement between the observations and Hindcast simulations regarding the direction of recent trends, the multimodel-mean trends are similar for all regions, while there is considerable regional variability in observed trends. Furthermore, the HistoricalNat simulations suggest that observed historical trends are a combination of natural variability and anthropogenic forcing. Thus, the influence of anthropogenic forcing on the magnitude of near-term future changes could be temporarily masked by natural variability. However, continued observed increases in extreme precipitation in the first decade (2006–15) of the “future” period partially confirm the Predict results, suggesting that incorporation of increases in planning would appear prudent.

scholarly journals A probabilistic gridded product for daily precipitation extremes over the United States

2019 ◽  
Vol 53 (5-6) ◽  
pp. 2517-2538 ◽  
Author(s):  
Mark D. Risser ◽  
Christopher J. Paciorek ◽  
Michael F. Wehner ◽  
Travis A. O’Brien ◽  
William D. Collins
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
The United States ◽  
Precipitation Extremes

scholarly journals COMPARATIVE PERFORMANCE OF FOUR CLAIMS-BASED FRAILTY MEASURES IN THE UNITED STATES MEDICARE DATA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S684-S685
Author(s):  
Dae H Kim ◽  
Elisabetta Patorno ◽  
Ajinkya Pawar ◽  
Hemin Lee ◽  
Sebastian Schneeweiss ◽  
...  
Keyword(s):  
United States ◽  
Health Outcomes ◽  
Activities Of Daily Living ◽  
Gait Speed ◽  
Daily Living ◽  
Correlation Coefficients ◽  
The United States ◽  
Medicare Data ◽  
C Statistic ◽  
Age And Sex

Abstract Background: There has been increasing effort to measure frailty in the United States Medicare data. The performance of claims-based frailty measures has not been compared. Methods: This retrospective cohort study included 2,326 community-dwelling Medicare beneficiaries who participated in the 2008 assessment of the Health and Retirement Study. The claims-based frailty measures developed by Davidoff, Faurot, Segal, and Kim were compared against clinical measures of frailty (gait speed, grip strength) using correlation coefficients and health outcomes (e.g., mortality, hospitalization, activities-of-daily-living disabilities) over 2 years using C-statistics. Results: The Davidoff, Faurot, Segal, and Kim indices were negatively correlated with gait speed (-0.19, -0.33, -0.37, and -0.37, respectively), but age and sex adjustment variably attenuated the correlation to -0.17, -0.22, -0.18, and -0.33, respectively. The corresponding correlation coefficients with grip strength were -0.17, -0.27, -0.35, and -0.24, which attenuated to -0.09, -0.14, -0.05, and -0.23 after age and sex adjustment, respectively. The models that included age, sex, and each of Davidoff, Faurot, Segal, and Kim indices showed C-statistics of 0.67, 0.71, 0.71, 0.75 for mortality (versus C-statistic for age and sex: 0.66); 0.59, 0.64, 0.63, 0.70 for hospitalization (versus C-statistic for age and sex: 0.58); and 0.64, 0.63, 0.63, 0.70 for activities-of-daily-living disabilities (versus C-statistic for age and sex: 0.61), respectively. Conclusions: The choice of a claims-based frailty measure results in a meaningful variation in the identification of frail older adults at high risk for adverse health outcomes. Claims-based frailty measures that included demographic variables offer limited risk adjustment beyond age and sex.

scholarly journals Coastal Resilience Against Storm Surge from Tropical Cyclones

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 725
Author(s):  
Robert Mendelsohn ◽  
Liang Zheng
Keyword(s):  
United States ◽  
Probability Distribution ◽  
Tropical Cyclones ◽  
Storm Surge ◽  
Urban Areas ◽  
Storm Surges ◽  
The United States ◽  
Atlantic Seaboard ◽  
Order Of Magnitude ◽  
Coastal Resilience

It is well known that seawalls are effective at stopping common storm surges in urban areas. This paper examines whether seawalls should be built to withstand the storm surge from a major tropical cyclone. We estimate the extra cost of building the wall tall enough to stop such surges and the extra flood benefit of this additional height. We estimate the surge probability distribution from six tidal stations spread along the Atlantic seaboard of the United States. We then measure how valuable the vulnerable buildings behind a 100 m wall must be to justify such a tall wall at each site. Combining information about the probability distribution of storm surge, the average elevation of protected buildings, and the damage rate at each building, we find that the value of protected buildings behind this 100 m wall must be in the hundreds of millions to justify the wall. We also examine the additional flood benefit and cost of protecting a km2 of land in nearby cities at each site. The density of buildings in coastal cities in the United States are generally more than an order of magnitude too low to justify seawalls this high. Seawalls are effective, but not at stopping the surge damage from major tropical cyclones.

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