Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States

For the Committee on the Handicapped, the Chairman commented as follows: Dr. Mock emphasizes an important aspect which was alluded to in the original statement in the recommendation that if a hearing loss was suspected, the child should be referred for a thorough diagnostic evaluation following which a plan would be developed to meet the child's physical, psychological, and social needs. The audiogram is only a small part of the overall assessment. For a detailed description of hard of hearing vs. deaf, refer to: Educational Audiology Hard of Hearing, Frederick S. Berg, Ph.D., United States Office of Education, Bureau of Education for the Handicapped, May 1973.

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Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1529-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1529-1568

Author(s):

Samuel Saxe ◽

William Farmer ◽

Jessica Driscoll ◽

Terri S. Hogue

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Water Equivalent ◽

Reanalysis Data ◽

Data Sets ◽

Hydrologic Models ◽

Study Results ◽

Hydrologic System ◽

Snow Water ◽

Multimodel Ensembles

Abstract. Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

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Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0187.1 ◽

2015 ◽

Vol 17 (1) ◽

pp. 73-98 ◽

Cited By ~ 40

Author(s):

Naoki Mizukami ◽

Martyn P. Clark ◽

Ethan D. Gutmann ◽

Pablo A. Mendoza ◽

Andrew J. Newman ◽

...

Keyword(s):

Climate Change ◽

United States ◽

Statistical Downscaling ◽

Precipitation Amount ◽

Hydrologic Model ◽

Shortwave Radiation ◽

Hydrologic Models ◽

Community Land Model ◽

Model Version ◽

Downscaling Method

Abstract Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

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Diagnostic Evaluation of Hydrologic Models Employing Flow Duration Curve

Journal of Hydrologic Engineering ◽

10.1061/(asce)he.1943-5584.0001778 ◽

2019 ◽

Vol 24 (6) ◽

pp. 05019009 ◽

Cited By ~ 2

Author(s):

Vinod Chilkoti ◽

Tirupati Bolisetti ◽

Ram Balachandar

Keyword(s):

Diagnostic Evaluation ◽

Flow Duration Curve ◽

Hydrologic Models ◽

Flow Duration

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Toward improved identification of hydrological models: A diagnostic evaluation of the “abcd” monthly water balance model for the conterminous United States

Water Resources Research ◽

10.1029/2009wr008294 ◽

2010 ◽

Vol 46 (8) ◽

Cited By ~ 78

Author(s):

Guillermo F. Martinez ◽

Hoshin V. Gupta

Keyword(s):

United States ◽

Water Balance ◽

Water Balance Model ◽

Diagnostic Evaluation ◽

Balance Model ◽

Hydrological Models ◽

Monthly Water Balance

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Adding Person-Centered Measures to Research on Detection and Diagnosis of Dementia

Innovation in Aging ◽

10.1093/geroni/igab046.1009 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 263-263

Author(s):

Katie Maslow

Keyword(s):

United States ◽

The United States ◽

Diagnostic Evaluation ◽

Screening Tests ◽

Initial Screening ◽

Diagnosis Of Dementia ◽

Detection And Diagnosis ◽

The U.S

Abstract In the United States, numerous studies on detection and diagnosis of dementia show that large proportions of subjects refuse initial screening tests. Moreover, among those who accept the tests, score poorly, and are therefore referred for a diagnostic evaluation, large proportions do not follow up to get the evaluation. Available data on characteristics of subjects who refuse initial screening and follow-up evaluation suggest that incorporating procedures based on person-centered concepts and practices, such as procedures that acknowledge individuals’ unique characteristics and attempt to involve, enable, and empower them, could lead to more effective detection and diagnosis. Based on results of an analysis of measures used in studies conducted in the U.S. and elsewhere, this presentation will describe frequently used measures and identify person-centered measures that could be added to studies of alternate procedures intended to increase detection and diagnosis.

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