scholarly journals Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1629 ◽  
Author(s):  
Sydney S. Foks ◽  
Jeff P. Raffensperger ◽  
Colin A. Penn ◽  
Jessica M. Driscoll
Keyword(s):  
United States ◽  
Goodness Of Fit ◽  
Hydrologic Model ◽  
Specific Conductance ◽  
Base Flow ◽  
Hydrograph Separation ◽  
Calibration Dataset ◽  
Runoff Modeling ◽  
Watershed Slope ◽  
Model Components

Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow, added to gwres_flow, produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.

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 Groundwater contribution to river flows – using hydrograph separation, hydrological and hydrogeological models in a southern Quebec aquifer

2010 ◽  
Vol 7 (5) ◽  
pp. 7809-7838 ◽  
Author(s):  
M. Larocque ◽  
V. Fortin ◽  
M. C. Pharand ◽  
C. Rivard
Keyword(s):  
Groundwater Flow ◽  
Flow Model ◽  
Regional Scale ◽  
Significant Proportion ◽  
Base Flow ◽  
Groundwater Flow Model ◽  
Hydrograph Separation ◽  
River Flows ◽  
Groundwater Contribution ◽  
Base Flows

Abstract. Groundwater contribution to river flows, generally called base flows, often accounts for a significant proportion of total flow rate, especially during the dry season. The objective of this work is to test simple approaches requiring limited data to understand groundwater contribution to river flows. The Noire river basin in southern Quebec is used as a case study. A lumped conceptual hydrological model (the MOHYSE model), a groundwater flow model (MODFLOW) and hydrograph separation are used to provide estimates of base flow for the study area. Results show that the methods are complementary. Hydrograph separation and the MOHYSE surface flow model provide similar annual estimates for the groundwater contribution to river flow, but monthly base flows can vary significantly between the two methods. Both methods have the advantage of being easily implemented. However, the distinction between aquifer contribution and shallow subsurface contribution to base flow can only be made with a groundwater flow model. The aquifer renewal rate estimated with the MODFLOW model for the Noire River is 30% of the recharge estimated from base flow values. This is a significantly difference which can be crucial for regional-scale water management.

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.

scholarly journals A Pragmatic Slope-Adjusted Curve Number Model to Reduce Uncertainty in Predicting Flood Runoff from Steep Watersheds

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1469 ◽  
Author(s):  
Muhammad Ajmal ◽  
Muhammad Waseem ◽  
Dongwook Kim ◽  
Tae-Woong Kim
Keyword(s):  
Goodness Of Fit ◽  
Curve Number ◽  
Natural Environments ◽  
Factor Effect ◽  
Average Slope ◽  
Slope Factor ◽  
Value Estimation ◽  
Watershed Slope ◽  
Runoff Events ◽  
Good Agreement

The applicability of the curve number (CN) model to estimate runoff has been a conundrum for years, among other reasons, because it presumes an uncertain fixed initial abstraction coefficient (λ = 0.2), and because choosing the most suitable watershed CN values is still debated across the globe. Furthermore, the model is widely applied beyond its originally intended purpose. Accordingly, there is a need for more case-specific adjustments of the CN values, especially in steep-slope watersheds with diverse natural environments. This study scrutinized the λ and watershed slope factor effect in estimating runoff. Our proposed slope-adjusted CN (CNIIα) model used data from 1779 rainstorm–runoff events from 39 watersheds on the Korean Peninsula (1402 for calibration and 377 for validation), with an average slope varying between 7.50% and 53.53%. To capture the agreement between the observed and estimated runoff, the original CN model and its seven variants were evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PB), and 1:1 plot. The overall lower RMSE, higher NSE, better PB values, and encouraging 1:1 plot demonstrated good agreement between the observed and estimated runoff by one of the proposed variants of the CN model. This plausible goodness-of-fit was possibly due to setting λ = 0.01 instead of 0.2 or 0.05 and practically sound slope-adjusted CN values to our proposed modifications. For more realistic results, the effects of rainfall and other runoff-producing factors must be incorporated in CN value estimation to accurately reflect the watershed conditions.

scholarly journals Modeling Relations between Compacted and Uncompacted Crown Ratio for the Northern United States

2019 ◽  
Vol 65 (5) ◽  
pp. 593-601
Author(s):  
James A Westfall ◽  
Megan B E Westfall ◽  
KaDonna C Randolph
Keyword(s):  
United States ◽  
Random Effects ◽  
Goodness Of Fit ◽  
Tree Mortality ◽  
Mixed Model ◽  
The United States ◽  
Repeated Measurements ◽  
Growth Potential ◽  
Model Framework ◽  
Northern United States

Abstract Tree crown ratio is useful in various applications such as prediction of tree mortality probabilities, growth potential, and fire behavior. Crown ratio is commonly assessed in two ways: (1) compacted crown ratio (CCR—lower branches visually moved upwards to fill missing foliage gaps) and (2) uncompacted crown ratio (UNCR—no missing foliage adjustment). The national forest inventory of the United States measures CCR on all trees, whereas only a subset of trees also are assessed for UNCR. Models for 27 species groups are presented to predict UNCR for the northern United States. The model formulation is consistent with those developed for other US regions while also accounting for the presence of repeated measurements and heterogeneous variance in a mixed-model framework. Ignoring random-effects parameters, the fit index values ranged from 0.43 to 0.78, and root mean squared error spanned 0.08–0.15; considerable improvements in both goodness-of-fit statistics were realized via inclusion of the random effects. Comparison of UNCR predictions with models developed for the southern United States exhibited close agreement, whereas comparisons with models used in Forest Vegetation Simulator variants indicated poor association. The models provide additional analytical flexibility for using the breadth of northern region data in applications where UNCR is the appropriate crown characteristic.

scholarly journals The Influence of Atlantic Tropical Cyclones on Drought over the Eastern United States (1980–2007)

2013 ◽  
Vol 26 (10) ◽  
pp. 3067-3086 ◽  
Author(s):  
Jonghun Kam ◽  
Justin Sheffield ◽  
Xing Yuan ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Tropical Cyclones ◽  
Land Surface ◽  
Hydrologic Model ◽  
Eastern United States ◽  
Short Term ◽  
Drought Duration ◽  
Study Region

Abstract To assess the influence of Atlantic tropical cyclones (TCs) on the eastern U.S. drought regime, the Variable Infiltration Capacity (VIC) land surface hydrologic model was run over the eastern United States forced by the North American Land Data Assimilation System phase 2 (NLDAS-2) analysis with and without TC-related precipitation for the period 1980–2007. A drought was defined in terms of soil moisture as a prolonged period below a percentile threshold. Different duration droughts were analyzed—short term (longer than 30 days) and long term (longer than 90 days)—as well as different drought severities corresponding to the 10th, 15th, and 20th percentiles of soil moisture depth. With TCs, droughts are shorter in duration and of a lesser spatial extent. Tropical cyclones variously impact soil moisture droughts via late drought initiation, weakened drought intensity, and early drought recovery. At regional scales, TCs decreased the average duration of moderately severe short-term and long-term droughts by less than 4 (10% of average drought duration per year) and more than 5 (15%) days yr−1, respectively. Also, they removed at least two short-term and one long-term drought events over 50% of the study region. Despite the damage inflicted directly by TCs, they play a crucial role in the alleviation and removal of drought for some years and seasons, with important implications for water resources and agriculture.

scholarly journals Detection and Attribution of Streamflow Timing Changes to Climate Change in the Western United States

2009 ◽  
Vol 22 (13) ◽  
pp. 3838-3855 ◽  
Author(s):  
H. G. Hidalgo ◽  
T. Das ◽  
M. D. Dettinger ◽  
D. R. Cayan ◽  
D. W. Pierce ◽  
...  
Keyword(s):  
Climate Change ◽  
United States ◽  
Greenhouse Gases ◽  
River Basin ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Hydrologic Model ◽  
Western United States ◽  
Detection And Attribution

Abstract This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.

Applicability Evaluation of WASMOD in Tarim Basin

2014 ◽  
Vol 522-524 ◽  
pp. 902-906
Author(s):  
Jue Yi Peng ◽  
Zhan Ling Li ◽  
Zhi Xia Xu
Keyword(s):  
Tarim Basin ◽  
Hydrologic Model ◽  
Base Flow ◽  
Monthly Precipitation ◽  
Snow Melt ◽  
North West ◽  
Speed Flow ◽  
Optimum Model ◽  
Fast Flow ◽  
Applicability Evaluation

Tarim basin,which located in north-west of China, is a very important zone both on ecology and economy. How much discharge could it produce has significant importance to the residents' life and the progress of this region. Beside of rainfall, the glaciers that existed plentifully in the upstreams have a great role in this basin which due to the very quantity of snow-melt. In this study, we choose Akesu river that belongs to this basin to be object. The WASMOD hydrologic model used in this program has both snow-melt and rainfall modules with eight sub-models. This article probed into different sub models applicabilities to Akesu river and came to a conclusion of which sub model was best suitable to it. On the basis of optimum model which calibrated by data from 1978-1987 monthly precipitation, evaporation, fast speed flow and base flow, we simulated real evaporation, slow flow, fast flow and total volume of runoff around 2001-2004.The result showed a good applicability of WASMOD in Tarim Basin.

scholarly journals Spatial Characterization of Flood Magnitudes over the Drainage Network of the Delaware River Basin

2017 ◽  
Vol 18 (4) ◽  
pp. 957-976 ◽  
Author(s):  
Ping Lu ◽  
James A. Smith ◽  
Ning Lin
Keyword(s):  
United States ◽  
Tropical Cyclones ◽  
River Basin ◽  
Hydrologic Model ◽  
Drainage Network ◽  
Eastern United States ◽  
Delaware River ◽  
Flood Peak ◽  
Landfalling Tropical Cyclones ◽  
Flood Index

Abstract A framework to characterize the distribution of flood magnitudes over large river networks is developed using the Delaware River basin in the northeastern United States as a principal study region. Flood magnitudes are characterized by the flood index, which is defined as the ratio of the flood peak for a flood event to the historical 10-yr flood magnitude. Event flood peaks are computed continuously over the drainage network using a distributed hydrologic model, CUENCAS, with high-resolution radar rainfall fields as the principal forcing. The historical 10-yr flood is calculated based on scaling relationships between the 10-yr flood and drainage area. Summary statistics for characterizing the probability distribution and spatial correlation of flood magnitudes over the drainage network are developed based on the flood index. This framework is applied to four flood events in the Delaware River basin that reflect the principal flood-generating mechanisms in the eastern United States: landfalling tropical cyclones (Hurricane Ivan in September 2004 and Hurricane Irene in August 2011), late winter/early spring extratropical systems (April 2005), and warm season convective systems (June 2006). The framework can be utilized to characterize the spatial distribution of floods, most notably for floods caused by landfalling tropical cyclones, which play an important role in controlling the upper tail of flood peak magnitudes over much of the eastern United States.

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.

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