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Eleonora M. C. Demaria
,
David C. Goodrich
, and
Kenneth E. Kunkel

Abstract

The detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

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Efrat Morin
,
Robert A. Maddox
,
David C. Goodrich
, and
Soroosh Sorooshian

Abstract

Radar-based estimates of rainfall rates and accumulations are one of the principal tools used by the National Weather Service (NWS) to identify areas of extreme precipitation that could lead to flooding. Radar-based rainfall estimates have been compared to gauge observations for 13 convective storm events over a densely instrumented, experimental watershed to derive an accurate reflectivity–rainfall rate (i.e., ZR) relationship for these events. The resultant ZR relationship, which is much different than the NWS operational ZR, has been examined for a separate, independent event that occurred over a different location. For all events studied, the NWS operational ZR significantly overestimates rainfall compared to gauge measurements. The gauge data from the experimental network, the NWS operational rain estimates, and the improved estimates resulting from this study have been input into a hydrologic model to “predict” watershed runoff for an intense event. Rainfall data from the gauges and from the derived ZR relation produce predictions in relatively good agreement with observed streamflows. The NWS ZR estimates lead to predicted peak discharge rates that are more than twice as large as the observed discharges. These results were consistent over a relatively wide range of subwatershed areas (4–148 km2). The experimentally derived Z–R relationship may provide more accurate radar estimates for convective storms over the southwest United States than does the operational convective ZR used by the NWS. These initial results suggest that the generic NWS ZR relation, used nationally for convective storms, might be substantially improved for regional application.

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Jackson Tan
,
Walter A. Petersen
,
Gottfried Kirchengast
,
David C. Goodrich
, and
David B. Wolff

Abstract

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

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Timothy M. Lahmers
,
Hoshin Gupta
,
Christopher L. Castro
,
David J. Gochis
,
David Yates
,
Aubrey Dugger
,
David Goodrich
, and
Pieter Hazenberg

Abstract

In August 2016, the National Weather Service Office of Water Prediction (NWS/OWP) of the National Oceanic and Atmospheric Administration (NOAA) implemented the operational National Water Model (NWM) to simulate and forecast streamflow, soil moisture, and other model states throughout the contiguous United States. Based on the architecture of the WRF-Hydro hydrologic model, the NWM does not currently resolve channel infiltration, an important component of the water balance of the semiarid western United States. Here, we demonstrate the benefit of implementing a conceptual channel infiltration function (from the KINEROS2 semidistributed hydrologic model) into the WRF-Hydro model architecture, configured as NWM v1.1. After calibration, the updated WRF-Hydro model exhibits reduced streamflow errors for the Walnut Gulch Experimental Watershed (WGEW) and the Babocomari River in southeast Arizona. Model calibration was performed using NLDAS-2 atmospheric forcing, available from the NOAA National Centers for Environmental Prediction (NCEP), paired with precipitation forcing from NLDAS-2, NCEP Stage IV, or local gauge precipitation. Including channel infiltration within WRF-Hydro results in a physically realistic hydrologic response in the WGEW, when the model is forced with high-resolution, gauge-based precipitation in lieu of a national product. The value of accounting for channel loss is also demonstrated in the Babocomari basin, where the drainage area is greater and the cumulative effect of channel infiltration is more important. Accounting for channel infiltration loss thus improves the streamflow behavior simulated by the calibrated model and reduces evapotranspiration bias when gauge precipitation is used as forcing. However, calibration also results in increased high soil moisture bias, which is likely due to underlying limitations of the NWM structure and calibration methodology.

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Efrat Morin
,
Witold F. Krajewski
,
David C. Goodrich
,
Xiaogang Gao
, and
Soroosh Sorooshian

Abstract

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the ZR transformation equation. The scale dependency of the power-law ZR parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for ZR relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

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Patrick Broxton
,
Peter A. Troch
,
Mike Schaffner
,
Carl Unkrich
, and
David Goodrich

Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorological observations and predictions, as well as calibrated hydrological models, which should effectively simulate how a catchment filters rainfall inputs into streamflow. Furthermore, because of the requirement of both hydrological and meteorological components in flash flood forecasting systems, there must be extensive data handling capabilities built in to force the hydrological model with a variety of available hydrometeorological data and predictions, as well as to test the model with hydrological observations. The authors have developed a working prototype of such a system, called KINEROS/hsB-SM, after the hydrological models that are used: the Kinematic Erosion and Runoff (KINEROS) and hillslope-storage Boussinesq Soil Moisture (hsB-SM) models. KINEROS is an event-based overland flow and channel routing model that is designed to simulate flash floods in semiarid regions where infiltration excess overland flow dominates, while hsB-SM is a continuous subsurface flow model, whose model physics are applicable in humid regions where saturation excess overland flow is most important. In addition, KINEROS/hsB-SM includes an energy balance snowmelt model, which gives it the ability to simulate flash floods that involve rain on snow. There are also extensive algorithms to incorporate high-resolution hydrometeorological data, including stage III radar data (5 min, 1° by 1 km), to assist in the calibration of the models, and to run the model in real time. The model is currently being used in an experimental fashion at the National Weather Service Binghamton, New York, Weather Forecast Office.

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David M. Goodrich
,
William C. Boicourt
,
Peter Hamilton
, and
Donald W. Pritchard

Abstract

Multiyear continuous observations of velocity and salinity in the Chesapeake Bay indicate that wind-induced destratification occurs frequently from early fall through midspring over large areas of the estuary. Storm-driven breakdown of summer stratification was observed to occur near the autumnal equinox in two separate years. Surface cooling plays an important, though secondary, role in the fall destratification by reducing the vertical temperature gradient in the days prior to the mixing event. Large internal velocity shear precedes mixing events, suggesting a mechanism involving the generation of dynamic instability across the pycnocline. Destratification is shown to fundamentally alter the response of the velocity field to subsequent wind forcing; in stratified conditions, response is depth-dependent, while after mixing a depth-independent response is observed.

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Eyal Amitai
,
Carl L. Unkrich
,
David C. Goodrich
,
Emad Habib
, and
Bryson Thill

Abstract

The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999–2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products.

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Long Yang
,
James Smith
,
Mary Lynn Baeck
,
Efrat Morin
, and
David C. Goodrich

Abstract

The hydroclimatology, hydrometeorology, and hydrology of flash floods in the arid/semiarid southwestern United States are examined through empirical analyses of long-term, high-resolution rainfall and stream gauging observations, together with hydrological modeling analyses of the 19 August 2014 storm based on the Kinematic Runoff and Erosion Model (KINEROS2). The analyses presented here are centered on identifying the structure and evolution of flood-producing storms, as well as the interactions of space–time rainfall variability and basin characteristics in determining the upper-tail properties of rainfall and flood magnitudes over this region. This study focuses on four watersheds in Maricopa County, Arizona, with contrasting geomorphological properties. Flash floods over central Arizona are concentrated in both time and space, reflecting controls of the North American monsoon and complex terrain. Thunderstorm systems during the North American monsoon, as represented by the 19 August 2014 storm, are the dominant flood agents that determine the upper tail of flood frequency over central Arizona and that also shape the envelope curve of floods for watersheds smaller than 250 km2. Flood response for the 19 August 2014 storm is associated with storm elements of comparable spatial extent to the drainage area and slow movement for the three compact, headwater watersheds. Flood response for the elongated and relatively flat Skunk Creek highlights the importance of the spatial distribution of rainfall for transmission losses in arid/semiarid watersheds.

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Susan Stillman
,
Xubin Zeng
,
William J. Shuttleworth
,
David C. Goodrich
,
Carl L. Unkrich
, and
Marek Zreda

Abstract

The Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona covers ~150 km2 and receives the majority of its annual precipitation from highly variable and intermittent summer storms during the North American monsoon. In this study, the patterns of precipitation in the U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) 88-rain-gauge network are analyzed for July through September from 1956 to 2011. Because small-scale convective systems generate most of this summer rainfall, the total (T), intensity (I), and frequency (F) exhibit high spatial and temporal variability. Although subsidiary periods may have apparent trends, no significant trends in T, I, and F were found for the study period as a whole. Observed trends in the spatial coverage of storms change sign in the late 1970s, and the multidecadal variation in I and spatial coverage of storms have statistically significant correlation with the Pacific decadal oscillation and the Atlantic multidecadal oscillation indices. Precipitation has a pronounced diurnal cycle with the highest T and F occurring between 1500 and 2200 LT, and its average fractional coverage over 2- and 12-h periods is less than 40% and 60% of the gauges, respectively. Although more gauges are needed to estimate area-averaged daily precipitation, 5–11 gauges can provide a reasonable estimate of the area-averaged monthly total precipitation during the period from July through September.

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