Dynamic Hydrologic Simulation of the Bear Brook Watershed in Maine (BBWM)

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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A framework for evaluating regional hydrologic sensitivity to climate change using archetypal watershed modeling

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3077-2013 ◽

2013 ◽

Vol 17 (8) ◽

pp. 3077-3094 ◽

Cited By ~ 6

Author(s):

S. R. Lopez ◽

T. S. Hogue ◽

E. D. Stein

Keyword(s):

Climate Change ◽

Watershed Modeling ◽

Suspended Sediments ◽

Precipitation Variability ◽

Sediment Flux ◽

Urban Systems ◽

Climate Scenarios ◽

Hydrologic Simulation ◽

Change Temperature ◽

Land Covers

Abstract. The current study focuses on the development of a regional framework to evaluate hydrologic and sediment sensitivity, at various stages of urban development, due to predicted future climate variability. We develop archetypal watersheds, which are regional representations of observed physiographic features (i.e., geomorphology, land cover patterns, etc.) with a synthetic basin size and reach network. Each of the three regional archetypes (urban, vegetated and mixed urban/vegetated land covers) simulates satisfactory regional hydrologic and sediment behavior compared to historical observations prior to a climate sensitivity analysis. Climate scenarios considered a range of increasing temperatures, as estimated by the IPCC, and precipitation variability based on historical observations and expectations. Archetypal watersheds are modeled using the Environmental Protection Agency's Hydrologic Simulation Program–Fortran model (EPA HSPF) and relative changes to streamflow and sediment flux are evaluated. Results indicate that the variability and extent of vegetation play a key role in watershed sensitivity to predicted climate change. Temperature increase alone causes a decrease in annual flow and an increase in sediment flux within the vegetated archetypal watershed only, and these effects are partially mitigated by the presence of impervious surfaces within the urban and mixed archetypal watersheds. Depending on the extent of precipitation variability, urban and moderately urban systems can expect the largest alteration in flow regimes where high-flow events increase in frequency and magnitude. As a result, enhanced wash-off of suspended sediments from available pervious surfaces is expected.

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Physical Characterization of the Navarro Watershed for Hydrologic Simulation

GIS for Water Resource and Watershed Management ◽

10.1201/9780203217917.ch15 ◽

2002 ◽

pp. 179-187

Author(s):

Jeffrey Colby

Keyword(s):

Physical Characterization ◽

Hydrologic Simulation

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The SPAW Model for Agricultural Field and Pond Hydrologic Simulation

Watershed Models ◽

10.1201/9781420037432-29 ◽

2010 ◽

pp. 425-460

Keyword(s):

Agricultural Field ◽

Hydrologic Simulation

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Ensemble long-term soil moisture forecast using hydrological modeling

RBRH ◽

10.1590/2318-0331.252020190036 ◽

2020 ◽

Vol 25 ◽

Author(s):

Bibiana Rodrigues Colossi ◽

Carlos Eduardo Morelli Tucci

Keyword(s):

Soil Moisture ◽

Hydrological Modeling ◽

Hydrological Model ◽

Computing Time ◽

Ensemble Member ◽

Lead Times ◽

Hydrologic Simulation ◽

The Mean ◽

Group B

ABSTRACT Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

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