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  • 1
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    Reservoir Evaporation in the Western United States: Current Science, Challenges, and Future Needs (2018)
    American Meteorological Society
    Details
    Publication Date: 2018-01-01
    Description: One way to adapt to and mitigate current and future water scarcity is to manage and store water more efficiently. Reservoirs act as critical buffers to ensure agricultural and municipal water deliveries, mitigate flooding, and generate hydroelectric power, yet they often lose significant amounts of water through evaporation, especially in arid and semiarid regions. Despite this fact, reservoir evaporation has been an inconsistently and inaccurately estimated component of the water cycle within the water resource infrastructure of the arid and semiarid western United States. This paper highlights the increasing importance and challenges of correctly estimating and forecasting reservoir evaporation in the current and future climate, as well as the need to bring new ideas and state-of-the-art practices for the estimation of reservoir evaporation into operational use for modern water resource managers. New ideas and practices include i) improving the estimation of reservoir evaporation using up-to-date knowledge, state-of-the-art instrumentation and numerical models, and innovative experimental designs to diagnose processes and accurately forecast evaporation; ii) improving our understanding of spatial and temporal variations in evaporative water loss from existing reservoirs and transferring this knowledge when expanding reservoirs or siting new ones; and iii) implementing an adaptive management plan that incorporates new knowledge, observations, and forecasts of reservoir evaporation to improve water resource management.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
    Published by American Meteorological Society
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  • 2
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    Characterizing Antecedent Conditions Prior to Annual Maximum Flood Events in a High-Elevation Watershed Using Self-Organizing Maps (2018)
    American Meteorological Society
    Details
    Publication Date: 2018-10-31
    Description: Flood frequency estimates are required for many water management and design engineering projects, including dam safety risk management activities. Most studies assume that annual or peak-over-threshold flood events are sampled from a single homogeneous population, an assumption that is sometimes invalid. In this study, we characterize conditions prior to annual maximum flood events in the Taylor Park watershed between water years 1981 and 2016 using historical observations and the self-organizing maps (SOM) algorithm. Inputs to the SOM algorithm include annual maximum daily reservoir inflow, annual maximum snow water equivalent (SWE), SWE melt length, and 4-day antecedent precipitation. Four-day antecedent precipitation is defined as the precipitation accumulated over the 3 days prior to and on the day of the annual maximum event. Results based on a 2 × 2 SOM output map, which represents four flood categories, suggest that 58% of events are the result of snowmelt with a near-negligible contribution from antecedent precipitation, 17% of events are the result of snowmelt combined with large antecedent precipitation, and the remaining 25% of events are the result of snowmelt with no contribution from antecedent precipitation. These results, which highlight the existence of more than one flood mechanism, may have implications for future flood frequency analyses in this watershed and other watersheds within the region.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 3
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    Improving Climate Sensitivity of Deep Lakes within a Regional Climate Model and Its Impact on Simulated Climate (2014)
    American Meteorological Society
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    Publication Date: 2014-04-10
    Description: Regional climate models aim to improve local climate simulations by resolving topography, vegetation, and land use at a finer resolution than global climate models. Lakes, particularly large and deep lakes, are local features that significantly alter regional climate. The Hostetler lake model, a version of which is currently used in the Community Land Model, performs poorly in deep lakes when coupled to the regional climate of the International Centre for Theoretical Physics (ICTP) Regional Climate Model, version 4 (RegCM4). Within the default RegCM4 model, the lake fails to properly stratify, stifling the model’s ability to capture interannual variability in lake temperature and ice cover. Here, the authors improve modeled lake stratification and eddy diffusivity while correcting errors in the ice model. The resulting simulated lake shows improved stratification and interannual variability in lake ice and temperature. The lack of circulation and explicit mixing continues to stifle the model’s ability to simulate lake mixing events and variability in timing of stratification and destratification. The changes to modeled lake conditions alter seasonal means in sea level pressure, temperature, and low-level winds in the entire model domain, highlighting the importance of lake model selection and improvement for coupled simulations. Interestingly, changes to winter and spring snow cover and albedo impact spring warming. Unsurprisingly, regional climate variability is not significantly altered by an increase in lake temperature variability.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 4
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    Influence of the Background State on Rossby Wave Propagation into the Great Lakes Region Based on Observations and Model Simulations* (2014)
    American Meteorological Society
    Details
    Publication Date: 2014-12-10
    Description: The authors investigate the relationship between hydrology in the Great Lakes basin—namely, overlake precipitation and transient Rossby waves—using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data and historical output from phase 3 of the Coupled Model Intercomparison Project (CMIP3). The preferred path of observed Rossby wave trains associated with overlake precipitation on Lake Superior depends strongly on season and appears to be related to the time-mean, upper-level flow. During summer and fall, the Northern Hemisphere extratropical jet is relatively narrow and acts as a waveguide, such that Rossby wave trains traversing the Great Lakes region travel along the extratropical Pacific and Atlantic jets. During other months, the Pacific jet is relatively broad, which allows more wave activity originating in the tropics to penetrate into the midlatitudes and influence Lake Superior precipitation. Analysis is extended to CMIP3 models and is intended to 1) further understanding of how variations in the mean state influence transient Rossby waves and 2) assess models’ ability to capture observed features, such as wave origin and track. Results indicate that Rossby wave train propagation in twentieth-century simulations can significantly differ by model. Unlike observations, some models do not produce a well-defined jet across the Pacific Ocean during summer and autumn. In these models, some Rossby waves affecting the Great Lakes region originate in the tropics. Collectively, observations and model results show the importance of the time-mean upper-level flow on Rossby wave propagation and therefore on the relative influence of the tropics versus the extratropics on the hydroclimate of the Great Lakes region.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 5
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    Understanding Simulated Extreme Precipitation Events in Madison, Wisconsin, and the Role of Moisture Flux Convergence during the Late Twentieth and Twenty-First Centuries* (2012)
    American Meteorological Society
    Details
    Publication Date: 2012-06-01
    Description: Understanding extreme precipitation events in the current and future climate system is an important aspect of climate change for adaptation and mitigation purposes. The current study investigates extreme precipitation events over Madison, Wisconsin, during the late twentieth and late twenty-first centuries using 18 coupled ocean–atmosphere general circulation models that participated in the Coupled Model Intercomparison Project (CMIP3). An increase of ~10% is found in the multimodel average of annual precipitation received in Madison by the end of the twenty-first century, with the largest increases projected to occur during winter [December–February (DJF)] and spring [March–May (MAM)]. It is also found that the observed seasonal cycle of precipitation in Madison is not accurately captured by the models. The multimodel average shows a strong seasonal peak in May, whereas observations peak during midsummer. Model simulations also do not accurately capture the annual cycle of extreme precipitation events in Madison, which also peak in summer. Instead, the timing of model-simulated extreme events exhibits a bimodal distribution that peaks during spring and fall. However, spatial composites of average daily precipitation simulated by GCMs during Madison’s wettest 1% of precipitation events during the twentieth century strongly resemble the spatial pattern produced in observations. The role of specific humidity and vertically integrated moisture flux convergence (MFC) during extreme precipitation events in Madison is investigated in twentieth- and twenty-first-century simulations. Spatial composites of MFC during the wettest 1% of days during the twentieth-century simulations agree well with results from the North American Regional Reanalysis dataset (NARR), suggesting that synoptic-scale dynamics are vital to extreme precipitation events.
    Print ISSN: 1525-755X
    Electronic ISSN: 1525-7541
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 6
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    Process-Based Flood Frequency Analysis in an Agricultural Watershed Exhibiting Nonstationary Flood Seasonality (2018)
    Copernicus
    Details
    Publication Date: 2018-11-06
    Description: Floods are the product of complex interactions of processes including rainfall, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into process interactions and how they shape the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use requires deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative process-based FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm scenarios based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied in Turkey River in the Midwestern United States, a watershed that is undergoing significant climatic and hydrologic change. We show that using only 15 years of rainfall records, our methodology can produce more accurate estimates of present-day flood frequency than is possible using longer discharge or rainfall records. We found that shifts in the seasonality of soil moisture conditions and extreme rainfall in Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. Such mistakes are avoidable, however, and our approach may provide a clearer pathway toward understanding current and future flood frequency in nonstationary conditions compared with more conventional methods.
    Print ISSN: 1812-2108
    Electronic ISSN: 1812-2116
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 7
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    Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality (2019)
    Copernicus
    Details
    Publication Date: 2019-05-07
    Description: Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 8
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    Rescaling Transposed Extreme Rainfall within a Heterogeneous Region (2019)
    American Society of Civil Engineers
    Details
    Publication Date: 2019-06-01
    Print ISSN: 1084-0699
    Electronic ISSN: 1943-5584
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by American Society of Civil Engineers
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  • 9
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    Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range (2021)
    American Geophysical Union
    Details
    Publication Date: 2021-04-01
    Print ISSN: 0043-1397
    Electronic ISSN: 1944-7973
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by American Geophysical Union
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  • 10
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    Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system (2021)
    Copernicus
    Details
    Publication Date: 2021-10-25
    Description: This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.
    Print ISSN: 1027-5606
    Electronic ISSN: 1607-7938
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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