Browsing by Author "Ramirez, Jorge, committee member"

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Open Access
A simple parameterization of aerosol emissions in RAMS
(Colorado State University. Libraries, 2013) Letcher, Theodore, author; Cotton, William, advisor; Kreidenweis, Sonia, committee member; Ramirez, Jorge, committee member
Throughout the past decade, a high degree of attention has been focused on determining the microphysical impact of anthropogenically enhanced concentrations of Cloud Condensation Nuclei (CCN) on orographic snowfall in the mountains of the western United States. This area has garnered a lot of attention due to the implications this effect may have on local water resource distribution within the Region. Recent advances in computing power and the development of highly advanced microphysical schemes within numerical models have provided an estimation of the sensitivity that orographic snowfall has to changes in atmospheric CCN concentrations. However, what is still lacking is a coupling between these advanced microphysical schemes and a real-world representation of CCN sources. Previously, an attempt to representation the heterogeneous evolution of aerosol was made by coupling three-dimensional aerosol output from the WRF Chemistry model to the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) (Ward et al. 2011). The biggest problem associated with this scheme was the computational expense. In fact, the computational expense associated with this scheme was so high, that it was prohibitive for simulations with fine enough resolution to accurately represent microphysical processes. To improve upon this method, a new parameterization for aerosol emission was developed in such a way that it was fully contained within RAMS. Several assumptions went into generating a computationally efficient aerosol emissions parameterization in RAMS. The most notable assumption was the decision to neglect the chemical processes in formed in the formation of Secondary Aerosol (SA), and instead treat SA as primary aerosol via short-term WRF-CHEM simulations. While, SA makes up a substantial portion of the total aerosol burden (much of which is made up of organic material), the representation of this process is highly complex and highly expensive within a numerical model. Furthermore, SA formation is greatly reduced during the winter months due to the lack of naturally produced organic VOC's. Because of these reasons, it was felt that neglecting SOA within the model was the best course of action. The actual parameterization uses a prescribed source map to add aerosol to the model at two vertical levels that surround an arbitrary height decided by the user. To best represent the real-world, the WRF Chemistry model was run using the National Emissions Inventory (NEI2005) to represent anthropogenic emissions and the Model Emissions of Gases and Aerosols from Nature (MEGAN) to represent natural contributions to aerosol. WRF Chemistry was run for one hour, after which the aerosol output along with the hygroscopicity parameter (κ) were saved into a data file that had the capacity to be interpolated to an arbitrary grid used in RAMS. The comparison of this parameterization to observations collected at Mesa Verde National Park (MVNP) during the Inhibition of Snowfall from Pollution Aerosol (ISPA-III) field campaign yielded promising results. The model was able to simulate the variability in near surface aerosol concentration with reasonable accuracy, though with a general low bias. Furthermore, this model compared much better to the observations than did the WRF Chemistry model using a fraction of the computational expense. This emissions scheme was able to show reasonable solutions regarding the aerosol concentrations and can therefore be used to provide an estimate of the seasonal impact of increased CCN on water resources in Western Colorado with relatively low computational expense.
Open Access
A value-function based method for incorporating ensemble forecasts in real-time optimal reservoir operations
(Colorado State University. Libraries, 2020) Peacock, Matthew E., author; Labadie, John W., advisor; Ramirez, Jorge, committee member; Anderson, Chuck, committee member; Johnson, Lynn, committee member
Increasing stress on water resource systems has led to a desire to seek methods of improving the performance of reservoir operations. Water managers face many challenges including changes in demand, variable hydrological input and new environmental pressures. These issues have led to an interest in using ensemble streamflow forecasts to improve the performance of a reservoir system. The currently available methods for using ensemble forecasts encounter difficulties as the resolution of the analysis increases in order to accurately model a real-world system. One of the difficulties is due to the "curse of dimensionality'' as computing time exponentially increases when the discretization of the state and action spaces becomes finer or when more state or action variables are considered. Another difficulty is the problem of delayed rewards. When the time step of the analysis becomes shorter than the travel time due to routing, rewards may not be realized in the same time step as the action which caused them. Current methods such as dynamic programming or scenario-tree based methods are not able to handle delayed rewards. This research presents a value function-based method which separates the problem into two subproblems: computing the state-value function in the no-forecast condition, and finding optimal sequences of decisions given the ensemble forecast with the state-value function providing information about the value at any state at the end of the forecast horizon. A continuous action deep reinforcement learning algorithm is used to overcome the problems of dimensionality and delayed rewards, and a particle swarm method is used to find optimal decisions during the forecast horizon. The method is applied to a case study in the Russian River basin and compared to an idealized operating rule. The results show that the reinforcement learning process is able to generate policies that skillfully operate the reservoir without forecasts. When forecasts are used, the method is able to produce non-dominated performance measures. When the water stress to the system is increased by removing a transbasin diversion, the method outperforms the idealized operations.
Open Access
Ensemble-based analysis of Front Range severe convection on 6-7 June 2012: forecast uncertainty and communication of weather information to Front Range decision-makers
(Colorado State University. Libraries, 2014) Vincente, Vanessa, author; Schumacher, Russ, advisor; Johnson, Richard, committee member; Ramirez, Jorge, committee member
The variation of topography in Colorado not only adds to the beauty of its landscape, but also tests our ability to predict warm season severe convection. Deficient radar coverage and limited observations make quantitative precipitation forecasting quite a challenge. Past studies have suggested that greater forecast skill of mesoscale convection initiation and precipitation characteristics are achievable considering an ensemble with explicitly predicted convection compared to one that has parameterized convection. The range of uncertainty and probabilities in these forecasts can help forecasters in their precipitation predictions and communication of weather information to emergency managers (EMs). EMs serve an integral role in informing and protecting communities in anticipation of hazardous weather. An example of such an event occurred on the evening of 6 June 2012, where areas to the lee of the Rocky Mountain Front Range were impacted by flash-flood-producing severe convection that included heavy rain and copious amounts of hail. Despite the discrepancy in the timing, location and evolution of convection, the convection-allowing ensemble forecasts generally outperformed those of the convection-parameterized ensemble in representing the mesoscale processes responsible for the 6-7 June severe convective event. Key features sufficiently reproduced by several of the convection-allowing ensemble members resembled the observations: 1) general location of a convergence boundary east of Denver, 2) convective initiation along the boundary, 3) general location of a weak cold front near the Wyoming/Nebraska border, and 4) cold pools and moist upslope characteristics that contributed to the backbuilding of convection. Members from the convection-parameterized ensemble that failed to reproduce these results displaced the convergence boundary, produced a cold front that moved southeast too quickly, and used the cold front for convective initiation. The convection-allowing ensemble also showed greater skill in forecasting heavy precipitation amounts in the vicinity of where they were observed during the most active convective period, particularly near urbanized areas. A total of 9 Front Range EMs were interviewed to research how they understood hazardous weather information, and how their perception of forecast uncertainty would influence their decision making following a heavy rain event. Many of the EMs use situational awareness and past experiences with major weather events to guide their emergency planning. They also highly valued their relationship with the National Weather Service to improve their understanding of weather forecasts and ask questions about the uncertainties. Most of the EMs perceived forecast uncertainty in terms of probability and with the understanding that forecasting the weather is an imprecise science. The greater the likelihood of occurrence (implied by a higher probability of precipitation) showed greater confidence in the forecast that an event was likely to happen. Five probabilistic forecast products were generated from the convection-allowing ensemble output to generate a hypothetical warm season heavy rain event scenario. Responses varied between the EMs in which products they found most practical or least useful. Most EMs believed that there was a high probability for flooding, as illustrated by the degree of forecasted precipitation intensity. Most confirmed perceiving uncertainty in the different forecast representations, sharing the idea that there is an inherent uncertainty that follows modeled forecasts. The long-term goal of this research is to develop and add reliable probabilistic forecast products to the "toolbox" of decision-makers to help them better assess hazardous weather information and improve warning notifications and response.
Open Access
Examination of the potential impacts of dust and pollution aerosol acting as cloud nucleating aerosol on water resources in the Colorado River Basin
(Colorado State University. Libraries, 2016) Jha, Vandana, author; Cotton, William R., advisor; Rutledge, Steven A., committee member; Pierce, Jeffrey, committee member; Ramirez, Jorge, committee member
In this study we examine the cumulative effect of dust acting as cloud nucleating aerosol (cloud condensation nuclei (CCN), giant cloud condensation nuclei (GCCN), and ice nuclei (IN)) along with anthropogenic aerosol pollution acting primarily as CCN, over the entire Colorado Rocky Mountains from the months of October to April in the year 2004-2005; the snow year. This ~6.5 months analysis provides a range of snowfall totals and variability in dust and anthropogenic aerosol pollution. The specific objectives of this research is to quantify the impacts of both dust and pollution aerosols on wintertime precipitation in the Colorado Mountains using the Regional Atmospheric Modeling System (RAMS). In general, dust enhances precipitation primarily by acting as IN, while aerosol pollution reduces water resources in the CRB via the so-called “spill-over” effect, by enhancing cloud droplet concentrations and reducing riming rates. Dust is more episodic and aerosol pollution is more pervasive throughout the winter season. Combined response to dust and aerosol pollution is a net reduction of water resources in the CRB. The question is by how much are those water resources affected? Our best estimate is that total winter-season precipitation loss for for the CRB the 2004-2005 winter season due to the combined influence of aerosol pollution and dust is 5,380,00 acre-feet of water. Sensitivity studies for different cases have also been run for the specific cases in 2004-2005 winter season to analyze the impact of changing dust and aerosol ratios on precipitation in the Colorado River Basin. The dust is varied from 3 to 10 times in the experiments and the response is found to be non monotonic and depends on various environmental factors. The sensitivity studies show that adding dust in a wet system increases precipitation when IN affects are dominant. For a relatively dry system high concentrations of dust can result in over-seeding the clouds and reductions in precipitation. However, when adding dust to a system with warmer cloud bases, the response is non-monotonical, and when CCN affects are dominant, reductions in precipitation are found.
Open Access
Fate of snowmelt in complex subalpine terrain
(Colorado State University. Libraries, 2016) Webb, Ryan W., author; Gooseff, Michael, advisor; Fassnacht, Steven, committee member; Ramirez, Jorge, committee member; Niemann, Jeffrey, committee member
Snow is important to human communities and natural ecosystems around the world that rely on snowmelt runoff for as much as 80% or more of streamflow. In addition to streamflow, snowmelt can drive hydrological processes such as groundwater recharge, soil moisture dynamics, forest ecosystem dynamics, and potentially cause high damage flooding. Multiple environmental controls will cause snow to vary in depth, density, and snow crystal metamorphism causing a complex three dimensional matrix of ice, air, water vapor, and liquid water (during melt) that is non-uniform across a landscape and varies in time at the daily and even hourly scale. Because of the non-uniform dynamics of snow and snowmelt processes, multi-dimensional studies are necessary to determine hydrological flow paths during spring snowmelt. The goal of this dissertation is to investigate the physical processes that control the fate of snowmelt during spring runoff in complex subalpine terrain. These processes were investigated through 1) observing the diurnal pattern of snowmelt in Colorado's Front Range, 2) testing the diversion potential of hydraulic barriers within a layered snowpack through numerical modeling, 3) collecting field data to investigate the spatio-temporal patterns of water distribution during spring snowmelt, and 4) analyzing a network of soil moisture sensors in California's Southern Sierra Nevada to determine the variability of infiltration in a headwater catchment. Observations of the diurnal temporal pattern of snowmelt resulted in a relatively simple method to capture the outflow from a snowpack using hourly snow water equivalent data. The resulting temporal pattern is comparable to design rainfall distribution types specifically for snowmelt that can be important for flood risk analysis or design of channels in previously unmonitored headwater systems. The observed temporal patterns were also used to inform numerical simulations in the modeling package TOUGH2 that utilized additional data from NASA CLPX datasets to simulate meltwater percolation through a melting snowpack. Results of this component of the dissertation displays the potential for hydraulic barriers to form on south, flat, and north aspect hillslopes and potentially divert downward flowing water at similar scales as the topographic or land cover variability. Hydraulic barriers in simulations were permeability barriers only on the south and flat aspect slopes and capillary barriers only on the north aspect slopes. The dynamic nature of a snowpack in the presence of water implies that the capillary barriers are likely short-lived relative to permeability barriers and thus capillary barriers may be important at the day or week timescale and permeability barriers may be more influential at the monthly or seasonal time scale. Field observations near Steamboat Springs, Colorado were made for above normal, relatively normal, and below normal snow seasons including measurements of bulk snow water equivalent and soil moisture on varying slope, aspect, soil parameters, and canopy conditions with results displaying the variability from these influences. Evidence was present of meltwater flowing above the soil surface and through the snowpack. At the base of the north aspect slope the water table rose above the soil surface and the snowpack added storage capacity to the vadose zone. The variability of snowmelt and resulting soil moisture and infiltration dynamics was supported by the analysis of a network of soil moisture sensors in California’s Southern Sierra Nevada. This component of the dissertations displayed the high variability of wetting and drying dynamics beneath a snowpack at the sub-hillslope and watershed scale. Results of this dissertation display that the snowpack acts as an extension of the vadose zone during spring snowmelt and that one-dimensional assumptions are not appropriate in headwater catchments during this time. Consideration of the snowpack and soil together will improve modeling, remote sensing, and water balance calculations for hydrologic studies during spring snowmelt and improvements upon allocation of streamflow, groundwater recharge, and evapotranspiration.
Open Access
Impacts of climate change on the hydrologic response of headwater basins in Colorado
(Colorado State University. Libraries, 2010) Foy, Caleb R., author; Arabi, Mazdak, advisor; Kampf, Stephanie, committee member; Ramirez, Jorge, committee member
The headwater basins of Colorado are heavily relied upon for freshwater resources on an annual basis. However, knowledge concerning generation of such resources, and implications of climate change on their availability in the future, is not well understood. Thus, this research has been undertaken to develop, calibrate, and test a comprehensive process-based model in four mountainous watersheds of Colorado, and investigate the potential impacts of changing climate on hydrologic response in these basins. Specifically, the four study watersheds considered for analysis include the Cache la Poudre, Gunnison, San Juan and Yampa River basins. Calibration of the model compared several parameter optimization techniques for performance in each of the study basins, which included the more common Shuffled Complex Evolution – University of Arizona (SCE-UA) method and a Markov Chain Monte Carlo (MCMC) method known as the Gibbs Sampler Algorithm (GSA). Fully calibrated and tested models were driven by a suite of 112 climate projections, downscaled both spatially and temporally, and were run on a daily time-step for a period of 90 years from 2010 – 2099. Results from model calibration indicate GSA outperformed SCE-UA in a majority of the study basins, in addition to revealing promising results from a two-stage method that combined the strengths of the two techniques. Error statistics showed very good (Nash-Sutcliffe coefficient of efficiency >0.75 and relative error <+/-10%) performance of monthly streamflow simulations compared to naturalized flows at the outlet of each watershed over a period of 16 years (1990 – 2005). Additionally, the models provided satisfactory results for simulating monthly streamflow at multiple sites nested within each watershed, which increased confidence in model parameterization and representation of dominant hydrologic processes. Results indicate that on an average annual basis, 55% – 65% of precipitation goes to evapotranspiration, and lateral flow contributes to between 64% and 82% of gross water yield. Results from future simulations over the course of the 21st century indicate inconsistent responses in streamflow to increasing temperature and variable precipitation projections. However, results did show consistency in the Yampa River basin, where 71 out of 112 future projections resulted in statistically significant (α<0.1) positive trends of average annual streamflow. Furthermore, all study basins exhibited a decreasing ratio of precipitation to potential evapotranspiration from emissions scenario ensemble averages, which suggest Colorado basins will become more arid over the 21st century. Future forecasting of water availability in Colorado may benefit from this research, as specific climate projections were provided that resulted in consistent responses (increasing and decreasing) in streamflow across all watersheds. Implications of this study are considerable, as management of water resources, both within the state and across the West, will be affected by freshwater availability in headwater basins of Colorado in the future.
Open Access
Impacts of climate change to breeding and migrating waterbirds in the Prairie Pothole Region
(Colorado State University. Libraries, 2016) Steen, Valerie, author; Noon, Barry, advisor; Skagen, Susan, advisor; Flather, Curt, committee member; Ramirez, Jorge, committee member
The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. Using a bioclimatic species distribution modeling (SDM) approach, I explored the potential effects of climate change on 31 breeding waterbird species. The approach involved using a random forest modeling algorithm and downscaled climate data from outputs of two future General Circulation Models (GCMs). By the 2040’s, species were projected, on average, to lose 46% of their current habitat in the U.S. portion of the PPR. Species specific projected impacts ranged widely, with three species (Wilson’s Snipe, Sora, and Franklin’s Gull) projected to lose close to 100% of their U.S. Prairie Pothole habitats and two species (Killdeer and Upland Sandpiper) projected to gain habitat. Bioclimatic SDM approaches, however, have been shown to produce varying projections of species climate change impacts depending on methodological decisions including: choice of GCM, choice of climate covariates, level of collinearity among climate variables, and thresholding procedure used to convert probability values to binary occurrence values. I explored these and found that median projected range loss, across species, was 35%. However, projections for individual species varied widely, typically spanning from 100% range loss to range increases. The largest source of uncertainty was choice of GCM, followed by choice of climate covariate, then thresholding procedure. Level of collinearity contributed relatively little uncertainty. To understand the potential impacts of climate change to migrating shorebirds, I explored climate change sensitivity using historic records from a dry year and a wet year. Using historic data to explore climate sensitivity of migrating shorebirds in the PPR avoids many of the uncertainties of the bioclimatic SDM approach, and can yield insights helpful to guide adaptation planning for climate change. Using binomial generalized linear models, I found shorebirds shifted at the regional scale and selected landscapes with different characteristics in a dry year versus a wet year. This result indicates shorebirds are able to find habitat in the PPR under varying climate conditions, and supports a model of resilience for migrating shorebirds under climate change if wetlands in these varying landscapes are protected from drainage.
Open Access
Investigation into a displacement bias in numerical weather prediction models' forecasts of mesoscale convective systems
(Colorado State University. Libraries, 2013) Yost, Charles, author; Schumacher, Russ, advisor; van den Heever, Sue, committee member; Ramirez, Jorge, committee member
Although often hard to correctly forecast, mesoscale convective systems (MCSs) are responsible for a majority of warm-season, localized extreme rain events. This study investigates displacement errors often observed by forecasters and researchers in the Global Forecast System (GFS) and the North American Mesoscale (NAM) models, in addition to the European Centre for Medium Range Weather Forecasts (ECMWF) and the 4-km convection allowing NSSL-WRF models. Using archived radar data and Stage IV precipitation data from April to August of 2009 to 2011, MCSs were recorded and sorted into unique six-hour intervals. The locations of these MCSs were compared to the associated predicted precipitation field in all models using the Method for Object-Based Diagnostic Evaluation (MODE) tool, produced by the Developmental Testbed Center and verified through manual analysis. A northward bias exists in the location of the forecasts in all lead times of the GFS, NAM, and ECMWF models. The MODE tool found that 74%, 68%, and 65% of the forecasts were too far to the north of the observed rainfall in the GFS, NAM and ECMWF models respectively. The higher-resolution NSSL-WRF model produced a near neutral location forecast error with 52% of the cases too far to the south. The GFS model consistently moved the MCSs too quickly with 65% of the cases located to the east of the observed MCS. The mean forecast displacement error from the GFS and NAM were on average 266 km and 249 km, respectively, while the ECMWF and NSSL-WRF produced a much lower average of 179 km and 158 km. A case study of the Dubuque, IA MCS on 28 July 2011 was analyzed to identify the root cause of this bias. This MCS shattered several rainfall records and required over 50 people to be rescued from mobile home parks from around the area. This devastating MCS, which was a classic Training Line/Adjoining Stratiform archetype, had numerous northward-biased forecasts from all models, which are examined here. As common with this archetype, the MCS was triggered by the low-level jet impinging on a stationary front, with the heaviest precipitation totals in this case centered along the tri-state area of Iowa, Illinois, and Wisconsin. Low-level boundaries were objectively analyzed, using the gradient of equivalent potential temperature, for all forecasts and the NAM analysis. In the six forecasts that forecasted precipitation too far to the north, the predicted stationary front was located too far to the north of the observed front, and therefore convection was predicted to initiate too far to the north. Forecasts associated with a northern bias had a stationary front that was too far to the north, and neutral forecasts' frontal locations were closer to the observed location.
Open Access
Modeling a variable surface resistance (rs) for alfalfa and assessing the ASCE rs performance in the reference evapotranspiration equation
(Colorado State University. Libraries, 2016) Subedi, Abhinaya, author; Chávez, José, advisor; Andales, Allan, advisor; Ramirez, Jorge, committee member; Ham, Jay, committee member
Accurate quantification of crop water requirement is necessary for proper irrigation water management. The knowledge of actual crop evapotranspiration (ETc) is important and is necessary for estimating irrigation water requirements. The most common procedure of obtaining actual crop evapotranspiration (ETc) is by first calculating the reference crop evapotranspiration (ETr) and then multiplying it with the appropriate crop coefficients (Kc). If the surface resistance (rs) of a particular crop can be modeled, then ETc can be directly calculated without using Kc. The overall objectives of this dissertation were to model surface resistance for alfalfa reference crop and to find an effective value of the surface resistance of alfalfa in the ASCE Standardized Reference ET equation. It has been found that using a single Kc curve for different climatic conditions can lead to significant error in estimating ETc. Hence it is important to find appropriate Kc for different crops for local climatic condition. Lysimeters are generally used to determine the values of Kc, as lysimetry is considered a reliable method of quantifying the ET losses from a control volume. This study found that using lysimeter ET data to obtain Kc can be problematic especially when the field is heterogeneous. In order to develop Kc for various crops, it is recommended to use some years of reliable data with uniform healthy and unstressed crop surface conditions both inside and outside the lysimeter. This study was focused on to develop a model for surface resistance (rs) of alfalfa in order to calculate alfalfa ETc in a one-step approach without the need for Kc values. Surface resistance was estimated by inverting the aerodynamic equation using ET measured from lysimeter and sensible heat flux (H) measured from large aperture scintillometer (LAS). This observed rs showed a very good correlation with leaf area index (LAI) and crop height (hc). The alfalfa rs was then modeled as a function of LAI and hc (which is referred to as rs(LAI) and rs(hc) respectively). Then these modeled rs s were incorporated into the Penman Monteith (PM) equation to estimate alfalfa hourly ET, which performed very well when compared with the measured hourly lysimeter ET. The conventional alfalfa rs, developed by Allen et al. (1989) was found to underestimate rs significantly especially when the crop height was short (less than 25 cm). It was found that ET_conventional_rs was not applicable to estimate alfalfa ET when the crop height was less than 25 cm. The modeled rs(LAI) and rs(hc) are constant throughout the day, but in reality, rs changes throughout the day. Hence hourly variable rs was also developed based on aerodynamic resistance (ra), canopy temperature (Tc) and vapor pressure deficit (VPD). It was found that PM equation incorporating the hourly variable rs improved the alfalfa ET estimation when compared with the conventional rs approach. ASCE-EWRI Standardized Reference ET for tall reference crop was found to underestimate measured ET by about 10 per cent. The equation assumes the value of rs for alfalfa as 30 s/m. When the value of rs was changed from 30 s/m to 10 s/m, the performance of the equation improved, resulting in no bias and root mean square error (RMSE) reduction from 0.08 mm/h (15.3%) to 0.06 mm/h (11.4%) in 2009 and from 0.09 mm/h (14.1%) to 0.06 mm/h (10.1%) in 2010.
Open Access
The hydroclimate of the Upper Colorado River Basin and the western United States
(Colorado State University. Libraries, 2014) Bolinger, Rebecca A., author; Kummerow, Christian D., advisor; Doesken, Nolan, committee member; Ramirez, Jorge, committee member; Rutledge, Steven, committee member; Vonder Haar, Tom, committee member
Understanding water budget variability of the Upper Colorado River Basin (UCRB) is critical, as changes can have major impacts on the region's vulnerable water resources. Using in situ, reanalysis, and satellite-derived datasets, surface and atmospheric water budgets of the UCRB are analyzed. All datasets capture the seasonal cycle for each water budget component. Most products capture the interannual variability, although there are some discrepancies with atmospheric divergence estimates. Variability and magnitude among storage volume change products also vary widely. With regards to the surface budget, the strongest connections exist between precipitation, evapotranspiration (ET), and soil moisture, while snow water equivalent is best correlated with runoff. Using the most ideal datasets for each component, the atmospheric water budget balances with 73 mm leftover. Increasing the best estimate of ET by 5% leads to a better long-term balance between surface storage changes, runoff, and atmospheric convergence. It also brings long-term atmospheric storage changes to a better balance of 13 mm. A statistical analysis and case study are performed to better understand the variability and predictability of the UCRB's hydroclimate. Results show significant correlations (at the 90% confidence level) between UCRB temperature and precipitation, and El Nino - Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) during the fall. However, correlations are typically not greater than 0.4. ENSO and PDO are associated with the second mode of variability in a Principal Component analysis, while the first mode of variability (57% of variance for precipitation and 74% of variance for temperature) displays a high year-to-year variability. A case study of a wet and a dry year (with similar ENSO/PDO conditions) shows that a few large accumulation events is what drives the seasonal variability. These large accumulation events are more dependent on a variety of more local synoptic conditions (e.g., location of low pressure, direction and speed of local winds, amount of moisture available). An analysis of ten winters shows that there are generally less than five large accumulating events in drier winters, with closer to ten in wetter winters. Overall, the statistics and case study show that a consistently accurate seasonal forecast for the UCRB is not achievable at this time. Expanding the ideal datasets selected over the UCRB, an analysis of the errors in atmospheric and surface water budgets is performed for every individual HUC4 basin over the western U.S. Surface water budgets show overall much smaller residual errors than the atmospheric water budgets over the region. Visually analyzing the balances and imbalances, we see that several different areas around the Continental Divide and the Great Basin balance well at the surface, but not as well in the atmosphere; around Arizona, most basins don't balance at either the surface or atmosphere; many of the Pacific coastal basins and basins in the northern Rocky mountains balance well at the surface and in the atmosphere. These balances/imbalances, climate variability, land cover, and topography are combined to delineate five hydroclimate zones. Seasonal and interannual variability is analyzed for each zone. The Pacific Coast zone shows strong agreement amongst the seasonal cycles of all the water budget components, while most of the other zones show an offset in peaks between components during the winter and summer.
Open Access
Using convection-allowing ensembles to understand the predictability of extreme rainfall
(Colorado State University. Libraries, 2016) Nielsen, Erik R., author; Schumacher, Russ, advisor; van den Heever, Susan, committee member; Ramirez, Jorge, committee member
The meteorological community has well established the usefulness of ensemble-based numerical weather prediction for precipitation guidance, since trusting one possible atmospheric solution can lead to, in some cases, particularly bad forecasts for precipitation guidance, owing to inherent uncertainties in precipitation processes that make deterministic prediction impractical. However, continued predictive challenges associated with intense convective rainfall has led to an increasing need to determine the most effective use of these ensemble systems in high impact, extreme precipitation events. Further, it cannot be assumed that ensembles will evolve similarly in both extreme precipitation and more benign events, due to the importance and error growth associated with convective-scale motions. This error growth associated with the chaotic nature of moist convective dynamics can also serve to limit the predictability of an extreme rainfall event (known as intrinsic predictability), in addition to predictability limits imposed by deficiencies in observing systems and numerical models (known as practical predictability). This research will focus on using a recently developed, operationally based ensemble dataset, specifically the National Oceanic and Atmospheric Administration's (NOAA) Second Generation Global Medium-Range Ensemble Reforecast Dataset (Reforecast-2), to create downscaled ensemble reforecasts of the extreme precipitation events. Some events examined during the course of this research are the inland movement of tropical storm Erin in 2007 and flooding associated with mesoscale convective vortices in Arkansas in 2010 and San Antonio, Texas in 2013. The global reforecasts are used to force an ensemble of convection-allowing WRF-ARW numerical simulations for the purpose of evaluating ensemble-based precipitation forecasts associated with specific extreme rainfall events. Using these ensemble forecasts, we address several questions related to the practical versus the intrinsic predictability of the extreme rainfall events examined. Experiments that vary the magnitude of the perturbations to the initial and lateral boundary conditions (ICs and LBCs) reveal a seemingly proportional scaling of ensemble spread early in the simulations associated with the magnitude of the perturbation, but this scaling is not maintained throughout the simulations. Additionally, a diurnal cycle in ensemble spread growth is observed with large growth associated with afternoon convection, but the growth rate then reduced after convection dissipates the next morning rather than continuing to grow. The specific characteristics of the diurnal cycle, however, vary based upon region and flow regime. Lastly, the ensemble spread was found to be influenced by the size of the IC perturbations out to at least 48 hours. These spread evolution characteristics speak to the viability of running convection-allowing ensembles for prediction on multi-day timescales, since no saturation of the ensemble spread is seen despite extreme precipitation within the modeled time period. In addition to the overall ensemble characteristics, terrain-induced precipitation variability associated with the terrain feature known as the Balcones Escarpment, located in central Texas, is analyzed in multiple instances of heavy rainfall in San Antonio and the surrounding area. Simulations in which the Balcones Escarpment is removed reveal that when the synoptic to mesoscale forcing for heavy rainfall are in place over the Balcones Escarpment, the terrain does not directly affect the occurrence or magnitude of precipitation. It does affect the spatial distribution of the precipitation in a small but consistent way. This shift in precipitation associated with removing the Balcones Escarpment, when compared to a WRF-ARW ensemble for the event, is smaller than shifts associated with typical atmospheric variability. The combined results of these experiments demonstrate that downscaled ensemble NWP systems using readily available global ensemble forecasts can faithfully represent previously unresolved mesoscale features, precipitation totals, and depict ensemble-spread characteristics associated with moist convection.
Open Access
Watershed-based methodology for assessment of nonpoint source pollution from inactive mines
(Colorado State University. Libraries, 1995) Caruso, Brian S., author; Loftis, Jim C., advisor; Ward, Robert C., committee member; Ramirez, Jorge, committee member; Walters, Richard W., committee member
A watershed-based methodology for the screening-level assessment of nonpoint source pollution from inactive and abandoned metal mines (lAMs) was developed, tested, and evaluated in this study. The methodology is intended for use by state and federal agencies responsible for management of these sites, and was designed to generate the common types of baseline site characterization information required for targeting streams and contaminant source areas for remediation. These information goals have been defined as part of this study prior to developing the assessment methodology, and are based on generalized but clearly stated lAM management goals that are most common among agencies. The research involved the following; (1) Identifying typical water quality and hydrologic characteristics of and assessment methods for lAMs. (2) Defining lAM management goals and information goals for targeting. (3) Identifying and evaluating attributes of data derived from typical synoptic surveys of lAMs. (4) Identifying common data gaps and data collection and analysis methods to fill these gaps. (5) Identifying and evaluating applicable assessment and data analysis methods to achieve the stated information goals. (6) Developing, testing, and evaluating the assessment methodology. The Cement Creek Basin, part of the Upper Animas River Basin above Silverton in the San Juan Mountains of southwestern Colorado, was used as the primary case study to develop the recommended methodology. The study showed that the potential error and uncertainty in the data and derived information should be considered explicitly in the assessment process in order to target remediation with a known degree of confidence. Confidence intervals, therefore, should be computed for statistical estimators. Visual aids for data presentation and usage should be used and include graphs, mapping of information, and if possible, GIS. Targeting in Cement Creek and at other sites can be accomplished effectively using the recommended methodology. Some data gaps exist in Cement Creek and at most lAMs with regard to targeting remediation. These can be filled when the required information goals are not met with existing data and when resources are available using some of the methods discussed in this study. The recommended methodology is applicable to and would be very useful for other lAMs.