Browsing by Subject "Rainfall"
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Item Investigating Rainwater Harvesting as a Stormwater Best Management Practice and as a Function of Irrigation Water Use(2012-02-14) Shannak, Sa'D Abdel-HalimStormwater runoff has negative impacts on water resources, human health and environment. In this research the effectiveness of Rain Water Harvesting (RWH) systems is examined as a stormwater Best Management Practice (BMP). Time-based, evapotranspiration-based, and soil moisture-based irrigation scheduling methods in conjunction with RWH and a control site without RWH were simulated to determine the effect of RWH as a BMP on a single-family residence scale. The effects of each irrigation scheduling method on minimizing water runoff leaving the plots and potable water input for irrigation were compared. The scenario that reflects urban development was simulated and compared to other RWH-irrigation scheduling systems by a control treatment without a RWH component. Four soil types (sand, sandy loam, loamy sand, silty clay) and four cistern sizes (208L, 416L, 624L, 833L) were evaluated in the urban development scenario. To achieve the purpose of this study; a model was developed to simulate daily water balance for the three treatments. Irrigation volumes and water runoff were compared for four soil types and four cistern sizes. Comparisons between total volumes of water runoff were estimated by utilizing different soil types, while comparisons between total potable water used for irrigation were estimated by utilizing different irrigation scheduling methods. This research showed that both Curve Number method and Mass-Balance method resulted in the greatest volumes of water runoff predicted for Silty Clay soil and the least volumes of water runoff predicted for Sand soil. Moreover, increasing cistern sizes resulted in reducing total water runoff and potable water used for irrigation, although not at a statistically significant level. Control treatment that does not utilize a cistern had the greatest volumes of predicted supplemental water among all soil types utilized, while Soil Moisture-based treatment on average had the least volume of predicted supplemental water.Item On the predictability of rainfall anomalies over the Southern Amazonia : a comparison between NMME and statistical models(2016-12) Zhang, Kai, M.S. in Statistics; Daniels, Michael JosephPrediction of rainfall over the Amazonian rainforest during wet season is fundamental to assess the regional water and energy balance and global carbon-climate feedbacks. Previous observational analysis has identified some large-scale atmospheric dynamic and thermodynamics conditions that can influence the rainfall anomalies during the wet season. Based on these observed persistent conditions that started between June and August (JJA, dry season), we have developed and evaluated several statistical models to predict rainfall conditions during September to November (SON, early wet season) for the Southern Amazonia (5-15oS, 50-70oW). Multivariate Empirical Orthogonal Function (EOF) Analysis is applied to the following four fields during JJA from the ECMWF Reanalysis (ERA-Interim) spanning from year 1979 to 2015: geopotential height at 200 hPa, surface relative humidity, convective inhibition energy (CIN) index and convective available potential energy (CAPE), to filter out noise and highlight the most coherent spatial and temporal variations. The first 10 EOF modes are retained for inputs to the statistical models, accounting for at least 70% of the total variance in the predictor fields. Then the 12-fold cross-validation method is used to estimate the tuning parameters used in the regression algorithms. Ridge Regression and Lasso Regression are able to capture the spatial pattern and magnitude of rainfall anomalies. Compared with the seasonal prediction based on dynamical models, this statistical prediction system has better predictions than the seasonal predictions of the dynamic climate model. The statistical models show longer and more accurate predictive persistence of the rainfall anomalies. In addition, we use Logistic regression and Neural Networks to predict the categorical states of rainfall over the Southern Amazon by classifying the rainfall states into two categories, i.e., dry and wet. Our statistical models show overall better predictions of categorical rainfall states than the magnitudes of rainfall in our study region. The accuracy of the statistical prediction based on Neural Networks method can reach greater than 90%, which is much higher than the simple logistic regression method, indicating the non-linearity of the atmospheric processes. Both predictions of the magnitudes and states of rainfall anomalies can be combined to provide more accurate information. The models we have developed have broad implications on the future development of seasonal climate prodictions and can be used for real-time forecasts in the future.Item Study on Poisson Cluster Stochastic Rainfall Generators(2011-02-22) Kim, Dong KyunThe purpose of this dissertation is to enhance the applicability and the accuracy of the Poisson cluster stochastic rainfall generators. Firstly, the 6 parameters of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall simulation model were regionalized across the contiguous United States. Each of the parameters of MBLRP model estimated at 3,444 National Climate Data Center (NCDC) rain gages was spatially interpolated based on the Ordinary Kriging technique to produce the parameter surface map for each of the 12 months of the year. Cross-validation was used to assess the validity of the parameter maps. The results indicate that the suggested maps reproduce well the statistics of the observed rainfall for different accumulation intervals, except for the lag-1 autocorrelation coefficient. The estimated parameter values were also used to produce the maps of storm and rain cell characteristics. Secondly, the relative importance of the rainfall statistics in the generation of watershed response characteristics was estimated based on regression analyses using the rainfall time series observed at 1099 NCDC rain gages. The result of the analyses was used to weigh the rainfall statistics differently in the parameter calibration process of MBLRP model. It was observed that synthetic rainfall time series generated weighing the precipitation statistics according to their relative importance outperformed those generated weighing all statistics equally in predicting watershed runoff depths and peak flows. When all statistics were given the same weight, runoff depths and peak flows were underestimated by 20 percent and 14 percent, respectively; while, when the statistics were weighed proportionally to their relative importance, the underestimation was reduced to 4 percent and 3 percent, which confirms the advantage of weighing the statistics differently. In general, the value of the weights depends on the hydrologic process being modeled. Lastly, a stochastic rainfall generation model that can integrate year-to-year variability of rainfall statistics is suggested. The new framework consists of two parts. The first part generates the short-term rainfall statistics based on the correlation between the observed rainfall statistics. The second part generates the rainfall time series using the modified Bartlett-Lewis rectangular pulse model based on the simulated rainfall statistics. The new approach was validated at 104 NCDC gages across the United States in its ability to reproduce rainfall and watershed response characteristics. The result indicates that the new framework outperformed the traditional approach in reproducing the distribution of monthly maximum rainfall depths, monthly runoff volumes and monthly peak flows.Item The Role of Climate in the Deformation of a Fold and Thrust Belt(2012-02-14) Steen, Sean KristianTheory and experiment show that the rate and geographic distribution of erosion control the rate and pattern of deformation in collisional mountain belts. Enhanced erosion reduces the mass of material that must be moved up and over ramps and uplifted in large folds. In order to test this and related ideas in a natural example, we have compared modeled rainfall to measured thrust sheet displacement, geometry, and internal deformation in the Appalachian fold and thrust belt. We use mean annual precipitation from a global climate model (GCM) as a proxy for rate of erosion. Deformation measurements were made on a portfolio of regional cross sections from Alabama to New England. During the Carboniferous Allegheny orogeny the Southern Appalachians moved from -30 ? to 0? latitude whereas the Central and Northern Appalachians lay between -15? and 5? latitude. Mean annual precipitation determined from the GENESIS 2 GCM (Grossman, per. comm.) varied from tropical to arid conditions as the collision both moved north and grew in breadth and height. The Southern Appalachians, which experienced more net rainfall than other regions, generally show more displacement, deeper present day exhumation, and shallower ramps than regions to the north. The vicinity of the Pine Mountain thrust sheet in the Southern Appalachians experienced the most displacement (~1.5X the Central Appalachian average) and bulk shortening (~1.6X the Central Appalachians) and produced the most eroded material (~1.5X the Central Appalachians). The latitude of the Pine Mountain thrust sheet in the Southern Appalachians received ~20% more rainfall than the Central Appalachians. Although the number of regional detachments and lithologies change from Southern to Central and Northern Appalachians, the change in rainfall both regionally at any one time and as the collision progressed may explain part of the change in structural style from south to north.