A STATISTICAL SIMULATION TECHNIQUE FOR NORTHERN NEW JERSEY MONTHLY PRECIPITATION1

A 30-year record of monthly precipitation for Northern New Jersey was analyzed for its statistical components. With a weak annual periodicity eliminated, the series was found to be random. The data for each month were fit with a gamma distribution using Thom's suggested best estimates of the distribution parameters. A one-thousand-year simulated monthly precipitation series was generated using random values from the twelve gamma distributions. The statistical properties of the simulated and sample time series agreed well. Numerous anomalous precipitation regimes were observed in the simulated data.     

CHARACTERISTICS OF EXTREME PRECIPITATION1

ABSTRACT: Records of extreme precipitation were investigated using the Discrete Autoregressive Moving Average (DABMA) process, which can explain long persistences of wet and dry spells that exist in daily precipitation data. The results show that the daily precipitation with strong autocorrelation is inclined to be better fit by a Discrete Autoregressive (DAB) model. On the other hand, those data with weak autocorrelations tend to be best fit by a Discrete Moving Average (DMA) model. It can also be concluded that based on the records from extremely wet and dry regions there is no geographic preference regarding the selection of the best model.     

Wind energy potential assessment and techno-economic performance of wind turbines in coastal sites of Buenos Aires province,Argentina

An assessment of wind energy potential was carried out in five sites (four onshore and one offshore) in South-West (SW) of Buenos Aires province (Argentina). We use high-resolution wind data (2 and 5 min) for the period 2009–2012. The power law was used to estimate the wind speed at 30, 40, and 60 m height from the anemometer position. Turbulence intensity and wind direction were analyzed. Statistical analyses were conducted using two-parameter Weibull distribution. A techno-economic analysis based on a set of commercial wind turbines was performed in those sites. The results derived from this work indicate that the SW of Buenos Aires province represents a promising area for the wind energy extraction, which would encourage the construction of wind farms for electricity generation.     

GENERATION OF DAILY PRECIPITATION OVER AN AREA1

ABSTRACT: A model was developed of the periodic-stochastic structure of daily precipitation over an area. The model is based ona multivariate normal distribution. The square roots of daily precipitation at a point were found to approximate a sample from a univariate normal distribution that had been truncated at zero. The zero daily precipitation amounts were considered negative amounts of unknown quantity. The multivariate normal distribution was used to describe the variation of daily precipitation over an area. The periodic fluctuations of the model parameters were described with Fourier series. The model was tested using data from two areas of different precipitation characteristics. Data generated with the model contained many of the statistical characteristics observed in the historical data.     

Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

Scientific literature discussed various types of mixture models and models derived from maximum entropy principle using short-term wind speed data for their relative assessment. The literature on suitability of these mixture models for long-term data is rarely available. However, for correct assessment of wind power potential both wind speed and wind direction are equally important. Therefore, in this paper, both wind speed and wind direction are simultaneously analyzed using several types of mixture distribution and compared the same with conventional Weibull distribution. For wind speed and wind power density assessment, the mixture distributions such as Weibull--Weibull distribution, Gamma--Weibull distribution, Truncated Normal--Weibull distribution, Truncated Normal--Normal distribution, proposed Truncated Normal--Gamma distribution and Gamma--Gamma distribution along with MEP-distribution are compared with conventional 2-parameter Weibull distribution. Similarly, for wind direction analysis, the finite mixtures of von-Mises distribution are compared with conventional von-Mises distribution. Judgment criteria include R², RMSE, Kolmogorov--Smirnov test and relative percentage error in wind power density. The sites selected are the three onshore locations of India, viz., Calcutta, Trivandrum, and Ahmedabad. The results show that for wind speed assessment, mixture distribution performs better than the conventional Weibull distribution for analyzing wind power density. However, location wise comparison of all mixture distribution is of prime importance. For wind direction analysis, finite mixture of two von-Mises distributions proved to be a suitable candidate for Indian climatology.     

Magnitude,Frequency, and Duration Relations for Suspended Sediment in Stable (“Reference”) Southeastern Streams1

Abstract: Sediment is listed as one of the leading causes of water‐quality impairments in surface waters of the United States (U.S.). A water body becomes listed by a State, Territory or Tribe if its designated use is not being attained (i.e., impaired). In many cases, the prescribed designated use is aquatic health or habitat, indicating that total maximum daily loads (TMDL) targets for sediment should be functionally related to this use. TMDL targets for sediment transport have been developed for many ecoregions over the past several years using suspended‐sediment yield as a metric. Target values were based on data from “reference” streams or reaches, defined as those exhibiting geomorphic characteristics of equilibrium. This approach has proved useful to some states developing TMDLs for suspended sediment, although one cannot conclude that if a stream exceeds the target range, the aquatic ecosystem will be adversely impacted. To address this problem, historical flow‐transport and sediment‐transport data from hundreds of sites in the Southeastern U.S. were re‐examined to develop parameters (metrics) such as frequency and duration of sediment concentrations. Sites determined as geomorphically stable from field evaluations and from analysis of gauging‐station records were sorted by ecoregion. Mean‐daily flow data obtained from the U.S. Geological Survey were applied to sediment‐transport rating relations to determine suspended‐sediment load for each day of record. The frequency and duration that a given concentration was equaled or exceeded were then calculated to produce a frequency distribution for each site. “Reference” distributions were created using the stable sites in each ecoregion by averaging all of the distributions at specified exceedance intervals. As with the “reference” suspended‐sediment yields, there is a broad range of frequency and duration distributions that reflects the hydrologic and sediment‐transport regimes of the ecoregions. Ecoregions such as the Mississippi Valley Loess Plains (#74) maintain high suspended sediment concentrations for extended periods, whereas coastal plain ecoregions (#63 and 75) show much lower concentrations.     

MODELING THE BIG BLACK RIVER: A COMPARISON OF WATER QUALITY MODELS1

ABSTRACT: The Mississippi Department of Environmental Quality uses the Steady Riverine Environmental Assessment Model (STREAM) to establish effluent limitations. While the U.S. Environmental Protection Agency has approved of its use, questions arise regarding the model's simplicity. The objective of this research was to compare STREAM with the more commonly utilized Enhanced Stream Water Quality Model (QUAL2E). The comparison involved a statistical evaluation procedure based on sensitivity analyses, input probability distribution functions, and Monte Carlo simulation with site‐specific data from a 46‐mile (74‐km) reach of the Big Black River in central Mississippi. Site specific probability distribution functions were derived from measured rates of reaeration, sediment oxygen demand, photosynthesis, and respiration. Both STREAM and QUAL2E reasonably predicted daily average dissolved oxygen (DO) based on a comparison of output probability distributions with observed DO. Observed DO was consistently within 90 percent confidence intervals of model predictions. The STREAM approach generally overpredicted while QUAL2E generally matched observed DO. Using the more commonly assumed lognormal distribution as opposed to a Weibull distribution for two of the sensitive input parameters resulted in minimal differences in the statistical evaluations. The QUAL2E approach had distinct advantages over STREAM in simulating the growth cycle of algae.     

DERIVATION OF THE GAMMA DISTRIBUTION BY USING THE PRINCIPLE OF MAXIMUM ENTROPY (POME)1

The principle of maximum entropy (POME) was used to derive the two-parameter gamma distribution used frequently in synthesis of instantaneous or finite-period unit hydrographs. The POME yielded the minimally prejudiced gamma distribution by maximizing the entropy subject to two appropriate constraints which were the mean of real values and the mean of the logarithms of real values of the variable. It provided a unique method for parameter estimation. Experimental data were used to compare this method with the methods of moments, cumulants, maximum likelihood estimation, and least squares.     

Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin,Ethiopia1

Abstract: This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be ?7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.     

A COMPARISON OF METHODS FOR ESTIMATING LOW FLOW CHARACTERISTICS OF STREAMS1

ABSTRACT: Four methods for estimating the 7-day, 10-year and 7-day, 20-year low flows for streams are compared by the bootstrap method. The bootstrap method is a Monte Carlo technique in which random samples are drawn from an unspecified sampling distribution defined from observed data. The nonparametric nature of the bootstrap makes it suitable for comparing methods based on a flow series for which the true distribution is unknown. Results show that the two methods based on hypothetical distributions (Log-Pearson III and Weibull) had lower mean square errors than did the Box-Cox transformation method or the Log-Boughton method which is based on a fit of plotting positions.     

SPACE-TIME VALIDATION OF A THUNDERSTORM RAINFALL MODEL1

A probability model for predicting the occurrence and magnitude of thunderstorm rainfall developed in the southwestern United States was tested in the metropolitan Chicago area with reasonable success, especially for the moderate to the extreme runoff-producing events. The model requires the estimation of two parameters, the mean number of events per year and the conditional probability of rain given that an event has occurred. To tie in the data from more than one gage in an area, an event can be defined in several ways, such as the areal mean rainfall exceeding 0.50 inch and at least one gage receiving more than 1.0 inch. This type of definition allows both of the model parameters to be obtained from daily warm-season rainfall records. Regardless of the definition used a Poisson distribution adequately described the number of events per season. A negative binomial distribution was derived as representing the frequency density function for rainfall where several gages are employed in defining a storm. Chicago data fit both distributions very well at events with relatively high return periods. The results indicate the possibility of using the model on a regional basis where limited amount of data may be used to estimate parameters for extensive areas.     

PRECIPITATION DISTRIBUTION IN COASTAL BRITISH COLUMBIA1

ABSTRACT: The spatial distribution and the temporal and spatial variation of the annual, seasonal, and monthly precipitation in two mountainous watersheds in southwestern British Columbia, Canada, have been analyzed using a detailed database for 1971–1990 in the Capilano and Seymour watersheds. The analysis showed that the precipitation increases up to the mid-position of the watersheds, and then either levels off or decreases. Precipitation on mountain slopes and in the valley at the same distance from the beginning of the slope is similar, and the barrier height is identified as the dominant parameter which influences the precipitation distribution. The temporal variation of the precipitation is the smallest at the mid-position of the watersheds. This variability is the least in the fall and winter and largest in the summer. Correlation between the precipitation accumulations at various stations is large, ranging from 0.80 for the wet period of October-March to 0.65 for the dry period of April-September for distances less than 32 km. Comparison with other studies and the analyses of precipitation and runoff data from coastal British Columbia showed that the results of this study are perhaps general and thus transferable to similar areas in the coastal Pacific Northwest.     

FOREST HYDROLOGY IN THE PACIFIC NORTHWEST: ADDITIONAL RESEARCH NEEDS1

ABSTRACT: While much is known about the hydrology of forested mountain catchments in the Pacific Northwest, important research questions remain. For example, the dynamics of storm precipitation amounts and the modeling of catchment outflows represent a continuing research need. Without an improved understanding of the spatial and temporal aspects of storm precipitation patterns, our ability to evaluate and improve physically-based hydrologic models is limited. From a practical perspective, tens of thousands of kilometers of access roads have been constructed across forested catchments of the Pacific Northwest. Yet, few forestry research programs focus on road drainage (e.g., ditches, culverts, fords). The few studies that address this issue indicate road drainage systems need to function effectively over a wide range of flow events and terrain conditions. In addition, historical forest practices associated with hillslopes and riparian systems have altered the character of many Pacific Northwest aquatic ecosystems. If restoration of these systems is to be effective, research efforts are needed to better understand the linkages between riparian forests, geomorphic processes, and hydrologic disturbance regimes.     

GENERALIZED LOW-FLOW FREQUENCY RELATIONSHIPS FOR UNGAGED SITES IN MASSACHUSETTS1

ABSTRACT: Regional hydrologic procedures such as generalized least squares regression and streamflow record augmentation have been advocated for obtaining estimates of both flood-flow and low-flow statistics at ungaged sites. While such procedures are extremely useful in regional flood-flow studies, no evaluation of their merit in regional low-flow estimation has been made using actual streamflow data. This study develops generalized regional regression equations for estimating the d-day, T-year low-flow discharge, Q_{d, t}, at ungaged sites in Massachusetts where d = 3, 7, 14, and 30 days. A two-parameter lognormal distribution is fit to sequences of annual minimum d-day low-flows and the estimated parameters of the lognormal distribution are then related to two drainage basin characteristics: drainage area and relief. The resulting models are general, simple to use, and about as precise as most previous models that only provide estimates of a single statistic such as Q_7,10. Comparisons are provided of the impact of using ordinary least squares regression, generalized least squares regression, and streamflow record augmentation procedures to fit regional low-flow frequency models in Massachusetts.     

ACCEPTING THE STANDARDIZED PRECIPITATION INDEX: A CALCULATION ALGORITHM1

ABSTRACT: The Palmer Drought Severity Index (PDSI) has been calculated for about 30 years as a means of providing a single measure of meteorological drought severity. It was intended to retrospectively look at wet and dry conditions using water balance techniques. The Standardized Precipitation Index (SPI) is a probability index that was developed to give a better representation of abnormal wetness and dryness than the Palmer indices. Before the user community will accept the SPI as an alternative to the Palmer indices, a standard method must be developed for computing the index. Standardization is necessary so that all users of the index will have a common basis for both spatial and temporal comparison of index values. If different probability distributions and models are used to describe an observed series of precipitation, then different SPI values may be obtained. This article describes the effect on the SPI values computed from different probability models as well as the effects on dry event characteristics. It is concluded that the Pearson Type III distribution is the “best” universal model, and that the reliability of the SPI is sample size dependent. It is also concluded that because of data limitations, SPIs with time scales longer than 24 months may be unreliable. An internet link is provided that will allow users to access Fortran 77 source code for calculating the SPI.     

Assessment of Alternative Methods for Statistically Downscaling Daily GCM Precipitation Outputs to Simulate Regional Streamflow

This study applied three statistical downscaling methods: (1) bias correction and spatial disaggregation at daily time scale (BCSD_daily); (2) a modified version of BCSD which reverses the order of spatial disaggregation and bias correction (SDBC), and (3) the bias correction and stochastic analog method (BCSA) to downscale general circulation model daily precipitation outputs to the subbasin scale for west‐central Florida. Each downscaled climate input dataset was then used in an integrated hydrologic model to examine differences in ability to simulate retrospective streamflow characteristics. Results showed the BCSD_daily method consistently underestimated mean streamflow because the highly spatially correlated small precipitation events produced by this method resulted in overestimation of evapotranspiration. Highly spatially correlated large precipitation events produced by the SDBC method resulted in overestimation of the standard deviation of wet season daily streamflow and the magnitude/frequency of high streamflow events. BCSA showed better performance than the other methods in reproducing spatiotemporal statistics of daily precipitation and streamflow. This study demonstrated differences in statistical downscaling techniques propagate into significant differences in streamflow predictions, and underscores the need to carefully select a downscaling method that reproduces precipitation characteristics important for the hydrologic system under consideration.     

An Analysis of the Wind Energy Potential of Elazig,Turkey

Abstract

In this study, the wind energy potential of Elazig is statistically analyzed based on hourly measured wind speed data over the five-year period from 1998 to 2002. The probability density distributions are derived from cumulative distribution functions. Two probability density functions are fitted to the measured probability distribution on a yearly basis. The wind energy potential of the location is studied based on the Weibull and Rayleigh distributions. It was found that the numerical values of both Weibull parameters (k and c) for Elazig vary over a wide range. The yearly values of k range from 1.653 to 1.878 with an average value of 1.819, while those of c are in the range of 2.757–2.994 m/s with an average value of 2.824 m/s. In addition, yearly mean wind speed and mean power density of Elazig is found as 2.79 m/s and 38.76 W/m², respectively. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution, with a special case of the Weibull distribution for k = 2. As a result, the Rayleigh distribution is found to be suitable to represent the actual probability of wind speed data for Elazig.     

SERENDIPITY: CAPTURING A DESIGN LEVEL PRECIPITATION EVENT1

ABSTRACT: On February 23–24, 1998, a frontal system moved across the U.S. Department of Energy's 3,500 km² Nevada Test Site (NTS) and resulted in significant depths of precipitation at all recording gages on the NTS. A preliminary analysis suggested that this precipitation event was of the magnitude and duration for which many flood mitigation structures have been designed. Given the data and field observations available and the potential implications of the event on the methodologies used to size flood mitigation structures throughout the West, a detailed analysis of this event was undertaken. The goals of this study were to compare this event with the regulatory design precipitation event, compare the estimated peak flow rates from the rainfall/runoff model used to size the flood mitigation structures at a radioactive waste management site with the estimated peak flows from the precipitation event, and examine if modification of the standard source of the design depths of precipitation is warranted.     

GROUND WATER/SURFACE WATER RESPONSES TO GLOBAL CLIMATE SIMULATIONS,SANTA CLARA‐CALLEGUAS BASIN,VENTURA, CALIFORNIA1

ABSTRACT: Climate variations can play an important, if not always crucial, role in successful conjunctive management of ground water and surface water resources. This will require accurate accounting of the links between variations in climate, recharge, and withdrawal from the resource systems, accurate projection or predictions of the climate variations, and accurate simulation of the responses of the resource systems. To assess linkages and predictability of climate influences on conjunctive management, global climate model (GCM) simulated precipitation rates were used to estimate inflows and outflows from a regional ground water model (RGWM) of the coastal aquifers of the Santa Clara‐Calleguas Basin at Ventura, California, for 1950 to 1993. Interannual to interdecadal time scales of the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate variations are imparted to simulated precipitation variations in the Southern California area and are realistically imparted to the simulated ground water level variations through the climate‐driven recharge (and discharge) variations. For example, the simulated average ground water level response at a key observation well in the basin to ENSO variations of tropical Pacific sea surface temperatures is 1.2 m/°C, compared to 0.9 m/°C in observations. This close agreement shows that the GCM‐RGWM combination can translate global scale climate variations into realistic local ground water responses. Probability distributions of simulated ground water level excursions above a local water level threshold for potential seawater intrusion compare well to the corresponding distributions from observations and historical RGWM simulations, demonstrating the combination's potential usefulness for water management and planning. Thus the GCM‐RGWM combination could be used for planning purposes and — when the GCM forecast skills are adequate — for near term predictions.