HEAVY RAINFALL DISTRIBUTIONS BY SEASON IN LOUISIANA: SYNOPTIC INTERPRETATIONS AND QUANTILE ESTIMATES1

ABSTRACT: In most studies, quantile estimates of extreme 24-hour rainfall are given in annual probabilities. The probability of experiencing an excessive storm event, however, differs throughout the year. As a result, this paper explored the differences between heavy rainfall distributions by season in Louisiana. It was concluded by using the Kruskal-Wallis and Mann-Whitney tests that the distribution of heavy rainfall events differs significantly between particular seasons at the sites near the Gulf Coast. Furthermore, seasonal frequency curves varied dramatically at the four sites examined. Mixed distributions within these data were not found to be problematic, but the mechanisms that produced the events were found to change seasonally. Extreme heavy rainfall events in winter and spring were primarily generated by frontal weather systems, while summer and fall events had high proportions of events produced by tropical disturbances and airmass (free-convective) conditions.     

THE FLEXIBLE TOLERANCE METHOD FOR ESTIMATING HYDROLOGIC PARAMETERS IN THE ROOT ZONE1

In this study, a constrained minimization method, the flexible tolerance method, was used to solve the optimization problems for determining hydrologic parameters in the root zone: water uptake rate, spatial root distribution, infiltration rate, and evaporation. Synthetic soil moisture data were first generated using the Richards' equation and its associated initial and boundary conditions, and these data were then used for the inverse analyses. The results of inverse simulation indicate the following. If the soil moisture data contain no noise, the rate of estimated water uptake and spatial root distribution parameters are equal to the true values without using constraints. If there is noise in the observed data, constraints must be used to improve the quality of the estimate results. In the estimation of rainfall infiltration and surface evaporation, interpolation methods should be used to reduce the number of unknowns. A fewer number of variables can improve the quality of inversely estimated parameters. Simultaneous estimation of spatial root distribution and water uptake rate or estimation of evaporation and water uptake rate is possible. The method was used to estimate the water uptake rate, spatial root distribution, infiltration rate, and evaporation using long‐term soil moisture data collected from Nebraska's Sand Hills.     

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.     

Topographic Effects on Soil Organic Carbon in Louisiana Watersheds

Terrestrial carbon storage is influenced by a number of environmental factors, among which topographic and geomorphological features are of special significance. This study was designed to examine the relationships of soil organic carbon (SOC) density to various terrain parameters and watershed characteristics across Louisiana, USA. A polygon data set of 484 watersheds and 12 river drainage basins for Louisiana was used to form the landscape units. SOC densities were calculated for each soil map unit using the State Soil Geographic (STATSGO) database. Average drainage densities and average slopes at watershed and basin scales were quantified with the 1:24 K Digital Elevation Models (DEM) data, and the Louisiana hydrographic water features. Correlation and regression analyses were performed to determine relationships among drainage density, slope, elevation, and SOC. The study found an average watershed drainage density of 1.6 km/km² and an average watershed slope of 2.9 degrees in Louisiana. The results revealed that SOC density at both watershed and basin scales was closely related to drainage density, slope, and elevation. SOC density was positively correlated with watershed drainage density, but negatively correlated with watershed slope gradient and elevation. Regression models were developed for predicting SOC density at watershed and basin scales, obtaining regression coefficients (r ²) ranging from 0.43 to 0.83. The study showed that estimation of SOC at watershed and drainage basin scales combining DEM data can be a feasible approach to improve the understanding of the relationships among SOC, topographic, and geomorphological features.     

GENERALIZED SKEW COEFFICIENTS OF ANNUAL FLOODS FOR LOUISIANA STREAMS1

ABSTRACT: A generalized skew map for Louisiana streams was developed using data from Louisiana, Mississippi, Arkansas, and Texas with 20 or more years of annual flood records. A comparison between the newly developed Louisiana Generalized Skew Map (LGSM) and the generalized skew map recommended by the U.S. Water Resources Council (WRCGSM) was performed. The mean square error for the LGSM was 16 percent less than that of WRCGSM in direct application of the two maps. Performance of the new map was compared with the WRCGSM and with a regional analysis procedure through its application to the Log Pearson Type 3 (LP3) distribution. Two-thirds of the stations tested had lower standardized root mean square deviations (SRMSD) by a narrow margin using the skew coefficients obtained from LGSM instead of WRCGSM. The regional analysis also performed as well as the LGSM in terms of SRMSD. Thus, it was concluded that both LGSM and the regional analysis provide a more reliable tool for flood frequency analysis for Louisiana streams with short annual flood records.     

2013年秦皇岛连续两次降水过程对比分析

利用常规气象观测资料、自动站资料及卫星资料,从大尺度环流背景和水汽、动力、热力条件出发,对2013年8月底东北冷涡影响下秦皇岛两次降水过程进行对比分析。结果表明：由于处在东北冷涡发展的不同阶段,中低层温度场配置不同,两次过程降水特点极不相同。8月27日降水是一次连续的区域性降水过程,以层状云降水为主,降水均匀连续,8月31日至9月2日是冷涡后部西北气流影响下的数日局地性对流降水,具有很强的突发性。     

熵法估参在环境监测数据频率分析中的应用

以最大信息熵原理为理论基础的熵法估参方法，是一种具有严格物理和数学意义的新型参数估计方法，本文针对珠江广州河段主要污染物含量长年监测数据，对比熵法与传统方法矩法对四参数Г分布的估参结果，并以频率绝对离盖和最小为准则进行判定，结果表明，熵法估参结果与矩法总体上相当接近，且大部分样本的熵法估计参数优于矩法，在环境监测数据频率分析中具有实用性和推广价值。     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.     

Nonparametric Monte Carlo Simulation for Flood Frequency Curve Derivation: An Application to a Korean Watershed1

Abstract: A mix of causative mechanisms may be responsible for flood at a site. Floods may be caused because of extreme rainfall or rain on other rainfall events. The statistical attributes of these events differ according to the watershed characteristics and the causes. Traditional methods of flood frequency analysis are only adequate for specific situations. Also, to address the uncertainty of flood frequency estimates for hydraulic structures, a series of probabilistic analyses of rainfall‐runoff and flow routing models, and their associated inputs, are used. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated to evaluate the probability of floods. Therefore, the objectives of this study were to develop a flood frequency curve derivation method driven by multiple random variables and to develop a tool that can consider the uncertainties of design floods. This study focuses on developing a flood frequency curve based on nonparametric statistical methods for the estimation of probabilities of rare floods that are more appropriate in Korea. To derive the frequency curve, rainfall generation using the nonparametric kernel density estimation approach is proposed. Many flood events are simulated by nonparametric Monte Carlo simulations coupled with the center Latin hypercube sampling method to estimate the associated uncertainty. This study applies the methods described to a Korean watershed. The results provide higher physical appropriateness and reasonable estimates of design flood.     

REGIONAL APPLICATION OF AN APPROXIMATE STREAMFLOW PARTITIONING METHOD1

ABSTRACT: The approximate streamflow partitioning method which uses daily rainfall and streamfiow data was applied in Coastal Plain, Coastal Flatwoods, and Southern Piedmont physiographic regions for estimation of the surface and subsurface flow components of total streainflow. Sizes of the watersheds ranged from 9.6 km² to 1,030 km. Although the streamflow partitioning method was developed and tested on the Coastal Plain physiographic region, results indicate that the procedure can be applied to other physiographic regions where available data are limited to daily values. The effect of channelization on the partitioned flow components in the Coastal Plain and Coastal Flatwoods physiographic areas was also examined. While channelization was found to decrease the storm-time base, it had no significant effect on the relative percentages of the partitioned flow components.     

TEMPORAL FLUCTUATIONS OF HEAVY RAINFALL MAGNITUDES IN NEW ORLEANS,LOUISIANA: 1871–19911

ABSTRACT: Recent occurrences of heavy rainfall in New Orleans, Louisiana, have led to speculation that the local heavy rainfall regime has changed. To objectively determine the validity of these speculations, changing magnitudes of storm rainfall were investigated through an examination of the annual maximum storm series from 1871 to 1991 at the New Federal site. Although a long-term trend was not found, the Wilcoxon test indicated that magnitudes during the last 14 years (from 1978–1991) differed significantly from the rest of the series.     

Hydrologic Modeling Uncertainty Resulting From Land Cover Misclassification1

Abstract: A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One‐hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omission in land cover classes and is used to assess the uncertainty in hydrologic runoff simulation resulting from parameter estimation based on land cover. These land cover realizations were used in the GIS‐based Automated Geospatial Watershed Assessment tool in conjunction with topography and soils data to generate input to the physically‐based Kinematic Runoff and Erosion model. Uncertainties in modeled runoff volumes resulting from these land cover realizations were evaluated in the Upper San Pedro River basin for 40 watersheds ranging in size from 10 to 100 km² under two rainfall events of differing magnitudes and intensities. Simulation results show that model sensitivity to classification error varies directly with respect to watershed scale, inversely to rainfall magnitude and are mitigated or magnified by landscape variability depending on landscape composition.     

Bayesian approach to estimating margin of safety for total maximum daily load development

A Bayesian-updating approach is presented to the estimation of total uncertainty-based Margin of Safety (MOS) for Total Maximum Daily Load (TMDL) calculations. Probability distributions are presented to construct the likelihood function, the prior probability distribution, and the posterior (total uncertainty) probability distribution. The Bayesian-updating approach is demonstrated through a case study for the Lower Amite River, Louisiana. The posterior probability distribution-based on the Bayesian approach updates the standard deviation of summer dissolved oxygen in the Amite River from 1.88 mg/L to 2.10 mg/L when the total uncertainty is considered. Results from the Bayesian-updating approach are compared with two conventional methods. The dissolved oxygen reserve based on a conventional margin of safety of 20% is estimated to be 45,682.26 kg/Day. The second conventional method, where we consider the standard deviation of 1.88 mg/L, produces a dissolved oxygen reserve of 40,516.09 kg/Day. The Bayesian approach yields the dissolved oxygen reserve of 38,614.43 kg/Day with the first level (μ-σ) of MOS, producing a deficit of 5606.65 kg/Day in dissolved oxygen. The dissolved oxygen reserve deficit increases to 23,895.13 kg/Day when the second level (μ-2σ) of MOS is used, which escalated to 42,383.52 kg/Day when the highest level (μ-3σ) of MOS is used. While the total uncertainty-based Bayesian approach is demonstrated for a TMDL development on the Amite River, the overall approach could be applied in any river system with similar available data.     

EVIDENCE OF CHAOTIC BEHAVIOR IN SINGAPORE RAINFALL1

ABSTRACT: This paper focuses on the investigation of the existence of chaotic behavior in the Singapore rainfall data. The procedure for the determination of the minimum number of variables essential and the number of variables sufficient to model the dynamics of the rainfall process was studied. An analysis of the rainfall behavior of different time periods was also conducted. The correlation dimension was used as a basis for discriminating stochastic and chaotic behaviors. Daily rainfall records for durations of 30, 20, 10, 5, 4, 3, 2, and 1 years from six stations were analyzed. The delay time for the phase-space reconstruction was computed using the autocorrelation function approach. The results provide positive evidence of the existence of chaotic behavior in the daily rainfall data. The minimum number of variables essential to model the dynamics of the rainfall process was identified to be 3 while the number of variables sufficient to model the dynamics of the rainfall process ranges from 11 to 18. The results also suggest that the attractor dimensions of rainfall data of longer time periods are higher than that of shorter time periods. The study suggests a minimum number of 1500 data points required for the computation of the correlation dimension of the rainfall data.     

Defining the Diurnal Pattern of Snowmelt Using a Beta Distribution Function

Snow is an important component of the hydrologic cycle for many regions worldwide. In addition to vital water resources, snowmelt can be important for forest ecosystem dynamics and flood risk. However, standard design events in the United States lack a design snowmelt event, including only precipitation events, though snowmelt has been shown to be larger than rainfall. In this article, we present a method using hourly snow water equivalent data to develop and test a function for representing the diurnal pattern of snowmelt. A two‐parameter beta distribution function is modified for the purposes of this study and found to fit the pattern of snowmelt well with a root mean squared error of 0.008. Soil moisture sensors were additionally utilized to assess the timing of the snowmelt water outflow from the base of the snowpack that supports the shape of the function, but suggests that the timing of losses recorded on snow pillows lag as much as 3 h. Further testing of the function showed the shape of the function to be accurate. The methods developed and tested in this paper can be applied for design purposes comparing snowmelt and rainfall events or to improve hydrological models investigating processes such as streamflow or groundwater recharge.     

EQUIVALENCE BETWEEN TOPMODEL AND THE NRCS CURVE NUMBER METHOD IN PREDICTING VARIABLE RUNOFF SOURCE AREAS1

An equivalence is proposed between two rainfall‐runoff methods with a long history of use in the United States and Europe. In watersheds where variable source areas dominate runoff, the two methods can have comparable probability distribution functions of moisture deficit, and therefore predict similar saturated runoff source areas. A novel approach is introduced to determine the S parameter in the Natural Resources Conservation Service (NRCS) method. This approach constrains S by the physical soil and topography characteristics of the catchment and depth to water table. The NRCS curve number method is at the core of many rainfall‐runoff models in hydrology. As a simple lumped parameter method, it is often scrutinized because it is not obvious how to derive S from catchment hydromorphological characteristics. The novel approach provides a clear physical meaning for S, allowing better estimation of this parameter in humid shallow water table environments where the variable source area can be the dominant runoff mechanism.     

EFFECTS OF SIMULATED CLOUD SEEDING ON STREAMFLOW OF SELECTED WATERSHEDS IN PENNSYLVANIA1

A mathematical model was developed to simulate the hydrologic behavior of five small watersheds in central Pennsylvania. Continuous hydrographs for the 6-month period, April to September 1964, were simulated. Synthesized rainfall cycles consisting of increasing rainfall by 10, 20, and 30 percent to simulate the effects of cloud seeding were processed through the watershed model to determine the effects on low flow augmentation. Other rainfall cycles used consisted of increasing every third storm by 30 percent and of developing a rainfall cycle by processing daily radiosonde data through a mathematical cumulus cloud model to obtain a prediction of rainfall following seeding. A comparison of actual and predicted hydrographs indicated that simulated cloud seeding resulted in significant monthly and seasonal water yields. In general, the results of the study appear to indicate that on a theoretical basis cloud seeding would be a feasible method of augmenting low stream-flow during the summer months on watersheds in the northern Appalachian region.     

Hydrograph Separation by Incorporating Climatological Factors: Application to Small Experimental Watersheds 1

Abstract: Evaluating the relative amounts of water moving through the different components of the hydrological cycle is required for precise management and planning of water resources. An important aspect of this evaluation is the partitioning of streamflow into surface (quick flow) and base‐flow components. A prior study evaluated 40 different approaches for hydrograph‐partitioning on a field scale watershed in the Coastal Plain of the Southeastern United States and concluded that the Boughton’s method produced the most consistent and accurate results. However, its accuracy depends upon the proper estimation of: (1) the end of surface runoff, and (2) the fraction factor (α) that is function of many physical and hydrologic characteristics of a watershed. Proper identification of the end of surface runoff was accomplished by using a second derivative approach. Applying this approach to 12 years of separately measured surface and subsurface flow data from a field scale watershed (study area) proved to be accurate for 87% of the time. Estimation of the α value was accomplished in this study using two steps: (1) alpha was fitted to individual hydrographs: and, (2) a regression equation that determines these alpha values based on climatological factors (e.g., rainfall, evapotranspiration) was developed. Using these strategies improved the streamflow partitioning method’s performance significantly.     

NEAR REAL-TIME MAPPING OF THE 1975 MISSISSIPPI RIVER FLOOD IN LOUISIANA USING LANDSAT IMAGERY1

ABSTRACT: The project described in this report was undertaken by the Louisiana State Planning Office to establish the extent of backwater flooding in Louisiana in April 1975. Band 7 Landsat imagery, enlarged to a scale of 1:250,000 was used to visually identify flooded areas. Inundated areas were delineated on overlays keyed to 1:250,000 U.S. Geological Survey topographic quadrangles. Tabular data identifying acres flooded, according to land use type, were derived by merging the flood map overlays with computerized 1972 land use data. Approximately 1.12 million acres of the state were inundated by flood waters. The total acreage and land use types affected by flooding were determined within 72 hours from the time the flood areas were imaged. Flooded maps were prepared for 26 parishes. Field observations were made by Louisiana Cooperative Extension Service county agents in order to determine the accuracy of parish flood maps and flood acreage figures by land use type. Results indicated that this was a fast, accurate, and relatively inexpensive method of compiling flood data for disaster planning and postflood analysis.