RAINFALL POINT INTENSITIES IN AN AIR MASS THUNDERSTORM ENVIRONMENT: WALNUT GULCH,ARIZONA1

ABSTRACT: Point rainfall intensities for a given return period are often used to formulate design storms for rainfall/runoff models to simulate design floods. These design floods are in turn used to design bridges, culverts, and a variety of drainage and flood control structures. The projected rapid growth in the southwestern United States will require very substantial monetary investments in drainage infrastructure. Accurate estimates of point rainfall intensities are critical to ensure both safe designs while not wasting dollars in overdesign. Rainfall point intensities (accumulated rainfall depth over a specified duration) for 5‐, 15‐, 30‐, and 60‐minute durations for the 2‐, 5‐, 10‐, 25‐, 50‐, and 100‐year return periods were determined for southeast Arizona. Thirty‐five years of rainfall record (1961 to 1995) were used in this study. The records came from 20 stations that were grouped into five sets of four independent stations to extend the rainfall records. The stations are in the USDA‐ARS Walnut Gulch Experimental Watershed (WGEW), which is representative of large portions of the Southwest whose runoff generation is dominated by air‐mass thunderstorms. The 5‐, 15‐, 30‐, and 60‐minute maximum intensities per year followed log‐normal distributions. The mean point rainfall intensities of the five sets of gages are very close (between 0 and 11 percent) to the NOAA values of the 5‐, 15‐, 30‐, and 60‐minute durations for all return periods. Much larger differences between the mean point rainfall intensities for all durations were found when these results were compared to those of a previous study done with a shorter rainfall record (between 14 and 33 percent for the 25‐, 50‐, and 100‐year return‐periods). The difference between the largest and the smallest values of point rainfall intensities recorded by each group, for all durations, usually increases as the return period increases.     

TEMPORAL AND SPATIAL CHARACTERIZATION OF RAINFALL OVER CENTRAL AND SOUTH FLORIDA1

requency evaluation and spatial characterization of rainfall in Central and South Florida are presented. Point frequency analysis performed at all available sites has shown that the 2‐parameter Gamma probability density function is the best model for monthly rainfall frequency over Central and South Florida. The model's parameters estimated at 145 stations were used to provide monthly rainfall estimates for 10‐ and 100‐year dry and wet return periods. Experimental and theoretical variograms computed for these estimates, as well as the Kriging estimation variance maps, show that the existing rain gage network is less capable of resolving monthly rainfall variation in the wet season than the dry season. May is the dry‐to‐wet transition month, while October is the wet‐to‐dry transition month with average rainfall of 4.5 inches. Monthly average rainfall is above 7 inches during the wet season and below 3 inches during the dry season. Two‐thirds of the annual rainfall is accumulated in the wet season. Annual average rainfall is maximum (above 60 inches) in many areas along the east coast, and is minimum (below 45 inches) in many areas over Lake Okee‐chobee and Central Florida. Rainfall maps show a changing pattern between the wet and the dry seasons. Frontal rainfall occurs in the dry season, while convective rainfall, tropical depression, and hurricanes occur in the wet season. Average rainfall is higher along the east coast area in the dry season and it is higher along the west coast area in the wet season.     

COMPARING SOME METHODS OF ESTIMATING MEAN AREAL RAINFALL1

ABSTRACT: A comparison of 13 different methods of estimating mean areal rainfall was made on two areas in New Mexico, U.S.A., and one area in Great Britain. Daily, monthly and yearly rainfall data were utilized. All methods, in general, yielded comparable estimates, especially for yearly values. This suggested that a simpler method would be preferable for estimating mean areal rainfall in these areas.     

APPLICATION OF PARAMETER OPTIMIZATION METHOD FOR CALIBRATING TANK MODEL1

ABSTRACT: Many automatic calibration processes have been proposed to efficiently calibrate the 16 parameters involved in the four‐layered tank model. The Multistart Powell and Stuffed Complex Evolution (SCE) methods are considered the best two procedures. Two rainfall events were designed to compare the performance and efficiency of these two methods. The first rainfall event is short term and the second designed for long term rainfall data collection. Both rainfall events include a lengthy no‐rainfall period. Two sets of upper and lower values for the search range were selected for the numerical tests. The results show that the Multistart Powell and SCE methods are able to obtain the true values for the 16 parameters with a sufficiently long no‐rainfall period after a rainfall event. In addition, by using two selected objective functions, one based on root mean square error and one based on root mean square relative error criteria, it is found that the no‐rainfall period lengths necessary to obtain the converged true values for the 16 parameters are roughly the same. The SCE method provides a more efficient search based on an appropriate preliminary search range. The Multistart Powell method, on the other hand, leads to more accurate search results when there is no suitable search range selected based on the parameter calibration experience.     

FOREST HYDROLOGY AND EXTREME RAINFALL FREQUENCY1

Extreme rainfall frequency analysis provides one means to predict, within certain limits of probability, the average time interval between the recurrences of storms of a specified duration and magnitude. This in turn furnishes the forest hydrologist a valuable tool for engineering design and runoff and erosion forecast. A modification in the application of the annual maximum and annual exceedance series analysis described by V. T. Chow can, for special purposes, lead to an even more useful estimate of extreme events on a seasonal basis. This can be particularly important on small forested headwater watersheds where the runoff response to extreme rainfall may vary considerably with seasonal changes in canopy cover and soil moisture characteristics. Although the application of data covering a relatively short period of record has produced some inconsistencies among the frequency diagrams, under certain circumstances for short-term recurrence interval forecast and for non-critical application the analysis of extreme rainfall frequency from less than 20 years data seems justified.     

REGIONAL RAINFALL INTENSITY-DURATION-FREQUENCY CURVES FOR PENNSYLVANIA1

ABSTRACT: A statistical analysis of all available continuous hourly and 15-minute duration rainfall records for Pennsylvania was performed to develop an updated procedure to estimate design storms. As a resuit of this study, Pennsylvania was divided into five homogeneous rainfall regions and a set of rainfall intensity-duration curves developed for each region, for return periods of 1 to 100 years and durations ranging from 5 minutes to 24 hours. The PDT-IDF curves were judged to be a better representation of Pennsylvania rainfall than the nationwide TP-40 maps, particularly for storm events of 10-years and lower return periods. The average time distribution of 24-hour storms in Pennsylvania was found to be well represented by the SCS Type II distribution. The Corps of Engineers SPS 24-hour distribution was found to differ appreciably from both the SCS Type H and the Pennsylvania 24-hour storm distribution. For storm durations between 15 and 90 minutes the standard Yarnell intensity-duration curves closely resemble Pennsylvania storm distributions.     

METHODOLOGIES FOR CALIBRATION AND PREDICTIVE ANALYSIS OF A WATERSHED MODEL1

ABSTRACT: The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.     

A TECHNIQUE FOR MEASURING RAINFALL POTENTIAL1

ABSTRACT The roles played by thermodynamic stability and precipitable water in the production of rainfall are of prime importance. These two variables have been combined into a “Rainfall Potential” function by the use of multivariate statistical techniques. Data from weather stations in southern New Mexico and southwest Texas were used in the study. The results appear promising for further investigation, possibly in relation to the occurrence of rare but extremely heavy rains     

GENERATION OF HOURLY RAINFALL1

ABSTRACT: A rainfall model was developed to divide daily rainfall into storms and distribute storm depths over storm duration for input into the Stanford Watershed Model.     

A SIMULATION OF SMOOTH-VARIABLE INTENSITY RAINFALL PATTERNS1

ABSTRACT: Continuous rainfall patterns are currently simulated by approximating them by stair-stepped (piece-wise) patterns. The effects of this approximation on infiltration and runoff processes are not well known. A new technique for simulating smooth-variable intensity rainfall patterns is presented. This technique is based on the fundamental principles of a moving water head in a container. The proposed technique is general and capable of simulating any rainfall pattern. However, as the rainfall pattern gets more complicated, the equipment required for simulation becomes more involved. The proposed technique has been tested experimentally. A close agreement was found between the theoretical and experimental simulations. It is concluded that the proposed technique might be very useful in studying the infiltration and runoff processes under variable intensity rainfall, especially for simple convex patterns.     

AN EVALUATION OF FLOOD FREQUENCY ESTIMATES BASED ON RAINFALL/RUNOFF MODELING1

ABSTRACT: An evaluation of flood frequency estimates simulated from a rainfall/runoff model is based on (1) computation of the equivalent years of record for regional estimating equations based on 50 small stream sites in Oklahoma and (2) computation of the bias for synthetic flood estimates as compared to observed estimates at 97 small stream sites with at least 20 years of record in eight eastern states. Because of the high intercorrelation of synthetic flood estimates between watersheds, little or no regional (spatial) information may be added to the network as a result of the modeling activity. The equivalent years of record for the regional estimating equations based totally on synthetic flood discharges is shown to be considerably less than the length of rainfall record used to simulate the runoff. Furthermore, the flood estimates from the rainfall/runoff model consistently underestimate the flood discharges based on observed record, particularly for the larger floods. Depending on the way bias is computed, the synthetic estimate of the 100-year flood discharge varies from 11 to 29 percent less than the value based on observed record. In addition, the correlation between observed and synthetic flood frequency estimates at the same site is also investigated. The degree of correlation between these estimates appears to vary with recurrence interval. Unless the correlation between these two estimates is known, it is not possible to compute a weighted estimate with minimum variance.     

VALIDATION OF A HYDROLOGIC MODEL FOR TRANSPORT OF CHEMICALS IN RIVERS1

ABSTRACT: Hydrologic Transport Assessment System (HYTRAS) is a software package that models contaminant transport in rivers and streams, including volatilization, adsorption/desorption, sedimentation, settling, and resuspension. Biodegradation, photolysis, and any other process that can be modeled using a first‐order decay constant can be included as well. HYTRAS originally modeled the transport of radionuclides and has recently been expanded to include transport of chemicals. The transport of chemicals has been validated using data from an accidental release of the chemicals disulfoton and thiometon into the Rhine River in 1986. For these chemicals, sorption is not an important process. For the range of measured flow velocities, HYTRAS was found to bound the peak arrival times. For the range of measured degradation rates, HYTRAS was found to bound the peak concentrations within 400 km of the source and bound the peak concentrations within a factor of two out to 700 km.     

MODIFIED SMEMAX TRANSFORMATION FOR FREQUENCY ANALYSIS1

ABSTRACT: SMEMAX is a transformation technique suggested by Bethalmy to transform random hydrological series to a near normal series. This paper puts forward an alternative simpler form of the SMEMAX transformation. The modified SMEMAX transformation avoids use of trigonometric functions and the transformed variables range from 0 to 100.     

USING ARTIFICIAL NEURAL NETWORKS TO ESTIMATE MISSING RAINFALL DATA1

ABSTRACT: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.     

COMBINATION OF RADAR AND GAUGE DATA IN A RAINFALL ARCHIVE SYSTEM1

ABSTRACT: Near real time daily rainfall estimates for the UK are available from three sources: a sparse network of gauges, radar data, or radar data adjusted by the sparse gauges. The PARAGON rainfall archive system, which has been developed by the UK Meteorological Office, is able to produce these estimates in near real time on a 5 km grid. The ability of these estimates to reproduce the 5 km grid point field derived later from a dense network of gauges is compared using case studies. Five techniques have been used to assess the relative quality of the various estimates. There is general agreement between the results of the various techniques. For the London radar there are examples of days when the rainfall estimate was improved by incorporating radar data; conversely, there are days when the radar data make it worse. Overall little evidence was found to suggest that adjusted radar data are consistently markedly better than gauge estimates. Discriminate use of radar data is recommended.     

LABORATORY TESTS OF EROSION DEPENDENCE ON PROPERTIES OF SOILS AND RAINFALL1

ABSTRACT: One hundred and sixty-two rainfall-induced soil erosion tests were conducted to assist in predicting soil loss and subsequent increase in total suspended solids leaving a highway construction site during a rainfall event. A rainfall simulator and a water flume were constructed for the tests. Soil shear strength, compressive strength, rainfall intensity, and soil bed slope were treated as variables during the experiments. The soil with a higher shear strength resisted soil erosion better than lower strength soils. Soil loss was nearly independent of shear strength at low rainfall intensity but at high intensities, the shear strength was more important in resisting soil loss. Lower soil loss can be expected for cohesive soils if the compressive strength is high.     

THE CHANCE A FLOOD WILL BE EXCEEDED IN A PERIOD OF YEARS1

ABSTRACT. Much has been written about the chance that a hydrologic event, such as a flood peak of a given size or greater, will occur during a given period of years. Four variables are involved, and any one of the four can be the dependent variable: (1) the probability of encountering such an event in a single year, (2) the probability of encountering one or several of these events in a period of years, (3) the least number of times of encountering the event in that period of years, and (4) the number of years in the period involved. Most of these problems are not difficult to solve, but they are tedious to calculate, not well understood, and consequently seldom used in water resources planning and development. The most popular approach is based on the binomial distribution. Graphical procedures similar to those developed by Riggs [1961] were further elaborated and are presented here with illustrative examples to facilitate their use in solving the many related problems. The confidence that one can place in these probability estimates is also explained and illustrated by tables and further examples. To help assure proper use of these methods, commonly used terms such as “recurrence interval” and “partial duration series” are discussed. No new theory is developed: at issue is a deeper understanding of the significance of design levels and their ease of computation.     

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.     

STOCHASTIC MODELING OF MONTHLY AND DAILY RAINFALL SEQUENCES1

ABSTRACT: A monthly model and two daily models (I and II) are presented for the purpose of generating monthly and daily rainfall sequences in the Quae Yai river basin in Thailand. Performance of the models are evaluated by comparing the statistical parameters of the generated sequences with those from historical data. For monthly generation, Thomas-Fiering model worked satisfactorily in spite of the monthly correlations being weak, if any. Daily Model I, which assumes no persistence between daily rainfall amounts within the wet spells, could not preserve some important parameters regardless of the simplicity in model construction. Application of multi-state transition probability matrix model gave good results, although the user has to modify some parameters looking at the performance of the model for each historical record.