MASS LOAD ESTIMATION ERRORS UTILIZING GRAB SAMPLING STRATEGIES IN A KARST WATERSHED1

ABSTRACT: Developing a mass load estimation method appropriate for a given stream and constituent is difficult due to inconsistencies in hydrologic and constituent characteristics. The difficulty may be increased in flashy flow conditions such as karst. Many projects undertaken are constrained by budget and manpower and do not have the luxury of sophisticated sampling strategies. The objectives of this study were to: (1) examine two grab sampling strategies with varying sampling intervals and determine the error in mass load estimates, and (2) determine the error that can be expected when a grab sample is collected at a time of day when the diurnal variation is most divergent from the daily mean. Results show grab sampling with continuous flow to be a viable data collection method for estimating mass load in the study watershed. Comparing weekly, biweekly, and monthly grab sampling, monthly sampling produces the best results with this method. However, the time of day the sample is collected is important. Failure to account for diurnal variability when collecting a grab sample may produce unacceptable error in mass load estimates. The best time to collect a sample is when the diurnal cycle is nearest the daily mean.     

Influence of Sampling Frequency on Estimation of Annual Total Phosphorus and Total Suspended Solids Loads1

Abstract: The determination of sediment and nutrient loads is typically based on the collection and analysis of grab samples. The frequency and regularity of traditional sampling may not provide representation of constituent loading, particularly in systems with flashy hydrology. At two sites in the Little Bear River, Utah, continuous, high‐frequency turbidity was used with surrogate relationships to generate estimates of total phosphorus and total suspended solids concentrations, which were paired with discharge to estimate annual loads. The high frequency records were randomly subsampled to represent hourly, daily, weekly, and monthly sampling frequencies and to examine the effects of timing, and resulting annual load estimates were compared to the reference loads. Higher frequency sampling resulted in load estimates that better approximated the reference loads. The degree of bias was greater at the more hydrologically responsive site in the upper watershed, which required a higher sampling frequency than the lower watershed site to achieve the same level of accuracy in estimating the reference load. The hour of day and day of week of sampling impacted load estimation, depending on site and hydrologic conditions. The effects of sampling frequency on the determination of compliance with a water quality criterion were also examined. These techniques can be helpful in determining necessary sampling frequency to meet the objectives of a water quality monitoring program.     

Estimation of Catchment Nutrient Loads in New Zealand Using Monthly Water Quality Monitoring Data

Causes of variation between loads estimated using alternative calculation methods and their repeatability were investigated using 20 years of daily flow and monthly concentration samples for 77 rivers in New Zealand. Loads of dissolved and total nitrogen and phosphorus were calculated using the Ratio, L5, and L7 methods. Estimates of loads and their precision associated with short‐term records of 5, 10, and 15 years were simulated by subsampling. The representativeness of the short‐term loads was quantified as the standard deviation of the 20 realizations. The L7 method generally produced more realistic loads with the highest precision and representativeness. Differences between load estimates were shown to be associated with poor agreement between the data and the underlying model. The best method was shown to depend on the match between the model and functional and distributional characteristics of the data, rather than on the contaminant. Short‐term load estimates poorly represented the long‐term load estimate, and deviations frequently exceeded estimated imprecision. The results highlight there is no single preferred load calculation method, the inadvisability of “unsupervised” load estimation and the importance of inspecting concentration‐flow, unit load‐flow plots and regression residuals. Regulatory authorities should be aware that the precision of loads estimated from monthly data are likely to be “optimistic” with respect to the actual repeatability of load estimates.     

PRECIPITATION MEASUREMENT BIASES IN THE UNITED STATES1

ABSTRACT: Gage-induced biases in monthly precipitation are estimated and removed at 1818 stations across the continental United States from 1950 through 1987. Deleterious effects of the wind and wetting losses on the interior walls of the gage were considered. These “corrected” estimates were obtained using site-specific information including wind speed, shelter-height air temperature, gage height, and sheltering. Wind speed and air temperature were interpolated at stations for which these data were not available using a spherically-based, nearest neighbor interpolation procedure. Results indicate that, as expected, biases are greater in the winter than the summer owing to the increased problems (particularly wind-induced) of measuring snowfall. In summer, percent errors range between 4 and 6 percent over nearly three-quarters of the United States with slightly larger errors over the Rocky Mountains. By contrast, winter biases are highly correlated with snowfall totals and percentage errors increase poleward, mimicking patterns of snowfall frequency. Since these biases are not trivial, they must be accounted for in order to obtain accurate and reliable time-series. If these biases are not properly addressed, serious errors can be introduced into climate change, hydrologic modeling, and environmental impact research.     

GENERATION OF SYNTHETIC AND MISSING CLIMATIC DATA FOR PUERTO RICO1

ABSTRACT: Climatic data such as temperature, solar radiation, relative humidity, and wind speed have been widely used to estimate evapotranspiration. Moat of the solar radiation data and portions of the relative humidity data are either not available or missing from the records in Puerto Rico. Depending upon the availability and data characteristics of records, three methods (including a regression technique, an averaging of historical data, and a regional average) were used to generate missing data, and a time series analysis was used to synthesize a series of climatic data. The limitations and applicability of each method are discussed. The results showed that the time series analysis method can be successfully used to synthesize a series of monthly solar radiations for several stations. The regression technique and the regional average can be successfully applied to generate missing monthly solar radiation data. The regression technique and the averaging of historical data have been satisfactorily used to interpolate missing monthly relative humidity. The explained variance (R²) varied from 0.68 to 0.88, which are both significant at the 0.05 level of significance.     

Surveying macro-invertebrate diversity in the Tons river,Doon Valley,India

A survey of macro-invertebrates and their monthly variations occupying the Tons river in Doon Valley was conducted from August 2003–July 2004. Macro-invertebrate collections and water samples were taken from three sampling stations every month during the period of study. All the hydrological attributes were measured monthly for 1 year. The present study showed that the water velocity, hydromedian depth, turbidity and dissolved oxygen and nature and size of the bottom substrates do play a major role in determining the macro-invertebrate diversity of Tons river. The ecological relevance of the measured hydrological attributes was investigated by comparing their degree of correlation with invertebrate density and diversity. The Shannon–Wiener index (H′) of macro-invertebrates was found to be highest (3.60) during spring season (February and March) and lowest (2.59) during monsoon season (July and August). The high values of diversity index of macro-invertebrates at all the three sampling sites indicate diverse macro-invertebrate communities in the Tons river in Doon Valley, India.     

Evaluation of regression methodology with low-frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas

Water quality regulation and litigation have elevated the awareness and need for quantifying water quality and source contributions in watersheds across the USA. In the present study, the regression method, which is typically applied to large (perennial) rivers, was evaluated in its ability to estimate constituent loads (NO(3)-N, total N, PO(4)-P, total P, sediment) on three small (ephemeral) watersheds with different land uses in Texas. Specifically, regression methodology was applied with daily flow data collected with bubbler stage recorders in hydraulic structures and with water quality data collected with four low-frequency sampling strategies: random, rise and fall, peak, and single stage. Estimated loads were compared with measured loads determined in 2001-2004 with an autosampler and high-frequency sampling strategies. Although annual rainfall and runoff volumes were relatively consistent within watersheds during the study period, measured annual nutrient and sediment concentrations and loads varied considerably for the cultivated and mixed watersheds but not for the pasture watershed. Likewise, estimated loads were much better for the pasture watershed than the cultivated and mixed landuse watersheds because of more consistent land management and vegetation type in the pasture watershed, which produced stronger correlations between constituent loads and mean daily flow rates. Load estimates for PO(4)-P were better than for other constituents possibly because PO(4)-P concentrations were less variable within storm events. Correlations between constituent concentrations and mean daily flow rate were poor and not significant for all watersheds, which is different than typically observed in large rivers. The regression method was quite variable in its ability to accurately estimate annual nutrient loads from the study watersheds; however, constituent load estimates were much more accurate for the combined 3-yr period. Thus, it is suggested that for small watersheds, regression-based annual load estimates should be used with caution, whereas long-term estimates can be much more accurate when multiple years of concentration data are available. The predictive ability of the regression method was similar for all of the low-frequency sampling strategies studied; therefore, single-stage or random strategies are recommended for low-frequency storm sampling on small watersheds because of their simplicity.     

INFLUENCE OF DIFFERENT TEMPORAL SAMPLING STRATEGIES ON ESTIMATING TOTAL PHOSPHORUS AND SUSPENDED SEDIMENT CONCENTRATION AND TRANSPORT IN SMALL STREAMS1

ABSTRACT: Various temporal sampling strategies are used to monitor water quality in small streams. To determine how various strategies influence the estimated water quality, frequently collected water quality data from eight small streams (14 to 110 km²) in Wisconsin were systematically subsampled to simulate typically used strategies. These subsets of data were then used to estimate mean, median, and maximum concentrations, and with continuous daily flows used to estimate annual loads (using the regression method) and volumetrically weighted mean concentrations. For each strategy, accuracy and precision in each summary statistic were evaluated by comparison with concentrations and loads of total phosphorus and suspended sediment estimated from all available data. The most effective sampling strategy depends on the statistic of interest and study duration. For mean and median concentrations, the most frequent fixed period sampling economically feasible is best. For maximum concentrations, any strategy with samples at or prior to peak flow is best. The best sampling strategy to estimate loads depends on the study duration. For one‐year studies, fixed period monthly sampling supplemented with storm chasing was best, even though loads were overestimated by 25 to 50 percent. For two to three‐year load studies and estimating volumetrically weighted mean concentrations, fixed period semimonthly sampling was best.     

STREAM NITRATE-N LOADS IN RELATION TO VARIATIONS IN ANNUAL AND SEASONAL RUNOFF REGIMES1

ABSTRACT: The calculation of stream nutrient loads from a sampling period of one year or, at most, a few years may provide an inaccurate estimate of average seasonal or annual loads due to considerable year-to-year variations in hydrological regime. The number of years of record required to give a reliable estimate of long-term average NO₃-N loads was analyzed for E. Duffin Creek and the Nottawasaga River in Ontario, Canada. Nitrate load rating relationships were used in combination with a continuous stream discharge record for 22 years (E. Duffin Creek) and 34 years (Nottawasaga River) to simulate long-term seasonal and annual variation in NO₃.N loads. The errors involved in calculating average loads were examined by comparing the loads derived from sampling periods of one or more consecutive years duration with the estimated long-term average load for the two rivers. Annual NO₃-N loads for a single year deviated from the long-term average load by ± 20 to 53 percent in 8 out of 22 years in E. Duffin Creek and in 13 of 34 years in the Nottawasaga River. Six consecutive years of record would be required for both rivers to ensure that an error of > ± 20 percent would occur in only 5 percent of these observation periods. February-April NO₃-N loads for a single year could deviate by up to +90 percent or -61 percent from the long-term average spring period load for the two rivers. A sampling period of at least 6–7 years would be needed to estimate average NO₃-N loads for the spring runoff season with an error <± 20 percent.     

Hydrology and Water Budget for a Forested Atlantic Coastal Plain Watershed,South Carolina1

Abstract: Increases in timber demand and urban development in the Atlantic Coastal Plain over the past decade have motivated studies on the hydrology, water quality, and sustainable management of coastal plain watersheds. However, studies on baseline water budgets are limited for the low‐lying, forested watersheds of the Atlantic Coastal Plain. The purpose of this study was to document the hydrology and a method to quantify the water budget of a first‐order forested watershed, WS80, located within the USDA Forest Service Santee Experimental Forest northeast of Charleston, South Carolina. Annual Rainfall for the 2003 and 2004 periods were 1,671 mm (300 mm above normal) and 962 mm (over 400 mm below normal), respectively. Runoff coefficients (outflow as a fraction of total rainfall) for the 2003 and 2004 periods were 0.47 and 0.08, respectively, indicating a wide variability of outflows as affected by antecedent conditions. A spreadsheet‐based Thornthwaite monthly water balance model was tested on WS80 using three different potential evapotranspiration estimators [Hamon, Thornthwaite, and Penman‐Monteith (P‐M)]. The Hamon and P‐M‐based methods performed reasonably well with average absolute monthly deviations of 12.6 and 13.9 mm, respectively, between the measured and predicted outflows. Estimated closure errors were all within 9% for the 2003, 2004, and seasonal water budgets. These results may have implications on forest management practices and provide necessary baseline or reference information for Atlantic Coastal Plain watersheds.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

A MONTHLY TIME SERIES MODEL OF MUNICIPAL WATER DEMAND1

A multivariate time series model is formulated to study monthly variations in municipal water demand. The left hand side variable in the multivariate regression model is municipal water demand (gallons per connection per day) and the right hand side contains (explanatory) variables which include price (constant dollars), average temperature, total precipitation, and percentage of daylight hours. The application of the regression model to Salt Lake City Water Department data produced a high multiple correlation coefficient and F-statistic. The regression coefficients for the right hand side variables all have the appropriate sign. In an ex post forecast, the model accurately predicts monthly variations in municipal water demand. The proposed monthly multivariate model is not only found useful for forecasting water demand, but also useful for predicting and studying the impact of nonstructural management decisions such as the effect of price changes, peak load pricing methods, and other water conservation programs.     

TEMPORAL VARIABILITY OF WATER QUALITY IN THE ST. LUCIE ESTUARY,SOUTH FLORIDA1

ABSTRACT: Four years of monthly freshwater discharge and constituent concentration data from three tributaries were related to a concurrent series of data for three segments of the St. Lucie Estuary in South Florida using multiple regression and time-series analysis techniques. Water quality parameters examined were dissolved inorganic and total nitrogen and phosphorus, chlorophyll a, total suspended solids, turbidity, and color. On monthly time scales, a multiple regression, which included freshwater discharge, freshwater constituent concentration, and dilution with ocean water (salinity) as independent variables, explained 50 percent or less of the variability in estuarine constituents. No single independent variable explained more variation than another. By contrast, on seasonal (wet, dry) time scales, freshwater discharge explained the bulk of variation in estuarine water quality (up to 93 percent). On monthly time scales, variability in concentrations of nutrients and other constituents may be largely controlled by processes internal to the system. At seasonal time scales, freshwater discharge appears to drive variability in most estuarine water quality parameters examined. Results indicate that management of tributary input on a seasonal basis, with the expectation of achieving seasonal concentration goals in the estuary, would have a higher probability of success than managing on a monthly basis.     

Enhancing Hyrdological Simulation Program – FORTRAN Model Channel Hydraulic Representation1

Abstract: The hydrological simulation program – FORTRAN (HSPF) is a comprehensive watershed model that employs depth‐area‐volume‐flow relationships known as the hydraulic function table (FTABLE) to represent the hydraulic characteristics of stream channel cross‐sections and reservoirs. An accurate FTABLE determination for a stream cross‐section site requires an accurate determination of mean flow depth, mean flow width, roughness coefficient, longitudinal bed slope, and length of stream reach. A method that uses regional regression equations to estimate mean flow depth, mean flow width, and roughness coefficient is presented herein. FTABLES generated by the proposed method (Alternative Method) and FTABLES generated by Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) were compared. As a result, the Alternative Method was judged to be an enhancement over the BASINS method. First, the Alternative Method employs a spatially variable roughness coefficient, whereas BASINS employs an arbitrarily selected spatially uniform roughness coefficient. Second, the Alternative Method uses mean flow width and mean flow depth estimated from regional regression equations whereas BASINS uses mean flow width and depth extracted from the National Hydrography Dataset (NHD). Third, the Alternative Method offers an option to use separate roughness coefficients for the in‐channel and floodplain sections of compound channels. Fourth, the Alternative Method has higher resolution in the sense that area, volume, and flow data are calculated at smaller depth intervals than the BASINS method. To test whether the Alternative Method enhances channel hydraulic representation over the BASINS method, comparisons of observed and simulated streamflow, flow velocity, and suspended sediment were made for four test watersheds. These comparisons revealed that the method used to estimate the FTABLE has little influence on hydrologic calibration, but greatly influences hydraulic and suspended sediment calibration. The hydrologic calibration results showed that observed versus simulated daily streamflow comparisons had Nash‐Sutcliffe efficiencies ranging from 0.50 to 0.61 and monthly comparisons had efficiencies ranging from 0.61 to 0.84. Comparisons of observed and simulated suspended sediments concentrations had model efficiencies ranging from 0.48 to 0.56 for the daily, and 0.28 to 0.70 for the monthly comparisons. The overall results of the hydrological, hydraulic, and suspended sediment concentration comparisons show that the Alternative Method yielded a relatively more accurate FTABLE than the BASINS method. This study concludes that hydraulic calibration enhances suspended sediment simulation performance, but even greater improvement in suspended sediment calibration can be achieved when hydrological simulation performance is improved. Any improvements in hydrological simulation performance are subject to improvements in the temporal and spatial representation of the precipitation data.     

WEST AFRICA: VOLTA DISCHARGE DATA QUALITY ASSESSMENT AND USE1

ABSTRACT: Water resource management in West Africa is often a complicated process due to inadequate resources, climatic extremes, and insufficient hydrological information. Insufficient data hinder sustainable watershed management practices, one of the top priorities in the Volta River Basin. This research properly fills in missing data by modeling the hydrological distribution in the Volta River Basin. On average, discharge gages across the basin are missing 20 percent of their monthly data over 20 years. Two methods were used to supplement missing data: a statistically linear model and a conceptual hydrological model. A linear equation, developed from the regression of precipitation and runoff, was used to evaluate the quality of existing data. The hydrological model separates the system into root and groundwater zones. Measured values were used to calibrate the hydrological model and to validate the statistical model. The quality of existing data was analyzed and organized for usability. Accuracy of the hydrological model was also evaluated for its effectiveness using R² and standard error. It was found that the hydrological model was an improvement from the linear model on a monthly basis; R² values improved by as much as 0.5 and monthly error decreased. Monthly predictions of the hydrological model were used to fill gaps of measured data sets.     

STREAM TEMPERATURE CORRELATIONS WITH AIR TEMPERATURES IN MINNESOTA: IMPLICATIONS FOR CLIMATE WARMING1

ABSTRACT: Air temperatures are sometimes used as easy substitutes for stream temperatures. To examine the errors associated with this substitution, linear relationships between 39 Minnesota stream water temperature records and associated air temperature records were analyzed. From the lumped data set (38,082 daily data pairs), equations were derived for daily, weekly, monthly, and annual mean temperatures. Standard deviations between all measured and predicted water temperatures were 3.5°C (daily), 2.6°C (weekly), 1.9°C (monthly), and 1.3°C (annual). Separate analyses for each stream gaging station gave substantially lower standard deviations. Weather monitoring stations were, on average, 37.5 km from the stream. The measured water temperatures follow the annual air temperature cycle closely. No time lags were taken into account, and periods of ice cover were excluded from the analysis. If atmospheric CO₂ doubles in the future, air temperatures in Minnesota are projected (CCC GCM) to rise by 4.3°C in the warm season (April-October). This would translate into an average 4.1°C stream temperature rise, provided that stream shading would remain unaltered.     

流域污染物通量测算方法研究

流域水系内污染物通量不仅能够用于评价各类污染源的水污染物入河负荷,也是对流域污染特征,水污染物在河流水体中复杂迁移、转化过程的最直观反应。准确测算流域水系内污染物跨界通量及其时空分布是进行流域水环境风险预警和风险管理的重要前提之一。针对目前多种污染物通量测算方法在进行污染物年通量估算时,结果不确定性大这一突出问题,以流域水质监测站年内逐日流量、悬浮颗粒物监测数据作为悬浮颗粒物年通量参考值,基于以月、半月、周为周期的监测策略,将逐日流量、悬浮颗粒物监测数据重新筛选抽样构造,由此,系统分析了不同流域集水面积、污染通量监测频次和目前常用通量估算方法对污染物年通量估算不确定性的影响。所得方法和结论可为进一步制定流域污染物通量的测算规范提供方法指引和技术支持。     

Modeling flow and nitrate fate at catchment scale in Brittany (France)

In the intensive pig-farming (Sus scrofa) area of Brittany (western France), many surface and subsurface water resources are contaminated by nitrate (NO3) with concentrations that chronically exceed the European Community 50 mg L(-1) drinking standard. To ensure sustainable water supply, the fate of NO3 must be considered in both surface water and ground water. The fate of N was investigated in a Britain catchment, the Co?t-Dan watershed, with an integrated management tool: the hydrological SWAT model coupled with the ground water model MODFLOW, and its companion contaminant and solute transport model MT3DMS. The model was validated with respect to water quantity during a 6-yr period and for the NO3 concentration during a 44-mo period, at two gauging stations in the catchment. The coupled models reproduced accurately the measurements. At the basin outlet, the Nash-Sutcliffe coefficients were 0.88 for monthly flow for the entire period and 0.87 for monthly N load. Alternative scenarios were simulated and showed potential benefits of decreasing manure application from 210 to 170 kg N ha(-1) as required by the European Commission Nitrates Directive.     

Evaluating and Extending CLIGEN Precipitation Generation for the Loess Plateau of China1

Abstract: Climate generator (CLIGEN) is widely used in the United States to generate long‐term climate scenarios for use with agricultural systems models. Its applicability needs to be evaluated for use in a new region or climate. The objectives were to: (1) evaluate the reproducibility of the latest version of CLIGEN v5.22564 in generating daily, monthly, and yearly precipitation depths at 12 stations, as well as storm patterns including storm duration (D), relative peak intensity (ip), and peak intensity (rp) at 10 stations dispersed across the Loess Plateau and (2) test whether an exponential distribution for generating D and a distribution‐free approach for inducing desired rank correlation between precipitation depth and D can improve storm pattern generations. Mean absolute relative errors (MAREs) for simulating daily, monthly, annual, and annual maximum daily precipitation depth across all 12 stations were 3.5, 1.7, 1.7, and 5.0% for the mean and 5.0, 4.5, 13.0, and 13.6% for the standard deviations (SD), respectively. The model reproduced the distributions of monthly and annual precipitation depths well (p > 0.3), but the distribution of daily precipitation depth was less well produced. The first‐order, two‐state Markov chain algorithm was adequate for generating precipitation occurrence for the Loess Plateau of China; however, it underpredicted the longest dry periods. The CLIGEN‐generated storm patterns poorly. It underpredicted mean and SD of D for storms ≥10 mm by ?60.4 and ?72.6%, respectively. Compared with D, ip, and rp were slightly better reproduced. The MAREs of mean and SD were 21.0 and 52.1% for ip, and 31.2 and 55.2% for rp, respectively. When an exponential distribution was used to generate D, MAREs were reduced to 2.6% for the mean and 7.8% for the SD. However, ip estimation became much worse with MAREs being 128.9% for the mean and 241.1% for the SD. Overall, storm pattern generation needs improvement. For better storm pattern generation for the region, precipitation depth, D, and rp may be generated correlatively using Copula methods.     

ON THE EVALUATION OF OPERATIONAL CLOUD SEEDING PROJECTS1

ABSTRACT Operational cloud seeding projects, those designed to produce a desired change in the weather and that are nonexperimental in nature, continue to be pursued widely in the United States. A recurring question by scientists, project sponsors, and cloud seeders has been, “was the weather altered and if so, by how much?” Evaluation of such projects is now recognized as having scientific benefits, and a four-year study has addressed various techniques and statistical methods to perform evaluations and to learn more about how to modify the weather. Most such evaluations hinge on some type of space-time comparisons, but valid comparisons can be obtained only avoiding biases in the project design and operation. Through simulated changes in weather conditions, it was determined that the principal component regression techniques were used to evaluate selected rain and hail modification projects, revealing modification in certain projects and none in others. Various relevant issues have been examined such as use of other weather variables (covariates) to increase detection power, the validity of using historical data as controls for discrete operational periods, possible randomization options during cloud seeding operations, and analyses of individual rain events versus that based on monthly or seasonal units.