PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

Snowmelt Runoff and Water Yield Along Elevation and Temperature Gradients in California’s Southern Sierra Nevada1

Hunsaker, Carolyn T., Thomas W. Whitaker, and Roger C. Bales, 2012. Snowmelt Runoff and Water Yield Along Elevation and Temperature Gradients in California’s Southern Sierra Nevada. Journal of the American Water Resources Association (JAWRA) 48(4): 667‐678. DOI: 10.1111/j.1752‐1688.2012.00641.x Abstract: Differences in hydrologic response across the rain‐snow transition in the southern Sierra Nevada were studied in eight headwater catchments – the Kings River Experimental Watersheds – using continuous precipitation, snowpack, and streamflow measurements. The annual runoff ratio (discharge divided by precipitation) increased about 0.1 per 300 m of mean catchment elevation over the range 1,800‐2,400 m. Higher‐elevation catchments have lower vegetation density, shallow soils with rapid permeability, and a shorter growing season when compared with those at lower elevations. Average annual temperatures ranged from 6.8°C at 2,400 m to 8.6 at 1,950 m elevation, with annual precipitation being 75‐95% snow at the highest elevations vs. 20‐50% at the lowest. Peak discharge lagged peak snow accumulation on the order of 60 days at the higher elevations and 20 to 30 days at the lower elevations. Snowmelt dominated the daily streamflow cycle over a period of about 30 days in higher elevation catchments, followed by a 15‐day transition to evapotranspiration dominating the daily streamflow cycle. Discharge from lower elevation catchments was rainfall dominated in spring, with the transition to evapotranspiration dominance being less distinct. Climate warming that results in a longer growing season and a shift from snow to rain would result in earlier runoff and a lower runoff ratio.     

Improved Bankfull Discharge Prediction Using 2‐Year Recurrence‐Period Discharge1

Abstract:  Knowledge of bankfull discharge (Q_bf) is essential for planners, engineers, geomorphologists, environmentalists, agricultural interests, developments situated on flood prone lands, surface mining and reclamation activities, and others interested in floods and flooding. In conjunction with estimating Q_bf, regionalized bankfull hydraulic geometry relationships, which relate Q_bf and associated channel dimensions (i.e., width, depth, and cross‐section area) to drainage basin area (A_da), are often used. This study seeks to improve upon the common practice of predicting Q_bf using A_da exclusively. Specifically, we hypothesize that predictions of Q_bf can be improved by including estimates of the 2‐year recurrence‐period discharge (Q₂) in regression models for predicting Q_bf. For testing this hypothesis, we used Q_bf estimates from 30 reports containing data for streams that span 34 hydrologic regions in 16 states. Corresponding values of Q₂ and A_da were compiled from flood‐frequency reports and other sources. By comparing statistical measures (i.e., root mean squared error, coefficient of determination, and Akaike’s information criterion), we determined that predicting Q_bf from Q₂ rather than A_da yields consistently better estimates of Q_bf. Other principal findings are (1) data are needed for at least 12 sites in a region for reliable hydraulic geometry model selection and (2) an approximate range of values for Q_bf/Q₂ is 0.10‐3.0.     

Analysis of Appropriate Timescales for Water Diversion Permits in Brazil

Agreement on the criteria for granting the right to use water resources between governing bodies represents a significant advance in the process of sharing water use. To aid water resource management agencies in optimizing water use, the impact of using different criteria for permitting water use in the Paracatu river basin, Brazil, was evaluated in this study. The streamflow criteria corresponding to 30 % of the annual Q_7,10 (used by the governing body of Minas Gerais), 70 % of the annual Q₉₅ (used by the governing body of the union), 30 % of the monthly Q_7,10, and 70 % of the monthly Q₉₅ were evaluated. The use of criteria based on the monthly streamflow allows for better management of water use because it allows for greater utilization of this resource in times when there is high water availability and imposes a more realistic restriction during critical periods. Substitution of the annual Q_7,10 for the monthly Q_7,10 significantly increases the streamflow permitted in some months, for example, from December to May. Use of the criterion of 70 % of the annual Q₉₅ involves a high risk of drought in critical months, while the criterion of 70 % of the monthly Q₉₅ minimizes this risk.     

BASE FLOW RECESSION RATES,LOW FLOWS,AND HYDROLOGIC FEATURES OF SMALL WATERSHEDS IN PENNSYLVANIA,USA1

This paper examines the relationships between measurable watershed hydrologic features, base flow recession rates, and the Q_7,10 low flow statistic (the annual minimum seven‐day average streamflow occurring once every 10 years on average). Base flow recession constants were determined by analyzing hydrograph recession data from 24 small (>130 km²), unregulated watersheds across five major physiographic provinces of Pennsylvania, providing a highly variable dataset. Geomorphic, hydrogeologic, and land use parameters were determined for each watershed. The base flow recession constant was found to be most strongly correlated to drainage density, geologic index, and ruggedness number (watershed slope); however, these three parameters are intercorrelated. Multiple regression models were developed for predicting the recession rate, and it was found that only two parameters, drainage density and hydrologic soil group, were required to obtain good estimates of the recession constant. Equations were also developed to relate the recession rates to Q_7,10 per unit area, and to the Q_7,10/Q₅₀ ratio. Using these equations, estimates of base flow recession rates, Q_7,10, and streamflow reduction under drought conditions can be made for small, ungaged basins across a wide range of physiography.     

STATISTICAL ANALYSIS OF THE IMPACT OF GROUND WATER PUMPAGE ON LOW-FLOW HYDROLOGY1

ABSTRACT: Ground-water pumpage withdrew 57 cubic feet per second from aquifers beneath the Yahara River Basin in 1970. Forty-six cubic feet per second were exported by the diversion of treated wastewater from the drainage basin. The low-flow hydrology of the upper Yahara River has been impacted by this diversion. Prior to 1959, the wastewater was discharged into the river, augmenting the baseflow during low-flow periods. As much as 85% of streamflow was due to effluent discharge. In 1959 the wastewater was transferred from the river basin. The result was a decrease of about one-third in mean annual streamflow, and a decrease of more than 50% in the 7Q₂ and 7Q₁₀. Regression analysis showed the annual 7-day low-flow and 60-day low-flow have a statistically significant correlation with mean annual flow. Using predictions of future mean annual discharge of the river with increasing interbasin transfers, it is shown that by 1990 there is a significant probability that in some years the 60-day low-flow in the river will be zero.     

ESTIMATING LOW-FLOW QUANTILES FROM DRAINAGE-BASIN CHARACTERISTICS IN NEW HAMPSHIRE AND VERMONT1

ABSTRACT: This study systematically develops, validates, and compares alternative approaches for estimating quantiles of the distribution of annual minimum seven-day-average flows (7Q) for ungaged, unregulated drainage basins in New Hampshire and Vermont via regression on map-measurable drainage-basin characteristics. At 47 gaging stations in the region, the hypotheses that 7Q is log normally distributed and serially independent are not rejected, and the regional average spatial correlation is R= 0.35. Step-forward examination of a suite of potential predictor variables revealed that logarithm of drainage area, mean elevation, and fraction of basin covered with sand and gravel deposits are significant predictors of quantiles of 7Q. The regression equations were incorporated into four approaches to estimate the 7Q value with a nonexceedence probability of 0.1, 7Q10. Comparison of observed values and values predicted via a delete-one jackknife resampling validation indicates that one of the approaches gives estimates with acceptable bias and precision, with median relative error of 33 percent and prediction error of 64 percent. This is equivalent to the precision obtainable with only one to two years of gaging records. In spite of this limited precision, the approaches developed herein are useful for predicting 7Q quantiles at ungaged sites. Further improvement in precision will likely be possible only by exploiting the spatial correlation of annual 7Q.     

Decoupling Streamflow Responses to Climate Variability and Land Use/Cover Changes in a Watershed in Northern China

Restored annual streamflow (Q_r) and measured daily streamflow of the Chaohe watershed located in northern China and associated long‐term climate and land use/cover data were used to explore the effects of land use/cover change and climate variability on the streamflow during 1961‐2009. There were no significant changes in annual precipitation (P) and potential evapotranspiration, whereas Q_r decreased significantly by 0.81 mm/yr (p < 0.001) over the study period with a change point in 1999. We used 1961‐1998 as the baseline period (BP) and 1999‐2009 the change period (CP). The mean Q_r during the CP decreased by 39.4 mm compared with that in the BP. From 1979 to 2009, the grassland area declined by 69.6%, and the forest and shrublands increased by 105.4 and 73.1%, respectively. The land use/cover change and climate variability contributed for 58.4 and 41.6% reduction in mean annual Q_r, respectively. Compared with the BP, median and high flows in the CP decreased by 38.8 and up to 75.5%, respectively. The study concludes that large‐scale ecological restoration and watershed management in northern China has greatly decreased water yield and reduced high flows due to the improved land cover by afforestation leading to higher water loss through evapotranspiration. At a large watershed scale, land use/cover change could play as much of an important role as climate variability on water resources.     

Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge1

He, Laien and Gregory V. Wilkerson, 2011. Improved Bankfull Channel Geometry Prediction Using Two‐Year Return‐Period Discharge. Journal of the American Water Resources Association (JAWRA) 47(6):1298–1316. DOI: 10.1111/j.1752‐1688.2011.00567.x Abstract:  Bankfull discharge (Q_bf) and bankfull channel geometry (i.e., width, W_bf; mean depth, D_bf; and cross‐section area, A_bf) are important design parameters in stream restoration, habitat creation, mined land reclamation, and related projects. The selection of values for these parameters is facilitated by regional curves (regression models in which Q_bf, W_bf, D_bf, and A_bf are predicted as a function of drainage area, A_da). This paper explores the potential for the two‐year return‐period discharge (Q₂) to improve predictions of W_bf, D_bf, and A_bf. Improved predictions are expected because Q₂ estimates integrate the effects of basin drainage area, climate, and geology. For conducting this study, 29 datasets (each representing one hydrologic region) spanning 14 states in the United States were analyzed. We assessed the utility of using Q₂ by comparing statistical measures of regression model performance (e.g., coefficient of determination and Akaike’s information criterion). Compared to using A_da, Q₂ is shown to be a “clearly superior” predictor of W_bf, D_bf, and A_bf, respectively, for 21, 13, and 25% of the datasets. By contrast, A_da yielded a clearly superior model for predicting W_bf, D_bf, and A_bf, respectively, for 0, 0, and 14% of the datasets. Our conclusion is that it alongside with developing conventional regional curves using A_da it is prudent to develop regional curves that use Q₂ as an independent variable because in some cases the resulting model will be superior.     

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.     

Estimating Annual Groundwater Evapotranspiration from Phreatophytes in the Great Basin Using Landsat and Flux Tower Measurements

Escalating concerns about water supplies in the Great Basin have prompted numerous water budget studies focused on groundwater recharge and discharge. For many hydrographic areas (HAs) in the Great Basin, most of the recharge is discharged by bare soil evaporation and evapotranspiration (ET) from phreatophyte vegetation. Estimating recharge from precipitation in a given HA is difficult and often has significant uncertainty, therefore it is often quantified by estimating the natural discharge. As such, remote sensing applications for spatially distributing flux tower estimates of ET and groundwater ET (ET_g) across phreatophyte areas are becoming more common. We build on previous studies and develop a transferable empirical relationship with uncertainty bounds between flux tower estimates of ET and a remotely sensed vegetation index, Enhanced Vegetation Index (EVI). Energy balance‐corrected ET measured from 40 flux tower site‐year combinations in the Great Basin was statistically correlated with EVI derived from Landsat imagery (r² = 0.97). Application of the relationship to estimate mean‐annual ET_g from four HAs in western and eastern Nevada is highlighted and results are compared with previous estimates. Uncertainty bounds about the estimated mean ET_g allow investigators to evaluate if independent groundwater discharge estimates are “believable” and will ultimately assist local, state, and federal agencies to evaluate expert witness reports of ET_g, along with providing new first‐order estimates of ET_g.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.     

Linking Riparian Dynamics and Groundwater: An Ecohydrologic Approach to Modeling Groundwater and Riparian Vegetation

The growing use of global freshwater supplies is increasing the need for improved modeling of the linkage between groundwater and riparian vegetation. Traditional groundwater models such as MODFLOW have been used to predict changes in regional groundwater levels, and thus riparian vegetation potential attributable to anthropogenic water use. This article describes an approach that improves on these modeling techniques through several innovations. First, evapotranspiration from riparian/wetland systems is modeled in a manner that more realistically reflects plant ecophysiology and vegetation complexity. In the authors’ model programs (RIP-ET and PRE-RIP-ET), the single, monotonically increasing evapotranspiration flux curve in traditional groundwater models is replaced with a set of ecophysiologically based curves, one for each plant functional group present. For each group, the curve simulates transpiration declines that occur both as water levels decline below rooting depths and as waters rise to levels that produce anoxic soil conditions. Accuracy is further improved by more effective spatial handling of vegetation distribution, which allows modeling of surface elevation and depth to water for multiple vegetation types within each large model cell. The use of RIP-ET in groundwater models can improve the accuracy of basin scale estimates of riparian evapotranspiration rates, riparian vegetation water requirements, and water budgets. Two case studies are used to demonstrate that RIP-ET produces significantly different evapotranspiration estimates than the traditional method. When combined with vegetation mapping and a supporting program (RIP-GIS), RIP-ET also enables predictions of riparian vegetation response to water use and development scenarios. The RIP-GIS program links the head distribution from MODFLOW with surface digital elevation models, producing moderate- to high-resolution depth-to-groundwater maps. Together with information on plant rooting depths, these can be used to predict vegetation response to water allocation decisions. The different evapotranspiration outcomes produced by traditional and RIP-ET approaches affect resulting interpretations of hydro-vegetation dynamics, including the effects of groundwater pumping stress on existing habitats, and thus affect subsequent policy decisions.     

EVALUATION OF AN INDUCED INFILTRATION MODEL AS APPLIED TO GLACIAL AQUIFER SYSTEMS1

ABSTRACT: Numerical models were used to examine the limitations of the assumptions used in an analytical induced infiltration model. The assumptions tested included negligible streambed effects, negligible areal recharge, two-dimensional ground water flow, fully penetrating rivers and wells, and constant surface water stage. For situations that deviate from the underlying assumptions, the analytical model becomes a less reliable predictor of induced infiltration. The numerical experiments show that streambed effects cannot be neglected if the streambed conductivity is more than one order of magnitude lower than the aquifer hydraulic conductivity. Areal recharge cannot be neglected if the ground water basin receives more than 5 in/yr of areal recharge. Three-dimensional flow effects due to well partial penetration cannot be neglected if the ratio of horizontal hydraulic conductivity to vertical hydraulic conductivity (K_h/K_u) is greater than 10. Surface water elevation fluctuations can significantly influence the induced infiltration rate, depending on the degree of fluctuations and the ground water hydraulic gradient.     

SPATIAL VARIABILITY IN WATER‐BALANCE MODEL PERFORMANCE IN THE CONTERMINOUS UNITED STATES1

ABSTRACT: A monthly water‐balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil‐moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R‐square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long‐term mean and standard deviation of annual precipitation; temperature and runoff; and low‐flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R‐square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest. Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.     

APPLICATION OF KRIGING TO ESTIMATING MEAN ANNUAL PRECIPITATION IN A REGION OF OROGRAPHIC INFLUENCE1

ABSTRACT: Estimates of mean annual precipitation (MAP) over areas are the starting point for all computations of water and chemical balances for drainage basins and surface water bodies. Any errors in the estimates of MAP are propagated through the balance computations. These errors can be due to: (1) failures of individual gages to collect the amount of precpitation that actually falls; (2) operator errors; and (3) failure of the raingage network to adequately sample the region of interest. This paper attempts to evaluate the last of these types of error by applying kriging in two different approaches to estimating MAP in New Hampshire and Vermont, USA. The data base is the 1951–1980 normal precipitation at 120 raingages in the two states and in adjacent portions of bordering states and provinces. In the first approach, kriging is applied directly to the MAP values, while in the second, kriging is applied to a “precipitation delivery factor” that represents the MAP with the orographic effect removed. The first approach gives slightly better kriged estimates of MAP at seven validation stations that were not included in the original analysis, but results in an error surface that is highly contorted and in larger maximum errors over most of the region. The second approach had a considerably smoother error surface and, thus, is generally preferable as a basis for point and areal estimates of MAP. MAP estimates in the region have 95 percent confidence intervals of about 20 cm/yr at low and moderate elevations, and up to 35 cm/yr at high elevations. These uncertainties amount to about 20 percent of estimated MAP values.     

ESTIMATING NUTRIENT AND SEDIMENT THRESHOLD CRITERIA FOR BIOLOGICAL IMPAIRMENT IN PENNSYLVANIA WATERSHEDS1

ABSTRACT: This study employs a simple nonlinear statistical approach to establish nitrogen, phosphorus, and sediment concentration and unit area load thresholds to aid in the evaluation of aquatic biological health of watersheds within the state of Pennsylvania. Flow, nitrogen and phosphorus species, sediment, basin area, land cover, and biological assessment data were assembled for 29 Pennsylvania watersheds. For each watershed, rating curves depicting flow versus load relationships were developed using the U.S. Environmental Protection Agency's (USEPA's) storage and retrieval database (STORET) flow and concentration data, then applied to daily flow data obtained from U.S. Geological Survey (USGS) daily flow gauging stations to estimate daily load between 1989 and 1999. The load estimates and concentration data were then sorted into six sets of data: mean annual unit area nitrogen, phosphorus, and sediment loads; and average nitrogen, phosphorus, and sediment concentrations. Results of Mann‐Whitney tests conducted on each of the six datasets indicate that there is a statistically significant difference between the concentrations and unit area loads of nitrogen, phosphorus, and sediment in impaired and unimpaired watersheds. Concentration thresholds, calculated as the midpoint between the impaired and unimpaired watersheds’ 95 percent confidence interval for the median, were estimated to be 2.01 mg/L, 0.07 mg/L, and 197.27 mg/L for nitrogen, phosphorus, and sediment, respectively. Annual unit area load thresholds were estimated to be equal to 8.64 kg/ha, 0.30 kg/ha, and 785.29 kg/ha, respectively, for nitrogen, phosphorus, and sediment species.     

EVAPOTRANSPIRATION MEASUREMENT AND ESTIMATION OF THREE WETLAND ENVIRONMENTS IN THE UPPER ST. JOHNS RIVER BASIN,FLORIDA1

ABSTRACT: Accurate estimates of evapotranspiration from areas dominated by wetland vegetation are needed in the water budget of the Upper St. Johns River Basin. However, local data on evapotranspiration rates, especially in wetland environments, were lacking in the project area. In response to this need, the St. Johns River Water Management District collected evapotranspiration field data in Fort Drum Marsh Conservation Area over the period 1996 through 1999. Three large lysimeters were installed to measure the evapotranspiration from different wetland environments: sawgrass (Cladium jamaicense), cattail (Typha domingensis), and open water. In addition, pan evaporation was measured with a standard class “A” pan. Concurrently, meteorological data including rainfall, solar radiation, wind speed, relative humidity, air temperature, and atmospheric pressure were collected. By comparing computed evapotranspiration rates with those measured in the lysimeters, parameters in the Penman‐Monteith, the Priestley‐Taylor, and Reference‐ET methods, and evaporation pan coefficients were estimated for monthly and seasonal cycles. The results from the data collected in this study show that mean monthly evapotranspiration rates, computed by the different methods, are relatively close. From a practical point of view, results indicate that the evaporation pan can be used equally well as the more complex and data‐intensive methods. This paper presents the measured evapotranspiration rates, evaporation pan coefficients, and the estimated parameter values for three different methods to compute evapotranspiration in the project area. Since local data on evaporation are often scarce or lacking, this information may be useful to watershed hydrologists for practical application in other project regions.     

Evaluation of large-scale CO2 storage on fresh-water sections of aquifers: An example from the Texas Gulf Coast Basin

Large-scale injections of CO₂ into subsurface saline aquifers have been proposed to remediate climate change related to buildup of green house gases in the atmosphere. The pressure buildup caused by such injections may impact a volume of the basin significantly larger than the CO₂ plume itself. In areas with hydrological settings similar to the Gulf Coast Basin, the perturbation of the flow-field in deep parts of the basin could result in brines or brackish water being pushed up-dip into unconfined sections of the same formations or into the capture zone of fresh-water wells. The premise of the current study is that the details of multiple-phase flow processes necessary to model the near field evolution of the CO₂ plume are not necessary to describe the impact of the pressure anomaly on up-dip aquifers. This paper quantitatively explores conditions under which shallow groundwater would be impacted by up-dip displacement of brines, utilizing an existing carefully calibrated flow model. Modeling an injection of water, arguably equivalent to 50 million tons of CO₂/year for 50 years resulted in an average water-table rise of 1 m, with minor increase in stream baseflow and larger increase in ground water evapotranspiration, but no significant change in salinity.     

SAMPLING STRATEGIES FOR ESTIMATING ACUTE AND CHRONIC EXPOSURES OF PESTICIDES IN STREAMS1

ABSTRACT: The Food Quality Protection Act of 1996 requires that human exposure to pesticides through drinking water be considered when establishing pesticide tolerances in food. Several systematic and seasonally weighted systematic sampling strategies for estimating pesticide concentrations in surface water were evaluated through Monte Carlo simulation, using intensive datasets from four sites in northwestern Ohio. The number of samples for the strategies ranged from 4 to 120 per year. Sampling strategies with a minimal sampling frequency outside the growing season can be used for estimating time weighted mean and percentile concentrations of pesticides with little loss of accuracy and precision, compared to strategies with the same sampling frequency year round. Less frequent sampling strategies can be used at large sites. A sampling frequency of 10 times monthly during the pesticide runoff period at a 90 km² basin and four times monthly at a 16,400 km² basin provided estimates of the time weighted mean, 90^th, 95^th, and 99^th percentile concentrations that fell within 50 percent of the true value virtually all of the time. By taking into account basin size and the periodic nature of pesticide runoff, costs of obtaining estimates of time weighted mean and percentile pesticide concentrations can be minimized.