Benthic Algal (Periphyton) Growth Rates in Response to Nitrogen and Phosphorus: Parameter Estimation for Water Quality Models

Nitrogen (N) and phosphorus (P) are significant pollutants that can stimulate nuisance blooms of algae. Water quality models (e.g., Water Quality Simulation Program, CE‐QUAL‐R1, CE‐QUAL‐ICM, QUAL2k) are valuable and widely used management tools for algal accrual due to excess nutrients in the presence of other limiting factors. These models utilize the Monod and Droop equations to associate algal growth rate with dissolved nutrient concentration and intracellular nutrient content. Having accurate parameter values is essential to model performance; however, published values for model parameterization are limited, particularly for benthic (periphyton) algae. We conducted a 10‐day mesocosm experiment and measured diatom‐dominated periphyton biomass accrual through time as chlorophyll a (chl a) and ash‐free dry mass (AFDM) in response to additions of N (range 5–11,995 µg nitrate as nitrogen [NO₃‐N]/L) and P (range 0.89–59.51 µg soluble reactive phosphorus/L). Resulting half‐saturation coefficients and growth rates are similar to other published values, but minimum nutrient quotas are higher than those previously reported. Saturation concentration for N ranged from 150 to 2,450 µg NO₃‐N/L based on chl a and from 8.5 to 60 µg NO₃‐N/L when based on AFDM. Similarly, the saturation concentration for P ranged from 12 to 29 µg‐P/L based on chl a, and from 2.5 to 6.1 µg‐P/L based on AFDM. These saturation concentrations provide an upper limit for streams where diatom growth can be expected to respond to nutrient levels and a benchmark for reducing nutrient concentrations to a point where benthic algal growth will be limited.     

Assessment of Future Climate Change Impact on Water Quality of Chungju Lake,South Korea,Using WASP Coupled with SWAT

This study is to evaluate the future potential impact of climate change on the water quality of Chungju Lake using the Water Quality Analysis Simulation Program (WASP). The lake has a storage capacity of 2.75 Gm³, maximum water surface of 65.7 km², and forest‐dominant watershed of 6,642 km². The impact on the lake from the watershed was evaluated by the Soil and Water Assessment Tool (SWAT). The WASP and SWAT were calibrated and validated using the monthly water temperatures from 1998 to 2003, lake water quality data (dissolved oxygen, total nitrogen [T‐N], total phosphorus [T‐P], and chlorophyll‐a [chl‐a]) and daily dam inflow, and monthly stream water quality (sediment, T‐N, and T‐P) data. For the future climate change scenario, the MIROC3.2 HiRes A1B was downscaled for 2020s, 2050s, and 2080s using the Change Factor statistical method. The 2080s temperature and precipitation showed an increase of +4.8°C and +34.4%, respectively, based on a 2000 baseline. For the 2080s watershed T‐N and T‐P loads of up to +87.3 and +19.6%, the 2080s lake T‐N and T‐P concentrations were projected to be 4.00 and 0.030 mg/l from 2.60 and 0.016 mg/l in 2000, respectively. The 2080s chl‐a concentration in the epilimnion and the maximum were 13.97 and 52.45 μg/l compared to 8.64 and 33.48 μg/l in 2000, respectively. The results show that the Chungju Lake will change from its mesotrophic state of 2000 to a eutrophic state by T‐P in the 2020s and by chl‐a in the 2080s. Editor's note: This paper is part of a featured series on Korean Hydrology. The series addresses the need for a new paradigm of river and watershed management for Korea due to climate and land use changes.     

Environmental Control and Limnological Impacts of a Large Recurrent Spring Bloom in Lake Washington,USA

A series of statistical analyses were used to identify temporal and spatial patterns in the phytoplankton and nutrient dynamics of Lake Washington, an mesotrophic lake in Washington State (USA). These analyses were based on fortnightly or monthly samples of water temperature, Secchi transparency, ammonium (NH₄), nitrate (NO₃), inorganic phosphorus (IP), total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), pH and chlorophyll a (chl a) collected during 1995–2000 from 12 stations. Lake Washington has a very consistent and pronounced annual spring diatom bloom which occurs from March to May. During this bloom, epilimnetic chl a concentrations peak on average at 10 μg/L, which is 3 times higher than chl a concentrations typically seen during summer stratified conditions. The spring bloom on average comprised 62% diatoms, 21% chlorophytes and 8% cyanobacteria. During summer stratification, diatoms comprised 26% of the phytoplankton community, chlorophytes 37% and cyanobacteria 25%. Cryptophytes comprised approximately 8% of the community throughout the year. Overall, 6 phytoplankton genera (i.e., Aulacoseira, Fragilaria, Cryptomonas, Asterionella, Stephanodiscus, and Ankistrodesmus) cumulatively accounted for over 50% of the community. These analyses also suggest that the phytoplankton community strongly influences the seasonality of NO₃, IP, DO, pH and water clarity. According to a MANOVA, seasonal fluctuations explained 40% of the total variability for the major parameters, spatial heterogeneity explained 10% of variability, and the seasonal-spatial interaction explained 10% of variability. Distinctive patterns were identified between offshore and inshore sampling stations. The results of our analyses also suggest that spatial variability was substantial, but much smaller than temporal variability.     

ESTIMATION OF COASTAL WATER CONTAMINATION LONG-TERM TREND USING SEASONAL LINEAR MODELS1

ABSTRACT: Bivalves are used as bioindicators to assess trends of the chemical quality of coastal and marine environments due to their ability to concentrate chemicals. These shellfish are subject to seasonal physiological changes influencing the chemical concentration. Using quarterly data, we model concentration via linear regression with a biologically based seasonal component. This was applied to cadmium concentration measured in the blue mussel (Mytilus edulis) at three sites in the Seine estuary (Normandy, France). In this case we have a high concentration season from January to June and a “low concentration” season from July to December. This season definition was checked a posteriori, using box-and-whisker plots and a statistical test of comparison of pair-wise adjusted least-squares mean differences, and it appears to be very reasonable. We averaged data by season and across sites. Our final model (R²= 0.846 with N= 27 observations) includes highly significant terms: a season effect, which accounts for 45% of the total variability, a linear and a quadratic time term. Outliers were identified by high Studentized residual values and attributed to bias in the temporal sampling schemes. The methodology developed will further be used with other shellfish and/or other trace elements and organic chemicals.     

Generalized Nondimensional Depth‐Discharge Rating Curves Tested on Florida Streamflow1

Abstract: A discharge rating is a relationship between stage and discharge at a specific point in a river stream or lake outlet structure. Rating curves are useful for interpolating and perhaps extrapolating flow measurements and for use directly in storage routing models. However, rating data and stations are limited. A generalized nondimensional mathematical expression that describes the rating relation of depth and discharge has been developed and tested against observations from 46 stations in West‐Central Florida. Three approaches were tested in sequence to select the best fit. The proposed model is a log‐linear equation with zero intercept and a slope that fits more than 50% of the stations were analyzed. The model is normalized by the depth and discharge values at 10% exceedance from data published by the U.S. Geological Survey. For ungauged applications, Q₁₀ and d₁₀ were derived from a relationship shown to be reasonably well correlated to the watershed drainage area. The average relative error for this parameter set shows that for the flow range up to the Q₁₀ discharge, better than 30% agreement with the USGS rating data can be expected for about 50% of the stations. Further analysis is required to determine why so many stations exhibit such similar behavior and to identify the criteria or parameters governing the differences.     

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

A statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.     

PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN1

ABSTRACT: Models for the prediction of chlorophyll a concentrations were developed and tested using data on 223 Florida lakes. A statistical analysis showed that the best model was log (Chl a) =?2.49 + 0.269 log (TP) + 1.06 log (TN) or log (Chl a) =?2.49 + 1.06 log (TN/TP) + 1.33 log (TP) where Chl a is the chlorophyll a concentration (mg m^-3), TP is the total phosphorus concentration (mg m^-3) and TN is the total nitrogen concentration (mg m^-3). The model yields unbiased estimates of chlorophyll a concentrations over a wide range of lake types and has a 95 percent confidence interval of 29–319 percent of the calculated chlorophyll a concentrations. Other models, especially the published Dillon-Rigler and Jones-Bachmann phosphorus-chlorophyll models, are less precise when applied to Florida lakes. The data support the hypothesis that nitrogen is an important limiting nutrient in hypereutrophic lakes.     

The Impact of Asynchronicity on Event‐Flow Estimation in Basin‐Scale Hydrologic Model Calibration1

Abstract: The calibration of basin‐scale hydrologic models consists of adjusting parameters such that simulated values closely match observed values. However, due to inevitable inaccuracies in models and model inputs, simulated response hydrographs for multiyear calibrations will not be perfectly synchronized with observed response hydrographs at the daily time step. An analytically derived formula suggests that when timing errors are significant, traditional calibration approaches may generally underestimate the total event‐flow volume. An event‐adaptive time series is developed and incorporated into the Nash‐Sutcliffe Efficiency objective function to diagnose the potential impact of event‐flow synchronization errors. Test sites are the 50 km² Subwatershed I of the Little River Experimental Watershed (LREWswI) in southeastern Georgia, and the 610 km² Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma, with the Soil and Water Assessment Tool used as the hydrologic model. Results suggest that simulated surface runoff generation is 55% less for LREWswI when the daily time series is used compared with when the event‐adaptive technique is used. Event‐flow generation may also be underestimated for LWREW, but to a lesser extent than it may be for LREWswI, due to a larger portion of the event flow being lateral flow.     

Modeling of Escherichia coli Fluxes on a Catchment and the Impact on Coastal Water and Shellfish Quality1

Bougeard, Morgane, Jean‐Claude Le Saux, Nicolas Pérenne, Claire Baffaut, Marc Robin, and Monique Pommepuy, 2011. Modeling of Escherichia coli Fluxes on a Catchment and the Impact on Coastal Water and Shellfish Quality. Journal of the American Water Resources Association (JAWRA) 1‐17. DOI: 10.1111/j.1752‐1688.2011.00520.x Abstract: The simulation of the impact of Escherichia coli loads from watersheds is of great interest for assessing estuarine water quality, especially in areas with shellfish aquaculture or bathing activities. For this purpose, this study investigates a model association based on the Soil and Water Assessment Tool (SWAT) coupled with a hydrodynamic model (MARS 2D; IFREMER). Application was performed on the catchment and estuary of Daoulas area (France). The daily E. coli fluxes simulated by SWAT are taken as an input in the MARS 2D model to calculate E. coli concentrations in estuarine water and shellfish. Model validation is based on comparison of frequencies: a strong relationship was found between calculated and measured E. coli concentrations for river quality (r² = 0.99) and shellfish quality (r² = 0.89). The important influence of agricultural practices and rainfall events on the rapid and large fluctuations in E. coli fluxes from the watershed (reaching three orders of magnitude in <24 hours) is one main result of the study. Response time in terms of seawater quality degradation ranges from one to two days after any important rainfall event (greater than 10 mm/day) and the time for estuary to recover good water quality also mainly depends on the duration of the rainfall. In the estuary, three effects (rainfall, tidal dilution, and manure spreading) have been identified as important influences.     

Evaluation of a Watershed Model for Estimating Daily Flow Using Limited Flow Measurements1

Abstract: The Soil and Water Assessment Tool (SWAT) model was evaluated for estimation of continuous daily flow based on limited flow measurements in the Upper Oyster Creek (UOC) watershed. SWAT was calibrated against limited measured flow data and then validated. The Nash‐Sutcliffe model Efficiency (NSE) and mean relative error values of daily flow estimations were 0.66 and 15% for calibration, and 0.56 and 4% for validation, respectively. Also, further evaluation of the model’s estimation of flow at multiple locations was conducted with parametric paired t‐test and nonparametric sign test at a 95% confidence level. Among the five main stem stations, four stations were statistically shown to have good agreement between predicted and measured flows. SWAT underestimated the flow of the fifth main stem station possibly because of the existence of complex flood control measures near to the station. SWAT estimated the daily flow at one tributary station well, but with relatively large errors for the other two tributaries. The spatial pattern of predicted flows matched the measured ones well. Overall, it was concluded from the graphical comparisons and statistical analyses of the model results that SWAT was capable of reproducing continuous daily flows based on limited flow data as is the case in the UOC watershed.     

INVESTIGATION OF TURBIDITY MAXIMUM IN A MESOTIDAL ESTUARY,TAIWAN1

ABSTRACT: To comprehend the distributions of salinity, temperature, and suspended sediment in the Danshuei River estuary in Taiwan, monthly field surveys were conducted in 2003. These included several high and low slackwater surveys and intensive surveys. The results show that the Danshuei River estuary is predominately a partially mixed estuary. The highest concentration of suspended sediment is typically observed at the Chung‐Hsin Bridge, the most upstream sampling station. The suspended sediment concentration exhibits a general decreasing trend in the downstream direction. It may be concluded that the sediments mostly come from the upstream reach. A locally high concentration of suspended sediment is found at the Kuan‐Du station because of the local deep channel bathymetry and two‐layered estuarine circulation. A vertical two‐dimensional hydrodynamic and sediment transport model is applied to investigate the tidally averaged salinity distribution, residual circulation, and suspended sediment concentration. The modeling results reveal that, under the Q₇₅ flow condition (i.e., low flow), a turbidity maximum occurs at the Kuan‐Du station due to the strong estuarine circulation. The model simulation with a much higher river flow condition results in a weaker residual circulation and weaker turbidity maximum.     

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.     

Field Evaluation of DRAINMOD 5.1 Under a Cold Climate: Simulation of Daily Midspan Water Table Depths and Drain Outflows1

Abstract: The hydrologic performance of DRAINMOD 5.1 was assessed for the southern Quebec region considering freezing/thawing conditions. A tile drained agricultural field in the Pike River watershed was instrumented to measure tile drainage volumes. The model was calibrated using water table depth and subsurface flow data over a two‐year period, while another two‐year dataset served to validate the model. DRAINMOD 5.1 accurately simulated the timing and magnitude of subsurface drainage events. The model also simulated the pattern of water table fluctuations with a good degree of accuracy. The R² between the observed and simulated daily WTD for calibration was >0.78, and that for validation was 0.93. The corresponding coefficients of efficiency (E) were >0.74 and 0.31. The R² and E values for calibration/validation of subsurface flow were 0.73/0.48 and 0.72/0.40, respectively. DRAINMOD simulated monthly subsurface flow quite accurately (E > 0.82 and R² > 0.84). The model precisely simulated daily/monthly drain flow over the entire year, including the winter months. Thus DRAINMOD 5.1 performed well in simulating the hydrology of a cold region.     

Simulation of Daily Flow Pathways,Tile‐Drain Nitrate Concentrations,and Soil‐Nitrogen Dynamics Using SWAT

Tile drainage significantly alters flow and nutrient pathways and reliable simulation at this scale is needed for effective planning of nutrient reduction strategies. The Soil and Water Assessment Tool (SWAT) has been widely utilized for prediction of flow and nutrient loads, but few applications have evaluated the model's ability to simulate pathway‐specific flow components or nitrate‐nitrogen (NO₃‐N) concentrations in tile‐drained watersheds at the daily time step. The objectives of this study were to develop and calibrate SWAT models for small, tile‐drained watersheds, evaluate model performance for simulation of flow components and NO₃‐N concentration at daily intervals, and evaluate simulated soil‐nitrogen dynamics. Model evaluation revealed that it is possible to meet accepted performance criteria for simulation of monthly total flow, subsurface flow (SSF), and NO₃‐N loads while obtaining daily surface runoff (SURQ), SSF, and NO₃‐N concentrations that are not satisfactory. This limits model utility for simulating best management practices (BMPs) and compliance with water quality standards. Although SWAT simulates the soil N‐cycle and most predicted fluxes were within ranges reported in agronomic studies, improvements to algorithms for soil‐N processes are needed. Variability in N fluxes is extreme and better parameterization and constraint, through use of more detailed agronomic data, would also improve NO₃‐N simulation in SWAT. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Effects of Land Use and Sample Location on Nitrate‐Stream Flow Hysteresis Descriptors during Storm Events

The U.S. Geological Survey's New Jersey and Iowa Water Science Centers deployed ultraviolet‐visible spectrophotometric sensors at water‐quality monitoring sites on the Passaic and Pompton Rivers at Two Bridges, New Jersey, on Toms River at Toms River, New Jersey, and on the North Raccoon River near Jefferson, Iowa to continuously measure in‐stream nitrate plus nitrite as nitrogen (NO₃ + NO₂) concentrations in conjunction with continuous stream flow measurements. Statistical analysis of NO₃ + NO₂ vs. stream discharge during storm events found statistically significant links between land use types and sampling site with the normalized area and rotational direction of NO₃ + NO₂‐stream discharge (N‐Q) hysteresis patterns. Statistically significant relations were also found between the normalized area of a hysteresis pattern and several flow parameters as well as the normalized area adjusted for rotational direction and minimum NO₃ + NO₂ concentrations. The mean normalized hysteresis area for forested land use was smaller than that of urban and agricultural land uses. The hysteresis rotational direction of the agricultural land use was opposite of that of the urban and undeveloped land uses. An r² of 0.81 for the relation between the minimum normalized NO₃ + NO₂ concentration during a storm vs. the normalized NO₃ + NO₂ concentration at peak flow suggested that dilution was the dominant process controlling NO₃ + NO₂ concentrations over the course of most storm events.     

Development and Application of an Agricultural Intensity Index to Invertebrate and Algal Metrics from Streams at Two Scales

Research was conducted at 28‐30 sites within eight study areas across the United States along a gradient of nutrient enrichment/agricultural land use between 2003 and 2007. Objectives were to test the application of an agricultural intensity index (AG‐Index) and compare among various invertebrate and algal metrics to determine indicators of nutrient enrichment nationally and within three regions. The agricultural index was based on total nitrogen and phosphorus input to the watershed, percent watershed agriculture, and percent riparian agriculture. Among data sources, agriculture within riparian zone showed significant differences among values generated from remote sensing or from higher resolution orthophotography; median values dropped significantly when estimated by orthophotography. Percent agriculture in the watershed consistently had lower correlations to invertebrate and algal metrics than the developed AG‐Index across all regions. Percent agriculture showed fewer pairwise comparisons that were significant than the same comparisons using the AG‐Index. Highest correlations to the AG‐Index regionally were ?0.75 for Ephemeroptera, Plecoptera, and Trichoptera richness (EPTR) and ?0.70 for algae Observed/Expected (O/E), nationally the highest was ?0.43 for EPTR vs. total nitrogen and ?0.62 for algae O/E vs. AG‐Index. Results suggest that analysis of metrics at national scale can often detect large differences in disturbance, but more detail and specificity is obtained by analyzing data at regional scales.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

Groundwater Level Trends and Drivers in Two Northern New England Glacial Aquifers

We evaluated long‐term trends and predictors of groundwater levels by month from two well‐studied northern New England forested headwater glacial aquifers: Sleepers River, Vermont, 44 wells, 1992‐2013; and Hubbard Brook, New Hampshire, 15 wells, 1979‐2004. Based on Kendall Tau tests with Sen slope determination, a surprising number of well‐month combinations had negative trends (decreasing water levels) over the respective periods. Sleepers River had slightly more positive than negative trends overall, but among the significant trends (p < 0.1), negative trends dominated 67 to 40. At Hubbard Brook, negative trends outnumbered positive trends by a nearly 2:1 margin and all seven of the significant trends were negative. The negative trends occurred despite generally increasing trends in monthly and annual precipitation. This counterintuitive pattern may be a result of increased precipitation intensity causing higher runoff at the expense of recharge, such that evapotranspiration demand draws down groundwater storage. We evaluated predictors of month‐end water levels by multiple regression of 18 variables related to climate, streamflow, snowpack, and prior month water level. Monthly flow and prior month water level were the two strongest predictors for most months at both sites. The predictive power and ready availability of streamflow data can be exploited as a proxy to extend limited groundwater level records over longer time periods.     

Monitored and Modeled Correlations of Sediment and Nutrients with Chesapeake Bay Water Clarity

This article analyzes the correlations of the observed and modeled light attenuation coefficient, Kd, with in situ total suspended solids (TSS) and chlorophyll‐a concentrations in Chesapeake Bay (CB) tidal waters, and with sediment and nutrient loads from the Chesapeake watershed. Light attenuation is closely related to in situ TSS and chlorophyll‐a concentrations, however, the strength of the correlation differs among the CB segments. There are distinct differences between saline and tidal fresh segments in the main Bay, but less distinction among saline and tidal fresh segments in the tidal tributaries. The correlation between Kd with sediment and nutrient loads is complicated by the lag times of TSS and the chlorophyll‐a responses to reductions in nutrient and sediment loads from the watershed, and also due to the diverse load sources. Three sets of model sensitivity scenarios were performed with: (1) differential sediment and nutrient loads; (2) selective sediment source types; and (3) geographically isolated inputs. The model results yield similar findings as those based on observed data and provide information regarding the effect of sediment on specific water bodies. Based on the model results a method was developed to determine sediment and nutrient load reductions needed to achieve the water clarity standards of the CB segments.