Recent studies of Chesapeake Bay hypoxia suggest higher susceptibility to hypoxia in years after the 1980s. We used two simple mechanistic models and Bayesian estimation of their parameters and prediction uncertainty to explore the nature of this regime shift. Model estimates show increasing nutrient conversion efficiency since the 1980s, with lower DO concentrations and large hypoxic volumes as a result. In earlier work, we suggested a 35% reduction from the average 1980-1990 total nitrogen load would restore the Bay to hypoxic volumes of the 1950s-1970s. With Bayesian inference, our model indicates that, if the physical and biogeochemical processes prior to the 1980s resume, the 35% reduction would result in hypoxic volume averaging 2.7 km3 in a typical year, below the average hypoxic volume of 1950s–1970s. However, if the post-1980 processes persist the 35% reduction would result in much higher hypoxic volume averaging 6.0 km3. Load reductions recommended in the 2003 agreement will likely meet dissolved oxygen attainment goals if the Bay functions as it did prior to the 1980s; however, it may not reach those goals if current processes prevail.

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM),was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R2=0.80. For SEM, Chl a and eight variableswere used: NH4-N (ammonianitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence ofwater chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu.

The susceptibility of estuaries to nutrient loading is an important issue that cuts across a range of management needs. We used a theory-driven but data-tested simple model to assist classifying estuaries according to their susceptibility to nutrients. This simple nutrient-driven phytoplankton model is based on fundamental principles of mass balance and empirical response functions for a wide variety of estuaries in the United States. Phytoplankton production was assumed to be stoichiometrically proportional to nitrogen load and an introduced "efficiency factor" intended to capture the myriad processes involved in converting nitrogen load to algal production. A Markov Chain Monte Carlo algorithm of Baye sianinference was then employed for parameter estimation. The model performed remarkably well for chlorophyll estimates, and the predicted estimates of primary production, grazing, and sinking losses are consistent with measurements reported in the literature from a wide array of systems. Analysis of the efficiency factor suggests that estuaries with the ratio of river inflow to estuarine volume (Q/V) greater than 2.0 per year are less susceptible to nutrient loads, and those with Q/V between 0.3 and 2.0 per year are moderately susceptible. This simple model analysis provides a first-order screening tool for estuarine susceptibility classification.

The water supply to Chinese cities is increasingly degrading from pollution due to watershed activities. Consequently, water source protection requires urgent action using optimal land-use management efforts. An inexact linear programming model for optimal land-use management of surface water source area was developed. The model was proposed to balance the economic benefits of land-use development and water source protection. The maximum net economic benefit (NEB) was chosen as the objective of land-use management. The total environmental capacity (TEC) of rivers and the minimum water supply (MWS) were considered key constraints. Other constraints included forest coverage, government requirements concerning the proportions of various land-use types, soil loss, slope lands, and technical constraints. A case study was conducted for the Songhuaba Watershed, a reservoir supplying water to Kunming City, the third largest city in southwestern China. A 15-year (2006 to 2020) optimal model for land-use management was developed to better protect this water source and to gain maximum benefits from development. Ten constraints were involved in the optimal model, and results indicated that NEB ranged between 893 and 1,459 million US$. The proposed model will allow local authorities to better understand and address complex land-use systems and to develop optimal land-use management strategies for balancing source water protection and local economic development.

A Bayesian approach was applied to river water quality modeling (WQM) for load and parameter estimation. A distributed-source model (DSM) was used as the basic model to support load reduction and effective water quality management in the Hun-Taizi River system, northeastern China. Water quality was surveyed at 18 sites weekly from 1995 to 2004; biological oxygen demand (BOD) and ammonia (NH4+) were selected as WQM variables. The first-order decay rate (ki) and load (Li) of the 16 river segments were estimated using the Bayesian approach. The maximum pollutant loading (Lm) of NH4 + and BOD for each river segment was determined based on DSM and the estimated parameters of ki. The results showed that for most river segments, the historical loading was beyond the Lm threshold; thus, reduction for organic matter and nitrogen is necessary to meet water quality goals. Then the effects of inflow pollutant concentration (Ci-1) and water velocity (vi) on water quality standard compliance were used to demonstrate how the proposed model can be applied to water quality management. The results enable decision makers to decide load reductions and allocations among river segments under different Ci-1 and vi scenarios.