The obvious decline in stream flow to the Biliu River reservoir over the period 1990-2005 has raised increasing concerns. Climate change and human activities, which mainly include land use changes, hydraulic constructions and artificial water consumption, are considered to be the most likely reasons for the decline in stream flow. This study centres on a detailed analysis of the runoff response to changes in human activities. Using a distributed hydrological model, (Soil and Water Assessment Tool), we simulated runoffs under different human activity and climate scenarios to understand how each scenario impacts stream flow. The results show that artificial water consumption correlates with the precipitation (wet, normal and dry) of the year in question and is responsible for most of the decrease in runoff during each period and for each different wetness year. A Fuzzy Inference Model is also used to find the relationship between the precipitation and artificial water consumption for different years, as well as to make inferences regarding the future average impact on runoff. Land use changes in the past have increased the runoff by only a small amount, while another middle reservoir (Yunshi) has been responsible for a decrease in runoff since operation began in 2001. We generalized the characteristics of the human activities to predict future runoff using climate change scenarios. The future annual flow will increase by approximately 10% from 2011 to 2030 under normal human activities and future climate change scenarios, as indicated by climate scenarios with a particularly wet year in the next 20 years. This study could serve as a framework to analyse and predict the potential impacts of changes both in the climate and human activities on runoff, which can be used to inform the decision making on the river basin planning and management.

Abstract. Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH 4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near-optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH 4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50 %. The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are used. For the tropics, the optimized parameters lead to lower emission predictions than the default parameters.

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Abstract. Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50%. The CLM predictions with the optimized parameters agree for northern and tropical latitudes more with the observed data than when using the default parameters and the emission predictions are higher than with default settings in northern latitudes and lower than default settings in the tropics.

In this study, two different versions of the Soil and Water Assessment Tool (SWAT) model were used to simulate the hydrology and biogeochemical response of the Cannonsville Reservoir watershed, in New York. The first version distributes overland flow in ways that are consistent with variable source area (VSA) hydrology driven by saturation excess runoff, whereas the second version is the standard version of SWAT. These two models were each calibrated for streamflow (Flow), particulate phosphorus (PP), total dissolved phosphorus (TDP), and sediment (Sed) against measured data from the 1,200 km2 Cannonsville watershed. The standard version of the model yielded an r2 between the measured and simulated data of 0.85, 0.73, 0.70, and 0.72 for Flow, Sed, TDP, and PP, respectively. The VSA version yielded an r2 of 0.84, 0.69, 0.72, and 0.53 for Flow, Sed, TDP, and PP, respectively. The two models were then used to determine the maximum upper bound on the reduction in phosphorus loading by removing all of the corn in the watershed. The average reductions between the two models were 65 and 37% for PP and TDP, respectively. The VSA version was also used to estimate the effect of moving corn land in the watershed from the wettest, most runoff prone areas to the driest, least runoff prone areas, which cannot be done directly with the standard SWAT model.