Development and evaluation of a real-time forecasting framework for daily water quality forecasts for Lake Chaohu to Lead time of six days

摘要

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu. (C) 2019 Elsevier B.V. All rights reserved.