Total phosphorus (TP) is a vitally important water quality index in shallow reservoirs and is closely connected with hydrological variability, anthropogenic water diversion and meteorological forcing. However, it is still unclear to what extent the TP concentration in a complex shallow reservoir system attributes to each type of forcing. To resolve this issue, this study proposed a TP concentration contribution index (TPI) to assess the contribution of each forcing, using the data assimilation (DA) method, the Ensemble Kalman Filter (EnKF), which was applied in the shallow Yuqiao Reservoir, China. The EnKF model was conducted based on the Vollenweider model and logistic regression models with datasets of 1989-2015. The results showed that human-originated activities forcing (water diversion) contributed the maximum TPI (40%), followed by hydrological variability forcing (37%). Finally, meteorological forcing (air temperature and wind included) only accounted for 23%. Furthermore, the seasonal analyses also showed that the TPI of hydrological variability dominated in spring and winter, with 65% and 73% respectively. However, the contributions of meteorological forcing (air temperature and wind) accounted for a larger proportion of 63% and 57% in summer and autumn. The benefit of our EnKF model denoises the Gaussian noise contained in observation and simulation, which offers a chance to isolate and identify even a minor driving factor (i.e., meteorological forcing) from a complex river and lake system with limited data. The study provides a method to assess the influence of direct and indirect forcing on TP concentration in shallow reservoirs from a quantitative perspective. Thus, it may serve as a useful tool for water quality management in water-receiving systems.
Keywords: Data assimilation; Ensemble Kalman Filter; Quantitative assessment; Shallow reservoir; Total phosphorus.
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