12.3.3 Riverine N Inputs
The data-model comparisons in Fig. 12.7a reveal that the simulated daily
streamflows were generally consistent with the observations of 16 rivers for the
period 2009–2011, with NSE of 0.70, BIAS of 1.4%, and RSR of 0.40. The
DC-HSPF model also reproduced the seasonal variations of the simulated
streamflows (Fig. 12.7b). The calibrated DC-HSPF model was further validated
against multiple independent observations from the YHB. First, the observed daily
streamflows of Panlong and Baoxiang Rivers from 1999 to 2008 were well captured
(Fig. 12.7c) with NSE of 0.82, BIAS of 15.7%, and RSR of 0.29. Second, the mean
of long-term total surface inflows of 16 rivers and direct runoff areas from 1999 to
2011 was estimated as 586.8 Â 10
6 m
3
Áyear
À1 , which differs by only 2.5% compared
to the value of 572.5 Â 10
6 m
3
Áyear
À1 reported in a detailed YHB investigation
(Fig. 12.7d). Therefore, the calibrated DC-HSPF model can serve as a reliable
predictor for the hydrological simulation of lake inflows.
Calibration results provide a reasonable and statistically significant fit between
observed and estimated N inputs for both Caohai and Waihai portions. Using the
observed daily discharge as an independent variable, the LOADEST was able to
reproduce observed N inputs from watersheds to Caohai, as shown by a coefficient
of determination (R
2 ) of 0.94 and the AIC of 1.4 (Fig. 12.8). In the case of riverine N
inputs to Waihai, R
2 and AIC were 0.89 and 1.8 (Fig. 12.8), respectively. Thus, N
inputs from 20 rivers estimated by the LOADEST are acceptable to convert
biweekly observations of N inputs into daily ones. A Monte Carlo ensemble
simulation (Zhou et al. 2014) was performed to estimate the uncertainty of the
Fig. 12.6 Ratio of dry and wet N on annual (a) or biweekly interval (b) and ratio of reduced and
oxidized N on annual (c) or biweekly interval (d). (This figure was adapted from Zhan et al. (2017)
with permission by the American Chemical Society)
12 Impacts of Nitrogen Deposition on China’s Lake Ecosystems. . .
277
The data-model comparisons in Fig. 12.7a reveal that the simulated daily
streamflows were generally consistent with the observations of 16 rivers for the
period 2009–2011, with NSE of 0.70, BIAS of 1.4%, and RSR of 0.40. The
DC-HSPF model also reproduced the seasonal variations of the simulated
streamflows (Fig. 12.7b). The calibrated DC-HSPF model was further validated
against multiple independent observations from the YHB. First, the observed daily
streamflows of Panlong and Baoxiang Rivers from 1999 to 2008 were well captured
(Fig. 12.7c) with NSE of 0.82, BIAS of 15.7%, and RSR of 0.29. Second, the mean
of long-term total surface inflows of 16 rivers and direct runoff areas from 1999 to
2011 was estimated as 586.8 Â 10
6 m
3
Áyear
À1 , which differs by only 2.5% compared
to the value of 572.5 Â 10
6 m
3
Áyear
À1 reported in a detailed YHB investigation
(Fig. 12.7d). Therefore, the calibrated DC-HSPF model can serve as a reliable
predictor for the hydrological simulation of lake inflows.
Calibration results provide a reasonable and statistically significant fit between
observed and estimated N inputs for both Caohai and Waihai portions. Using the
observed daily discharge as an independent variable, the LOADEST was able to
reproduce observed N inputs from watersheds to Caohai, as shown by a coefficient
of determination (R
2 ) of 0.94 and the AIC of 1.4 (Fig. 12.8). In the case of riverine N
inputs to Waihai, R
2 and AIC were 0.89 and 1.8 (Fig. 12.8), respectively. Thus, N
inputs from 20 rivers estimated by the LOADEST are acceptable to convert
biweekly observations of N inputs into daily ones. A Monte Carlo ensemble
simulation (Zhou et al. 2014) was performed to estimate the uncertainty of the
Fig. 12.6 Ratio of dry and wet N on annual (a) or biweekly interval (b) and ratio of reduced and
oxidized N on annual (c) or biweekly interval (d). (This figure was adapted from Zhan et al. (2017)
with permission by the American Chemical Society)
12 Impacts of Nitrogen Deposition on China’s Lake Ecosystems. . .
277