5. Hydrologic
Simulation
5.1 Overall sequence
analysis
The four machine learning methods, i.e., MLR, SVM, ANN and MLP, were
used to simulate the runoff, and the historical observation climate and
hydrological data were brought into the model for calibration and
verification. In order to fully analyze the simulation accuracy of
hydrological models, this part carries out daily and monthly runoff
simulation respectively. And the performance of the hydrological
simulation in the three basins is illustrated in Table 3 and Table 4.
Daily simulation result of verification period in Xiangxi River shows
Pearson correlation coefficient and the Nash of MLP are the highest,
meanwhile RMSE and RRSE are the smallest. This means MLP has the best
simulation accuracy. And Nash is 0.71, indicating the simulation results
are credible. In addition to MLP, the other three hydrological models
have similar simulation effects. Simulation results of ANN are slightly
lower than MLR and SVM. Compared with daily runoff simulation,
simulation accuracy in monthly runoff are significantly improved. The
average Spearman correlation coefficient is increased by 48%, and Nash
is increased by 53%. Compared with the other three models, ANN has a
slightly poorer simulation effect in the monthly runoff simulation.
In Jinghe River, the MLR, SVM and MLP show better daily simulation
accuracy than ANN, and MLP is slightly better than the others. Monthly
runoff simulation effect is better than the daily. In monthly runoff
simulation, there is no significant difference in simulation results of
four hydrological models. MLP has smaller simulation bias and slightly
better simulation performance, but the correlation between simulated and
actual runoff value is slightly lower than the others. In general, the
four hydrological models have a general runoff simulation accuracy in
Jinghe River. This may be due to the fact that the two climatic factors
of precipitation and temperature have little impact on the overall
runoff, and the relationship between climatic factors and runoff is not
strong.
In daily performance of hydrological simulation in Zhongzhou River, the
effects of the four models show strong differences. Simulation effect of
MLP is significantly better than the others. In verification period,
RMSE in MLP is about 38% of the SVM model. It can be seen simulation
accuracy of MLP is significantly higher than the others. Fig. 8 shows
the simulation sequence of the daily runoff of SVM and MLP. It can be
seen MLP can better restore the runoff condition and simulation of
runoff peak also has a higher accuracy. MLR is the best but MLP in
hydrological simulation, and it exhibits superior simulation results in
daily runoff simulation. This may be due to the fact that precipitation
has a greater impact on runoff and presents a strong linear relationship
in Zhongzhou River. SVM and ANN behave similarly, and the simulation
performance is general. In the monthly runoff simulation, the simulation
performance of MLP is slightly higher than the other three hydrological
models. In general, MLP has obvious advantages in runoff simulation.
Based on the hydrological simulation above, it can be found runoff
simulation accuracy of MLP is better than the other three models. SVM
and ANN have similar simulation performance. MLR exhibits excellent
simulation effects when there is a strong linear relationship between
inputs and outputs, while the overall performance is slightly worse than
the SVM and ANN models in opposite cases. In different basins, the
simulation effects of hydrological models vary greatly. Simulation
accuracy in Zhongzhou River are the best, while in Jinghe River are the
worst. This is because there are large differences in the effects of
climatic factors, i.e., precipitation and temperature, on runoff in
different watersheds. Within the scope of climate factor impact, MLP can
more fully explore its potential relationship with runoff.
5.2 Seasonality of Modeling
Accuracies
Through overall sequence analysis for hydrological simulation, it can be
seen MLP shows its greatest advantage in hydrological forecast in daily
runoff forecast at Zhongzhou River. Therefore, this part selects the
daily runoff forecast at Zhongzhou River as research object to analyze
runoff simulation of four hydrological models during the four seasons.
The data sequence is divided into four parts, i.e., spring (from March
to May), summer (from June to August), autumn (from September to
November), and winter (from December to February). Select simulation
results of MLR, the most commonly used method, as baseline. Since Nash
has negative value and RRSE is consistent with Nash on certain extent,
this part does not analyze Nash. Analysis results in verification period
are shown in Fig. 9.
It can be seen from the results that MLP shows the best simulation
accuracy in four seasons compared with the others. Simulation
performance in spring and summer is significantly higher. RMSE value of
MLP in summer is 54.08% lower than that of MLR, and simulation
deviation is greatly reduced. SVM and ANN models show a slightly worse
simulation performance than MLR, which is consistent with performance of
the four models in the hydrological simulation. It can be seen from the
seasonal analysis that, compared with the others, the accuracy of MLP
for the prediction in summer runoff peaks is significantly improved.
5.3 Streamflow Magnitudes
In order to further understand the simulation effect of hydrological
models in each runoff interval, simulation sequence was subjected to
magnitudes analysis. The sequence is arranged in ascending order
according to observed runoff, and magnitudes are divided into 0-5%,
5-15%, 15-25%, 25-50%, 50-75%, 75-85%, 85-95% and 95-100%.
Results of daily runoff simulation in Zhongzhou River are shown in Fig.
10.
From analysis results of Pearson correlation coefficient and RMSE,
runoff simulation effects of the four hydrological models are not much
different in 0-95% quantile interval. Overall, MLP is slightly better
than the others. In 95-100% interval, the difference is significantly
increased. MLP has obvious advantages and simulation accuracy is greatly
improved. From the results of RRSE analysis, SVM shows the best
simulation effect in 0-50% quantile interval. MLP is similar to MLR,
while ANN is slightly worse. In 50-100% interval, difference among
simulation results of the four models are reduced. Especially in ANN and
SVM, and simulation results are almost identical. However, MLP is stable
and exhibits better simulation results than the other three models.
5.4 Inter-Annual Variation of Runoff
Changes
The RCM-driven hydrological model was used to forecast runoff during the
period 2021-2050. MLP was used for runoff forecasting. Corrected RCMs
climate prediction results were used as inputs. The forecast was carried
out in Xiangxi River, Jinghe River and Zhongzhou River, respectively.
Hydrological forecast results were analyzed based on historical
hydrological simulation data.
The annual variability is shown in Fig. 11. It can be seen that annual
runoff in the three basins all have upward trend in the next 30 years.
And this trend is more obvious under RCP8.5 emission scenario. Among
them, average annual runoff of Jinghe River is increasing year by year,
and the increase is the largest in the three basins. Hydrological
forecast results show that average annual runoff in Jinghe River will
increase by about 50% until 2050. While the annual average runoff in
Xiangxi River is increasing slightly. Under the RCP4.5 emission
scenario, the annual average runoff in Xiangxi River reached its highest
level in 2036, an increase of 27.04%. The future annual average runoff
in Zhongzhou River is more gradual than that of the other two basins,
but the overall trend is increased.
5.5 Intra-Annual Variation of Runoff
Changes
Like inter-annual variation, based on historical observation data,
intra-annual runoff change trend in three basins under two emission
scenarios was analyzed. The results are shown in Fig. 12.
It can be seen that monthly average runoff in the three river basins in
the next 30 years show different trends. The average monthly runoff in
Xiangxi River increased slightly, and the increase mainly occurred in
the peak period (from May to August) of the runoff. Like the Xiangxi
River, the runoff growth in Jinghe River is mainly concentrated in
summer. The increase is significantly larger than that in Xiangxi River,
and this trend is more obvious under RCP8.5 emission scenario. In
addition, the future winter (December to February) runoff in Jinghe
River has also increased significantly, which may improve the winter
runoff in the basin. Future monthly average runoff variation trend in
Zhongzhou River is different from the others. The runoff growth is
mainly concentrated from February to April, while the flow in other
months has no obvious change trend. This means that the peak runoff in
Zhongzhou River may move forward.