CN110942257A - Method for quantitatively analyzing water temperature change of downstream river by reservoir regulation and environmental factors - Google Patents

Method for quantitatively analyzing water temperature change of downstream river by reservoir regulation and environmental factors Download PDF

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CN110942257A
CN110942257A CN201911239918.0A CN201911239918A CN110942257A CN 110942257 A CN110942257 A CN 110942257A CN 201911239918 A CN201911239918 A CN 201911239918A CN 110942257 A CN110942257 A CN 110942257A
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王远坤
陶雨薇
王栋
倪玲玲
吴吉春
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Abstract

The invention discloses a quantitative analysis method of reservoir regulation and environmental factors on the water temperature change of a downstream river, which comprises the steps of collecting hydrological meteorological data in a water collection area, respectively constructing linear water temperature regression models for a natural period and an influence period before and after reservoir storage month by month, reconstructing natural runoff of the influence period, reconstructing a natural water temperature sequence, and analyzing the contribution of the reservoir regulation and the environmental factors on the downstream water temperature change by utilizing the difference between the reconstructed natural water temperature and the actually measured water temperature; according to the method, the binary attribution analysis of reservoir regulation and environmental factors on the water temperature change is subdivided into the quaternary attribution analysis of water temperature-air temperature interaction change and runoff mode change under the reservoir regulation effect and air temperature change and flow change under a natural situation, the influence degree of the reservoir on the water temperature can be scientifically and accurately judged, and the reason of the water temperature change of the downstream river of the reservoir can be quantitatively evaluated.

Description

Method for quantitatively analyzing water temperature change of downstream river by reservoir regulation and environmental factors
Technical Field
The invention relates to a hydrographic water resource application technology, in particular to a method for quantitatively analyzing water temperature change of a downstream river by reservoir regulation and environmental factors.
Background
The water temperature is one of the important factors in the river ecosystem, has different degrees of influence on the survival, metabolism, reproduction behaviors of aquatic organisms and the structure and distribution of population, and determines the overall health of the aquatic ecosystem. Changes in water temperature are affected by factors such as natural conditions and man-made disturbances, e.g., human activities and climate changes. After a dam is built to form a reservoir in human activities, non-seasonal cold water is released in spring and summer, the water temperature of river flow is changed, the propagation, growth and distribution of fishes are influenced, and the population structure and biological diversity of a river ecological system are further influenced. The existing research on river water temperature under the influence of the reservoir only leads the water temperature change before and after dam building to reservoir regulation and storage effects, and does not consider the influence of environmental factor change on the water temperature. Therefore, the influence degree of the reservoir on the water temperature is scientifically and accurately judged, the reason of the water temperature change of the downstream river of the reservoir is quantitatively evaluated, and the method has important significance for maintaining the health of the river ecosystem.
Since air temperature directly affects the heat flux of the water surface and is close to the equilibrium temperature of the water body, it is often used as an independent variable for returning the river water temperature. In addition, the flow rate is an important factor affecting the water temperature. The water temperature is predicted by the correlation between the water temperature and the air temperature and flow, for example, a linear regression model is widely applied to the prediction of the river water temperature.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a reservoir regulation and environment factor water temperature change quantitative analysis method for reservoir regulation and environment factor water temperature change of downstream rivers, which is characterized in that binary attribution analysis of reservoir regulation and environment factor water temperature change is subdivided into quaternary attribution analysis of water temperature-air temperature interaction change and runoff mode change under the reservoir regulation effect and air temperature change and flow change under a natural situation.
The technical scheme is as follows: the method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors comprises the following steps:
(1) collecting river hydrological meteorological data and reservoir regulation data, dividing the water storage into a natural period pre before and dividing the water storage into an influence period post;
(2) respectively constructing a water temperature linear regression model of a reservoir affected site A month by month according to the hydrological meteorological data of a natural-stage pre and an affected-stage post;
(3) natural runoff Q of site A influence period is reconstructed by utilizing hydrological data of a plurality of sites upstream of site AnaA sequence;
(4) using reconstructed natural runoff QnaAnd the influence period air temperature Ta,postSubstituting the natural water temperature WT into the river water temperature linear regression model in the natural phase to reconstruct the natural water temperature WT in the influence phasenat,postA sequence;
(5) natural water temperature WT using reconstructed influence phasenat,postWater temperature WT in natural periodsim,preAnalyzing the contribution of the environmental factors to the water temperature change of the river;
(6) natural water temperature WT using reconstructed influence phasenat,postAnd the influence period water temperature WTsim,postThe contribution of reservoir regulation to the river water temperature change is analyzed.
In the step (1), the collecting river hydrological meteorological data comprises collecting the temperature WT and temperature T of the solar wateraAnd the daily runoff Q, and the collected reservoir regulation data comprises collected water storage starting and stopping dates.
The concrete formula for constructing the water temperature linear regression model is as follows:
WT(t)＝β0+β1Ta(t-l)+β2Q(t) (1)
wherein ,β0，β1 and β2Is the regression model coefficient; t is time; l is time lag in days.
The water temperature linear regression model parameters are calibrated by adopting a root mean square error RMSE, the smaller the RMSE value is, the smaller the residual error is represented, the more reasonable the corresponding regression model is, and the specific formula is as follows:
Figure BDA0002305921100000021
wherein ,
Figure BDA0002305921100000022
is an analog value; y istIs the measured value; n is the sample size.
The hydrological data in the step (3) is runoff Q, and a generalized regression neural network GRNN method is used for reconstructing the natural runoff Q of the site AnaAnd (4) sequencing.
The GRNN model parameter spread is calibrated by adopting the NSC, namely the model effect is optimal when the NSC value is maximum, and the specific formula is as follows:
Figure BDA0002305921100000023
wherein ,
Figure BDA0002305921100000024
is an analog value; y istIs the measured value; n is the sample size.
Natural runoff Q using a reconstructed influence periodnaAnd the influence period air temperature Ta,postSubstituted into the river water temperature linear regression model β in the natural period0,pre，β1,pre，β2,preIn the reconstruction of the Natural Water temperature WT in the affected phasenat,postThe sequence is shown as the following specific formula:
WTnat,post(t)＝β0,pre+β1,preTa,post(t-l)+β2,preQna(t) (4)
the total variation of the site A measured water temperature before and after water storage is expressed as deltaTOTThe specific calculation formula is as follows:
Figure BDA0002305921100000031
wherein ,WTobs,postThe measured water temperature of the station A after water storage is indicated; WT (WT)obs,preMeasured Water temperature of site A before Water storage, Delta β0The water temperature variation caused by other influencing factors except reservoir regulation and environmental factors is indicated; deltaNCThe water temperature variation caused by the change of the environmental factors; deltaReservoirDelta β is the variation of water temperature caused by reservoir regulation0＝β0,post-β0,preAnd epsilon (═ 0) is the average residual in the linear regression model for water temperature.
The contribution of the environmental factors to the river water temperature change is subdivided into the contribution of the air temperature change and the flow change to the water temperature change under the natural situation, and the specific formula is as follows:
Figure BDA0002305921100000032
in the formula ,ΔNCRefers to the water temperature variation, delta T, caused by the air temperature variation and flow variation under natural conditionsaDenotes the temperature change, Δ Q, in the natural contextnatRefers to the change in flow rate in a natural situation.
The contribution of reservoir regulation to river water temperature change is subdivided into the contribution of water temperature-air temperature interaction change and runoff mode change to water temperature change under the influence of a reservoir, and the specific formula is as follows:
Figure BDA0002305921100000033
wherein ,Ta,post(t-lpre)*Δβ1+ΔTa,lageffect*β1,postRepresenting the water temperature variation caused by the interaction change of the water temperature and the air temperature under the reservoir regulation effect; qna,pre*Δβ2+β2,post*ΔQReserviorRepresenting the amount of change in water temperature caused by the changed runoff mode under the reservoir regulation effect.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: (1) by dividing the binary attribution analysis of reservoir regulation and environmental factors on water temperature change into quaternary attribution analysis of water temperature-air temperature interaction change and runoff mode change under the reservoir regulation action and air temperature change and flow change under a natural situation, the influence degree of the reservoir on the water temperature can be scientifically and accurately judged, and the reason of the water temperature change of a downstream river of the reservoir can be quantitatively evaluated; (2) the analysis result is accurate and reliable; (3) and a Generalized Regression Neural Network (GRNN) model is adopted, so that the structure is simple and the calculation is rapid.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to specific embodiments and the attached drawing figures.
As shown in fig. 1, the present invention specifically includes the following steps: (1) study area overview: collecting river hydrological meteorological data and reservoir regulation data, dividing the water storage into a natural period pre before and dividing the water storage into an influence period post, wherein the collected hydrological meteorological data comprises a daily water temperature WT and a daily air temperature TaAnd the daily runoff Q, and the reservoir regulation data comprises the collected water storage starting and stopping date.
(2) Constructing a water temperature linear regression model: respectively constructing a water temperature linear regression model of a station A influenced by the reservoir according to the hydrological meteorological data of a natural-stage pre and an influence stage post; actually measuring water temperature WT and air temperature T by adopting site AaAnd constructing a linear water temperature linear regression model month by month according to the runoff Q. Research has shown that the water temperature has a hysteresis phenomenon in comparison with the air temperature on a daily scale, so that a specific formula is as follows when a time lag of l days is introduced into a linear regression model of the water temperature:
WT(t)＝β0+β1Ta(t-l)+β2Q(t) (1)
wherein ,β0，β1 and β2Is the water temperature linear regression model coefficient; t is time; l is time lag in days.
The water temperature linear regression model parameters are calibrated by adopting a root mean square error RMSE, the smaller the RMSE value is, the smaller the residual error is represented, the more reasonable the corresponding regression model is, and the specific formula is as follows:
Figure BDA0002305921100000041
wherein ,
Figure BDA0002305921100000042
is an analog value; y istIs the measured value; n is the sample size.
(3) Reconstructing natural runoff: using a plurality of stations upstream of station A (B)C …) reconstructing the natural runoff sequence of site a influence phase post; because the generalized regression neural network GRNN is a highly parallel radial basis network, is a 4-layer network consisting of an input layer, a mode layer, a summation layer and an output layer, and has the characteristics of simple structure and rapid calculation, the generalized regression neural network GRNN adopts a GRNN model, takes the daily runoff Q of a plurality of stations (B, C …) at the upstream of the station A as an input variable, and outputs the natural runoff Q of the station A under the condition of no dam in the influence periodna. The GRNN model parameter spread is calibrated by adopting the NSC, namely the model effect is optimal when the NSC value is maximum, and the specific formula is as follows:
Figure BDA0002305921100000043
wherein ,
Figure BDA0002305921100000044
is an analog value; y istIs the measured value; n is the sample size.
(4) Reconstructing the natural water temperature: using reconstructed natural runoff QnaAnd air temperature T of the influencing perioda,postSubstituted into the river water temperature linear regression model β in the natural period0,pre，β1,pre，β2,preIn the reconstruction of the Natural Water temperature WT in the affected phasenat,postThe sequence is shown as the following specific formula:
WTnat,post(t)＝β0,pre+β1,preTa,post(t-l)+β2,preQna(t) (4)
(5) analyzing the contribution amount of the environmental factors to the change of the river water temperature: natural water temperature WT using reconstructed influence phasenat,postWater temperature WT in natural periodsim,preAnalyzing the contribution of the environmental factors to the water temperature change of the river; the total variation of the site A measured water temperature before and after water storage is expressed as deltaTOTThe specific calculation formula is as follows:
Figure BDA0002305921100000051
wherein ,WTobs,postThe measured water temperature of the station A after water storage is indicated; WT (WT)obs,preMeasured Water temperature of site A before Water storage, Delta β0The water temperature variation caused by other influencing factors except reservoir regulation and environmental factors is indicated; deltaNCThe water temperature variation caused by the change of the environmental factors; deltaReservoirDelta β is the variation of water temperature caused by reservoir regulation0＝β0,post-β0,preAnd epsilon (═ 0) is the average residual in the linear regression model for water temperature.
The contribution of the analysis environmental factors to the river water temperature change is subdivided into the contribution of the air temperature change and the flow change to the water temperature change under the natural situation, and the specific formula is as follows:
Figure BDA0002305921100000052
in the formula ,ΔNCRefers to the water temperature variation, delta T, caused by the air temperature variation and flow variation under natural conditionsaDenotes the temperature change, Δ Q, in the natural contextnatRefers to the change in flow rate in a natural situation.
(6) Analyzing the contribution of reservoir regulation to the change of river water temperature: natural water temperature WT using reconstructed influence phasenat,postAnd the influence period water temperature WTsim,postAnalyzing the contribution amount of reservoir regulation to the river water temperature change; the contribution of reservoir regulation to river water temperature change can be subdivided into the contribution of water temperature-air temperature interaction change and runoff mode change to water temperature change under the influence of a reservoir, and the specific formula is as follows:
Figure BDA0002305921100000053
wherein ,Ta,post(t-lpre)*Δβ1+ΔTa,lageffect*β1,postRepresenting the water temperature variation caused by the interaction change of the water temperature and the air temperature under the reservoir regulation effect; qna,pre*Δβ2+β2,post*ΔQReserviorRepresenting the amount of change in water temperature caused by the changed runoff mode under the reservoir regulation effect.
The following practical application of the invention is to make quantitative analysis of water temperature change of downstream Yichang station by three gorges reservoir:
the accuracy and the effectiveness of the method are verified by taking the Yichang station at the downstream of the Yangtze river main flow three gorges reservoir as an example and taking the sequence of the daily water temperature, the daily air temperature and the daily flow rate in 1983 and 2013 as samples.
(1) Overview of the study region
The three gorges reservoir is positioned in the main flow of the Yangtze river, and the water is stored to the elevation of 135m at the first 6 months in 2003, so that three staged targets of water storage, power generation and navigation of a ship lock are realized; the secondary water storage is carried out to 156m height from 9 months to 20 days to 10 months and 28 days in 2006, and the flood control storage capacity reaches 110 hundred million m3The flood control system in the middle and lower reaches of the Yangtze river is formed preliminarily; in 2009, the water level is gradually raised to a normal water storage level of 175m, and the flood control storage capacity is 221.5 hundred million m3. 393 hundred million m of total storage capacity of three gorges reservoir3Wherein the storage capacity is adjusted to 165 hundred million m3And accounts for about 3.7% of annual runoff at the dam site.
The Yichang hydrological station is positioned at the downstream 44km of the three gorges reservoir and is a storage control station of the three gorges reservoir.
(2) Construction of a Water temperature Linear regression model
Firstly, a water temperature regression model is respectively constructed for the natural stage pre (1983-2002) and the influence stage post (2003-2013) of the Yichang station, and the parameter values are shown in Table 1.
TABLE 1 results of regression model of temperature in Yichang station
Figure BDA0002305921100000061
(3) Restructuring natural runoff and natural water temperature
The natural runoff of the Yichang station influence period (2003-. The results show that the NSC value is maximal at 0.89 when the spread value is 0.03. The model effect is optimal at this time.
And substituting the reconstructed natural runoff and the air temperature in the affected period into the river water temperature linear regression model in the natural period to reconstruct a natural water temperature sequence in the affected period.
(4) Separating contribution of reservoir regulation and environmental factors to water temperature change
By using the formula (5), the contribution amounts of the regulation and storage of the three gorges reservoir and the environmental factors to the change of the water temperature are separated and displayed according to the seasons (spring, summer, autumn and winter), and the results are shown in table 2.
TABLE 2 contribution of each influence factor to the variation of water temperature in Yichang station after storage in three gorges reservoir
Time period ΔTOT(℃) Δβ0(℃) ΔNC(℃) ΔReservoir(℃)
3-5 (spring) -1.75 -0.70 0.24 -1.29
6-8 (summer) -0.15 0.10 0.32 -0.57
9-11 (autumn) 1.86 2.74 0.06 -0.95
12-2 (winter) 2.72 2.47 0.03 0.22
All year round 0.67 1.16 0.16 -0.65
As can be seen from Table 2, the influence of the Sanxia reservoir on the water temperature is greater than the environmental factor, the reservoir regulation reduces the annual water temperature by 0.65 ℃, the spring cooling effect is most significant at 1.29 ℃, and the influence of the winter reservoir on the water temperature is increased by 0.22 ℃.
The regulation and storage function of the three gorges reservoir is mainly embodied in two aspects of water temperature-air temperature interaction change and runoff mode change under the regulation and storage function of the reservoir, and the change of environmental factors is mainly embodied in two aspects of air temperature change and flow change under a natural situation. The results of the quaternary attribution analysis calculations are shown in table 3 according to equations (7) to (8).
TABLE 3 contribution of reservoir regulation and environmental factors to the water temperature change in Yichang station
Figure BDA0002305921100000071
As can be seen from Table 3, the influence of the air temperature change on the water temperature is greater than the flow change under natural circumstances; the water temperature is reduced by the interaction of the water temperature and the air temperature which change under the influence of the three gorges reservoir, and the annual average temperature is reduced by 0.77 ℃; the changed runoff mode has the effect of raising the temperature in summer and winter and the effect of lowering the temperature in spring and autumn.

Claims (10)

1. A method for quantitatively analyzing the water temperature change of a downstream river by reservoir regulation and environmental factors is characterized by comprising the following steps:
(1) collecting river hydrological meteorological data and reservoir regulation data, dividing the water storage into a natural period pre before and dividing the water storage into an influence period post;
(2) respectively constructing a water temperature linear regression model of a reservoir affected site A month by month according to the hydrological meteorological data of a natural-stage pre and an affected-stage post;
(3) natural runoff Q of site A influence period is reconstructed by utilizing hydrological data of a plurality of sites upstream of site AnaA sequence;
(4) using reconstructed natural runoff QnaAnd the influence period air temperature Ta,postSubstituting the natural water temperature WT into the river water temperature linear regression model in the natural phase to reconstruct the natural water temperature WT in the influence phasenat,postA sequence;
(5) natural water temperature WT using reconstructed influence phasenat,postWater temperature WT in natural periodsim,preAnalyzing the contribution of the environmental factors to the water temperature change of the river;
(6) natural water temperature WT using reconstructed influence phasenat,postAnd the influence period water temperature WTsim,postThe contribution of reservoir regulation to the river water temperature change is analyzed.
2. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 1, characterized in that: in the step (1), the collecting river hydrological meteorological data comprises collecting the temperature WT and temperature T of the solar wateraAnd the daily runoff Q, and the collected reservoir regulation data comprises collected water storage starting and stopping dates.
3. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 2, characterized in that: the concrete formula for constructing the water temperature linear regression model is as follows:
WT(t)＝β0+β1Ta(t-l)+β2Q(t) (1)
wherein ,β0，β1 and β2Is the regression model coefficient; t is time; l is time lag in days.
4. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 3, wherein the method comprises the following steps: the parameters of the water temperature linear regression model are calibrated by adopting the root mean square error RMSE, the smaller the RMSE value is, the smaller the residual error is, the more reasonable the corresponding water temperature linear regression model is, and the specific formula is as follows:
Figure FDA0002305921090000011
wherein ,
Figure FDA0002305921090000012
is an analog value; y istIs the measured value; n is the sample size.
5. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 1, characterized in that: the hydrological data in the step (3) is daily runoff Q, and a generalized regression neural network GRNN method is used for reconstructing the natural runoff Q of the site AnaAnd (4) sequencing.
6. The method for quantitatively analyzing the water temperature change of the downstream rivers by the reservoir regulation and the environmental factors according to claim 5, characterized in that: the GRNN model parameter spread is calibrated by adopting the NSC, namely the model effect is optimal when the NSC value is maximum, and the specific formula is as follows:
Figure FDA0002305921090000021
wherein ,
Figure FDA0002305921090000022
is an analog value; y istIs the measured value; n is the sample size.
7. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 3, wherein the method comprises the following steps: natural runoff Q using a reconstructed influence periodnaAnd the influence period air temperature Ta,postSubstituted into the river water temperature linear regression model β in the natural period0,pre，β1,pre，β2,preIn the reconstruction of the Natural Water temperature WT in the affected phasenat,postThe sequence is shown as the following specific formula:
WTnat,post(t)＝β0,pre+β1,preTa,post(t-l)+β2,preQna(t) (4)。
8. the method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 1, characterized in that: the total variation of the site A measured water temperature before and after water storage is expressed as deltaTOTThe specific calculation formula is as follows:
Figure FDA0002305921090000023
wherein ,WTobs,postThe measured water temperature of the station A after water storage is indicated; WT (WT)obs,preMeasured Water temperature of site A before Water storage, Delta β0The water temperature variation caused by other influencing factors except reservoir regulation and environmental factors is indicated; deltaNCThe water temperature variation caused by the change of the environmental factors; deltaReservoirDelta β is the variation of water temperature caused by reservoir regulation0＝β0,post-β0,preAnd epsilon (═ 0) is the average residual in the linear regression model for water temperature.
9. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 8, wherein the method comprises the following steps: the contribution of the environmental factors to the river water temperature change is subdivided into the contribution of the air temperature change and the flow change to the water temperature change under the natural situation, and the specific formula is as follows:
Figure FDA0002305921090000024
in the formula ,ΔNCRefers to the water temperature variation, delta T, caused by the air temperature variation and flow variation under natural conditionsaDenotes the temperature change, Δ Q, in the natural contextnatRefers to the change in flow rate in a natural situation.
10. The method for quantitatively analyzing the water temperature change of the downstream river by the reservoir regulation and the environmental factors according to claim 8, wherein the method comprises the following steps: the contribution of reservoir regulation to river water temperature change is subdivided into the contribution of water temperature-air temperature interaction change and runoff mode change to water temperature change under the influence of a reservoir, and the specific formula is as follows:
Figure FDA0002305921090000031
wherein ,Ta,post(t-lpre)*Δβ1+ΔTa,lageffect*β1,postRepresenting the water temperature variation caused by the interaction change of the water temperature and the air temperature under the reservoir regulation effect; qna,pre*Δβ2+β2,post*ΔQReserviorRepresenting the amount of change in water temperature caused by the changed runoff mode under the reservoir regulation effect.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112116147A (en) * 2020-09-16 2020-12-22 南京大学 River water temperature prediction method based on LSTM deep learning
CN115795258A (en) * 2022-10-27 2023-03-14 华能伊敏煤电有限责任公司 Method for quantitatively analyzing influence of changing water taking mode on concentration of pollutants in downstream of river
CN117150976A (en) * 2023-10-31 2023-12-01 长江三峡集团实业发展(北京)有限公司 Determination method, device, equipment and storage medium for water temperature change coefficient

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104318077A (en) * 2014-10-09 2015-01-28 水利部交通运输部国家能源局南京水利科学研究院 Quantitative analysis method for river runoff change caused by climate change and human activity
CN109408848A (en) * 2018-08-24 2019-03-01 河海大学 A kind of distributed attribution method considering Runoff Evolution temporal-spatial heterogeneity
CN109919491A (en) * 2019-03-08 2019-06-21 河海大学 A kind of Heat And Water Balance combined calculation method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104318077A (en) * 2014-10-09 2015-01-28 水利部交通运输部国家能源局南京水利科学研究院 Quantitative analysis method for river runoff change caused by climate change and human activity
CN109408848A (en) * 2018-08-24 2019-03-01 河海大学 A kind of distributed attribution method considering Runoff Evolution temporal-spatial heterogeneity
CN109919491A (en) * 2019-03-08 2019-06-21 河海大学 A kind of Heat And Water Balance combined calculation method

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