CN115238513A - River basin runoff ensemble forecasting method considering climate and land utilization changes - Google Patents

River basin runoff ensemble forecasting method considering climate and land utilization changes Download PDF

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CN115238513A
CN115238513A CN202210919438.4A CN202210919438A CN115238513A CN 115238513 A CN115238513 A CN 115238513A CN 202210919438 A CN202210919438 A CN 202210919438A CN 115238513 A CN115238513 A CN 115238513A
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李永平
徐志鹏
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Beijing Normal University
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Abstract

The invention discloses a river basin runoff ensemble forecasting method considering climate and land utilization changes, and belongs to the technical field of hydrological forecasting. The method comprises the following steps: collecting geographic data, hydrological meteorological data and human activity data of a research basin; and B: setting and simulating future land utilization and future climate change data as a scene for researching the future land utilization and the future climate change of the basin; and C: according to hydrological data of a historical period, a distributed hydrological model SWAT of a research basin is established, model parameters are calibrated, then the calibrated hydrological model is operated under different future land utilization and climatic change scenes to obtain the change range of future runoff under different influences, and the influence degree of the land utilization and the climatic change on the future runoff change is analyzed and quantized. The method can preliminarily judge and research the main factors influencing the runoff change in the runoff domain, thereby providing a certain scientific basis for the future water resource management and utilization.

Description

River basin runoff ensemble forecasting method considering climate and land utilization changes
Technical Field
The invention relates to the technical field of hydrological forecasting, in particular to a basin runoff ensemble forecasting method considering climate and land utilization changes.
Background
The river basin hydrological model is an important tool for river basin hydrological simulation, is a basis for water resource evaluation, configuration, development and utilization, and plays an important role in flood control and disaster reduction, water resource development and utilization, river basin water resource configuration, influence of human activities on river basin water resources and the like. In the hydrological simulation process, climate and land utilization changes often have a great influence on the runoff forecasting accuracy. The change of climate can directly affect rainfall, evapotranspiration, soil moisture content and the like, and the hydrological process and the circulation of the area can be changed accordingly. With the need for economic development, the increase in human activities has led to changes in land use types. Land utilization plays a crucial role in hydrological processes such as balance interception, surface runoff, groundwater replenishment and the like, and the change of the type and the area ratio of the land utilization means that hydrological circulation can be changed along with the land utilization. Accurate runoff simulation becomes more complex under the influence of climate and land use. Therefore, changes in climate and land utilization need to be taken into account during runoff forecasting to improve the accuracy of the simulation.
Climate and land use changes can have direct impact on the hydrological processes in the area, resulting in changes in runoff and changes in area available water resources. The middle Asia region has obvious characteristics of arid climate and semi-arid climate, and the hydrological processes such as the production confluence of rivers are more sensitive to the change of climate and human activities. The runoff trend of regional rivers may be moving in multiple directions under the influence of increasing human activities as a result of global climate warming and regional economic development needs. The water circulation process is fully known, the law is mastered, the utilization efficiency of water resources is increased, and the social and economic development is promoted. Therefore, an effective and accurate runoff forecasting method is urgently needed to be developed to explore the influence of climate and land utilization change on runoff and provide scientific basis for describing the future runoff change trend of the watershed.
Disclosure of Invention
The invention aims to provide a basin runoff ensemble forecasting method considering climate and land utilization changes, which is characterized by comprising the following steps:
step A: collecting geographic data, hydrological meteorological data and human activity data of a research basin;
and B: b, setting and simulating future land utilization and future climate change data according to the human activity data collected in the step A to be used for researching the future land utilization and the future climate change situation of the basin;
and C: according to hydrological data of a historical period, a distributed hydrological model SWAT of a research basin is established, model parameters are calibrated, then the calibrated hydrological model is operated under different future land utilization and climatic change scenes to obtain the change range of future runoff under different influences, and the influence degree of the land utilization and the climatic change on the future runoff change is analyzed and quantized.
The geographic data in the step A comprise river basin ranges, elevations and gradients; the hydrological meteorological data include runoff, precipitation and gas temperature over historical periods of the basin.
The model for simulating future land utilization in the step B is a CA-Markov model; the application process of the CA-Markov model comprises the following steps:
step B1: according to existing land utilization data of two different times, a Markov model generates a state transition probability matrix of each land utilization type, and then the area and the occupation ratio of each land utilization type in the next stage are calculated according to the state transition probability matrix;
and step B2: generating a conditional probability image of each cell according to the land use change driving factors and the weight thereof, wherein the weight is constructed by a CA filter;
and step B3: distributing the area obtained in the state transition probability matrix after each land utilization type change to a land utilization data graph obtained through simulation based on a land utilization type change rule appointed in the conditional probability image, so as to generate a land utilization scene result obtained through simulation of a CA-Markov model;
and step B4: the accuracy of the model was verified according to the Kappa index.
And B, the future climate change situation in the step B is high-precision reanalyzed data based on the GCM model.
The step C specifically comprises the following substeps:
step C1: combining different future land utilization and climate change scenes to serve as hydrological model input data to simulate runoff sequences, using the simulated maximum sequences and the simulated minimum sequences of runoff as the upper limit and the lower limit of the change range of the future runoff, and simultaneously comparing and judging the comprehensive influence of the future different land utilization and the climate change scenes on the runoff change;
and C2: combining historical period land utilization data with future climate scenes, simulating a runoff process by using a hydrological model, and taking the difference between a runoff simulation sequence and a runoff sequence in the historical period as runoff change caused by different climate change conditions; similarly, historical period meteorological data and future land utilization scenes are combined, and the difference between the simulated runoff sequence of the hydrological model under the condition and the historical period runoff sequence is used as runoff change caused by different land utilization change conditions, so that the independent influence of different land utilization conditions and climate change conditions on the runoff change is judged.
The distributed hydrological model SWAT in the step C is:
Figure BDA0003777012110000031
in the formula, SW t And SW 0 Time T and initial soil moisture, T is the calculation period, i is the time point, R is the time point day 、Q suff 、E a 、W seep And Q gw The precipitation, surface runoff, evaporation, infiltration and groundwater outflow at time i are respectively.
The objective function for calibrating the model parameters in the step C is as follows:
Figure BDA0003777012110000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003777012110000033
refers to the observed value at the time t,
Figure BDA0003777012110000034
refers to the analog value at time t,
Figure BDA0003777012110000035
represents the average of the observations.
The invention has the beneficial effects that:
the method can obtain the independent influence of land utilization and climate change on the runoff change, thereby preliminarily judging and researching the main factors influencing the runoff change in the runoff and providing a certain scientific basis for the future water resource management and utilization.
Drawings
Fig. 1 is a flow chart of the basin runoff ensemble forecasting method considering climate and land utilization changes according to the invention.
Fig. 2 is an overall schematic diagram of the basin runoff ensemble forecasting method considering climate and land utilization changes.
FIG. 3 is a schematic diagram of the application process of the CA-Markov model.
Fig. 4 is a runoff volume comparison diagram for different RCP scenarios when the land use scenario is a GM scenario.
Fig. 5 (a) and 5 (b) are radial flow rate comparison graphs in different climate models when the land use scenario is a GM scenario.
FIGS. 6 (a) and 6 (b) are comparative runoff values for different land use scenarios with the climate model BCC-CSM 1.1.
Fig. 7 (a) and 7 (b) are 6-month runoff volume comparison graphs in different climate models when the land use is changed to the GM scenario.
Detailed Description
The invention provides a river basin runoff ensemble forecasting method considering climate and land utilization changes, and the invention is further explained by combining the attached drawings and the specific embodiment.
Fig. 1 and fig. 2 are a flow chart and an overall schematic diagram of a basin runoff ensemble forecasting method considering climate and land utilization changes according to the present invention, respectively. The method comprises the following steps:
step A, data collection: collecting hydrological meteorological data for a historical period in a data area, comprising: runoff, rainfall, air temperature data, etc.; geographic data, including: geographical range, elevation, slope, etc. of the drainage basin; and data relating to human activity within the domain, including: land use data of different years in historical period, development policies related to future land use and the like.
Step B, future land utilization and climate change scene simulation: land use variation scenarios are predicted using the CA-Markov model. The principle of the model is as follows: the model combines a Cellular Automaton (CA) model and a Markov model. The CA model is a grid dynamic model with discrete time, space and state, and local space interaction and time causal relation. The complete model consists of four parts: cells and their states, cell space, neighborhood, and transfer rules. The next state of the central cell of each cell space depends on the current states of the cell and neighboring cells and some switching rules. This process can be expressed as follows:
S t+1 ＝f(S t ,N)
wherein S represents a finite discrete set of cell states, t and t +1 are the time at which the state is located, f is a transformation rule, and N represents a neighborhood of cells. However, CA only describes the interaction of the spatial neighborhood, and has no effect of temporal variation. The Markov model mainly reflects the state transition probability on a time scale, and the mathematical model of the Markov model is as follows:
V(t+1)＝V(t)·P
where V (t) and V (t + 1) represent the system state at time t and t +1, respectively, and P represents the transition probability matrix from the initial time to the end time, which can be expressed as follows:
Figure BDA0003777012110000041
wherein, P ij Is the probability of the next time period transitioning from state i to state j. But markov models reflect only quantitative changes and do not describe the spatial variation law. The combination of the Markov model and the CA model can fully utilize the advantages of the two methods and accurately simulate the change of the soil utilization on the quantity and the space.
FIG. 3 is a schematic diagram of an application process of the CA-Markov model, which comprises four steps: (1) According to the existing two land utilization data at different time, a Markov model generates a state transition probability matrix of each land utilization type, and then the area proportion of each land utilization type at the next stage is calculated according to the matrix; (2) Generating a conditional probability image of each cell according to land utilization change driving factors such as land policies, climate change and the like and the weight of the driving factors, wherein the weight is constructed by a CA filter, and a 5 multiplied by 5 filter is selected in the example (namely, a space formed by 5 multiplied by 5 cells around one cell has influence on the state change of the cell); (3) Distributing the area obtained in the state transition probability matrix after each land utilization type change to a land utilization data graph obtained through simulation based on a land utilization type change rule appointed in the conditional probability image, so as to generate a land utilization scene result obtained through simulation of a CA-Markov model; and (4) finally verifying the accuracy of the model by certain indexes.
The accuracy of the land use prediction result needs to be judged by using a corresponding index. The KAPPA index introduced by Cohen is a common criterion for judging the relationship between the predicted outcome and the actual situation, as shown below.
K＝(P o -P c )/(1-P c )
Wherein K is the Kappa index, P o Is the actual agreed unit proportion, P c Is a unit proportion where the expected variation is consistent. According to Viera and Garrett studies, kappa indices between 0.61 and 0.80 are considered "acceptable" and between 0.81 and 0.99 are considered "almost perfect".
In the embodiment, a CA-Markov model is constructed by using the 2005, 2010 and 2015 land use classification data collected in the step a as a reference, and a plurality of future land use change trends, namely a sustainable development scenario (SSP 1), an intermediate road development scenario (SSP 2), an unreasonable planning scenario (SSP 3), an area unequal development scenario (SSP 4), a traditional development scenario (SSP 5) and a glacier thawing scenario (GM), are set by combining with research area future land development policies and climate factors, so as to simulate land use scenario data under different development policies of 2020, 2030, 2040 and 2050.
Future Climate change scenarios are represented by high-precision reanalyzed data using various Global Climate Models (GCMs). GCM is an important tool for predicting future climate conditions and can provide current and future global climate data. However, because the calculated climate situation may deviate significantly from the actual situation due to differences in the parameter settings of the GCMs and the data used, etc., the calculation using multiple GCMs at the same time is generally better than the single GCM. At the same time, the spatial scale of GCM generation is much larger than the scale required for studying the hydrological process. Therefore, the accuracy of future meteorological data simulation can be effectively improved by selecting the high-precision re-analysis meteorological data based on the GCM. In this embodiment, the same GCM-based high-precision re-analysis meteorological data source, such as NEX-GDDP data set, should be determined, and then different meteorological data based on multiple GCMs should be selected as multiple future climate change trend scenarios. In this example, two RCP scenarios (i.e., RCP4.5 and RCP 8.5) of 6 GCMs (i.e., bcc-csm1.1, canESM2, CSIRO-Mk3.6.0, CNRM-CM5, IPSL-CM5A-LR, and NorESM 1-M) were selected to simulate climate change in 2021-2050.
Step C, hydrological model construction and runoff sequence simulation under different situations in the future: the distributed hydrological model used in the present embodiment is a SWAT (Soil and Water association Tool) model. The SWAT model is a comprehensive, time-continuous, semi-distributed and process-based hydrological model, the calculation of which needs data such as elevation, weather and land utilization, and can directly simulate the processes of water quantity change, sediment migration, nutrient substance circulation and the like. In the model, the surface hydrological simulation process is mainly based on water balance, and the equation is as follows:
Figure BDA0003777012110000061
in the formula, SW t And SW 0 Initial and time t, t is the calculation period, i is the time point, R day 、Q suff 、E a 、W seep And Q gw The precipitation, surface runoff, evaporation, infiltration and groundwater outflow at time i are respectively. In the SWAT model, the basin is divided into several sub-basins according to the distribution of the river network, and then further subdivided into Hydrological Response Units (HRUs) according to land utilization, soil type and slope characteristics. First, in each HRUHydrologic and environmental factors were modeled and weighted to the sub-watershed level. And then the water collecting loads of all the sub-basins are gathered through a river network and converged to the outlet of the basin. It can be seen from the water balance equation and the simulation process based on the model that climate and land utilization have a great influence on the runoff process.
In this embodiment, the historical hydrometeorological data, the geographic data, and the human activity data collected in step a are used as input data of the SWAT model, the historical period is divided into a preheating period, a calibration period, and a verification period, appropriate model parameters to be calibrated are selected, and the SWAT model is calibrated and verified, so that the flow process of the basin can be well simulated and researched. The parameter calibration of the model takes the maximum Nash-Sutcliffe efficiency coefficient (NSE) as an objective function, and the calculation formula is as follows:
Figure BDA0003777012110000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003777012110000063
refers to the observed value at the time t,
Figure BDA0003777012110000064
refers to the analog value at the time point t,
Figure BDA0003777012110000065
represents the average of the observed values.
And then combining the future land utilization and climate change situations in different time periods obtained by the CA-Markov model and the GCM model in the step B. In this embodiment, land use scenarios of 2020, 2030, 2040, and 2050 and climate change scenarios of 2015-2055 constitute hydrological model input data in combination. To illustrate the combined process, for example, the land use data of 2020 years and the climate data of 2015-2025 years are combined as input data and substituted into the SWAT model to simulate the daily runoff sequence of 2015-2025 years. And by analogy, combining 2030-year land utilization data with 2026-2035-year climate data, 2040-year land utilization data with 2036-2045-year climate data, and 2050-year land utilization data with 2046-2055-year climate data respectively. And then, connecting the calculated flow results of the four time periods together to generate a flow sequence under 72 scenes of 6 multiplied by 2 (6 land utilization scenes and 6 climate models under 2 RCP scenes) in 2021-2050, wherein the simulated maximum and minimum sequences of the runoff are regarded as the upper and lower limits of the possible variation range of the future runoff. Meanwhile, the runoff sequence under different situations is compared with the runoff process in the historical period, and the comprehensive influence degree of land utilization and climate change on future runoff change can be judged.
Table 1 shows the annual average runoff volume for 72 scenarios, and fig. 4, 5 (a), 5 (b), 6 (a), 6 (b), 7 (a), and 7 (b) are comparisons of partial aggregate runoff prediction results. As can be seen from the graph, the average flow rate in 2021-2050 in 72 scenes tends to decrease, and the average flow rate in the future year is 244.32-295.98m 3 Changes between/s and a reference period (317.03 m) 3 /s) ratio is reduced. Comparing the flow rates of different RCP scenarios in the same climate mode, the flow rate change in the RCP8.5 scenario will be relatively more severe. The runoff change characteristics of various climatic modes are also different, for example: the annual traffic variation in the IPSL-CM5A-LR model is relatively stable, while the variation is more significant in the CSIRO-Mk3.6.0 model. The runoff rate under the CanESM2 model with the most obvious temperature rise can be remarkably reduced, which indicates that the temperature can influence the annual change process of the runoff. However, the difference in annual average runoff change is not significant under various land use change scenarios. For space, fig. 5 shows only the flow comparison for different land use scenarios in BCC-CSM1.1 climate mode, and other climate modes have similar results, whereby land use changes have relatively little impact on the annual average runoff.
In month 6, the flow changes associated with glacier melting may reflect the effects of temperature and glacier area. Fig. 7 (a) and 7 (b) show the average flow change for 6 months per year 2021-2050 and the flow process for 6 months per year in the baseline period under the GM scenario. In 2021-2035, when the glacier area is reduced and the air temperature is not obviously raised, the flow rate under the future change situation is basically the same as the flow rate change trend of the reference period of 6 months. Glacier area will decline severely during the period 2036 to 2050 and the average temperature in the study area will gradually increase compared to the baseline period. In most future scenarios, the flow for 6 months will have a trend higher than the flow during the baseline period. The results show that a premature rise in air temperature over the year will lead to premature melting of glaciers, while runoff in summer will decrease due to insufficient ice and snow melt water supply, and imbalance between water resource allocation and water demand may be further exacerbated.
TABLE 1 annual average runoff in 72 scenarios (unit: m) 3 /s)
Figure BDA0003777012110000071
Figure BDA0003777012110000081
In order to quantify the independent influence of land utilization and climate change on runoff change, a future land utilization change scene and historical period meteorological data are combined and are brought into a SWAT model simulation runoff sequence, and similarly, the historical period land utilization data and the future climate change scene are combined and are simulated by the SWAT model to obtain a runoff forecast result. To quantitatively assess the contribution of land use and climate change to runoff, the following formula was used for calculation:
ΔQ L,C ＝Q L,C -Q baseline
in the formula,. DELTA.Q L,C Is the total change of future runoff compared to a reference period, Q L,C Runoff under the future climate and land utilization situation, Q baseline Is the runoff of the baseline period.
Figure BDA0003777012110000082
Figure BDA0003777012110000083
Wherein eta is L And η C Respectively representing the contribution degree of land utilization and climate change to the change of the runoff. Q L,baseline Is the runoff under the future land use scene and the climate condition of the reference period. In the same way, Q baseline,C Representing the runoff under a benchmark period land use scenario and future climate change conditions. Therefore, independent influences of land utilization and climate change on runoff change can be obtained respectively, so that main factors influencing the runoff change in the runoff domain are preliminarily judged and researched, and a certain scientific basis is provided for future water resource management and utilization.
Table 2 lists the degree of contribution of climate and land use changes to flow changes compared to a baseline period. It can be found that climate change has a greater impact on runoff than land use. In each scenario, the climate contributes 78.76% to 98.66% to the flow change, while the contribution to land utilization is only 1.34% to 21.24%. The reduction in glacier area increases the impact of land use on runoff. For example, in the GM scenario, the contribution of land use changes to runoff decline will be greater than in other land use change scenarios, even reaching 21.24% under RCP8.5 of the IPSL-CM5A-LR model. Glacier areas are considered to be of great importance in snow-based hydrological systems, such as the research area. The temperature also has a great influence on the contribution degree, and the runoff is indirectly influenced mainly by changing the glacier area. For example, in the RCP4.5 and RCP8.5 scenarios of the IPSL-CM5A-LR model, the precipitation difference is only 3.05mm and the temperature difference is 0.50 ℃. Under the warmer RCP8.5 scenario, the contribution of land use to runoff changes increased significantly. Thus, it can be seen that temperature also plays a crucial role in studying the change in runoff in the area.
TABLE 2 degree of contribution of climate and land use changes to runoff changes
Figure BDA0003777012110000084
Figure BDA0003777012110000091

Claims (7)

1. A method for ensemble forecasting of runoff in a river basin considering climate and land use changes, the method comprising the steps of:
step A: collecting geographic data, hydrological meteorological data and human activity data of a research basin;
and B: setting and simulating future land utilization and future climate change data according to the human activity data collected in the step A to be used for researching future land utilization and future climate change situations of the basin;
and C: according to hydrological data of a historical period, a distributed hydrological model SWAT of a research basin is established, model parameters are calibrated, then the calibrated hydrological model is operated under different future land utilization and climatic change scenes to obtain the change range of future runoff under different influences, and the influence degree of the land utilization and the climatic change on the future runoff change is analyzed and quantized.
2. The method for ensemble forecasting of runoff according to claim 1 wherein the geographical data in step a includes the range, elevation and slope of the watershed; the hydrometeorological data comprises runoff, precipitation and gas temperature of the basin in historical periods.
3. The method for ensemble forecasting of runoff according to claim 1 wherein the model simulating future land use in step B is a CA-Markov model; the application process of the CA-Markov model comprises the following steps:
step B1: according to existing land utilization data of two different times, a Markov model generates a state transition probability matrix of each land utilization type, and then the area and the occupation ratio of each land utilization type in the next stage are calculated according to the state transition probability matrix;
and step B2: generating a conditional probability image of each cell according to the land use change driving factors and the weight thereof, wherein the weight is constructed by a CA filter;
and step B3: distributing the area obtained in the state transition probability matrix after each land utilization type change to a land utilization data graph obtained through simulation based on a land utilization type change rule appointed in the conditional probability image, so as to generate a land utilization scene result obtained through simulation of a CA-Markov model;
and step B4: the accuracy of the model was verified according to the Kappa index.
4. The method for ensemble forecasting of runoff according to claim 1 and taking into account climate and land utilization changes, wherein the future climate change situation in step B is high-precision reanalysis data based on a GCM model.
5. The method for ensemble forecasting of runoff according to claim 1, taking into account the variations in climate and land utilization, characterized in that said step C comprises in particular the following sub-steps:
step C1: combining different future land utilization and climate change scenes to serve as hydrological model input data to simulate runoff sequences, using the simulated maximum sequences and the simulated minimum sequences of runoff as the upper limit and the lower limit of the change range of the future runoff, and simultaneously comparing and judging the comprehensive influence of the future different land utilization and the climate change scenes on the runoff change;
and step C2: combining historical period land utilization data with future climate scenes, simulating a runoff process by using a hydrological model, and taking the difference between a runoff simulation sequence and a runoff sequence in the historical period as runoff change caused by different climate change conditions; similarly, historical period meteorological data and future land utilization scenes are combined, and the difference between the simulated runoff sequence of the hydrological model under the condition and the historical period runoff sequence is used as runoff change caused by different land utilization change conditions, so that the independent influence of different land utilization conditions and climate change conditions on the runoff change is judged.
6. The method for ensemble forecasting of runoff according to claim 1 and taking into account the climate and land utilization changes, wherein the distributed hydrological model SWAT in step C is:
Figure FDA0003777012100000021
in the formula, SW t And SW 0 Time T and initial soil moisture, T is the calculation period, i is the time point, R day 、Q suff 、E a 、W seep And Q gw The precipitation, surface runoff, evaporation, infiltration and groundwater outflow at time i are respectively.
7. The method for ensemble forecasting of runoff according to claim 1 or 6 and taking account of climate and land utilization changes, wherein the objective function of the calibration model parameters in step C is:
Figure FDA0003777012100000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003777012100000023
refers to the observed value at the t-th time,
Figure FDA0003777012100000024
refers to the analog value at time t,
Figure FDA0003777012100000025
represents the average of the observed values.
CN202210919438.4A 2022-08-02 2022-08-02 River basin runoff ensemble forecasting method considering climate and land utilization changes Pending CN115238513A (en)

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CN117172037A (en) * 2023-11-03 2023-12-05 长江三峡集团实业发展(北京)有限公司 Distributed hydrologic forecasting method, device, computer equipment and medium
CN117172037B (en) * 2023-11-03 2024-01-26 长江三峡集团实业发展(北京)有限公司 Distributed hydrologic forecasting method, device, computer equipment and medium

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