Prediction and attribution methods, systems and media for seasonal runoff changes in watersheds

Abstract

The present application discloses a method, system and medium for predicting and attributing seasonal runoff changes in a watershed. The method includes seasonal division of the hydrological year based on the monthly precipitation, potential evapotranspiration and terrestrial water storage changes in the watershed; establishing multiple coupling models of GCM and ABCD to predict the runoff process in the watershed under the scenarios of only considering future climate change scenarios and considering both climate change and the impact of human activities; considering the seasonal scale Budyko model, quantifying the relative impact of climate change and human activities on future runoff changes on a seasonal scale. The present application has a strong statistical basis, can objectively reflect the laws of changes in watershed hydrological runoff, and can fully decouple the complex relationship between factors such as climate change and human activities on a seasonal time scale, providing a new technology for studying the driving mechanism of water cycle processes under a changing environment.

Description

Technical Field

[0001] The present application relates to the field of hydrology and water resources, and in particular to a method, system and medium for predicting and attributing seasonal runoff changes in a watershed. Background Art

[0002] Since the 21st century, significant climate change and extensive human activities have been the main factors changing the global hydrological cycle pattern. Climate change, such as changes in precipitation and potential evapotranspiration, affects hydrological runoff changes through runoff generation and runoff mechanisms. Human activities, such as land use / cover changes, reservoir operations, and water withdrawal projects, are also changing the spatiotemporal distribution of water resources by directly interfering with hydrological processes. In recent years, there has been a significant increase in related studies analyzing runoff trends within a basin and quantitatively attributing its changing factors. Due to the more drastic and complex changes in hydrological and climate variables and human activities within the year, it is more challenging and meaningful to increase the time scale of runoff attribution analysis from the (multi-)annual scale to the seasonal scale.

[0003] At the same time, these early studies mainly focused on historical observational data, ignoring uncertain future predictions. The evolution of hydrological runoff caused by climate and human factors under possible future scenarios still needs to be explored, which is important for managing water resource systems. In the existing technology, the methods for future runoff analysis mainly include dynamic hydrological modeling methods, elasticity-based analysis methods, and conceptualized water balance models based on the Budyko hypothesis.

[0004] 1. Dynamic hydrological modeling methods: For areas where future human activities are not expected to change significantly, a calibrated hydrological model can be used to predict future runoff by perturbing future climate conditions. Commonly used hydrological models include the Soil Moisture Assessment Tool (SWAT) and the Variable Infiltration Capacity (VIC) model. However, these models require a lot of input information and involve many parameters that need to be calibrated, which limits their application in watersheds with scarce hydrological data.

[0005] 2. Elasticity-based analysis: This method uses some key elasticity coefficients, such as the sensitivity of runoff to changes in meteorological factors (such as precipitation, potential evapotranspiration, and relative humidity), to predict future runoff based on expected climate conditions. However, this method has certain shortcomings in assessing the sensitivity of runoff to human activities.

[0006] 3. Conceptualized water balance model based on Budyko hypothesis: Due to its simplicity, this model is widely used to separate the impacts of climate change and direct human activities on historical runoff. However, this method cannot further decompose the impact of climate change into specific factors such as precipitation and potential evapotranspiration, and on a seasonal scale, the effect of basin water storage changes violating the multi-year average steady-state assumption deserves more attention.

[0007] In summary, existing technologies are mainly limited to the long-term process of runoff changes in future periods, and are unable to deeply analyze the complex changes on the seasonal scale, let alone decouple the complex impacts of climate change and human activities on future seasonal runoff changes. Summary of the invention

[0008] The purpose of the embodiments of the present application is to overcome the shortcomings of the prior art, obtain seasonal runoff information under future climate change scenarios based on the global climate model (GCM) and the ABCD four-parameter hydrological model, and adopt the vertical decomposition method of the extended Budyko hypothesis to provide a method, system and storage medium for predicting and attributing changes in seasonal runoff in a watershed taking into account climate change scenarios.

[0009] To achieve the above objectives, this application provides the following technical solutions:

[0010] In a first aspect, an embodiment of the present application provides a method for predicting and attributing seasonal runoff changes in a watershed, comprising the following steps:

[0011] Step 1: Based on the variation patterns of monthly precipitation, potential evapotranspiration and terrestrial water storage in the basin, the hydrological year is seasonally divided;

[0012] Step 2: Establish multiple coupling models of GCM and ABCD to predict the runoff process in the basin under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time;

[0013] Step 3, considering the seasonal scale Budyko model, quantify the relative impact of climate change and human activities on future runoff changes on a seasonal scale;

[0014] The specific seasonal scale Budyko model is:

[0015]

[0016] In the formula, It is the change in the sum of soil and groundwater storage at the end and beginning of the same period; represents available precipitation, i.e. effective precipitation; and represent the seasonal scale drought index and seasonal scale evaporation rate, respectively; is the Budyko shape parameter, which is a comprehensive parameter reflecting the underlying surface of the watershed. Except Another parameter represents the lower limit of the drought index.

[0017] In the step 1,

[0018] Considering the effective precipitation and potential evaporation in the seasonal Budyko model representing the water volume and energy boundary conditions of water, the multi-year average monthly drought index is introduced. To divide the hydrological year.

[0019] Multi-year average monthly drought index The definition is as follows:

[0020] (1)

[0021] In formula (1), and are the multi-year average monthly potential evaporation and monthly precipitation, respectively; is the multi-year average monthly land water storage change, and its value can be calculated based on the monthly water balance equation Get, where and is the multi-year average monthly actual evaporation and total runoff, if the multi-year average monthly drought index , the energy is limited, then the month belongs to the flood season; on the contrary, if the multi-year average monthly drought index , the water volume is limited, then the month is a non-flood season.

[0022] The two situations in step 2, namely, considering only the climate change scenario and considering both the climate change and human activity impact scenarios, are represented by two groups of ABCD hydrological models calibrated in the natural period and the human activity impact period, respectively.

[0023] The step 3 includes the following sub-steps:

[0024] (3.1) Fitting the seasonal Budyko hypothesis curve, using the Turc-Pike form of the seasonal scale Budyko model, extending the concept of effective precipitation at the annual scale to the seasonal scale, and obtaining the mathematical expression relationship between actual evapotranspiration and potential evapotranspiration. The parameters of the seasonal scale Budyko curve are fitted using the least squares method, and the coefficient of certainty and root mean square error are selected as fitting evaluation indicators;

[0025] (3.2) Use the vertical decomposition method of the Budyko hypothesis at a seasonal scale to quantify the impact of climate change and human activities on future runoff changes.

[0026] In a second aspect, the present application provides a system for predicting and attributing seasonal runoff changes in a watershed, including:

[0027] Seasonal classification module, which is used to divide the hydrological year into seasonal categories based on the monthly precipitation, potential evapotranspiration and groundwater storage changes in the basin;

[0028] Model building module, which builds multiple coupling models of GCM and ABCD to predict the runoff process in the basin under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time;

[0029] The quantification module considers the seasonal scale Budyko model to quantify the relative impact of climate change and human activities on future runoff changes on a seasonal scale.

[0030] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a program code. When the program code is executed by a processor, the steps of the method for predicting and attributing seasonal runoff changes in a watershed as described above are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. It can provide important and highly operational reference for seasonal runoff prediction in river basins under climate change conditions:

[0033] The present invention establishes a coupling model of GCM and ABCD, which can respectively predict the seasonal runoff variation process under the scenario of only considering future climate change and the scenario of simultaneously considering the impact of climate change and human activities, and has strong practicality.

[0034] 2. It can further quantify the impact of complex factors such as climate change and human activities on future seasonal runoff, and provide a new technology for studying the driving mechanism of water cycle processes under a changing environment.

[0035] This application uses the vertical decomposition method of the extended Budyko hypothesis to distinguish and quantify the relative impact of climate change and human activities on runoff changes on a seasonal scale. It has a strong statistical basis, can objectively reflect the law of hydrological runoff changes in the basin, and can provide reliable theoretical support for basin water resources management. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a specific flow chart of the method of the present invention;

[0038] Figure 2 This is the coupling principle diagram of the GCM climate model and the ABCD hydrological model;

[0039] Figure 3A schematic diagram of quantifying the impact of climate change and human activities on runoff based on the seasonal scale Byduko vertical decomposition method;

[0040] Figure 4 It is a system diagram of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0042] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0043] The terms "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and should not be understood as indicating or implying relative importance, nor should they be understood as requiring or implying any such actual relationship or order between these entities or operations.

[0044] This application first analyzes the annual variation patterns of historical runoff series in the basin, and subdivides the traditional annual scale analysis of runoff attribution into seasonal scales such as flood season and non-flood season; and based on the CMIP6 downscaled hydrological and meteorological data, drives two different hydrological models that only consider climate change and the impact of climate change and human activities at the same time, in order to predict the seasonal variation process of the basin under different future change scenarios. Finally, through the vertical decomposition method of the extended Budyko hypothesis, the relative impact of climate change and human activities on runoff changes on the seasonal scale is distinguished and quantified.

[0045] The specific implementation process of this application is shown in Figure 1 , the steps are as follows:

[0046] Step 1: Based on the monthly precipitation, potential evapotranspiration and groundwater storage changes in the basin, the hydrological year is seasonally divided.

[0047] The calculation idea of ​​this embodiment is: considering the effective precipitation and potential evaporation in the seasonal scale Budyko model to represent the water volume and energy boundary conditions of water, respectively, and introducing the multi-year average monthly drought index To divide the hydrological year (such as flood season and non-flood season), the definition is as follows.

[0048] (1)

[0049] In formula (1), and are the multi-year average monthly potential evaporation and monthly precipitation, respectively; is the multi-year average monthly land water storage change, and its value can be calculated based on the monthly water balance equation Get, where and is the multi-year average monthly actual evaporation and total runoff, if the multi-year average monthly drought index , the energy is limited, then the month belongs to the flood season; on the contrary, if the multi-year average monthly drought index , the water volume is limited, then the month is a non-flood season.

[0050] Step 2: Establish multiple coupling models of GCM and ABCD to predict the basin runoff process under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time.

[0051] Compared with the traditional distributed hydrological model, the lumped ABCD model has a simple structure. It only needs to determine the four parameters a, b, c, d and the initial soil water storage S0 and the initial groundwater storage G0 to obtain a good runoff simulation effect. The model has been successfully applied in many humid and arid basins in China, the United States, Africa, etc. More importantly, the ABCD hydrological model is based on the basic principle of water balance. It only uses meteorological precipitation and potential evapotranspiration as input to obtain the monthly soil water storage change and actual evapotranspiration. If it is superimposed on the seasonal scale, it can be applied to the extended Budyko model on the seasonal scale. The specific structure of the model is as follows.

[0052] (2)

[0053] In formula (2), a represents the tendency of runoff to occur before the soil is fully saturated, and its value range is (0, 1); b represents the upper limit of evaporation and soil water content; i represents the time period; Yi is the maximum possible evaporation, which is the sum of evaporation Ei within the time period and soil water storage SSi at the end of the time period; Available precipitation.

[0054] The balance of available water minus possible evapotranspiration can be divided into direct runoff Di and groundwater recharge Ri:

[0055] (3)

[0056] (4)

[0057] In formulas (3)-(4), c is the groundwater recharge coefficient.

[0058] The model simulates the groundwater layer using the linear reservoir method:

[0059] (5)

[0060] In formula (5), Gi is the groundwater storage, Fi is the base flow, and d is the groundwater storage coefficient.

[0061] In order to ensure the accuracy of the calibration results, the Nash efficiency coefficient NSE and relative error RE are selected as evaluation indicators for calibration and verification of hydrological model parameters. Formula (5) is used as the objective function for parameter calibration.

[0062] (6)

[0063] In formula (6), is the weight coefficient. The smaller the objective function Obj of parameter calibration is, the better the calibration result will be.

[0064] In this embodiment, two different sets of parameter combinations (a, b, c, d) obtained by calibrating the natural period and the human activity impact period represent two situations in which only the climate change scenario is considered and the scenario in which both the climate change and the human activity impact are considered.

[0065] For the prediction of future runoff, we first apply the statistical downscaling method to the global climate model GCM to predict and analyze the future changes in precipitation and temperature in the basin. Then, we use the downscaled output (precipitation and temperature) of GCM as the input of the ABCD calibrated hydrological model to simulate and predict the runoff process at the seasonal scale of the basin outlet section under future scenarios. The coupling mechanism of the ABCD model and GCM based on NCEP observation data and statistical downscaling method is detailed in Figure 2 .

[0066] Step 3, considering the seasonal scale Budyko model, quantify the relative impacts of climate change and human activities on future runoff changes on a seasonal scale.

[0067] Step 3 further includes the following sub-steps:

[0068] (3.1) Fitting seasonal Budyko hypothesis curve. The present invention adopts the seasonal scale Budyko model as shown in formula (7), expands the concept of annual scale effective precipitation to the season, adopts the Turc-Pike form, and obtains the mathematical expression relationship between actual evapotranspiration and potential evapotranspiration.

[0069] (7)

[0070] In formula (7), It is the change in the sum of soil and groundwater storage at the end and beginning of the same period; represents available precipitation, i.e. effective precipitation; and represent the seasonal scale drought index and seasonal scale evaporation rate, respectively; is the Budyko shape parameter, which is a comprehensive parameter reflecting the underlying surface of the watershed (related to vegetation and soil type);

[0071] Except Another parameter represents the lower limit of the drought index.

[0072] For formula (7) and The least square method is used to fit the parameters, and the coefficient of certainty and root mean square error are used as evaluation indicators.

[0073] (3.2) Use the vertical decomposition method of the Budyko hypothesis at a seasonal scale to quantify the impact of climate change and human activities on future runoff changes.

[0074] In this embodiment, the seasonal vertical decomposition method assumes that climate change changes runoff by changing absolute physical quantities such as effective precipitation and potential evapotranspiration, while human activities change runoff by redistributing the proportion of effective precipitation between actual evapotranspiration and runoff at the basin outlet. In the seasonal vertical decomposition method of the present invention, if the model is not affected by human activities, Then move along the Budyko curve (such as Figure 3 Moving along the Budyko curve includes For P and The vertical movement is caused by human activities. and Changes. Figure 3 As shown, the changes in the coordinate points on the Budyko curve of the basin in the horizontal direction are only caused by climate change, and the changes in the vertical direction are caused by climate change and human activities.

[0075] The specific operation steps are as follows: If only the impact of climate change is considered, the hydrological state of the basin will change from Figure 3 Point A ( ) moves along the Budyko curve to point B ( ). The climate conditions at points B and C are the same, and human activities only cause changes in the vertical direction, the amount of change is Therefore, the change in runoff under the influence of human activities is as follows.

[0076] (8)

[0077] In formula (8), The change in runoff caused by human activities.

[0078] The runoff change caused by climate change is the difference between the total runoff change and the runoff change caused by human activities.

[0079] (9)

[0080] In formula (9), is the total runoff change; is the change in runoff caused by climate change.

[0081] Therefore, the relative impacts of climate change and human activities on future seasonal runoff changes are:

[0082] (10)

[0083] In formula (10), and is the relative contribution of climate change and human activities, ranging from 0 to 1.

[0084] like Figure 4 The embodiment of the present application provides a system for predicting and attributing seasonal runoff changes in a watershed, including:

[0085] Seasonal classification module 1 is used to divide the hydrological year into seasonal categories based on the monthly precipitation, potential evapotranspiration and groundwater storage changes in the basin;

[0086] Model building module 2, building multiple coupling models of GCM and ABCD to predict the basin runoff process under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time;

[0087] Quantification module 3 considers the seasonal scale Budyko model to quantify the relative impact of climate change and human activities on future runoff changes on a seasonal scale.

[0088] An embodiment of the present application provides a computer-readable storage medium, which stores program code. When the program code is executed by a processor, the steps of the method for predicting and attributing seasonal runoff changes in a watershed as described above are implemented.

[0089] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0095] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0096] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting and attributing seasonal runoff changes in a watershed, characterized in that: The following steps are involved: Step 1: Based on the variation patterns of monthly precipitation, potential evapotranspiration and terrestrial water storage in the basin, the hydrological year is seasonally divided; Step 2: Establish multiple coupling models of GCM and ABCD to predict the runoff process in the basin under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time; Step 3, considering the seasonal scale Budyko model, quantify the relative impact of climate change and human activities on future runoff changes on a seasonal scale; The specific seasonal scale Budyko model is: , In the formula, It is the change in the sum of soil and groundwater storage at the end and beginning of the same period; represents available precipitation, i.e. effective precipitation; and represent the seasonal scale drought index and seasonal scale evaporation rate, respectively; is the Budyko shape parameter, which is a comprehensive parameter reflecting the underlying surface of the watershed. Except Another parameter, representing the lower limit of drought index; The step 3 includes the following sub-steps: (3.1) Fitting the seasonal Budyko hypothesis curve, using the Turc-Pike form of the seasonal scale Budyko model, extending the concept of effective precipitation at the annual scale to the seasonal scale, and obtaining the mathematical expression relationship between actual evapotranspiration and potential evapotranspiration. The parameters of the seasonal scale Budyko curve are fitted using the least squares method, and the coefficient of certainty and root mean square error are selected as fitting evaluation indicators; (3.2) Quantify the impact of climate change and human activities on future runoff changes using the vertical decomposition method of the Budyko hypothesis at the seasonal scale; For future runoff prediction, the statistical downscaling method is first applied to the global climate model GCM to realize the prediction and analysis of future precipitation and temperature changes in the basin. Then, the downscaled precipitation and temperature output of GCM are used as the input of the ABCD calibrated hydrological model to simulate and predict the runoff process of the seasonal scale basin outlet section under future scenarios.

2. A method for predicting and attributing seasonal runoff changes in a watershed according to claim 1, characterized in that: In the step 1, Considering the effective precipitation and potential evaporation in the seasonal scale Budyko model representing the water volume and energy boundary conditions of water, the multi-year average monthly drought index is introduced. To divide the hydrological year.

3. A method for predicting and attributing seasonal runoff changes in a watershed according to claim 2, characterized in that: Multi-year average monthly drought index The definition is as follows: （1） In formula (1), and are the multi-year average monthly potential evaporation and monthly precipitation, respectively; is the multi-year average monthly land water storage change, and its value can be calculated based on the monthly water balance equation Get, where and is the multi-year average monthly actual evaporation and total runoff, if the multi-year average monthly drought index , the energy is limited, then the month belongs to the flood season; on the contrary, if the multi-year average monthly drought index , the water volume is limited, then the month is a non-flood season.

4. A method for predicting and attributing seasonal runoff changes in a watershed according to claim 1, characterized in that: The two situations in step 2, namely, considering only the climate change scenario and considering both the climate change and human activity impact scenarios, are represented by two groups of ABCD hydrological models calibrated in the natural period and the human activity impact period, respectively.

5. A system for predicting and attributing seasonal runoff changes in a watershed, used to implement the method described in any one of claims 1 to 4, characterized in that: include, Seasonal classification module, which is used to divide the hydrological year into seasonal categories based on the monthly precipitation, potential evapotranspiration and terrestrial water storage changes in the basin; Model building module, which builds multiple coupling models of GCM and ABCD to predict the runoff process in the basin under the scenarios of only considering future climate change and considering the impact of climate change and human activities at the same time; The quantification module considers the seasonal scale Budyko model to quantify the relative impact of climate change and human activities on future runoff changes on a seasonal scale.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, and when the program codes are executed by a processor, the steps of the method for predicting and attributing seasonal runoff changes in a watershed as described in any one of claims 1 to 4 are implemented.

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