A method for assessing characteristics of drought and flood encounters in a river basin

Abstract

The application discloses a kind of watershed weather-hydrology drought and flood encounter characteristic evaluation methods, belong to disaster evaluation technical field, comprising the following steps: according to the natural geographical features of watershed, the index that can represent weather drought and flood and hydrology drought and flood is selected, respectively the evolution law of drought and flood event in watershed source area-upstream-middle and downstream is analyzed;Overall identification is carried out to the response relationship and lag effect between different partitions and different types of drought and flood in watershed based on digital signal processing technology;Based on Copula function, construct two-dimensional joint distribution model, quantitatively analyze the joint occurrence probability of 9 kinds of combined events of weather-hydrology, different partition hydrology drought and flood encounter in the same partition;In-depth analysis of the influence when flood-flood, drought-drought superposition and flood-drought occur under different scenarios of drought and flood synchronization and asynchrony.The application solves the problem that there is no drought and flood encounter evaluation method that comprehensively reflects the multivariate attributes, multi-temporal and spatial scale and multi-watershed regional characteristics.

Description

Technical Field

[0001] This invention belongs to the field of watershed drought and flood disaster assessment technology, specifically relating to a method for assessing the meteorological-hydrological drought and flood encounter characteristics of a watershed. Background Technology

[0002] Against the backdrop of global climate change and intense human activities, the uncertainty of extreme hydrological events is increasing, and multi-temporal and spatial drought and flood events are occurring more frequently. The risk of simultaneous drought and flooding among different sub-basins is rising, profoundly impacting the water security, ecological security, and sustainable socio-economic development of these basins. Large river basins such as the Yangtze and Yellow Rivers span multiple climate zones, with significant spatial heterogeneity in topography and climate across different regions. Influenced by complex underlying surfaces and geographical environments, drought and flood events exhibit significant regional characteristics, and severe drought and flood events have become increasingly frequent in recent years. The drought and flood experiences in different regions of the basin are essentially a multi-regional combination problem of meteorological and hydrological variables. On the one hand, once meteorological drought and flood events occur, they may affect soil water and runoff abundance or scarcity through the water cycle, thereby impacting the water resource security of the basin and regional water use scheduling. On the other hand, the source region-upstream-middle-downstream spatial domain is influenced by a complex interplay of meteorological, ecological, and socio-economic systems, resulting in complex relationships of mutual influence and time-delayed transmission of drought and flood events. Therefore, understanding the patterns of drought and flood occurrence in different zones and types of watersheds, and clarifying the response relationships and potential propagation mechanisms among various systems, is crucial for improving the watershed's disaster early warning, risk management, and water resource allocation capabilities.

[0003] Current research focuses on the evolution of overall drought and flood characteristics in a specific watershed or the propagation mechanisms of different types of drought and flood. However, the issue of drought and flood encounters in a watershed involves multiple regions (source area - upper-middle-lower reaches) and multiple levels (meteorological - hydrological drought and flood). Especially in recent years, under the background of climate change driven by natural and human activities, the transformation relationships between water cycle elements have become more complex, making the drought and flood risk in this region highly complex and uncertain. Further research is needed to accurately identify the response relationships and hysteresis effects between drought and flood propagation using digital signal processing technology, from the perspectives of multivariate attributes, multi-temporal and spatial scales, and multi-watershed regional characteristics of drought and flood disasters, and to systematically analyze the patterns of drought and flood encounters among different types and regions of the watershed. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a method for assessing the characteristics of drought and flood encounters in watersheds based on meteorological and hydrological conditions. This method comprehensively considers the combined influence of the watershed source region, upstream, midstream, and downstream spatial domains, and meteorological and hydrological systems. It identifies propagation lag time based on digital signal processing technology, applies the copula function to quantitatively analyze the joint probability of drought and flood encounters for nine combined events, and deeply analyzes the superposition effect of drought and flood and the drought mitigation effect under different scenarios of synchronous and asynchronous drought and flood. This method solves the problem of lacking a comprehensive method for assessing watershed drought and flood encounters that fully reflects multivariate attributes, multiple spatiotemporal scales, and multi-watershed regional characteristics.

[0005] To achieve the above-mentioned objectives, the technical solution adopted by this invention is: a method for assessing the characteristics of drought and flood encounters in a watershed's meteorological-hydrological processes, comprising the following steps:

[0006] S1. Based on the natural geographical characteristics of the watershed, select indicators to characterize meteorological drought and flood and hydrological drought and flood, and analyze the evolution pattern of drought and flood events in the source area, upper, middle and lower reaches of the watershed;

[0007] S2. Based on the evolution of drought and flood events in the source area, upper, middle and lower reaches of the watershed, analyze the response relationship and lag effect among different regions and different types of drought and flood in the watershed;

[0008] S3. Quantitatively analyze the probability of the joint occurrence of different combinations of meteorological-hydrological and hydrological drought and flood events in the same region and in different sub-regions;

[0009] S4. Based on the combined events of drought and flood and their lag time, analyze the impact of flood-flood, drought-drought and flood-drought events to achieve an impact assessment of drought and flood events.

[0010] The beneficial effects of this invention are as follows: This invention provides a method for assessing the meteorological-hydrological drought and flood encounter characteristics of a watershed. It considers the multivariate attributes, multi-temporal scales, and multi-watershed regional characteristics of drought and flood disasters, comprehensively takes into account the meteorological-hydrological response relationship across multiple spatial domains of the watershed, selects digital signal processing technology, analyzes the spectral variation characteristics of drought and flood in the watershed under changing environments, clarifies the lag days between the two, constructs a two-dimensional joint distribution model based on the Copula function, and quantitatively analyzes the joint probability and impact of different combinations of events. This achieves a comprehensive assessment of the meteorological-hydrological drought and flood encounter characteristics of the watershed, providing important support for improving the watershed's disaster early warning, risk management, and water resource allocation capabilities.

[0011] Further, step S1 specifically includes:

[0012] S11. Divide the watershed into source area, upper-middle-lower reaches catchment area;

[0013] S12. Collect precipitation data from meteorological stations in the basin and calculate the indicators representing meteorological drought and flood in the source area, upper, middle and lower reaches respectively;

[0014] S13. Collect runoff data from hydrological stations in the watershed and calculate indicators representing hydrological drought and flood in the source area, upper, middle and lower reaches respectively;

[0015] S14. Based on the calculated indicators representing meteorological and hydrological droughts and floods in the source region, upper, middle and lower reaches, analyze the evolution patterns of drought and flood events in the source region, upper, middle and lower reaches of the basin.

[0016] Furthermore, in step S12, the index representing meteorological drought and flood in the source region-upper-middle-lower reaches is the Standardized Precipitation Index (SPI), and the index representing hydrological drought and flood in the source region-upper-middle-lower reaches is the Standardized Runoff Index (SRI).

[0017] The formulas for calculating the Standardized Precipitation Index (SPI) or the Standardized Runoff Index (SRI) are as follows:

[0018] When 0 < F ( z When )≤0.5, let :

[0019]

[0020] When 0.5 < F ( z When )≤1, let :

[0021]

[0022] In the formula, F ( z ( ) refers to the decrease in water volume or runoff on a certain time scale. z The probability density function that satisfies the gamma distribution. k Here are the parameters for the transformation between the gamma distribution and the standard normal distribution: c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, c3 = 1.432788, c4 = 0.189269, c5 = 0.001308; where, , Let Γ be a function of precipitation / runoff z. and The parameter is estimated using the maximum likelihood method.

[0023] The beneficial effects of the above-mentioned further solutions are as follows: The present invention systematically collects and organizes historical hydrological and meteorological data of large watersheds, constructs meteorological and hydrological drought and flood sequences considering different time scales of different sections of the watershed from the source area to the upper, middle and lower reaches; it mines and integrates actual extreme hydrological event data of the watershed, and adopts a combination of mathematical statistics and spatiotemporal differentiation methods to focus on analyzing the trend, periodicity and spatial variability of the changes of various elements in extreme hydrological years.

[0024] Further, step S2 specifically includes:

[0025] S21. Analyze the correlation between SPI and SRI in the same region and between SRI and SRI in different partitions at different time scales, and select the SPI and SRI sequences at the time scale with the highest correlation.

[0026] S22. Calculate the coherence, gain, and phase between SPI sequences and SRI sequences in different selected regions and under different drought and flood types;

[0027] S23. Based on the calculated coherence, gain, and phase, analyze the response relationship and hysteresis effect between different zones and different types of drought and flood.

[0028] Furthermore, in step S22, the coherence calculation formula is:

[0029]

[0030] In the formula, For coherence, For SPI sequences, It is an SRI sequence;

[0031] In step S22, the gain The calculation formula is:

[0032]

[0033] In the formula, For gain spectrum, Let be the real part of the complex transfer function. This represents the imaginary part of the complex transfer function;

[0034] phase ( The calculation formula is:

[0035]

[0036] In step S23, the method for analyzing whether a response relationship exists is as follows:

[0037] Based on the calculated coherence, the knife-cut method is used to determine the significance threshold of coherence at a 95% confidence level. That is, if the coherence of a given signal frequency is greater than the threshold, then the response relationship of that frequency is statistically significant.

[0038] In step S23, the method for analyzing the hysteresis effect is as follows:

[0039] Converting phase lag to time lag in the time domain, for a time P where the response relationship is significant, the formula for converting phase lag to time lag is:

[0040]

[0041] In the formula, To analyze and estimate the phase lag between two signals for the transfer function, This corresponds to the time lag.

[0042] The beneficial effects of the above-mentioned further scheme are as follows: The above scheme adopts integrated digital signal processing technology, and analyzes the spectral variation characteristics of meteorological-hydrological drought and flood in different spatial domains from the perspectives of coherence, intensity and time delay, effectively quantifies the response intensity between the two, accurately identifies the lag time (in days), and explores the response relationship and lag effect of drought and flood encounters at different temporal and spatial scales in the watershed.

[0043] Further, step S3 specifically includes:

[0044] S31. For each region's SPI and SRI sequences, select their corresponding marginal distribution functions, and then construct a Copula joint distribution model for the SPI and SRI sequences.

[0045] S32. The AIC minimum criterion method is used to select the function with the highest fit from the Archimedean Copula functions;

[0046] S33. Based on the selected Copula function with the highest fitting degree, calculate the drought and flood evolution probability of different combinations of drought and flood events;

[0047] S34. Based on the calculated drought and flood evolution probability, calculate the joint probability of different combinations of drought and flood events in the same region and in different sub-regions at different time scales.

[0048] Furthermore, in step S33, the combined drought and flood events include simultaneous flooding, simultaneous normality, simultaneous drought, flood and drought, drought and flood, flood and normality, normality and flood, normality and drought, and drought and normality events under synchronous and asynchronous scenarios. The corresponding drought and flood evolution probability calculation formulas are as follows:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, The drought and flood evolution probabilities were calculated for the selected Copula function. X For SPI sequences, Y It is an SRI sequence. , and These are the marginal distribution functions of the SPI sequence, the SRI sequence, and the Copula functions of both the SPI and SRI sequences. and These are the SPI and SRI drought and flood thresholds, respectively.

[0059] The beneficial effects of the above-mentioned further scheme are as follows: The above scheme constructs a two-dimensional joint distribution model of drought and flood coupled with meteorology and hydrology, quantitatively identifies the joint probability of drought and flood encounter in the watershed, and lays the foundation for further analysis of the impact of drought-drought, flood-flood superposition and flood-drought occurrence under different scenarios of drought and flood synchronization and asynchronous occurrence.

[0060] Further, step S4 specifically includes:

[0061] S41. Select event pairs that occur simultaneously in synchronous and asynchronous scenarios, such as SPI sequences and SRI sequences in the same region and SRI sequences in different partitions, and simultaneously in flood and drought scenarios.

[0062] Select flood-drought event pairs that occur in synchronous and asynchronous scenarios between SPI sequences and SRI sequences in the same region, and between SRI sequences and SRI sequences in different partitions.

[0063] S42. For the selected event pair, compare its corresponding current sequence with its lagged sequence determined according to the lag time, and calculate the difference between the lagged SRI sequence and the current SRI sequence.

[0064] S43. Based on the calculated difference, assess the impact of simultaneous drought and flood events, as well as flood-drought events, to achieve a drought and flood encounter characteristic assessment.

[0065] Among them, when drought and flood events occur simultaneously, the assessment will determine whether the severity of drought and flood in the watershed will worsen;

[0066] When a flood-drought event occurs, assess whether the severity of the drought will be mitigated.

[0067] The beneficial effects of the above-mentioned further scheme are as follows: In the above scheme, drought-drought, flood-flood (unfavorable scenario) and flood-drought (favorable scenario) event pairs are selected from different drought-flood combination scenarios. The exponential anomalies of the current sequence and the lagged sequence are compared to determine the impact of the three drought-flood encounter scenarios. Attached Figure Description

[0068] Figure 1This is a flowchart illustrating the steps of a method for assessing the characteristics of drought and flood encounters in a watershed based on meteorological and hydrological conditions, as described in an embodiment of the present invention.

[0069] Figure 2 This is a diagram showing the impact of flooding on upstream SPI-upstream SRI and upstream SRI-midstream SRI in an embodiment of the present invention.

[0070] Figure 3 This diagram illustrates the impact of drought on upstream SPI-upstream SRI and upstream SRI-midstream SRI in an embodiment of the present invention.

[0071] Figure 4 This is a diagram showing the impact of flooding on the upstream SPI-upstream SRI and upstream SRI-midstream SRI in an embodiment of the present invention. Detailed Implementation

[0072] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0073] This invention provides a method for assessing the meteorological-hydrological characteristics of drought and flood encounters in a watershed, such as... Figure 1 As shown, it includes the following steps:

[0074] S1. Based on the natural geographical characteristics of the watershed, select indicators to characterize meteorological drought and flood and hydrological drought and flood, and analyze the evolution pattern of drought and flood events in the source area, upper, middle and lower reaches of the watershed;

[0075] S2. Based on the evolution of drought and flood events in the source area, upper, middle and lower reaches of the watershed, analyze the response relationship and lag effect among different regions and different types of drought and flood in the watershed;

[0076] S3. Quantitatively analyze the probability of the joint occurrence of different combinations of meteorological-hydrological and hydrological drought and flood events in the same region and in different sub-regions;

[0077] S4. Based on the combined events of drought and flood and their lag time, analyze the impact of flood-flood, drought-drought and flood-drought events to achieve an impact assessment of drought and flood events.

[0078] In this embodiment of the invention, based on monthly precipitation data from meteorological stations in the Yangtze River Basin from 1957 to 2020, and monthly runoff data from the Zhimenda, Yichang, Hukou, and Datong hydrological stations, data analysis and drought / flood encounter characteristics assessment are performed. Therefore, step S1 of this embodiment of the invention specifically includes:

[0079] S11. Divide the watershed into source area, upper-middle-lower reaches catchment area;

[0080] S12. Collect precipitation data from meteorological stations in the basin and calculate the indicators representing meteorological drought and flood in the source area, upper, middle and lower reaches respectively;

[0081] S13. Collect runoff data from hydrological stations in the watershed and calculate indicators representing hydrological drought and flood in the source area, upper, middle and lower reaches respectively;

[0082] S14. Based on the calculated indicators representing meteorological and hydrological droughts and floods in the source region, upper, middle and lower reaches, analyze the evolution patterns of drought and flood events in the source region, upper, middle and lower reaches of the basin.

[0083] In this embodiment, four representative hydrological stations—Zhimenda, Yichang, Hukou, and Datong—are selected to divide the watershed into source, upper, middle, and lower reaches.

[0084] In step S12 of this embodiment, the index representing meteorological drought and flood in the source region-upper-middle-lower reaches is the Standardized Precipitation Index (SPI), and the index representing hydrological drought and flood in the source region-upper-middle-lower reaches is the Standardized Runoff Index (SRI).

[0085] The formulas for calculating the Standardized Precipitation Index (SPI) or the Standardized Runoff Index (SRI) are as follows:

[0086] When 0 < F ( z When )≤0.5, let :

[0087]

[0088] When 0.5 < F ( z When )≤1, let :

[0089]

[0090] In the formula, F ( z ( ) refers to the decrease in water volume or runoff on a certain time scale. z The probability density function that satisfies the gamma distribution. k Here are the parameters for the transformation between the gamma distribution and the standard normal distribution: c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, c3 = 1.432788, c4 = 0.189269, c5 = 0.001308; where, , Let Γ be a function of precipitation / runoff z. and As parameters, the maximum likelihood method is used to estimate them, and the cumulative probability of each term is calculated. F ( z Normalization yields the corresponding SPI (SRI).

[0091] In this embodiment of the invention, step S2 specifically includes:

[0092] S21. Analyze the correlation between SPI and SRI in the same region and between SRI and SRI in different partitions at different time scales, and select the SPI and SRI sequences at the time scale with the highest correlation.

[0093] S22. Calculate the coherence, gain, and phase between SPI sequences and SRI sequences in different selected regions and under different drought and flood types;

[0094] S23. Based on the calculated coherence, gain, and phase, analyze the response relationship and hysteresis effect between different zones and different types of drought and flood.

[0095] In step S21 of this embodiment, the SPI and SRI in the same region include source region SRI-source region SPI, upstream SRI-upstream SPI, midstream SRI-midstream SPI, and downstream SRI-downstream SPI; the SRI and SRI in different partitions include source region SRI-upstream SRI, upstream SRI-midstream SRI, and midstream SRI-downstream SRI.

[0096] In step 22 of this embodiment, the coherence calculation formula is as follows:

[0097]

[0098] In the formula, For coherence, For SPI sequences, It is an SRI sequence;

[0099] In step S22, the gain The calculation formula is:

[0100]

[0101] In the formula, For gain spectrum, Let be the real part of the complex transfer function. This represents the imaginary part of the complex transfer function;

[0102] phase ( The calculation formula is:

[0103]

[0104] In step S23, the method for analyzing whether a response relationship exists is as follows:

[0105] Based on the calculated coherence, the knife-cut method is used to determine the significance threshold of coherence at a 95% confidence level. That is, if the coherence of a given signal frequency is greater than the threshold, then the response relationship of that frequency is statistically significant.

[0106] In step S23 of this embodiment, the method for analyzing the hysteresis effect is as follows:

[0107] Converting phase lag to time lag in the time domain, for a time P where the response relationship is significant, the formula for converting phase lag to time lag is:

[0108]

[0109] In the formula, For the phase lag between two signals estimated by transfer function analysis (TFA), This corresponds to the time lag.

[0110] In this embodiment, based on the aforementioned data selection, firstly, coherence analysis was performed on the SPI-SRI and SRI-SRI sequences at 1, 3, 6, and 12-month scales in different spatial domains of the Yangtze River Basin. Secondly, the 12-month scale sequence with the highest correlation was selected, and the transfer function analysis method was applied to analyze the coherence, gain, and phase between meteorological and hydrological drought and flood indices, as well as between different regional hydrological drought and flood indices. The calculated correlation coefficients in this embodiment are shown in Table 1. The correlation coefficients between meteorological drought and flood indices in the Yangtze River source region and the hydrological drought and flood indices in the source region, upper reaches, middle reaches, and lower reaches are 0.577, 0.129, 0.185, and 0.228, respectively, indicating a strong correlation between meteorological drought and flood indices and hydrological drought and flood indices in the source region. The correlation coefficients between meteorological drought and flood indices in the upper, middle, and lower reaches and hydrological drought and flood indices reach 0.725, 0.545, and 0.630, respectively, all showing strong correlations. The correlation coefficients between hydrological drought and flood indices in the source region and hydrological drought and flood indices in the upper, middle, and lower reaches also decrease sequentially.

[0111] Table 1:

[0112]

[0113] In this embodiment, the transfer function analysis method is applied to distinguish the coherence between two sequences, identify the gain between two sequences, and the phase between two sequences. In this embodiment, the results of the transfer function analysis show that, regardless of whether it is between SPI and SRI in the same region or between SRI and SRI in different partitions, the lag period is successively extended. The sensitive distance is from the local area to the next watershed interval. The lag period results are shown in Table 2.

[0114] Table 2:

[0115]

[0116] Step S3 in this embodiment of the invention is specifically as follows:

[0117] S31. For each region's SPI and SRI sequences, select their corresponding marginal distribution functions, and then construct a Copula joint distribution model for the SPI and SRI sequences.

[0118] S32. The AIC minimum criterion method is used to select the function with the highest fit from the Archimedean Copula functions;

[0119] S33. Based on the selected Copula function with the highest fitting degree, calculate the drought and flood evolution probability of different combinations of drought and flood events;

[0120] S34. Based on the calculated drought and flood evolution probability, calculate the joint probability of different combinations of drought and flood events in the same region and in different sub-regions at different time scales.

[0121] In step S31 of this embodiment, for each region's SPI and SRI sequences, the KS test is applied to select the most suitable marginal distribution function from generalized extreme value distributions, normal distributions, Weiber distributions, etc. F 1( x ), F 2( y ).

[0122] In step S32 of this embodiment, the AIC minimum criterion method is used to select the function with the highest good fit from the above Archimedesian Copula functions. The expression of the AIC criterion method is as follows:

[0123]

[0124]

[0125] In the formula, n For the sample size, m For the number of parameters, P ei , P i These are the empirical probability and the theoretical calculated probability of the joint distribution, respectively. The smaller the value of AIC, the better the fit of the Copula function.

[0126] In step S33 of this embodiment, the combined drought and flood events include simultaneous flooding, simultaneous normality, simultaneous drought, flood and drought, drought and flood, flood and normality, normality and drought, normality and drought, and drought and normality events under synchronous and asynchronous scenarios. The corresponding drought and flood evolution probability calculation formulas are as follows:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] In the formula, The drought and flood evolution probabilities were calculated for the selected Copula function. X For SPI sequences, Y It is an SRI sequence. , and These are the marginal distribution functions of the SPI sequence, the SRI sequence, and the Copula functions of both the SPI and SRI sequences. and These are the SPI and SRI thresholds for drought and flood (SPI / SRI > 0.5 indicates flooding, and SPI / SRI < -0.5 indicates drought).

[0137] In this embodiment, a two-dimensional joint distribution model is constructed based on the Copula function. Appropriate marginal distribution functions are selected respectively, and the Copula function with the best fit is selected from Clayton, Frank and Gumbel. Finally, the joint probability results of nine combinations of meteorological-hydrological events in the same part of the Yangtze River Basin and drought and flood events in different parts are obtained, as shown in Table 3.

[0138] Table 3:

[0139]

[0140] In step S4 of this embodiment of the invention, based on nine possible combinations of meteorological-hydrological events in the same region of the Yangtze River Basin and hydrological events in different regions, flood-flood and drought-drought event pairs (unfavorable scenarios) are extracted. The exponential anomalies of the current sequence and the lagged sequence are compared to determine the impact of the three flood-drought scenarios. Therefore, step S4 of this embodiment of the invention specifically includes:

[0141] S41. Select event pairs that occur simultaneously in synchronous and asynchronous scenarios, such as SPI sequences and SRI sequences in the same region and SRI sequences in different partitions, and simultaneously in flood and drought scenarios.

[0142] Select flood-drought event pairs that occur in synchronous and asynchronous scenarios between SPI sequences and SRI sequences in the same region, and between SRI sequences and SRI sequences in different partitions.

[0143] S42. For the selected event pair, compare its corresponding current sequence with its lagged sequence determined according to the lag time, and calculate the difference between the lagged SRI sequence and the current SRI sequence.

[0144] S43. Based on the calculated difference, assess the impact of simultaneous drought and flood events, as well as flood-drought events, to achieve a drought and flood encounter characteristic assessment.

[0145] Among them, when drought and flood events occur simultaneously, the assessment will determine whether the severity of drought and flood in the watershed will worsen;

[0146] When a flood-drought event occurs, assess whether the severity of the drought will be mitigated.

[0147] In this embodiment, taking upstream SPI-upstream SRI and upstream SRI-midstream SRI as examples, the results of the three drought and flood combination scenarios are as follows: Figure 2-4 As shown, most events are aggravated when both drought and flood occur simultaneously, and drought is mitigated when both occur simultaneously.

[0148] In the middle reaches of the Yangtze River, 36 meteorological-hydrological flood-flood event pairs were successfully matched. Among them, 18 hydrological flood events were aggravated by meteorological floods, of which 5 had significant adverse effects and escalated in severity. In the upper and middle reaches of the Yangtze River, 66 hydrological flood-flood event pairs were successfully matched. Among them, 25 were aggravated by upstream floods, of which 3 had significant adverse effects.

[0149] In the middle reaches of the Yangtze River, 58 meteorological-hydrological drought-drought event pairs were successfully matched. Among them, 35 hydrological drought events were aggravated by meteorological drought, accounting for 60%. Among these, 10 had significant adverse effects and escalated in severity. In the upper and middle reaches, 90 drought-drought event pairs were successfully matched. Among them, 41 drought events were aggravated by drought in the upper reaches, of which 18 had significant adverse effects.

[0150] Three meteorological-hydrological flood-drought event pairs were successfully matched in the middle reaches of the Yangtze River Basin, with two hydrological drought events alleviated due to meteorological floods. Sixty-nine hydrological flood-drought event pairs were successfully matched in the upper and middle reaches, with 46 drought events alleviated due to upstream floods.

[0151] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0152] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for assessing the meteorological-hydrological characteristics of drought and flood encounters in a watershed, characterized in that, Includes the following steps: S1. Based on the natural geographical characteristics of the watershed, select indicators to characterize meteorological drought and flood and hydrological drought and flood, and analyze the evolution pattern of drought and flood events in the source area, upper, middle and lower reaches of the watershed; Step S1 specifically involves: S11. Divide the watershed into source area, upper-middle-lower reaches catchment area; S12. Collect precipitation data from meteorological stations in the basin and calculate the indicators representing meteorological drought and flood in the source area, upper, middle and lower reaches respectively; S13. Collect runoff data from hydrological stations in the watershed and calculate indicators representing hydrological drought and flood in the source area, upper, middle and lower reaches respectively; S14. Based on the calculated indicators representing meteorological and hydrological droughts and floods in the source region, upper, middle and lower reaches, analyze the evolution patterns of drought and flood events in the source region, upper, middle and lower reaches of the basin. S2. Based on the evolution of drought and flood events in the source area, upper, middle and lower reaches of the watershed, analyze the response relationship and lag effect among different regions and different types of drought and flood in the watershed; Step S2 specifically involves: S21. Analyze the correlation between SPI and SRI in the same region and between SRI and SRI in different partitions at different time scales, and select the SPI and SRI sequences at the time scale with the highest correlation. S22. Calculate the coherence, gain, and phase between SPI sequences and SRI sequences in different selected regions and under different drought and flood types; S23. Based on the calculated coherence, gain, and phase, analyze the response relationship and hysteresis effect between different zones and different types of drought and flood; S3. Quantitatively analyze the probability of the joint occurrence of different combinations of meteorological-hydrological and hydrological drought and flood events in the same region and in different sub-regions; Step S3 specifically involves: S31. For each region's SPI and SRI sequences, select their corresponding marginal distribution functions, and then construct a Copula joint distribution model for the SPI and SRI sequences. S32. The AIC minimum criterion method is used to select the function with the highest fit from the Archimedean Copula functions; S33. Based on the selected Copula function with the highest fitting degree, calculate the drought and flood evolution probability of different combinations of drought and flood events; S34. Based on the calculated drought and flood evolution probability, calculate the joint probability of different combinations of drought and flood events in the same region and in different sub-regions at different time scales. S4. Based on the combined events of drought and flood and their lag time, analyze the impact of flood-flood, drought-drought and flood-drought events to achieve an impact assessment of drought and flood events. Step S4 specifically involves: S41. Select event pairs that occur simultaneously with floods, droughts, or flood-drought events in synchronous and asynchronous scenarios between SPI sequences and SRI sequences in the same region and between SRI sequences in different partitions. S42. For the selected event pair, compare its corresponding current sequence with its lagged sequence determined according to the lag time, and calculate the difference between the lagged SRI sequence and the current SRI sequence. S43. Based on the calculated difference, assess the impact of simultaneous drought and flood events, as well as flood-drought events, to achieve a drought and flood encounter characteristic assessment. Among them, when drought and flood events occur simultaneously, the assessment will determine whether the severity of drought and flood in the watershed will worsen; When a flood-drought event occurs, assess whether the severity of the drought will be mitigated.

2. The method for assessing the characteristics of drought and flood encounters in a watershed based on meteorological and hydrological conditions according to claim 1, characterized in that, In step S22, the coherence calculation formula is as follows: In the formula, For coherence, For SPI sequences, It is an SRI sequence; In step S22, the gain The calculation formula is: In the formula, For gain spectrum, Let be the real part of the complex transfer function. This represents the imaginary part of the complex transfer function; phase ( The calculation formula is: In step S23, the method for analyzing whether a response relationship exists is as follows: Based on the calculated coherence, the knife-cut method is used to determine the significance threshold of coherence at a 95% confidence level. That is, if the coherence of a given signal frequency is greater than the threshold, then the response relationship of that frequency is statistically significant. In step S23, the method for analyzing the hysteresis effect is as follows: Converting phase lag to time lag in the time domain, for a time P where the response relationship is significant, the formula for converting phase lag to time lag is: In the formula, To analyze and estimate the phase lag between two signals for the transfer function, This corresponds to the time lag.

3. The method for assessing the characteristics of drought and flood encounters in a watershed based on meteorological and hydrological conditions according to claim 1, characterized in that, In step S33, the combined drought and flood events include simultaneous flood, simultaneous level, simultaneous drought, flood and drought, drought and flood, flood and level, level and flood, level and drought, level and drought, and drought and level events under synchronous and asynchronous scenarios, and calculate the corresponding drought and flood evolution probability.

Images