A method and system for evaluating hydrological variation of a complex river basin

Abstract

The application discloses a kind of complex basin's hydrological variation evaluation method and system, it is related to hydrological evaluation technical field, the method includes: obtaining the surface water resource quantity data stream of target complex basin, multidimensional climate change data stream and dynamic underlying surface change data stream;Carry out spatio-temporal variation significance detection;Surface water resource quantity data stream is carried out spatio-temporal characteristic driving variation evaluation, obtains hydrological variation evaluation graph;Obtain hydrological variation climate contribution graph;Carry out multidimensional contribution compensation, obtain hydrological variation contribution distribution graph.The present application solves the technical problems that the prior art cannot accurately identify and quantitatively characterize the spatio-temporal characteristics of complex basin hydrological variation, resulting in insufficient overall assessment and quantification of basin hydrological variation, achieves spatio-temporal accurate detection and coupled synergistic effect quantification of complex basin hydrological variation, and improves the overall assessment and accuracy of basin hydrological variation.

Description

Technical Field

[0001] This invention relates to the field of hydrological assessment technology, specifically to a method and system for assessing hydrological variability in complex watersheds. Background Technology

[0002] Accurate analysis of the spatiotemporal variability patterns and driving mechanisms of water resources is a crucial prerequisite for refined management and scientific regulation of water resources in complex watersheds, and a key research focus in the field of hydrological assessment. Current research on watershed hydrological variability has developed diverse analytical methods. Some studies combine water resources survey and assessment results to identify variability characteristics and attribute driving forces, while others rely on relevant diagnostic methods to complete systematic analyses of watershed hydrological variability. However, existing research still has significant limitations: it often focuses on hydrological variability analysis in single or simple watersheds. For watershed hydrological variability with complex topography, river systems, and underlying surface types, a systematic assessment system that considers both spatiotemporal dimensions has not yet been established, making it difficult to accurately detect and quantify the temporal characteristics and spatial distribution of hydrological variability. Furthermore, existing driving attribution analyses often revolve around single factors such as climate or underlying surface, or only conduct simple multi-factor overlay analyses, failing to fully consider the coupling and synergistic effects between climate and underlying surface factors. This makes it impossible to quantify the coupling and synergistic effects between the two, and thus difficult to comprehensively and accurately reveal the overall characteristics and integrated driving mechanisms of hydrological variability in complex watersheds. Existing technologies struggle to accurately identify and quantitatively characterize the spatiotemporal features of complex watershed hydrological variations, resulting in insufficient comprehensiveness and quantification in the assessment of watershed hydrological variations. Summary of the Invention

[0003] This application provides a method and system for assessing hydrological variability in complex watersheds, which addresses the technical problem that existing technologies struggle to accurately identify and quantitatively characterize the spatiotemporal features of hydrological variability in complex watersheds, resulting in insufficient comprehensiveness and quantification in assessing watershed hydrological variability.

[0004] In view of the above problems, this application provides a hydrological variability assessment system for complex watersheds.

[0005] A first aspect of this application provides a method for assessing hydrological variability in complex watersheds, the method comprising: This process involves acquiring surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for a target complex watershed; performing multi-node spatiotemporal variability significance detection on the surface water resources data streams to obtain the distribution of hydrological temporal and spatial variability significance; conducting spatiotemporal characteristic-driven variability assessment on the surface water resources data streams based on the hydrological temporal and spatial variability significance distributions to obtain a hydrological variability assessment map; analyzing the contribution of climate factors to the hydrological variability assessment map based on the multi-dimensional climate change data streams to obtain a hydrological variability climate contribution map; introducing a climate-underlying surface coupled collaborative contribution model, and combining it with the dynamic underlying surface change data streams to perform multi-dimensional contribution compensation under the underlying surface contribution analysis on the hydrological variability climate contribution map to obtain a hydrological variability contribution distribution map.

[0006] A second aspect of this application provides a hydrological variability assessment system for complex watersheds, the system comprising: The system comprises the following modules: a data acquisition module for acquiring surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for a target complex watershed; a significance detection module for performing multi-node spatiotemporal variability significance detection on the surface water resources data streams to obtain the distribution of hydrological temporal variability significance and the distribution of hydrological spatial variability significance; a hydrological variability assessment map acquisition module for performing spatiotemporal characteristic-driven variability assessment on the surface water resources data streams based on the distribution of hydrological temporal variability significance and the distribution of hydrological spatial variability significance to obtain a hydrological variability assessment map; a climate factor contribution analysis module for analyzing the contribution of climate factors on the hydrological variability assessment map based on the multi-dimensional climate change data streams to obtain a hydrological variability climate contribution map; and a multi-dimensional contribution compensation module for introducing a climate-underlying surface coupled collaborative contribution model and combining it with the dynamic underlying surface change data streams to perform multi-dimensional contribution compensation on the hydrological variability climate contribution map under the underlying surface contribution analysis to obtain a hydrological variability contribution distribution map.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This method acquires surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for a target complex watershed; performs multi-node spatiotemporal variability significance detection to obtain the distribution of hydrological temporal and spatial variability significance; assesses the spatiotemporal characteristic-driven variability of the surface water resources data stream to obtain a hydrological variability assessment map; analyzes the contribution of climate factors to the hydrological variability assessment map to obtain a hydrological variability climate contribution map; and combines the dynamic underlying surface change data stream to perform multi-dimensional contribution compensation under the underlying surface contribution analysis on the hydrological variability climate contribution map to obtain a hydrological variability contribution distribution map. This achieves precise spatiotemporal detection and quantitative assessment of coupled synergistic effects of hydrological variability in complex watersheds, improving the comprehensiveness and accuracy of watershed hydrological variability assessment. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of the hydrological variability assessment method for complex watersheds in this application; Figure 2 This is a schematic diagram of the hydrological variability assessment system for complex watersheds in this application.

[0010] Figure labeling: Data acquisition module 10, significance detection module 20, hydrological variability assessment map acquisition module 30, climate factor contribution analysis module 40, multidimensional contribution compensation module 50. Detailed Implementation

[0011] This application provides a method and system for assessing hydrological variability in complex watersheds, which addresses the technical problem that existing technologies struggle to accurately identify and quantitatively characterize the spatiotemporal features of hydrological variability in complex watersheds, resulting in insufficient comprehensiveness and quantification in assessing watershed hydrological variability.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] Example 1 like Figure 1As shown, this application provides a method for assessing hydrological variability in complex watersheds, the method comprising: Step S100: Obtain surface water resources data stream, multi-dimensional climate change data stream, and dynamic underlying surface change data stream for the target complex watershed.

[0014] Specifically, comprehensive hydrological data collection and integration will be carried out for complex watersheds, and surface water resources data streams that reflect the core hydrological conditions of the watershed will be acquired in a targeted manner, covering surface water resources monitoring data at different monitoring nodes and time scales within the watershed. Simultaneously, multi-dimensional climate change data streams containing indicators such as temperature, precipitation, humidity, and evaporation will be acquired, as well as dynamic underlying surface change data streams that reflect changes in land use type, vegetation cover, topographic evolution, and soil physicochemical properties within the watershed.

[0015] Step S200: Perform multi-node spatiotemporal variation significance detection on the surface water resources data stream to obtain the hydrological temporal variation significance distribution and the hydrological spatial variation significance distribution.

[0016] Specifically, the surface water resources data stream is first divided according to multiple time scales to obtain multiple time-scale hydrological sequences. Joint detection of Hearst variation and sliding variation is then performed on these sequences. The sliding variation coefficient is obtained by calculating the Hearst variation coefficient, the mean and standard deviation of each time series, and the ratio. The two types of coefficients are then normalized, and a weighted fusion calculation is performed based on the hydrological variation significance conditions containing Hearst variation weights and sliding variation weights to obtain the significant distribution of hydrological temporal variation. Simultaneously, the surface water resources data stream is divided according to multiple sub-basins of the target complex watershed to obtain multiple spatial node hydrological sequences. The same Hearst variation and sliding variation joint detection method as the time-node hydrological sequences is used to complete coefficient calculation, normalization, and weighted fusion operations, ultimately obtaining the significant distribution of hydrological spatial variation.

[0017] Step S300: Based on the significant distribution of hydrological temporal variation and the significant distribution of hydrological spatial variation, perform spatiotemporal characteristic-driven variation assessment on the surface water resources data stream to obtain a hydrological variation assessment map.

[0018] Specifically, based on predetermined significance values, the significance distributions of hydrological temporal variation and spatial variation are tested to identify the first and second prominent significance distributions. Based on these two types of prominent significance distributions, multiple prominently correlated hydrological sequences are selectively extracted from the surface water resources data stream. These sequences are then input into a hydrological variation assessment model for quantitative analysis, yielding multiple hydrological variation assessment results. Finally, the spatiotemporal characteristics and quantitative data of all hydrological variation assessment results are integrated to construct a hydrological variation assessment map that intuitively reflects the spatiotemporal variation characteristics of surface water resources in the target complex watershed.

[0019] Step S400: Analyze the contribution of climate factors to the hydrological variability assessment map based on the multidimensional climate change data stream to obtain a hydrological variability climate contribution map.

[0020] Specifically, based on the spatiotemporal variation characteristics of the hydrological variability assessment map, multi-dimensional climate change data streams are accurately mapped and matched to obtain climate characteristic matching matrices corresponding to each hydrological variability assessment result. Causal tracing analysis is then conducted on the corresponding hydrological variability assessment results based on these climate characteristic matching matrices, identifying multiple hydrological variability climate tracing paths. For each hydrological variability climate tracing path, contribution evaluation samples are retrieved to determine multiple climate factor contribution sample areas. The average value of the climate contribution data for each sample area is calculated to obtain the contribution degree of multiple climate factors. Finally, the hydrological variability assessment results, the identified hydrological variability climate tracing paths, and the calculated climate factor contribution degrees are integrated to generate a hydrological variability climate contribution map that clearly reflects the contribution patterns of climate factors to the hydrological variability of the watershed.

[0021] Step S500: Introduce the climate underlying surface coupled collaborative contribution model, and combine the dynamic underlying surface change data stream to perform multidimensional contribution compensation under the underlying surface contribution analysis of the hydrological variation climate contribution map, and obtain the hydrological variation contribution distribution map.

[0022] Specifically, the dynamic underlying surface change data stream is first mapped and matched based on the spatiotemporal variation characteristics of the hydrological variability assessment map to obtain the underlying surface change matching matrix corresponding to each hydrological variability assessment result. Based on this matrix, causal tracing and underlying surface factor contribution evaluation are carried out for each hydrological variability assessment result to obtain the hydrological variability underlying surface contribution map. Then, a climate-underlying surface coupled synergistic contribution model is introduced. The contribution degree of climate factors in the hydrological variability climate contribution map and the contribution degree of underlying surface factors in the hydrological variability underlying surface contribution map are substituted into the model. The coupled synergistic contribution analysis is completed by combining the corresponding synergistic coefficients of the two to obtain the coupled synergistic contribution distribution. Finally, based on the hydrological variability underlying surface contribution map and the coupled synergistic contribution distribution, multi-dimensional contribution compensation of the underlying surface contribution analysis dimension is performed on the hydrological variability climate contribution map. The contribution results of climate factors, underlying surface factors and their coupled synergistic effects are comprehensively integrated to finally obtain a hydrological variability contribution distribution map that can comprehensively reflect the contribution law of each driving factor of hydrological variability in the target complex watershed.

[0023] In one possible implementation, step S200 further includes: Step S210: Divide the surface water resources data stream according to multi-level time scales to obtain multiple time-scale hydrological sequences.

[0024] Step S220: Perform joint detection of Hurst variation and sliding variation on the multiple time-scale hydrological sequences to obtain the significance distribution of the hydrological time variation.

[0025] Step S230: Divide the surface water resources data stream according to multiple sub-basins of the target complex watershed, and obtain multiple spatial node hydrological sequences.

[0026] Step S240: Perform joint detection of Hurst variation and sliding variation on the hydrological sequences of the multiple spatial nodes to obtain the significance distribution of the hydrological spatial variation.

[0027] Specifically, for the surface water resources data stream of the target complex watershed, based on the time dimension research requirements of hydrological variation analysis, the monitoring data of the entire series of surface water resources is split, classified and normalized according to the time dimension of the preset multi-level time scales such as year, season and month. Invalid monitoring data is eliminated and missing key data is supplemented, and finally multiple time-scale hydrological sequences corresponding to different time scales, with continuous data and unified dimensions are formed.

[0028] Joint detection and analysis of Hurst variability and sliding variability were conducted on the hydrological sequences obtained from multiple time scales. First, Hurst variability was evaluated based on the temporal characteristics of the hydrological sequences at each time node, and multiple Hurst variability coefficients were calculated. Then, the mean and standard deviation of each hydrological sequence at each time scale were calculated, and the ratio of the standard deviation to the mean of each sequence was calculated to obtain multiple sliding variability coefficients. Subsequently, the Hurst variability coefficients and sliding variability coefficients were normalized to obtain the corresponding normalized values. Finally, the two types of normalized values ​​were weighted and fused based on the hydrological variability significance conditions that include Hurst variability weights and sliding variability weights. Based on the calculation results, a hydrological temporal variability significance distribution that can accurately reflect the variability characteristics and significance of surface water resources in the target complex watershed at different time scales was constructed and obtained.

[0029] Based on the water resources zoning plan of the target complex watershed, multiple sub-basins are delineated. Hydrological monitoring stations within each sub-basin serve as the core data collection benchmark. Spatial dimension decomposition, reorganization, and matching processing are carried out on the surface water resources data stream of the entire target complex watershed. Surface water resources monitoring data within the corresponding monitoring range are selected according to the sub-basin zoning. Invalid monitoring data across zoning is eliminated, and key hydrological data missing in each sub-basin are supplemented. At the same time, the hydrological data of each sub-basin are standardized and reorganized to ensure that the statistical caliber and time scale of the data in each zoning are consistent. Finally, multiple spatial node hydrological sequences that correspond one-to-one with each sub-basin, are continuous, and have unified dimensions are obtained.

[0030] A joint detection and analysis of Hearst variation and sliding variation was conducted on the hydrological sequences of multiple spatial nodes obtained from the sub-basin division. First, Hearst variation was evaluated based on the characteristics of the hydrological sequences of each spatial node, and the Hearst variation coefficient of each sub-basin was calculated. Then, the mean and standard deviation of the hydrological sequence of each spatial node were calculated, and the ratio of the standard deviation to the mean of each sequence was calculated to obtain the sliding variation coefficient of each sub-basin. Subsequently, all Hearst variation coefficients and sliding variation coefficients were normalized to achieve dimensional unification of the two types of coefficients. Finally, based on the hydrological variation significance condition including Hearst variation weight and sliding variation weight, the two types of normalized values ​​corresponding to each sub-basin were weighted and fused to obtain the hydrological spatial variation significance value of each sub-basin. Combining the spatial location and zoning characteristics of each sub-basin, all significance values ​​were systematically integrated and characterized according to the spatial distribution of the basin. Finally, the hydrological spatial variation significance distribution that can accurately characterize the degree of significance and distribution pattern of surface water resource spatial variation in each sub-basin of the target complex watershed was obtained.

[0031] In one possible implementation, step S220 further includes: Step S221: Evaluate the Hurst variation based on the multiple time-scale hydrological sequences and obtain multiple Hurst variation coefficients.

[0032] Step S222: Calculate the mean for each hydrological sequence at each time scale to obtain the mean of each time series, and calculate the standard deviation for each hydrological sequence at each time scale to obtain the standard deviation of each time series.

[0033] Step S223: Calculate the ratio of the standard deviation of each time series to the mean of each time series to obtain multiple moving coefficients of variation.

[0034] Step S224: Normalize the multiple Hearst variation coefficients and the multiple sliding variation coefficients to obtain multiple Hearst variation normalized values ​​and multiple sliding variation normalized values.

[0035] Step S225: Based on the hydrological variability significance condition, perform weighted fusion calculation on the multiple Hurst variability normalized values ​​and the multiple sliding variability normalized values ​​to obtain the hydrological time variability significance distribution.

[0036] Specifically, for each time scale hydrological sequence ,in For time sequence number, For the first The surface water resources monitoring values ​​at each time point are denoted as follows: The total length of the sequence is denoted as... The Hurst variation was evaluated using rescaled range R / S analysis. Selecting different time scale parameters within a time scale range The hydrological sequence at this time point is divided into non-overlapping subsequences ,in The subsequence number. , This represents the floor operation; for each subsequence Calculate its mean ,in The index of the data point within the subsequence. , For the i-th subsequence, the th... The monitoring values ​​of surface water resources at each location were used to calculate the cumulative deviation series based on the mean. ,in For the index variable of cumulative summation, Then the range of each subsequence is calculated. ,in This indicates the operation of finding the maximum value. This represents the minimum value operation and the sample standard deviation. Complete a single time scale After calculating all subsequences, calculate the average of the rescaled ranges of all subsequences for that time scale. Based on rescaled range With time scale Power-law relationship satisfied Convert it to a double logarithmic linear form ,in Represents the natural logarithm operation. Let be the Hearst coefficient of variation to be solved. Let be the linear fitting constant, with For independent variable, A univariate linear regression analysis is performed with the dependent variable, and the slope of the fitted line is the Hurst coefficient of variation for that hydrological time point. The above-described complete recalibrated range analysis process was repeated sequentially for all hydrological sequences at all time points to obtain multiple Hurst coefficients of variation that correspond one-to-one with the hydrological sequences at each time point.

[0037] For each hydrological series at a given time scale, the arithmetic mean of all surface water resources monitoring values ​​within the series is first calculated. The sum of all monitoring values ​​is then divided by the total number of monitoring points in the series to obtain the mean of each time series corresponding to that time node. Subsequently, based on this time series mean, the difference between each monitoring value in the series and the mean is calculated. The differences are squared and summed, and the sum is then divided by the number of monitoring points in the series minus one. Finally, the square root of the result is taken to obtain the standard deviation of each time series corresponding to that time node. This process of calculating the mean and standard deviation is repeated for all hydrological series at all time nodes to ultimately obtain the mean and standard deviation of each time series corresponding to each hydrological series at each time node.

[0038] For each hydrological sequence at a given time scale, the standard deviation of the corresponding time series is compared with the mean of the corresponding time series. That is, by dividing the standard deviation of each time series of the hydrological sequence at that time node by the mean of each time series, the sliding coefficient of variation, which characterizes the relative dispersion of the hydrological sequence at that time node, is obtained. The above ratio calculation process is repeated for all hydrological sequences at all time nodes, and finally multiple sliding coefficients of variation corresponding to each hydrological sequence at each time node are obtained.

[0039] For the acquired multiple Hearst variability coefficients and multiple sliding variability coefficients, extreme value normalization was used for normalization. First, extreme values ​​were extracted for all Hearst variability coefficients to determine their maximum and minimum values. Then, each Hearst variability coefficient was substituted into the normalization formula to calculate its corresponding normalized value. Simultaneously, extreme values ​​were extracted for all sliding variability coefficients to determine their maximum and minimum values. Each sliding variability coefficient was substituted into the same extreme value normalization formula to calculate its corresponding normalized value. Finally, multiple normalized values ​​of Hearst variability and multiple normalized values ​​of sliding variability were obtained, each corresponding one-to-one with the hydrological sequence at each time point. This achieved dimensional unification of the two types of coefficients, laying the foundation for subsequent weighted fusion calculations.

[0040] Based on the preset hydrological variability significance conditions including Hurst variation weights and sliding variation weights, a weighted fusion calculation is performed on the Hurst variation normalized value and sliding variation normalized value corresponding to the hydrological sequence at each time point. The formula for calculating the significance value of hydrological time variation is as follows: ,in Let be the significance value of the hydrological time variation corresponding to the hydrological sequence at time point t. The preset Hearst mutation weights, Let be the normalized value of the Hurst variation corresponding to the hydrological sequence at time t. The preset sliding variation weights, Let be the normalized moving variability value corresponding to the hydrological sequence at time t, and let the Hurst variability weight and the moving variability weight satisfy . The hydrological sequences at all time points are sequentially substituted into the formula to complete the weighted fusion calculation, resulting in multiple hydrological time variation significance values ​​that correspond one-to-one with the hydrological sequences at each time point. Then, combined with the time scale characteristics of each time point, all hydrological time variation significance values ​​are arranged and integrated in an orderly manner according to the time dimension. Finally, the hydrological time variation significance distribution can accurately characterize the degree of variation and distribution pattern of surface water resources in the target complex watershed at different time scales.

[0041] In one possible implementation, step S300 further includes: Step S310: Test the significance distribution of hydrological time variation according to the predetermined significance value of variation to obtain the first prominent significance distribution of variation.

[0042] Step S320: Test the significance distribution of hydrological spatial variation according to the predetermined significance value of variation to obtain the second prominent significance distribution of variation.

[0043] Step S330: Based on the first prominent variation significance distribution and the second prominent variation significance distribution, perform correlation data extraction on the surface water resources data stream to obtain multiple prominent correlation hydrological sequences.

[0044] Step S340: Input the multiple prominently correlated hydrological sequences into the hydrological variability assessment model to obtain multiple hydrological variability assessment results, and construct the hydrological variability assessment map based on the multiple hydrological variability assessment results.

[0045] Specifically, a predetermined significance value for variation required for watershed hydrological variation analysis is first determined as the threshold for time dimension variation testing. For each time node in the hydrological time variation significance distribution, a threshold comparison test is carried out one by one. The hydrological time variation significance values ​​with values ​​greater than or equal to the predetermined significance value, the corresponding time node information, and time scale characteristics are extracted and stored. The extracted data are then systematically integrated and structured according to the time scale and time node sequence. Finally, the relevant data set that meets the threshold conditions is constructed as the first prominent variation significance distribution. This distribution accurately represents the node location and significance characteristics of significant variation in the surface water resources of the target complex watershed in the time dimension.

[0046] Using the same predetermined significance value as in step S310 as the spatial dimension variation test threshold, threshold comparison tests are carried out one by one for the significance values ​​of hydrological spatial variation corresponding to each sub-basin in the hydrological spatial variation significance distribution. After the threshold comparison test is carried out one by one, the hydrological spatial variation significance values ​​with values ​​greater than or equal to the predetermined significance value, the corresponding spatial partition number and spatial location information are extracted and retained. The extracted relevant data are regularized and integrated according to the order of watershed spatial partitions, and finally a second prominent variation significance distribution is formed that can characterize the areas and significance of significant variation in the spatial dimension of surface water resources in the target complex watershed.

[0047] Based on the time node parameters of each significant variation in the first significant variation distribution and the spatial partition parameters of each significant variation in the second significant variation distribution, spatiotemporal correlation data are extracted from the surface water resources data stream. Each significant variation parameter in the first significant variation distribution corresponds to a set of significant variation hydrological data sequences in the time dimension, and each significant variation parameter in the second significant variation distribution corresponds to a set of significant variation hydrological data sequences in the spatial dimension. All extracted significant variation hydrological data are standardized and normalized to finally obtain multiple prominent correlation hydrological sequences that correspond one-to-one with each significant variation parameter.

[0048] The multiple prominently correlated hydrological sequences are input into the hydrological variability assessment model to obtain multiple hydrological variability assessment results, and the hydrological variability assessment map is constructed based on these results. The hydrological variability assessment model is a dedicated model for diagnosing hydrological sequence variability through multi-algorithm fusion. It is an unsupervised hydrological statistical analysis model that requires no training iteration. It relies on mature hydrological statistical verification algorithms to construct a three-level progressive modular internal structure, consisting of a trend mutation discrimination module, a precise location verification module, and a degree quantification output module. Each module works in a unidirectional, progressive manner to complete the full-dimensional analysis of hydrological variability. The model uses a combination of the Mann-Kendall trend and mutation detection algorithm, the Pettitt mutation point detection algorithm, and the coefficient of variation quantification algorithm to achieve the entire calculation process. The trend mutation discrimination module uses the Mann-Kendall trend and mutation detection algorithm to calculate forward and reverse order statistics, determine the trend Z-value and mutation UF / UB curve for each prominently correlated hydrological sequence, and classify the sequence as an upward trend variability, a downward trend variability, or a mutation-type variability, initially determining the variability type and the time of suspected variability occurrence. The precise location verification module uses the Pettitt mutation... The point test algorithm, using the suspected variation time output by the trend discrimination module as a reference, accurately locates significant abrupt changes in the hydrological sequence through sequence cumulative anomaly calculation and significance level testing. It also verifies and corrects the specific spatiotemporal nodes of variation occurrence identified in previous tests, ensuring the accuracy of abrupt change locations. The degree quantification output module, equipped with a coefficient of variation quantification algorithm, calculates the ratio of the standard deviation to the mean of each hydrological sequence to obtain the relative dispersion of the sequence, thus quantifying the degree of variation. It also integrates variation type and abrupt change location data from previous modules, combined with the significance level values ​​from each testing stage, to standardize and output multiple hydrological variation assessment results including variation type, abrupt change location, variation degree, and significance. Finally, based on the multi-level time scales and sub-basin spatial information of the target complex watershed, the hydrological variation assessment results are precisely matched with corresponding spatiotemporal units, numerically graded and mapped, and integrated into visualization layers to form a hydrological variation assessment map that comprehensively and intuitively represents the spatiotemporal variation characteristics, distribution location, and significance of surface water resources.

[0049] In one possible implementation, step S400 further includes: Step S410: Map and match the multi-dimensional climate change data stream according to the hydrological variability assessment map to obtain the matching matrix of each climate characteristic corresponding to each hydrological variability assessment result.

[0050] Step S420: Based on the matching matrix of each climate characteristic, perform causal tracing of each hydrological variation assessment result to obtain multiple hydrological variation climate tracing paths.

[0051] Step S430: Based on each hydrological variation climate tracing path, retrieve contribution evaluation samples to obtain multiple climate factor contribution sample areas.

[0052] Step S440: Calculate the mean for each climate factor contribution sample area to obtain the contribution of multiple climate factors.

[0053] Step S450: Organize the hydrological variation assessment results, the multiple hydrological variation climate tracing paths, and the contribution of multiple climate factors to generate the hydrological variation climate contribution map.

[0054] Specifically, using the spatiotemporal location, variation period, and spatial partition corresponding to each hydrological variation assessment result in the hydrological variation assessment map as the matching benchmark, the data of climate elements such as temperature, precipitation, evaporation, and wind speed in the same spatiotemporal range in the multidimensional climate change data stream are time-synchronized and spatially overlaid for mapping and matching. The temporal changes and spatial distribution characteristics of each climate element are correlated and aligned with the corresponding hydrological variation assessment results point by point and time by time. A matching matrix of each climate characteristic that can characterize the spatiotemporal correspondence between each climate element and hydrological variation is constructed and obtained.

[0055] Based on the spatiotemporal correspondence between climate elements and hydrological variability assessment results in each climate characteristic matching matrix, correlation analysis and time-by-time element-by-element tracing are used to conduct causal tracing of climate driving factors for each hydrological variability assessment result, identify key climate elements, change processes and transmission logic that cause hydrological variability, and sort out multiple hydrological variability climate tracing paths with complete correlation links from abnormal changes in climate elements to significant changes in hydrological sequences.

[0056] Based on the key climate elements, the time period of variation, and the spatial distribution range corresponding to each hydrological variation climate tracing path, spatiotemporal matching retrieval is performed in the multi-dimensional historical monitoring dataset. Historical samples with meteorological conditions and hydrological response characteristics highly similar to the path are selected. Each historical time period and spatial region obtained by matching are respectively designated as climate factor contribution sample areas, so that each hydrological variation climate tracing path corresponds to multiple climate factor contribution sample areas.

[0057] For each sample area contributing to climate factors, the arithmetic mean of the corresponding time-series data of climate elements and hydrological variability index data within the area is calculated to obtain the statistical mean of climate elements and hydrological variability index for each sample area. Then, by calculating the relative response between the mean of hydrological variability index and the mean of climate elements in the sample area, the contribution of multiple climate factors that characterize the magnitude of the influence of each climate factor on hydrological variability is quantified.

[0058] The results of various hydrological variability assessments, multiple hydrological variability climate tracing paths, and the contribution of multiple climate factors are uniformly organized, spatiotemporally aligned, and coupled. Using the time scale of the target complex watershed and the spatial information of its sub-watersheds as spatiotemporal benchmarks, the location, type, climate driving path, and contribution magnitude of variability are hierarchically labeled and visualized and integrated. Finally, a hydrological variability climate contribution map that can intuitively and comprehensively reflect the spatiotemporal characteristics of hydrological variability, the climate driving transmission path, and the strength of the influence of various climate factors is generated.

[0059] In one possible implementation, step S500 further includes: Step S510: Map and match the dynamic underlying surface change data stream according to the hydrological variability assessment map to obtain the underlying surface change matching matrix corresponding to each hydrological variability assessment result.

[0060] Step S520: Based on the matching matrix of each underlying surface change, perform causal tracing and evaluation of the contribution of underlying surface factors to each hydrological variation assessment result, and obtain the underlying surface contribution map of hydrological variation.

[0061] Step S530: Based on the climate underlying surface coupled synergistic contribution model, perform synergistic contribution analysis on the hydrological variation climate contribution map and the hydrological variation underlying surface contribution map to obtain the coupled synergistic contribution distribution.

[0062] Step S540: Perform multidimensional contribution compensation on the hydrological variation climate contribution map based on the hydrological variation underlying surface contribution map and the coupled synergistic contribution distribution to obtain the hydrological variation contribution distribution map.

[0063] Specifically, using the spatiotemporal location, variation period, and spatial zoning corresponding to each hydrological variation assessment result in the hydrological variation assessment map as the matching benchmark, the underlying surface element data such as land use, vegetation cover, topographic slope, river system, and engineering construction within the same spatiotemporal range in the dynamic underlying surface change data stream are time-synchronized and spatially overlaid for mapping and matching. The temporal changes and spatial distribution characteristics of each underlying surface element are correlated and aligned with the corresponding hydrological variation assessment results point by point and time by time. The underlying surface change matching matrix that can characterize the spatiotemporal correspondence between each underlying surface element and hydrological variation is constructed and obtained.

[0064] Based on the spatiotemporal correspondence between underlying surface elements and hydrological variability assessment results established by the matching matrices of various underlying surface changes, causal tracing of each hydrological variability assessment result is performed. By comparing and analyzing the timing and magnitude of changes in underlying surface elements such as land use change, vegetation cover change, water conservancy project construction, river channel modification, and topographic changes on a time-by-time and spatial-unit basis, consistency is determined with the timing and degree of trend variations and abrupt changes in the hydrological sequence. This identifies the driving relationship and transmission path between changes in underlying surface elements and hydrological variability. Simultaneously, an evaluation of the contribution of underlying surface factors is conducted, employing a five-dimensional weighted contribution quantification method to assess land use change and vegetation cover change. Five types of underlying surface factors—type transformation, increase or decrease in vegetation cover, impact of water conservancy project scheduling, expansion of impervious surfaces, and adjustment of topography and water system—are used to set corresponding analysis weights. The contribution of each type of underlying surface factor to hydrological variability is calculated by combining the intensity of change, spatial coverage, and hydrological variability response of each factor. The results of causal tracing and the five-dimensional weighted contribution evaluation are integrated and visualized to express the underlying surface driving mechanism, impact path, and contribution degree of each hydrological variability. Finally, a hydrological variability underlying surface contribution map that can comprehensively reflect the spatiotemporal distribution of hydrological variability, the underlying surface driving mechanism, and the contribution strength of each underlying surface factor is generated.

[0065] The hydrological variability climate contribution map and the hydrological variability underlying surface contribution map are input into a pre-constructed climate-underlying surface coupled collaborative contribution model. This model, with spatiotemporal matching and multi-factor collaborative analysis as its core architecture, is constructed using a random forest feature interaction algorithm and a gradient boosting regression (GBDT) coupled contribution decomposition algorithm. The overall structure consists of four layers: a spatiotemporal alignment layer, a feature fusion layer, a coupled interaction learning layer, and a collaborative contribution calculation layer. First, the spatiotemporal alignment layer accurately matches the climate contribution and underlying surface contribution within the same spatial unit and the same variation period. Then, the feature fusion layer combines the contribution of climate elements, the contribution of underlying surface factors, the variation type, and the variation... Multidimensional indicators such as heterogeneous intensity are fused into the model input features. Then, in the coupled interaction learning layer, the random forest algorithm is used to identify the nonlinear interaction relationship between climate and underlying surface. The GBDT algorithm is used to perform hierarchical fitting and decoupling of single driving contribution and cross-coupling contribution, eliminate independent components, and specifically quantify the coupled driving effect of mutual superposition, mutual amplification or mutual constraint between the two. Finally, the synergistic contribution solution layer outputs the coupled synergistic contribution values ​​generated by the combined action of climate and underlying surface in each spatiotemporal unit, completes the synergistic contribution analysis, and finally obtains the coupled synergistic contribution distribution that can reflect the magnitude, spatial distribution and temporal variation of the coupling effect.

[0066] Using the hydrological variability underlying surface contribution map as an independent driving reference and the coupled synergistic contribution distribution as the basis for interactive driving correction, multidimensional contribution compensation is performed on the climate contribution components of each spatiotemporal unit in the hydrological variability climate contribution map. On the basis of the original climate driving contribution values, the interactive contribution components brought about by the coupled synergistic effect are superimposed, while the parts that are repeatedly calculated with the underlying surface driving are eliminated. The unified reduction and error correction of single driving and coupled driving are completed. The independent climate contribution, the independent underlying surface contribution, and the climate-underlying surface coupled synergistic contribution are spatiotemporally integrated and quantified and normalized. Finally, a hydrological variability contribution distribution map that comprehensively and accurately reflects the overall contribution size, spatial distribution and temporal evolution of various driving factors to hydrological variability is generated.

[0067] In one possible implementation, step S500 further includes: The functional expression of the climate underlying surface coupled collaborative contribution model is as follows: ; in, The synergistic contribution of climate underlying surface to the assessment result of the nth hydrological variability is characterized, where n is a positive integer. Characterize the contribution of climate factors to the assessment results of the nth hydrological variability. Characterize the contribution of underlying surface factors to the assessment results of the nth hydrological variability. The synergy coefficient characterizes the climatic tracing path of hydrological variability corresponding to the nth hydrological variability assessment result and the underlying surface tracing path of hydrological variability corresponding to the nth hydrological variability assessment result.

[0068] Specifically, the core function expression of the climate underlying surface coupled collaborative contribution model is as follows: ,in This represents the synergistic contribution of the climate underlying surface to the nth hydrological variability assessment result, where n is a positive integer. The contribution of climate factors to the assessment result of the nth hydrological variability is given. The contribution of underlying surface factors to the nth hydrological variation assessment result. Let be the synergy coefficient between the hydrological variation climate tracing path and the hydrological variation underlying surface tracing path corresponding to the nth hydrological variation assessment result. This model quantifies the coupled synergistic contribution by linearly superimposing the independent contributions of climate factors and underlying surface factors, combined with their interaction term. In the interaction term... As a core correction coefficient, it is used to characterize the strength and direction of the nonlinear synergistic effect between climate and underlying surface driving paths. Its determination method is as follows: First, extract the spatiotemporal matching characteristics, element response characteristics, and driving transmission characteristics of the climate tracing path and the underlying surface tracing path corresponding to the nth hydrological variability assessment result. Obtain the initial synergistic coefficient through correlation analysis and path coupling degree calculation. Then, combine the random forest feature interaction algorithm to identify the nonlinear interaction relationship of each driving element in the two paths, correcting the initial synergistic coefficient. Simultaneously, introduce path synergistic verification samples from historical hydrological variability cases to calibrate the accuracy of the corrected coefficient, ultimately obtaining a coefficient that accurately reflects the actual synergistic relationship between the two tracing paths. ,when When the value is positive, it indicates a positive synergistic effect between climate and underlying surface driving pathways, with both amplifying their impact on hydrological variability. When the value is negative, it indicates that the two have a negative synergistic effect, mutually restricting each other's influence on hydrological variability. When the value is 0, it indicates that there is no significant synergistic effect between the two driving paths, and that climate and underlying surface factors are driving the process independently.

[0069] In one possible implementation, step S225 further includes: The significance criteria for hydrological variability include Hurst variability weight and sliding variability weight.

[0070] Specifically, the hydrological variability significance condition includes two core weight parameters: Hurst variation weight and sliding variation weight. These two parameters serve as the quantitative basis for determining the significance of hydrological variability and jointly participate in the weighted fusion calculation of the Hurst variation normalized value and the sliding variation normalized value, thereby achieving a comprehensive determination of the significance of hydrological series variability. The Hurst variation weight is used to assign a weight percentage to the Hurst variation normalized value. This weight is set based on the importance of the long-term trend variation characteristics of the hydrological series reflected by the Hurst variation coefficient, focusing on characterizing the contribution of the hydrological series to the persistence and trend variation over time. The sliding variation weight is used to assign a weight percentage to the sliding variation normalized value. This weight is set based on the importance of the discrete and fluctuating variation characteristics of the hydrological series reflected by the sliding variation coefficient, focusing on characterizing the contribution of the hydrological series to the short-term fluctuation and abrupt variation over time. Both weights are normalized, and the weight sum is 1. They can be dynamically adjusted according to the hydrological characteristics, watershed scale, and research focus of the target complex watershed. Through differentiated weight assignment, the requirements for determining the significance of hydrological variation under different watersheds and different spatiotemporal dimensions are accurately matched, avoiding the result bias caused by the determination of a single variation feature, and improving the accuracy and pertinence of the calculation of the significance distribution of hydrological temporal / spatial variation.

[0071] In one possible implementation, step S500 further includes: Based on the hydrological variation contribution distribution map, a hydrological variation early warning command is generated.

[0072] Specifically, based on the hydrological variability contribution distribution map, and combined with the water resource management threshold, hydrological safety standards, and hydrological variability impact level classification rules for the target complex watershed, a comprehensive assessment is conducted on the magnitude, spatial distribution characteristics, and temporal evolution trends of the independent climate contribution, independent underlying surface contribution, and climate-underlying surface coupled synergistic contribution of each spatiotemporal unit in the map. For spatiotemporal areas where the contribution value exceeds the safety threshold, the variability trend shows a continuous strengthening trend, the coupled synergistic effect is significantly amplified, and it is prone to triggering watershed hydrological anomalies, three levels of warning are classified according to the degree of variability impact: general warning, moderate warning, and severe warning. At the same time, the impact range, variability type, core driving factors, and risk development trend corresponding to each warning level are clearly defined. Based on the above assessment results, standardized hydrological variability warning instructions are generated. The instructions include key information such as warning level, warning area, warning period, core causative factors, variability development trend, and targeted prevention and control suggestions, so as to achieve accurate early warning and targeted management of watershed hydrological variability risks and provide a decision-making basis for watershed hydrological regulation, water resource optimization, and ecological protection measures.

[0073] Example 2 Based on the same inventive concept as the hydrological variability assessment method for complex watersheds described in the foregoing embodiments, such as Figure 2 As shown, this application provides a hydrological variability assessment system for complex watersheds. The system and method embodiments in this application are based on the same inventive concept. The system includes: The data acquisition module 10 is used to acquire surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for the target complex watershed.

[0074] The significance detection module 20 is used to perform multi-node spatiotemporal variation significance detection on the surface water resources data stream, and to obtain the hydrological temporal variation significance distribution and the hydrological spatial variation significance distribution.

[0075] The hydrological variability assessment map acquisition module 30 is used to perform spatiotemporal characteristic-driven variability assessment on the surface water resource data stream based on the significant distribution of hydrological temporal variability and the significant distribution of hydrological spatial variability, and to acquire a hydrological variability assessment map.

[0076] The climate factor contribution analysis module 40 is used to analyze the climate factor contribution of the hydrological variability assessment map based on the multi-dimensional climate change data stream, and obtain the hydrological variability climate contribution map.

[0077] The multidimensional contribution compensation module 50 is used to introduce a climate underlying surface coupled collaborative contribution model, and combine the dynamic underlying surface change data stream to perform multidimensional contribution compensation under the underlying surface contribution analysis of the hydrological variation climate contribution map, so as to obtain the hydrological variation contribution distribution map.

[0078] Furthermore, the system is also used to implement the following functions: The surface water resources data stream is divided into multiple time scales to obtain multiple time-scale hydrological sequences. Joint detection of Hearst variation and sliding variation is performed on the multiple time-scale hydrological sequences to obtain the significant distribution of hydrological temporal variation. The surface water resources data stream is also divided into multiple sub-basins of the target complex watershed to obtain multiple spatial node hydrological sequences. Joint detection of Hearst variation and sliding variation is performed on the multiple spatial node hydrological sequences to obtain the significant distribution of hydrological spatial variation.

[0079] Furthermore, the system is also used to implement the following functions: Hearst variability is evaluated based on the multiple time-scale hydrological sequences to obtain multiple Hearst variability coefficients; the mean of each time-scale hydrological sequence is calculated to obtain the mean of each time series, and the standard deviation of each time-scale hydrological sequence is calculated to obtain the standard deviation of each time series; the ratio of each time series standard deviation to each time series mean is calculated to obtain multiple moving variability coefficients; the multiple Hearst variability coefficients and the multiple moving variability coefficients are normalized to obtain multiple Hearst variability normalized values ​​and multiple moving variability normalized values; the multiple Hearst variability normalized values ​​and the multiple moving variability normalized values ​​are weighted and fused according to the hydrological variability significance condition to obtain the significance distribution of hydrological time variability.

[0080] Furthermore, the system is also used to implement the following functions: The significance distribution of hydrological temporal variation is tested according to a predetermined significance value to obtain a first prominent significance distribution of variation; the significance distribution of hydrological spatial variation is tested according to the predetermined significance value of variation to obtain a second prominent significance distribution of variation; the surface water resources data stream is correlated and extracted according to the first and second prominent significance distributions of variation to obtain multiple prominently correlated hydrological sequences; the multiple prominently correlated hydrological sequences are input into a hydrological variation assessment model to obtain multiple hydrological variation assessment results, and the hydrological variation assessment map is constructed based on the multiple hydrological variation assessment results.

[0081] Furthermore, the system is also used to implement the following functions: The multi-dimensional climate change data stream is mapped and matched based on the hydrological variability assessment map to obtain matching matrices for each climate characteristic corresponding to each hydrological variability assessment result; causal tracing is performed on each hydrological variability assessment result based on the climate characteristic matching matrices to obtain multiple hydrological variability climate tracing paths; contribution evaluation samples are retrieved based on each hydrological variability climate tracing path to obtain multiple climate factor contribution sample areas; the mean is calculated for each climate factor contribution sample area to obtain the contribution degree of multiple climate factors; the hydrological variability assessment results, the multiple hydrological variability climate tracing paths, and the multiple climate factor contribution degrees are organized to generate the hydrological variability climate contribution map.

[0082] Furthermore, the system is also used to implement the following functions: Mapping and matching the dynamic underlying surface change data stream based on the hydrological variability assessment map, obtaining the underlying surface change matching matrix corresponding to each hydrological variability assessment result; performing causal tracing and underlying surface factor contribution evaluation on each hydrological variability assessment result based on the underlying surface change matching matrix, obtaining the hydrological variability underlying surface contribution map; performing synergistic contribution analysis on the hydrological variability climate contribution map and the hydrological variability underlying surface contribution map based on the climate underlying surface contribution map and the hydrological variability underlying surface contribution map, obtaining the coupled synergistic contribution distribution; performing multidimensional contribution compensation on the hydrological variability climate contribution map based on the hydrological variability underlying surface contribution map and the coupled synergistic contribution distribution, obtaining the hydrological variability contribution distribution map.

[0083] Furthermore, the system is also used to implement the following functions: The functional expression of the climate underlying surface coupled collaborative contribution model is as follows: ;in, The synergistic contribution of climate underlying surface to the assessment result of the nth hydrological variability is characterized, where n is a positive integer. Characterize the contribution of climate factors to the assessment results of the nth hydrological variability. Characterize the contribution of underlying surface factors to the assessment results of the nth hydrological variability. The synergy coefficient characterizes the climatic tracing path of hydrological variability corresponding to the nth hydrological variability assessment result and the underlying surface tracing path of hydrological variability corresponding to the nth hydrological variability assessment result.

[0084] Furthermore, the system is also used to implement the following functions: The significance criteria for hydrological variability include Hurst variability weight and sliding variability weight.

[0085] Furthermore, the system is also used to implement the following functions: Based on the hydrological variation contribution distribution map, a hydrological variation early warning command is generated.

[0086] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Specific embodiments of this specification have been described above. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0087] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0088] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for assessing hydrological variability in complex watersheds, characterized in that, The method includes: Acquire surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for the target complex watershed; Multi-node spatiotemporal variation significance detection is performed on the surface water resources data stream to obtain the hydrological temporal variation significance distribution and the hydrological spatial variation significance distribution; Based on the significant distribution of hydrological temporal variation and the significant distribution of hydrological spatial variation, a spatiotemporal characteristic-driven variation assessment is performed on the surface water resources data stream to obtain a hydrological variation assessment map. Based on the multi-dimensional climate change data stream, the contribution of climate factors to the hydrological variability assessment map is analyzed to obtain a hydrological variability climate contribution map. A climate-underlying surface coupled collaborative contribution model is introduced, and multidimensional contribution compensation is performed on the hydrological variation climate contribution map under the analysis of the underlying surface contribution, based on the dynamic underlying surface change data stream, to obtain the hydrological variation contribution distribution map.

2. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, Multi-node spatiotemporal variability significance detection is performed on the surface water resources data stream to obtain the hydrological temporal variability significance distribution and the hydrological spatial variability significance distribution, including: The surface water resources data stream is divided according to multiple time scales to obtain hydrological sequences at multiple time scales. The Hurst variation and sliding variation of the multiple time-scale hydrological sequences are jointly detected to obtain the significance distribution of the hydrological time variation. The surface water resources data stream is divided into multiple sub-basins according to the target complex watershed, and multiple spatial node hydrological sequences are obtained; The hydrological sequences of the multiple spatial nodes are subjected to joint detection of Hurst variation and sliding variation to obtain the significance distribution of the hydrological spatial variation.

3. The method for assessing hydrological variability in complex watersheds according to claim 2, characterized in that, Joint detection of Hurst variation and sliding variation is performed on the multiple time-scale hydrological sequences to obtain the significance distribution of the hydrological time variation, including: Based on the multiple time-scale hydrological sequences, Hurst variability is evaluated to obtain multiple Hurst variability coefficients; The mean of each hydrological sequence at each time scale is calculated to obtain the mean of each time series, and the standard deviation of each hydrological sequence at each time scale is calculated to obtain the standard deviation of each time series. The ratio of the standard deviation of each time series to the mean of each time series is calculated to obtain multiple moving coefficients of variation. The multiple Hearst variation coefficients and the multiple sliding variation coefficients are normalized to obtain multiple Hearst variation normalized values ​​and multiple sliding variation normalized values. The weighted fusion calculation of the multiple Hearst variation normalized values ​​and the multiple sliding variation normalized values ​​is performed based on the hydrological variability significance condition to obtain the hydrological time variation significance distribution.

4. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, Based on the significant distribution of hydrological temporal variability and the significant distribution of hydrological spatial variability, a spatiotemporal characteristic-driven variability assessment is performed on the surface water resources data stream to obtain a hydrological variability assessment map, including: The significance distribution of hydrological time variation is tested according to a predetermined significance value to obtain the first prominent significance distribution of variation; The significance distribution of hydrological spatial variation is tested based on the predetermined significance value of variation to obtain the second prominent significance distribution of variation; Based on the first and second prominent variation significance distributions, the surface water resources data stream is correlated and extracted to obtain multiple prominent correlation hydrological sequences. The multiple prominently correlated hydrological sequences are input into the hydrological variability assessment model to obtain multiple hydrological variability assessment results, and the hydrological variability assessment map is constructed based on the multiple hydrological variability assessment results.

5. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, Based on the multi-dimensional climate change data stream, the hydrological variability assessment map is analyzed for climate factor contributions to obtain a hydrological variability climate contribution map, including: Based on the hydrological variability assessment map, the multidimensional climate change data stream is mapped and matched to obtain the matching matrix of each climate characteristic corresponding to each hydrological variability assessment result. Based on the climate characteristic matching matrix, the causal tracing of the hydrological variation assessment results is performed to obtain multiple hydrological variation climate tracing paths. Based on each hydrological variation climate tracing path, contribution evaluation samples are retrieved to obtain multiple climate factor contribution sample areas. The mean value of each climate factor contribution sample area is calculated to obtain the contribution degree of multiple climate factors. By compiling the assessment results of each hydrological variation, the climate tracing paths of the multiple hydrological variations, and the contribution of the multiple climate factors, a climate contribution map of the hydrological variations is generated.

6. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, A climate-underlying surface coupled collaborative contribution model is introduced. This model, combined with the dynamic underlying surface change data stream, performs multi-dimensional contribution compensation under the underlying surface contribution analysis on the hydrological variability climate contribution map, obtaining a hydrological variability contribution distribution map, including: Based on the hydrological variability assessment map, the dynamic underlying surface change data stream is mapped and matched to obtain the underlying surface change matching matrix corresponding to each hydrological variability assessment result. Based on the underlying surface change matching matrix, the causal tracing and underlying surface factor contribution evaluation of the hydrological variation assessment results are performed to obtain the underlying surface contribution map of hydrological variation. Based on the climate underlying surface coupled synergistic contribution model, the synergistic contribution analysis of the hydrological variation climate contribution map and the hydrological variation underlying surface contribution map is performed to obtain the coupled synergistic contribution distribution; Multidimensional contribution compensation is performed on the hydrological variation climate contribution map based on the underlying surface contribution map of the hydrological variation and the coupled synergistic contribution distribution to obtain the hydrological variation contribution distribution map.

7. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, The functional expression of the climate underlying surface coupled collaborative contribution model is as follows: ； in, The synergistic contribution of climate underlying surface to the assessment result of the nth hydrological variability is characterized, where n is a positive integer. Characterize the contribution of climate factors to the assessment results of the nth hydrological variability. Characterize the contribution of underlying surface factors to the assessment results of the nth hydrological variability. The synergy coefficient characterizes the climatic tracing path of hydrological variability corresponding to the nth hydrological variability assessment result and the underlying surface tracing path of hydrological variability corresponding to the nth hydrological variability assessment result.

8. The method for assessing hydrological variability in complex watersheds according to claim 3, characterized in that, The significance criteria for hydrological variability include Hurst variability weight and sliding variability weight.

9. The method for assessing hydrological variability in complex watersheds according to claim 1, characterized in that, Based on the hydrological variation contribution distribution map, a hydrological variation early warning command is generated.

10. A hydrological variability assessment system for complex watersheds, characterized in that, The system is used to implement the hydrological variability assessment method for complex watersheds according to any one of claims 1-9, the system comprising: The data acquisition module is used to acquire surface water resources data streams, multi-dimensional climate change data streams, and dynamic underlying surface change data streams for the target complex watershed; The significance detection module is used to perform multi-node spatiotemporal variation significance detection on the surface water resources data stream, and to obtain the hydrological temporal variation significance distribution and the hydrological spatial variation significance distribution. The hydrological variability assessment map acquisition module is used to perform spatiotemporal characteristic-driven variability assessment on the surface water resources data stream based on the significant distribution of hydrological temporal variability and the significant distribution of hydrological spatial variability, and to acquire a hydrological variability assessment map. The climate factor contribution analysis module is used to analyze the climate factor contribution of the hydrological variability assessment map based on the multi-dimensional climate change data stream, and obtain the hydrological variability climate contribution map. The multidimensional contribution compensation module is used to introduce a climate underlying surface coupled collaborative contribution model, and combine the dynamic underlying surface change data stream to perform multidimensional contribution compensation under the underlying surface contribution analysis of the hydrological variation climate contribution map, so as to obtain the hydrological variation contribution distribution map.

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