CN112765808B - Ecological drought monitoring and evaluating method - Google Patents
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Abstract
Description
技术领域technical field
本发明属于生态环境治理技术领域，具体涉及一种生态干旱的监测与评估方法的设计。The invention belongs to the technical field of ecological environment management, and in particular relates to the design of a method for monitoring and evaluating ecological drought.
背景技术Background technique
近年来，与生态文明建设相关的譬如生态水循环、生态干旱的监测与评估等研究方向逐渐成为研究热点。开展多因素影响下干旱对流域生态系统影响的研究，不仅对易发生态干旱区域的生态环境治理有重要的现实意义，而且还对促进生态学与水文学等学科的交叉性研究具有重大的促进和理论意义。In recent years, research directions related to the construction of ecological civilization, such as ecological water cycle and ecological drought monitoring and evaluation, have gradually become research hotspots. Carrying out research on the impact of drought on watershed ecosystems under the influence of multiple factors not only has important practical significance for the ecological environment governance in areas prone to ecological drought, but also has a great promotion for the interdisciplinary research of ecology and hydrology. and theoretical significance.
决策树是机器学习中的一种以实例为基础的归纳学习，采用的是自顶向下的归纳方法。决策树算法的最大优点它可以自主学习，并且在学习的过程中不需要使用者了解过多的知识背景，只需要对训练实例进行较好的标注，就能够进行学习，属于有监督学习。随机森林模型是使用随机的方式建立一个由很多决策树组成的模型，随机森林的每一棵决策树之间是没有关联的。随机森林模型通过自助法(bootstrap)重采样技术，从原始训练样本集N中有放回地重复随机抽取k个样本生成新的训练样本集合，然后根据自助样本集生成k个分类树组成随机森林，新数据的分类结果按分类树投票多少形成的分数而定，其实质是对决策树算法的一种改进，将多个决策树合并在一起，每棵树的建立依赖于一个独立抽取的样品，森林中的每棵树具有相同的分布，分类误差取决于每一棵树的分类能力和它们之间的相关性。特征选择采用随机的方法去分裂每一个节点，然后比较不同情况下产生的误差，能够检测到的内在估计误差、分类能力和相关性决定选择特征的数目。Decision tree is an example-based inductive learning in machine learning, which uses a top-down induction method. The biggest advantage of the decision tree algorithm is that it can learn independently, and in the process of learning, the user does not need to know too much knowledge background. It only needs to label the training examples well, and then it can learn, which belongs to supervised learning. The random forest model is to use a random method to build a model composed of many decision trees, and each decision tree in the random forest is not related. The random forest model uses bootstrap resampling technology to repeatedly randomly extract k samples from the original training sample set N with replacement to generate a new training sample set, and then generate k classification trees to form a random forest based on the self-service sample set , the classification result of the new data depends on the score formed by the number of votes of the classification tree. Its essence is an improvement of the decision tree algorithm. Multiple decision trees are merged together. The establishment of each tree depends on an independently drawn sample. , each tree in the forest has the same distribution, and the classification error depends on the classification ability of each tree and the correlation between them. Feature selection uses a random method to split each node, and then compares the errors generated in different situations. The internal estimation error, classification ability and correlation that can be detected determine the number of selected features.
现有的生态系统干旱损伤评估方法多以实验观测、遥感监测和模型模拟等定性或半定量评价为主。实验观测方法主要是利用实验和数理统计方法建立的损失统计模型；遥感监测方法利用遥感数据计算各种植被相关的干旱指数监测植被生长；模型模拟方法是通过温度、降水等气象数据计算植被生物期内各阶段相对蒸散发量，主要方法是通过构建水分生产函数和气候生产潜力模型开展植被减产评估。Existing evaluation methods for ecosystem drought damage are mostly qualitative or semi-quantitative evaluations such as experimental observation, remote sensing monitoring, and model simulation. The experimental observation method mainly uses the loss statistical model established by experiments and mathematical statistics methods; the remote sensing monitoring method uses remote sensing data to calculate various vegetation-related drought indices to monitor vegetation growth; the model simulation method uses temperature, precipitation and other meteorological data to calculate vegetation biological periods The relative evapotranspiration of each stage in the country is mainly measured by constructing water production function and climate production potential models to evaluate vegetation production reduction.
然而，现有的生态系统干旱损伤评估方法也存在相应的缺陷，实验观测方法相对简单，忽略了干旱的成灾过程对植被生长的物理影响，也未考虑土壤水分、养分以及二氧化碳对植被的作用，方法的时效性差、估算结果精度较低。模型模拟方法对植被生长机理过程描述较差，决定评估因素较少，模型参数多且不易确定与调节。However, the existing drought damage assessment methods for ecosystems also have corresponding defects. The experimental observation method is relatively simple, ignoring the physical impact of the drought process on vegetation growth, and not considering the effects of soil moisture, nutrients, and carbon dioxide on vegetation. , the timeliness of the method is poor, and the accuracy of the estimation result is low. The model simulation method has a poor description of the vegetation growth mechanism process, fewer decision and evaluation factors, and many model parameters that are difficult to determine and adjust.
发明内容Contents of the invention
本发明的目的是提出一种生态干旱的监测与评估方法，定量化揭示不同水文干旱的影响程度和时空规律，明确不同类型的水文干旱演变和生态系统之间的响应关系，构建水文干旱演变和生态系统损失评估的模型。The purpose of the present invention is to propose a monitoring and evaluation method for ecological drought, quantitatively reveal the impact degree and spatio-temporal law of different hydrological droughts, clarify the response relationship between the evolution of different types of hydrological droughts and ecosystems, and construct the evolution and analysis of hydrological droughts. Models for Ecosystem Loss Assessment.
本发明的技术方案为：一种生态干旱的监测与评估方法，包括以下步骤：The technical scheme of the present invention is: a monitoring and evaluation method for ecological drought, comprising the following steps:
S1、采集研究区域的多源数据，并利用多源数据构建生态干旱灾害数据库。S1. Collect multi-source data in the study area, and use multi-source data to build an ecological drought disaster database.
S2、根据多源数据计算研究区域的水文气象与水文干旱指标，并提取水文干旱特征。S2. Calculating hydrometeorological and hydrological drought indicators in the study area based on multi-source data, and extracting hydrological drought characteristics.
S3、对水文干旱特征进行时空规律与演变特征分析，根据分析结果实现对研究区域的生态干旱监测。S3. Analyze the spatio-temporal law and evolution characteristics of the hydrological drought characteristics, and realize the ecological drought monitoring of the research area according to the analysis results.
S4、根据多源数据构建Biome-BGC模型，计算得到研究区域的生态系统净初级生产力。S4. Construct the Biome-BGC model based on multi-source data, and calculate the net primary productivity of the ecosystem in the study area.
S5、根据水文干旱特征和生态系统净初级生产力，采用随机森林模型得到研究区域的综合生态系统生产力，并将综合生态系统生产力作为生态干旱损失的评估值，实现对研究区域的生态干旱评估。S5. According to the hydrological drought characteristics and the net primary productivity of the ecosystem, the random forest model is used to obtain the comprehensive ecosystem productivity of the research area, and the comprehensive ecosystem productivity is used as the evaluation value of the ecological drought loss to realize the ecological drought assessment of the research area.
进一步地，步骤S1中的多源数据包括自然地理资料数据、站点水文数据、水利普查数据、遥感资料数据、历年灾情统计资料数据和中国陆地生态系统通量观测研究网络的碳-水交换通量长期连续观测数据；自然地理资料数据包括数字高程模型、土壤墒情数据、水文地质资料数据和土地覆盖面积；站点水文数据包括逐月降水数据、蒸散发数据、径流数据、温度变化数据和土壤含水量；水利普查数据包括水库容量与数量和主要用水用户；遥感资料数据包括多期卫星遥感土地利用覆盖数据、遥感估算蒸散发数据和土壤湿度产品数据；历年灾情统计资料数据包括研究区域的各县旱灾损失数据、受旱面积、受旱人口和灾害损失量；中国陆地生态系统通量观测研究网络的碳-水交换通量长期连续观测数据包括生态系统总初级生产力、生态系统总呼吸、生态系统净碳交换、潜热、显热、不同生态系统生理生态参数、多年生物量和净初级生产力资料数据。Further, the multi-source data in step S1 includes physical geography data, station hydrology data, water conservancy census data, remote sensing data, disaster statistics data over the years and carbon-water exchange flux of China Terrestrial Ecosystem Flux Observation and Research Network Long-term continuous observation data; physical geographic data include digital elevation model, soil moisture data, hydrogeological data and land cover area; station hydrological data include monthly precipitation data, evapotranspiration data, runoff data, temperature change data and soil moisture content ; Water conservancy census data include reservoir capacity and quantity and main water users; remote sensing data include multi-period satellite remote sensing land use cover data, remote sensing estimated evapotranspiration data and soil moisture product data; historical disaster statistics data include drought disasters in the counties of the study area Loss data, drought-affected area, drought-affected population, and disaster losses; the long-term continuous observation data of carbon-water exchange flux of China Terrestrial Ecosystem Flux Observation and Research Network includes ecosystem total primary productivity, ecosystem total respiration, ecosystem net Carbon exchange, latent heat, sensible heat, physiological and ecological parameters of different ecosystems, annual biomass and net primary productivity data.
进一步地，步骤S2包括以下分步骤：Further, step S2 includes the following sub-steps:
S21、根据研究区域的水系分布，采用泰森多边形法对研究区域进行划分，将水文测站点作为多边形的离散数据中心点，根据逐日水文气象观测数据中的降水、河道径流、水库水位和地下水位，计算研究区域的水文气象与水文干旱指标；水文气象与水文干旱指标包括标准化降水指数、降水蒸散发指数、土壤湿度指数、河道流量指数、地下水位指数和水库蓄量指数。S21. According to the distribution of the water system in the study area, the Thiessen polygon method is used to divide the study area, and the hydrological observation station is used as the discrete data center point of the polygon. According to the precipitation, river runoff, reservoir water level and groundwater level in the daily hydrometeorological observation data , to calculate the hydrometeorological and hydrological drought indicators in the study area; the hydrometeorological and hydrological drought indicators include standardized precipitation index, precipitation evapotranspiration index, soil moisture index, river flow index, groundwater level index and reservoir storage index.
S22、采用阈值法进行不同时间尺度的水文干旱识别，并采用游程理论提取水文干旱特征；水文干旱特征包括干旱历时、干旱烈度、干旱峰度和干旱频率。S22. Use the threshold method to identify hydrological droughts at different time scales, and use run theory to extract hydrological drought characteristics; hydrological drought characteristics include drought duration, drought intensity, drought kurtosis and drought frequency.
进一步地，步骤S22中采用游程理论提取水文干旱特征的具体方法为：Further, in step S22, the specific method for extracting hydrological drought characteristics by using the run theory is as follows:
A1、设置截取水平R1为月降水量距平百分比的10％。A1. Set the interception level R1 as 10% of the monthly precipitation anomaly percentage.
A2、当月降水量序列Xi在一个或多个时间段都小于R1时，即出现了负游程，判定该时间段发生了干旱，将负游程的长度作为干旱历时，将负游程的面积作为干旱烈度，将负游程的极值作为干旱峰度，其中Xi＝{X1,X2,X3,...,XN}。A2. When the monthly precipitation sequence Xi is less than R1 in one or more time periods, a negative run has occurred, and it is determined that a drought has occurred in this time period, and the length of the negative run is regarded as the duration of the drought, and the area of the negative run is regarded as the drought Intensity, the extreme value of the negative run is taken as the drought kurtosis, where X i ={X 1 ,X 2 ,X 3 ,...,X N }.
A3、根据干旱判断及合并准则对干旱情况进行合并。A3. Merge the drought situation according to the drought judgment and the merger criteria.
进一步地，步骤A3中的干旱判断及合并准则具体为：Further, the drought judgment and merging criteria in step A3 are specifically:
(1)当降水量序列中某个单月降水量为X1，且满足X1<X2时，判定这个月是一次干旱事件；其中X2表示降水量为X1的单月的下一个月的降水量。(1) When the precipitation in a single month in the precipitation sequence is X 1 and satisfies X 1 < X 2 , it is determined that this month is a drought event; where X 2 represents the next month of the single month with the precipitation of X 1 monthly precipitation.
(2)当降水量序列中某个单月降水量为X1，且满足X1>X2时，判定这个月不是一次干旱事件。(2) When the precipitation in a single month in the precipitation series is X 1 and X 1 >X 2 is satisfied, it is determined that this month is not a drought event.
(3)当两次干旱事件间的时间间隔为一个月时，若该月的月降水量小于其相邻两个月降水量的平均值X0，则将这两场干旱事件合并为一场干旱事件；若该月的月降水量大于其相邻两个月降水量的平均值X0，则这两场干旱事件不需合并，即仍是两场干旱事件。(3) When the time interval between two drought events is one month, if the monthly precipitation of the month is less than the average value of the precipitation of the two adjacent months X 0 , then the two drought events are combined into one Drought event; if the monthly precipitation in this month is greater than the average value X 0 of the precipitation in the two adjacent months, then the two drought events do not need to be merged, that is, they are still two drought events.
进一步地，步骤S3包括以下分步骤：Further, step S3 includes the following sub-steps:
S31、根据水文干旱特征，基于ArcGIS进行空间分析，得到水文干旱时空规律与演变特征。S31. According to the characteristics of hydrological drought, spatial analysis is carried out based on ArcGIS, and the spatiotemporal law and evolution characteristics of hydrological drought are obtained.
S32、采用时间序列分析水文干旱时空规律与演变特征，得到水文干旱发生的周期性、突变性和趋势性。S32. Use time series to analyze the spatiotemporal law and evolution characteristics of hydrological drought, and obtain the periodicity, sudden change and trend of hydrological drought.
S33、采用小波变换法分解各个水文站径流水文时间序列及地下水位时间序列中的周期性成分、突变点和趋势。S33. Using the wavelet transform method to decompose the periodic components, mutation points and trends in the runoff hydrological time series of each hydrological station and the groundwater level time series.
S34、采用极值模态分解法与经验模态分解法分解水文时间序列变化中的气候变化成分。S34. Using the extreme value mode decomposition method and the empirical mode decomposition method to decompose the climate change component in the hydrological time series change.
S35、统计对比步骤S21划分的子区域内的干旱年和正常年，选取具有相似地理特征但具有不同功能、不同库容、不同运行调度规则的水库，采用多元回归对比分析建库前后及水库上下游、水库供水区与非供水区的水文干旱特征差异，根据地表水干旱指标、地下水位干旱指标及水库容量指标，定量分析水库与水库群调节对水文干旱特征的差异性影响。S35. Statistically compare the drought years and normal years in the sub-regions divided by step S21, select reservoirs with similar geographical features but different functions, different storage capacities, and different operation and scheduling rules, and use multiple regression to compare and analyze before and after the construction of the reservoir and upstream and downstream of the reservoir 1. According to the difference of hydrological drought characteristics between reservoir water supply area and non-water supply area, according to the surface water drought index, groundwater table drought index and reservoir capacity index, quantitatively analyze the differential influence of reservoir and reservoir group regulation on hydrological drought characteristics.
S36、采用主成分变换法将水文干旱时空规律与演变特征与归一化植被指数、湿度指数、干度指标以及热度指标进行耦合，得到从水文干旱到生态干旱的耦合模型，对研究区域进行生态干旱监测。S36. Using the principal component transformation method to couple the spatio-temporal law and evolution characteristics of hydrological drought with the normalized vegetation index, humidity index, dryness index, and heat index to obtain a coupled model from hydrological drought to ecological drought, and conduct an ecological analysis of the study area. Drought monitoring.
进一步地，步骤S4包括以下分步骤：Further, step S4 includes the following sub-steps:
S41、根据多源数据构建Biome-BGC模型。S41. Build a Biome-BGC model according to multi-source data.
S42、采用线性回归分析法、均方根误差法和显著性检验法验证Biome-BGC模型的模拟精度，模拟精度包括Biome-BGC模型的模拟值和观测值的精确度。S42. Verify the simulation accuracy of the Biome-BGC model by using the linear regression analysis method, the root mean square error method and the significance test method. The simulation accuracy includes the accuracy of the simulated value and the observed value of the Biome-BGC model.
S43、以气象数据为驱动，采用通过模拟精度验证的Biome-BGC模型分别模拟研究区域有干旱灾害发生的干旱年和无干旱灾害影响的正常年的植被生产力变化，计算得到研究区域的生态系统净初级生产力。S43. Driven by meteorological data, the Biome-BGC model that has passed the verification of simulation accuracy is used to simulate the changes in vegetation productivity in drought years and normal years without drought disasters in the study area, and calculate the net value of the ecosystem in the study area. primary productivity.
进一步地，步骤S41中构建Biome-BGC模型采用的驱动数据包括初始数据、气象数据和生理参数数据；初始数据包括水文站点的经纬度、海拔、土层深度、土壤质地和大气二氧化碳浓度；气象数据包括日降水量、日最高温度、日最低温度、日平均温度和辐射；生理参数数据包括气孔导度、冠层比叶面积、叶片和细根CN比。Further, the driving data used to construct the Biome-BGC model in step S41 includes initial data, meteorological data and physiological parameter data; initial data includes latitude and longitude, altitude, soil depth, soil texture and atmospheric carbon dioxide concentration of hydrological stations; meteorological data includes Daily precipitation, daily maximum temperature, daily minimum temperature, daily average temperature and radiation; physiological parameter data including stomatal conductance, canopy specific leaf area, leaf and fine root CN ratio.
进一步地，步骤S43中生态系统净初级生产力的计算公式为：Further, the calculation formula of ecosystem net primary productivity in step S43 is:
NPPBiome-BGC＝GPP-Pplant NPP Biome-BGC ＝GPP-P plant
其中NPPBiome-BGC表示生态系统净初级生产力，GPP表示生态系统总初级生产力，Pplant表示植被呼吸。Among them, NPP Biome-BGC represents the net primary productivity of the ecosystem, GPP represents the total primary productivity of the ecosystem, and P plant represents vegetation respiration.
进一步地，步骤S5包括以下分步骤：Further, step S5 includes the following sub-steps:
S51、基于水文模型和生态过程模型模拟得到的长时间水循环序列连续数据，构建随机森林模型。S51. Construct a random forest model based on the continuous data of the long-term water cycle sequence simulated by the hydrological model and the ecological process model.
S52、将社会水循环因素变化指标以及水文干旱特征作为随机森林模型的自变量，将生态系统净初级生产力作为随机森林模型的因变量，通过随机森林模型进行耦合模拟得到研究区域的综合生态系统生产力；社会水循环因素变化指标包括植被状态指数、温度状态指数、TRMM指数和土壤有效持水量。S52. Taking the change index of social water cycle factors and the characteristics of hydrological drought as independent variables of the random forest model, taking the net primary productivity of the ecosystem as the dependent variable of the random forest model, and performing coupling simulation through the random forest model to obtain the comprehensive ecosystem productivity of the research area; Change indicators of social water cycle factors include vegetation state index, temperature state index, TRMM index and soil effective water holding capacity.
S53、将综合生态系统生产力作为生态干旱损失的评估值，实现对研究区域的生态干旱评估。S53. Taking the comprehensive ecosystem productivity as the evaluation value of ecological drought loss to realize the evaluation of ecological drought in the research area.
本发明的有益效果是：The beneficial effects of the present invention are:
(1)本发明采用随机森林模型得出的综合生态系统生产力值，不仅考虑了生态因素，还包括导致水文干旱的因素与人为因素，对于生态干旱的评估更加全面而精确。(1) The comprehensive ecosystem productivity value obtained by the random forest model in the present invention not only considers ecological factors, but also includes factors and human factors that lead to hydrological drought, so that the evaluation of ecological drought is more comprehensive and accurate.
(2)本发明采用随机森林模型优化了传统模型模拟方法，解决了传统生态干旱损失评估方法考虑因素少和参数难以调节的问题。(2) The present invention optimizes the traditional model simulation method by adopting the random forest model, and solves the problems that the traditional ecological drought loss assessment method has few consideration factors and difficult adjustment of parameters.
附图说明Description of drawings
图1所示为本发明实施例提供的一种生态干旱的监测与评估方法流程图。Fig. 1 is a flowchart of a method for monitoring and evaluating ecological drought provided by an embodiment of the present invention.
具体实施方式Detailed ways
现在将参考附图来详细描述本发明的示例性实施方式。应当理解，附图中示出和描述的实施方式仅仅是示例性的，意在阐释本发明的原理和精神，而并非限制本发明的范围。Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the implementations shown and described in the drawings are only exemplary, intended to explain the principle and spirit of the present invention, rather than limit the scope of the present invention.
本发明实施例提供了一种生态干旱的监测与评估方法，如图1所示，包括以下步骤S1～S5：The embodiment of the present invention provides a method for monitoring and evaluating ecological drought, as shown in Figure 1, including the following steps S1-S5:
S1、采集研究区域的多源数据，并利用多源数据构建生态干旱灾害数据库。S1. Collect multi-source data in the study area, and use multi-source data to build an ecological drought disaster database.
本发明实施例中，多源数据包括自然地理资料数据、站点水文数据、水利普查数据、遥感资料数据、历年灾情统计资料数据和中国陆地生态系统通量观测研究网络的碳-水交换通量长期连续观测数据。In the embodiment of the present invention, the multi-source data includes physical geography data, station hydrology data, water conservancy survey data, remote sensing data, disaster statistics data over the years, and the carbon-water exchange flux long-term of China Terrestrial Ecosystem Flux Observation Research Network. continuous observation data.
其中，自然地理资料数据包括数字高程模型、土壤墒情数据、水文地质资料数据和土地覆盖面积。Among them, physical geographic data include digital elevation model, soil moisture data, hydrogeological data and land cover area.
站点水文数据包括逐月降水数据、蒸散发数据、径流数据、温度变化数据和土壤含水量。The hydrological data of the station include monthly precipitation data, evapotranspiration data, runoff data, temperature change data and soil moisture content.
水利普查数据包括水库容量与数量和主要用水用户。Water census data include reservoir capacity and quantity and major water users.
遥感资料数据包括多期卫星遥感土地利用覆盖数据、遥感估算蒸散发数据和土壤湿度产品数据。Remote sensing data include multi-period satellite remote sensing land use cover data, remote sensing estimated evapotranspiration data and soil moisture product data.
历年灾情统计资料数据包括研究区域的各县旱灾损失数据、受旱面积、受旱人口和灾害损失量。Disaster statistics over the years include drought loss data, drought-affected area, drought-affected population, and disaster loss in each county in the study area.
中国陆地生态系统通量观测研究网络的碳-水交换通量长期连续观测数据包括生态系统总初级生产力、生态系统总呼吸、生态系统净碳交换、潜热、显热、不同生态系统生理生态参数、多年生物量和净初级生产力资料数据。The long-term continuous observation data of carbon-water exchange flux of China Terrestrial Ecosystem Flux Observation and Research Network include ecosystem total primary productivity, ecosystem total respiration, ecosystem net carbon exchange, latent heat, sensible heat, physiological and ecological parameters of different ecosystems, Annual biomass and net primary productivity data.
S2、根据多源数据计算研究区域的水文气象与水文干旱指标，并提取水文干旱特征。S2. Calculating hydrometeorological and hydrological drought indicators in the study area based on multi-source data, and extracting hydrological drought characteristics.
步骤S2包括以下分步骤S21～S22：Step S2 includes the following sub-steps S21-S22:
S21、根据研究区域的水系分布，采用泰森多边形法对研究区域进行划分，将水文测站点作为多边形的离散数据中心点，根据逐日水文气象观测数据中的降水、河道径流、水库水位和地下水位，计算研究区域的水文气象与水文干旱指标。S21. According to the distribution of the water system in the study area, the Thiessen polygon method is used to divide the study area, and the hydrological observation station is used as the discrete data center point of the polygon. According to the precipitation, river runoff, reservoir water level and groundwater level in the daily hydrometeorological observation data , to calculate the hydrometeorological and hydrological drought indicators in the study area.
本发明实施例中，水文气象与水文干旱指标包括标准化降水指数、降水蒸散发指数、土壤湿度指数、河道流量指数、地下水位指数和水库蓄量指数。In the embodiment of the present invention, the hydrometeorological and hydrological drought indicators include standardized precipitation index, precipitation evapotranspiration index, soil moisture index, river flow index, groundwater level index and reservoir storage index.
S22、采用阈值法进行不同时间尺度的水文干旱识别，并采用游程理论提取水文干旱特征。S22. Using the threshold method to identify hydrological droughts on different time scales, and using run theory to extract the characteristics of hydrological droughts.
本发明实施例中，水文干旱特征包括干旱历时、干旱烈度、干旱峰度和干旱频率。In the embodiment of the present invention, the hydrological drought characteristics include drought duration, drought intensity, drought kurtosis and drought frequency.
游程理论是指在一类连续发生的事件前后发生的是另一类事件，如旱涝交替出现等自然现象。步骤S22中采用游程理论提取水文干旱特征的具体方法为：Run theory refers to the occurrence of another type of event before and after one type of continuous event, such as natural phenomena such as alternating droughts and floods. In step S22, the specific method of using the run theory to extract the characteristics of hydrological drought is as follows:
A1、设置截取水平R1为月降水量距平百分比的10％。A1. Set the interception level R1 as 10% of the monthly precipitation anomaly percentage.
A2、当月降水量序列Xi在一个或多个时间段都小于R1时，即出现了负游程，判定该时间段发生了干旱，将负游程的长度作为干旱历时，将负游程的面积作为干旱烈度，将负游程的极值作为干旱峰度，其中Xi＝{X1,X2,X3,...,XN}。A2. When the monthly precipitation sequence Xi is less than R1 in one or more time periods, a negative run has occurred, and it is determined that a drought has occurred in this time period, and the length of the negative run is regarded as the duration of the drought, and the area of the negative run is regarded as the drought Intensity, the extreme value of the negative run is taken as the drought kurtosis, where X i ={X 1 ,X 2 ,X 3 ,...,X N }.
A3、根据干旱判断及合并准则对干旱情况进行合并。A3. Merge the drought situation according to the drought judgment and the merger criteria.
由于在实际分析中，干旱情况较复杂，因此有时需要对干旱进行合并，本发明实施例中干旱判断及合并准则具体为：Since in actual analysis, the drought situation is more complicated, it is sometimes necessary to merge the droughts. The drought judgment and merger criteria in the embodiment of the present invention are specifically:
(1)当降水量序列中某个单月降水量为X1，且满足X1<X2时，判定这个月是一次干旱事件；其中X2表示降水量为X1的单月的下一个月的降水量。(1) When the precipitation in a single month in the precipitation sequence is X 1 and satisfies X 1 < X 2 , it is determined that this month is a drought event; where X 2 represents the next month of the single month with the precipitation of X 1 monthly precipitation.
(2)当降水量序列中某个单月降水量为X1，且满足X1>X2时，判定这个月不是一次干旱事件。(2) When the precipitation in a single month in the precipitation series is X 1 and X 1 >X 2 is satisfied, it is determined that this month is not a drought event.
(3)当两次干旱事件间的时间间隔为一个月时，若该月的月降水量小于其相邻两个月降水量的平均值X0，则将这两场干旱事件合并为一场干旱事件；若该月的月降水量大于其相邻两个月降水量的平均值X0，则这两场干旱事件不需合并，即仍是两场干旱事件。(3) When the time interval between two drought events is one month, if the monthly precipitation of the month is less than the average value of the precipitation of the two adjacent months X 0 , then the two drought events are combined into one Drought event; if the monthly precipitation in this month is greater than the average value X 0 of the precipitation in the two adjacent months, then the two drought events do not need to be merged, that is, they are still two drought events.
S3、对水文干旱特征进行时空规律与演变特征分析，根据分析结果实现对研究区域的生态干旱监测。S3. Analyze the spatio-temporal law and evolution characteristics of the hydrological drought characteristics, and realize the ecological drought monitoring of the research area according to the analysis results.
步骤S3包括以下分步骤S31～S36：Step S3 includes the following sub-steps S31-S36:
S31、根据水文干旱特征，基于ArcGIS进行空间分析，得到水文干旱时空规律与演变特征。S31. According to the characteristics of hydrological drought, spatial analysis is carried out based on ArcGIS, and the spatiotemporal law and evolution characteristics of hydrological drought are obtained.
S32、采用时间序列分析水文干旱时空规律与演变特征，得到水文干旱发生的周期性、突变性和趋势性。S32. Use time series to analyze the spatiotemporal law and evolution characteristics of hydrological drought, and obtain the periodicity, sudden change and trend of hydrological drought.
S33、采用小波变换法(Wavelet Transform)分解各个水文站径流水文时间序列及地下水位时间序列中的周期性成分、突变点和趋势。S33. Using Wavelet Transform to decompose the periodic components, mutation points and trends in the runoff hydrological time series and groundwater level time series of each hydrological station.
S34、采用极值模态分解法(ESMD)与经验模态分解法(EEMD)分解水文时间序列变化中的气候变化成分。S34. Using extreme value mode decomposition (ESMD) and empirical mode decomposition (EEMD) to decompose climate change components in hydrological time series changes.
S35、统计对比步骤S21划分的子区域内的干旱年和正常年，选取具有相似地理特征但具有不同功能、不同库容、不同运行调度规则的水库，采用多元回归对比分析建库前后及水库上下游、水库供水区与非供水区的水文干旱特征差异，根据地表水干旱指标、地下水位干旱指标及水库容量指标，定量分析水库与水库群调节对水文干旱特征的差异性影响。S35. Statistically compare the drought years and normal years in the sub-regions divided by step S21, select reservoirs with similar geographical features but different functions, different storage capacities, and different operation and scheduling rules, and use multiple regression to compare and analyze before and after the construction of the reservoir and upstream and downstream of the reservoir 1. According to the difference of hydrological drought characteristics between reservoir water supply area and non-water supply area, according to the surface water drought index, groundwater table drought index and reservoir capacity index, quantitatively analyze the differential influence of reservoir and reservoir group regulation on hydrological drought characteristics.
S36、采用主成分变换法将水文干旱时空规律与演变特征与归一化植被指数、湿度指数、干度指标以及热度指标进行耦合，得到从水文干旱到生态干旱的耦合模型，对研究区域进行生态干旱监测。S36. Using the principal component transformation method to couple the spatio-temporal law and evolution characteristics of hydrological drought with the normalized vegetation index, humidity index, dryness index, and heat index to obtain a coupled model from hydrological drought to ecological drought, and conduct an ecological analysis of the study area. Drought monitoring.
本发明实施例中，由于生态干旱的发生相比于水文干旱具有一定的滞后性，因此需要构建从水文干旱到生态干旱的耦合模型，通过气象数据结合生态环境指标监测生态干旱。In the embodiment of the present invention, since the occurrence of ecological drought has a certain lag compared with hydrological drought, it is necessary to construct a coupling model from hydrological drought to ecological drought, and monitor ecological drought through meteorological data combined with ecological environment indicators.
S4、根据多源数据构建Biome-BGC模型，计算得到研究区域的生态系统净初级生产力。S4. Construct the Biome-BGC model based on multi-source data, and calculate the net primary productivity of the ecosystem in the study area.
步骤S4包括以下分步骤S41～S43：Step S4 includes the following sub-steps S41-S43:
S41、根据多源数据构建Biome-BGC模型。S41. Build a Biome-BGC model according to multi-source data.
本发明实施例中，由于干旱对生态环境的影响最为严重的是植被生产力下降造成的植被退化，故采用陆地生态系统的净初级生产力(Net primary productivity，NPP)作为生态系统损失对干旱响应的评价指标。为定量化不同等级干旱对研究区域内生态系统的损失的影响程度，采用历史干旱期的生态过程模型Biome-BGC模型进行模拟揭示。In the embodiment of the present invention, since the most serious impact of drought on the ecological environment is the vegetation degradation caused by the decline in vegetation productivity, so the net primary productivity (Net primary productivity, NPP) of the terrestrial ecosystem is used as the evaluation of the ecosystem loss to the drought response index. In order to quantify the impact of different grades of drought on the loss of ecosystems in the study area, the Biome-BGC model, an ecological process model of historical drought periods, was used to simulate and reveal.
Biome-BGC模型主要基于光合作用、呼吸作用和蒸腾作用等算法对生态系统的碳循环、水循环进行模拟。Biome-BGC模型本身提供的默认生理生态参数是基于大量的文献研究和现有已验证的生理参数评价得出。利用研究区植被、气象、通量观测数据、土壤墒情等数据对模型参数进行适应性调整、修正及模拟效果校验，采用敏感系数对所构建模型进行参数敏感性分析。The Biome-BGC model is mainly based on algorithms such as photosynthesis, respiration and transpiration to simulate the carbon cycle and water cycle of the ecosystem. The default physiological and ecological parameters provided by the Biome-BGC model itself are based on a large number of literature studies and the evaluation of existing verified physiological parameters. The vegetation, meteorology, flux observation data, soil moisture and other data in the study area are used to adapt the model parameters, correct and verify the simulation effect, and use the sensitivity coefficient to analyze the parameter sensitivity of the constructed model.
本发明实施例中，构建Biome-BGC模型采用的驱动数据包括初始数据、气象数据和生理参数数据。In the embodiment of the present invention, the driving data used to build the Biome-BGC model includes initial data, meteorological data and physiological parameter data.
其中，初始数据包括水文站点的经纬度、海拔、土层深度、土壤质地和大气二氧化碳浓度。Among them, the initial data include the latitude and longitude, altitude, soil depth, soil texture and atmospheric carbon dioxide concentration of hydrological stations.
气象数据包括日降水量、日最高温度、日最低温度、日平均温度和辐射。Meteorological data include daily precipitation, daily maximum temperature, daily minimum temperature, daily average temperature, and radiation.
生理参数数据包括气孔导度、冠层比叶面积、叶片和细根CN比。Physiological parameter data included stomatal conductance, canopy specific leaf area, leaf and fine root CN ratio.
S42、采用线性回归分析法、均方根误差法和显著性检验法验证Biome-BGC模型的模拟精度，模拟精度包括Biome-BGC模型的模拟值和观测值的精确度。S42. Verify the simulation accuracy of the Biome-BGC model by using the linear regression analysis method, the root mean square error method and the significance test method. The simulation accuracy includes the accuracy of the simulated value and the observed value of the Biome-BGC model.
S43、以气象数据为驱动，采用通过模拟精度验证的Biome-BGC模型分别模拟研究区域有干旱灾害发生的干旱年和无干旱灾害影响的正常年的植被生产力变化，计算得到研究区域的生态系统净初级生产力，计算公式为：S43. Driven by meteorological data, the Biome-BGC model that has passed the verification of simulation accuracy is used to simulate the changes in vegetation productivity in drought years and normal years without drought disasters in the study area, and calculate the net value of the ecosystem in the study area. Primary productivity, the calculation formula is:
NPPBiome-BGC＝GPP-Pplant NPP Biome-BGC ＝GPP-P plant
其中NPPBiome-BGC表示生态系统净初级生产力，GPP表示生态系统总初级生产力，Pplant表示植被呼吸。Among them, NPP Biome-BGC represents the net primary productivity of the ecosystem, GPP represents the total primary productivity of the ecosystem, and P plant represents vegetation respiration.
将研究区域正常年的多年植被平均生产力作为对比标准，将不同干旱等级的干旱年植被生产力与正常年统计对比，可从统计角度得到不同等级干旱对研究区域内生态系统损失的影响程度。Taking the average annual vegetation productivity in the normal year of the study area as the comparison standard, and comparing the vegetation productivity in the drought year of different drought levels with the normal year, the degree of impact of different levels of drought on the ecosystem loss in the study area can be obtained from a statistical point of view.
S5、根据水文干旱特征和生态系统净初级生产力，采用随机森林模型得到研究区域的综合生态系统生产力，并将综合生态系统生产力作为生态干旱损失的评估值，实现对研究区域的生态干旱评估。S5. According to the hydrological drought characteristics and the net primary productivity of the ecosystem, the random forest model is used to obtain the comprehensive ecosystem productivity of the research area, and the comprehensive ecosystem productivity is used as the evaluation value of the ecological drought loss to realize the ecological drought assessment of the research area.
步骤S5包括以下分步骤S51～S53：Step S5 includes the following sub-steps S51-S53:
S51、基于水文模型和生态过程模型模拟得到的长时间水循环序列连续数据，构建随机森林模型，揭示水文干旱演变与生态系统损失的多过程耦合关系。S51. Based on the continuous data of long-term water cycle series simulated by hydrological models and ecological process models, a random forest model is constructed to reveal the multi-process coupling relationship between hydrological drought evolution and ecosystem loss.
S52、将社会水循环因素变化指标以及水文干旱特征作为随机森林模型的自变量，将生态系统净初级生产力作为随机森林模型的因变量，通过随机森林模型进行耦合模拟得到研究区域的综合生态系统生产力。S52. Taking the change index of social water cycle factors and the characteristics of hydrological drought as independent variables of the random forest model, taking the net primary productivity of the ecosystem as the dependent variable of the random forest model, and performing coupling simulation through the random forest model to obtain the comprehensive ecosystem productivity of the research area.
本发明实施例中，社会水循环因素变化指标不仅包括反映降水、土壤水分胁迫和植被生长状况等指标，还有反映土壤持水量、土地覆盖类型和地貌类型等指标，比如变化指标包括植被状态指数(VCI)、温度状态指数(TCI)、TRMM指数和土壤有效持水量(AWC)。In the embodiment of the present invention, the social water cycle factor change index includes not only indicators reflecting precipitation, soil moisture stress, and vegetation growth status, but also indicators reflecting soil water holding capacity, land cover type, and landform type. For example, the change index includes vegetation state index ( VCI), temperature regime index (TCI), TRMM index and soil effective water holding capacity (AWC).
S53、将综合生态系统生产力作为生态干旱损失的评估值，实现对研究区域的生态干旱评估。S53. Taking the comprehensive ecosystem productivity as the evaluation value of ecological drought loss to realize the evaluation of ecological drought in the research area.
本领域的普通技术人员将会意识到，这里所述的实施例是为了帮助读者理解本发明的原理，应被理解为本发明的保护范围并不局限于这样的特别陈述和实施例。本领域的普通技术人员可以根据本发明公开的这些技术启示做出各种不脱离本发明实质的其它各种具体变形和组合，这些变形和组合仍然在本发明的保护范围内。Those skilled in the art will appreciate that the embodiments described here are to help readers understand the principles of the present invention, and it should be understood that the protection scope of the present 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 revelations disclosed in the present invention without departing from the essence of the present invention, and these modifications and combinations are still within the protection scope of the present invention.
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