Ecological drought monitoring and evaluating method

Abstract

The invention discloses an ecological drought monitoring and evaluation method, which comprises the following steps: firstly, collecting multi-source data of a research area and constructing an ecological drought disaster database, then calculating hydrometeorological and hydrological drought indexes according to the multi-source data, extracting hydrological drought characteristics, then carrying out space-time law and evolution characteristic analysis on the hydrological drought characteristics, monitoring the ecological drought of the research area according to an analysis result, constructing a Biome-BGC model to calculate the net primary productivity of an ecological system, and finally obtaining the comprehensive ecological system productivity of the research area by adopting a random forest model, so as to achieve the ecological drought assessment of the research area. The comprehensive ecological system productivity value obtained by adopting the random forest model not only considers ecological factors, but also comprises factors causing hydrological drought and human factors, so that the evaluation of ecological drought is more comprehensive and accurate, and the problems of few considered factors and difficulty in parameter adjustment of a traditional ecological drought loss evaluation method are solved.

Description

technical field [0001] 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. Background technique [0002] 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. [0003] 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...

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Problems solved by technology

[0005] 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 evaluation factors, and many model parameters that are difficult to determine and adjust

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