249 results about "Climate variation" patented technology

The invention provides an urban intelligent drainage management system based on big data analysis, and relates to the technical field of urban drainage management, and the system comprises a multi-source data collection module which collects historical climate changes, rainfall curves, satellite meteorology, pipe network node water level flow and topological structure data in real time; the dynamic three-dimensional modeling module constructs a pipe network three-dimensional model according to a fluid mechanics principle, and binds real-time sensor data by a spatio-temporal data fusion algorithm; the drainage limit analysis module calculates the maximum drainage threshold value and the failure probability of the node under extreme rainfall; the intelligent early warning module fuses LSTM and ARIMA models, generates a drainage pressure thermodynamic diagram in the future 72 hours in combination with satellite meteorological data and triggers early warning; the health assessment module generates a node health index and a maintenance priority order; the resource scheduling engine generates a dynamic scheduling scheme. The urban drainage problem is effectively solved, the ability of a drainage system to deal with extreme weather and pipe network aging problems is improved, and accurate early warning and efficient resource allocation are achieved.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.

The invention relates to the technical field of hydrological assessment and environmental science, in particular to a watershed hydrological assessment method and system under climate change, and the method comprises the steps: collecting multi-source heterogeneous data, and generating fusion feature data and causal path data through multi-modal fusion; optimizing a watershed hydrological network structure based on reinforcement learning, performing runoff simulation by using an enhanced hydrological model, and generating optimized runoff prediction data; combining runoff prediction data and a run-length theory to extract drought characteristic data, and generating scene data for describing an extreme scene through a path constraint type generative adversarial network; and constructing a dynamic joint distribution model of the drought characteristics based on the scene data, and carrying out multi-scene risk assessment. According to the method, accurate modeling and risk quantification of drainage basin drought characteristics in a complex climate scene are realized, the precision and applicability of hydrological assessment are remarkably improved, and a scientific basis is provided for drainage basin water resource management.

A technological improvement to a system for vehicle navigation is provided. The improvement diminishes a contribution to global climate change, e.g., global warming, by generating a flight path (generated flight path) or modifying the flight path (modified flight path) of a vehicle to avoid an atmospheric region in which the vehicle would create a contrail if the vehicle travelled through the atmospheric region.

The invention relates to the field of ecological environment and climate change research, and discloses a vegetation phenology change trend comprehensive evaluation method, which comprises the following steps: acquiring climate data and tree ring data of a target area, simulating a relationship between radial growth and phenology of a tree by using a Vaganov-Shashki n model based on the acquired data, and generating a time sequence of phenology change; extracting phenological key nodes such as growing season starting time, end time and growing season length; analyzing the driving effect of climatic factors on phenological key nodes, and determining main influence factors; on the basis of the climate model and different emission scenes, predicting the vegetation phenological trend under future climate change; the response of the regional ecosystem to climate changes is evaluated based on historical and future data. Through simulation and quantitative analysis, a scientific basis is provided for long-term trend prediction and regional ecological management of vegetation phenological change, and the method has high applicability and reliability.

The invention discloses a drainage basin seasonal runoff change prediction and attribution method and system and a medium, and the method comprises the steps: carrying out the seasonal division of a hydrological year based on the monthly scale rainfall, potential evapotranspiration and land water reserve change rules in a drainage basin; establishing a plurality of coupling models of the GCM and the ABCD so as to predict the runoff process of the drainage basin under the conditions that only future climate change scenes are considered and climate change and human activity influence scenes are considered at the same time; a seasonal-scale Buddyko model is considered, and the relative influence of seasonal-scale climate change and human activities on future runoff change is quantified. The method has a strong statistical basis, can objectively reflect the change rule of the watershed hydrologic runoff, can fully decouple the complex relationship between climate change and human activity and other factors on a seasonal time scale, and provides a new technology for the research of a water circulation process driving mechanism in a changing environment.

The invention discloses a rapid positioning and early warning system for ambrosia trifida in forest planting, and the system comprises a climate and growth prediction module which analyzes historical climate data and the growth law of ambrosia trifida, combines with a climate model, and predicts the rapid positioning and early warning of ambrosia trifida under different climate change conditions. The growth mode, the expansion trend and the possible distribution area of ambrosia trifida are determined; the real-time monitoring and data acquisition module is used for collecting environmental data and the growth state of ambrosia trifida in real time; the data fusion and analysis module is used for comprehensively processing the multi-source data collected by the climate and growth prediction module and the real-time monitoring module, evaluating the growth trend, spatial distribution and ecological risk of ambrosia trifida and providing accurate data support for subsequent early warning and decision making; and the early warning and decision support module automatically generates early warning information of different risk levels according to the growth trend and the risk assessment result generated by the data fusion and analysis module. The method has the advantages of high positioning precision, strong adaptive adjustment capability, accurate grading early warning mechanism and the like.

The invention discloses a thermal power generating unit decommissioning path planning method and device based on meteorological and hydrological risks, equipment, a medium and a product, and relates to the field of power system decision optimization. The method comprises the steps of obtaining meteorological and hydrological risk data and a decommissioning index of a thermal power generating unit; according to the decommissioning index and the meteorological and hydrological risk data under the climate change, a progressive hierarchical screening logic method is adopted for screening and constraint progressive correction processing; performing mapping and nonlinear correlation interaction analysis by adopting Monte Carlo simulation and a gradient boosting decision tree and combining multiple groups of parameter combination data, such as water temperature and runoff volume in meteorological and hydrological variables; and performing collaborative optimization processing based on the adaptive strategy model and a set analysis function to obtain an optimal strategy, and then performing mapping and analysis processing in combination with an analysis result to obtain targeted strategy list information for planning the decommissioning path of the thermal power generating unit. The invention aims to realize the planning of the decommissioning path of the thermal power generating unit.

The invention discloses a flood season water environment pollution prevention and control method, and relates to the technical field of water pollution prevention and control, and the method comprises the following specific steps: data collection and preprocessing: collecting related data covering hydrology, water quality, land utilization, population distribution and industrial layout from each information department, according to the method, the influence of climate change on flood season water pollution is considered, historical meteorological data of a target area is collected and analyzed, a long-term climate change trend is accurately identified by using a meteorological analysis model, a climate background basis is provided for correlation analysis, and on the basis, a water pollution diffusion prediction model and a hydrological simulation model are combined, so that the flood season water pollution prediction accuracy is improved. The water regimen change characteristics and the water pollution risk degree of the flood season in the future specific prediction period are analyzed, the current flood season water pollution problem can be effectively solved, the adaptability to future climate change challenges can be improved, and a scientific basis is provided for making a long-term water pollution prevention and control strategy.

The invention provides an urban rainfall area IDF curve establishment constraint method and system. The method comprises the following steps: collecting actually measured rainfall data and GCM simulated rainfall data; establishing a spatial downscaling model, and performing spatial downscaling on the grid monthly rainfall estimated by the GCM in the future period; calculating the condition transition probability of rainfall occurrence in each calendar month in the future period; generating a parameter set for driving a random rainfall generator, and inputting the parameter set into the random rainfall generator to obtain a daily rainfall sequence in a future period; performing time discretization from the daily scale to the hour scale on the daily rainfall sequence in the future time period; extracting an annual maximum value sequence from the hour drop sequence in the future time period to carry out regional frequency analysis, and constructing an urban rainfall region IDF curve; constructing a random forest model, and deducing alternative factors of IDF curve migration change; and an emergence constraint model is constructed, and the uncertainty of urban rainfall risk prediction under future climate change is reduced. The accuracy of rainfall frequency analysis is improved.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention relates to the field of sedimentary stratigraphy, in particular to a method for analyzing a deep-time El Nino-Southern oscillation cycle based on a sea-lake facies sediment annual stripe layer. The method comprises the following steps: sampling and grinding a sheet, carrying out high-definition scanning, estimating a deposition rate, carrying out element plane distribution, carrying out principal component analysis on the content of each element, carrying out laser in-situ carbon and oxygen isotope test and time domain spectrum analysis on an annual stripe layer one by one, and finally analyzing the climatic change of the interannual scale in the geological historical period. According to the method, element data, the thickness of the annual lamina and the carbon and oxygen isotope of the annual lamina are analyzed and explained in a combined mode, relevance exists among the indexes, a new method is provided for identification and analysis of the deep-time El Nino-Southern oscillation cycle, a time scale with the year as the unit is established on the basis of the marine facies or lacustrine facies annual lamina, and the identification accuracy of the deep-time El Nino-Southern oscillation cycle is improved. And the climate fluctuation is subjected to high-precision analysis under the scale, so that the related technical blank is filled.

The invention discloses a glacier material balance simulation method and system based on feature interaction, and belongs to the technical field of glacier hydrology and climate change monitoring, and the method comprises the steps: sequentially carrying out the uniform format processing and slope feature enhancement of a multi-source data set of a glacier, and dividing the multi-source data set into a simulation data set and an observation data set; training a model based on a trans-attention Transform encoder framework by using the simulation data set to obtain a pre-training model; when one-glacier-leaving cross validation is carried out on the pre-trained model, multi-stage fine tuning training is carried out by using the observation data set to obtain a plurality of fine-tuned models; performing model integration based on performance index screening on the fine-tuned models, and constructing an integrated simulation model; and preprocessing the meteorological dynamic characteristics and the static topographic characteristics of the target glacier, inputting the preprocessed meteorological dynamic characteristics and static topographic characteristics into the integrated simulation model, and carrying out glacier material balance simulation to obtain a glacier material balance simulation result. According to the method, the simulation uncertainty is reduced, and the high-precision simulation of the glacier material balance is realized.

The invention discloses a method for predicting reservoir inflow and under-dam water resource change trend in a future change environment. The method comprises the steps of obtaining future monthly rainfall data, future monthly temperature data, future monthly evapotranspiration data and future land utilization data based on a GCM climate mode; the future runoff process of the reservoir control section is obtained through simulation in combination with an RCCC-WBM model; based on a data mining technology and an LSTM deep learning coupling model, mining relation characteristics of future reservoir runoff and the under-dam water resource quantity, and obtaining the under-dam water resource quantity by inputting the reservoir runoff; and a nonlinear trend analysis method and the like are adopted to analyze change trend characteristics of water resources under the dam in the future. The method can fully solve the problem of insufficient consideration of climate change and land utilization in the existing method, and can significantly improve the prediction precision of the water resource under the dam in the future.

The invention discloses a sudden snow disaster risk degree probability prediction method oriented to insufficient historical time sequences. The method comprises the following steps: step 1), constructing a multi-index risk index based on related data such as historical snow disasters; 2) performing time sequence modeling on the violent snow disaster risk index by adopting an autoregressive integral moving average model, and capturing trend and periodic characteristics of the violent snow disaster risk index; 3) discretizing a continuous risk index of modeling production into a plurality of risk levels, and constructing a state transition probability matrix; step 4), introducing space neighborhood influence and carrier exposure constraint, and constructing a Markov space weighted state transition model; 5) coupling CMIP6 climate scene data, and dynamically adjusting future state transition probability, and 6) fusing ARIMA trend prediction and Markov state transition probability, and outputting future multi-period snowstorm disaster risk level probability distribution.According to the method, the accuracy of snowstorm disaster risk prediction and the modeling ability and prediction adaptability of future climate change scenes are improved.

The invention provides a regional ecosystem carbon sink function evaluation system, which is based on remote sensing data and meteorological data, estimates vegetation net primary productivity (NPP) and soil heterotrophic organism respiration consumption (Rh) through a CASA model and a regression equation model respectively, and further calculates net ecosystem productivity (NEP) to evaluate the carbon sink function of a regional ecosystem. The system considers a plurality of parameters such as photosynthetically active radiation (APAR), actual light energy utilization rate (epsilon), a temperature stress factor, a water stress factor and the like, and ensures the accuracy and scientificity of estimation. The system can be widely applied to the fields of regional carbon sink monitoring, ecological environment evaluation, climate change research and the like, and provides a scientific basis for regional sustainable development.

The invention provides a drought and flood sudden change prediction method and system based on artificial intelligence, and the method comprises the steps: collecting the in-out reservoir runoff observation data of a reservoir in a target region, obtaining a restored natural reservoir runoff series based on a water balance method, calibrating a long-short-term memory model through a minimum batch gradient descent method, and carrying out the prediction of the drought and flood sudden change. Based on the natural reservoir runoff, the actual reservoir runoff and meteorological data, constructing a long-short-term memory model to simulate the influence of water conservancy project regulation and storage on the runoff; based on an earth system mode set and a multivariable deviation correction method, performing spatial downscaling on output data of earth system modes to obtain corrected meteorological variables; driving a multi-member set of an earth system mode, and separating by adopting a detection attribution technology to obtain a contribution rate of man-made climate compulsion to drought and flood sudden turning event evolution; and predicting a daily runoff process under future climate change, and predicting a future drought and flood sudden turning event by adopting deep learning and superposition of regulation and storage influence of a water conservancy project.

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

The invention belongs to the technical field of flood and drought disaster recognition and evaluation, and discloses a flood season anti-dry risk evaluation method based on a composite runoff index, and the method comprises the steps: obtaining geographic information data, hydro meteorological data and hydraulic engineering data of a target region; setting a drought threshold in combination with urban water supply requirements to select low-flow drought events in historical flood seasons; optimal probability distribution is selected, and marginal distribution characteristics of drought flow and duration of the low-flow drought event in the flood season are analyzed; selecting a Copula function, establishing joint distribution of drought flow and duration of the low-flow drought event in the flood season, and constructing a composite runoff index of the low-flow drought event in the flood season; and in consideration of different discharge scenes of future climate change, based on a drainage basin distributed hydrological model prediction result, analyzing occurrence risk response characteristics of future flood season anti-flood events by adopting a composite runoff index. The method can accurately evaluate the sudden rising characteristic of flood season anti-dry and the dry water characteristic of the flood season.

The invention discloses a corn planting risk prediction method and system based on a climate change trend, and relates to the technical field of environment prediction. The method comprises the following steps: acquiring climate-related data of corn planting; constructing a risk prediction model; the risk prediction model adopts an LSTM-Transform hybrid neural network to process time sequence meteorological data, introduces an SHAP value interpretation model to quantify the contribution degree of each climate factor to the output reduction risk, and couples a DSSAT crop model and a risk probability prediction module; inputting the climate-related data into the risk prediction model, and determining a final corn planting risk result through operation of the risk prediction model; the corn planting risk result comprises a regional risk grade map, planting window period optimization suggestions and extreme weather event probability early warning. According to the invention, the accuracy of corn planting risk prediction can be improved.

The invention relates to the field of carbon neutralization, and discloses a four-dimensional soil moisture drought event tracking and warming signal identification method, which comprises the following steps: data acquisition; four-dimensional soil moisture drought event identification; four-dimensional soil moisture drought event characteristic quantification; artificial warming signal detection of time evolution of four-dimensional soil moisture drought characteristics in historical periods; performing four-dimensional soil moisture drought future evolution estimation; four-dimensional soil moisture drought event driving factors; the method has the advantages that future space-time evolution characteristics of four-dimensional soil moisture drought are explored, driving factors and physical mechanisms of four-dimensional soil moisture drought are analyzed, and a powerful scientific basis is provided for coping with global climate changes, guaranteeing water resource safety and formulating disaster prevention and reduction strategies.

The invention of system of observation and monitoring the earth warming and changes in polar ice behavior using LoRa to satellite technology is a system for measuring, observing and monitoring weather and environmental conditions in order to determine the effects of environmental conditions and climate changes on the way ice masses change in the polar regions of the earth. This invention is related to the rocket equipped with several modules for measuring the conditions of the surrounding environment as well as data transmission through the LoraWan protocol to a fixed or deployable station. Also, in this invention, if there is a need to calibrate any of the modules in the meteorological rocket, the central server structure, which is equipped with an artificial intelligence analyzer, can calibrate the device by comparing the arrays received from the surrounding rockets and analyzing the data received from these systems.

The invention relates to the technical field of meteorological disaster assessment, in particular to a rainstorm flood-based risk assessment system, which comprises an acquisition terminal, a law analysis terminal, a prediction terminal and an assessment terminal which are in progressive signal connection, the prediction terminal is used for predicting future extreme rainfall and flood influence through climate change conditions and in combination with historical rainstorm flood evolution rules analyzed by the rule analysis terminal, the prediction terminal comprises a rainfall prediction module and a flood influence module, and the evaluation terminal is used for comprehensive prediction through the rainfall prediction module and the flood influence module. And setting an evaluation framework for the flood risk, and estimating a future flood risk change trend. According to the method, multiple factors are comprehensively considered, the prediction precision is improved, more comprehensive and accurate support is provided for prediction, evaluation and response of rainstorm flood disasters, and therefore comprehensive and multi-level evaluation of disaster loss is effectively reduced.

The invention relates to the technical field of environmental science, and discloses an SWAT-based BMP optimal configuration method adaptive to climate change, and the method comprises the steps: collecting pollution data of a target agricultural region, and constructing an NPS simulation model of the target agricultural region based on an SWAT model; collecting basic data of a target agricultural area, determining implementation data of a plurality of preset optimal management measures, and constructing an optimal management measure database; based on the optimal management measure database, performing parameter optimization on the NPS simulation model to form an NPS simulation optimization model; simulating climate change in future time according to the climate observation data and the historical meteorological data, and constructing a meteorological database; and based on the meteorological database, operating the NPS simulation optimization model, and performing optimal management measure optimization configuration analysis through a genetic algorithm to obtain an optimal management measure optimization result. According to the method, resources can be reasonably configured, and the flexibility, adaptability and actual effect of optimal management measures are improved.

The invention provides an urban extreme rainfall risk assessment and drainage optimization method fusing a composite threshold and a Chicago method, and relates to the field of power grid disaster prevention and reduction planning and meteorological and hydrological analysis, and the method comprises the following steps: employing a quartile method and a composite threshold method to recognize and classify extreme rainfall events; constructing an extreme rainfall pattern model, and determining a minute-level rainfall intensity curve by using the extreme rainfall pattern model; generating an extreme rainfall spatial-temporal distribution characteristic spectrum according to the extreme rainfall event set in combination with a spatial interpolation technology fused with a deep learning model; the drainage capacity of the urban drainage system is evaluated, and the urban drainage system is optimized according to the evaluation result. The method has high adaptability, can be popularized to urban areas with different terrains and climate conditions, provides an efficient technical tool for coping with extreme weather events under the climate change background and improving the disaster prevention and reduction capacity of a power system, and has important social and economic values.

The invention relates to the field of carbon neutralization, and discloses a climate-driven analysis method, equipment and medium based on time-space evolution of soil moisture, and the method comprises the following steps: using multiple sets of surface soil moisture data sets including satellite remote sensing, land assimilation, a diagnosis model, a reanalysis model and climate mode simulation; quantifying the temporal and spatial change of global surface soil moisture under the background of artificial climate change, and clarifying the consistency of long-term temporal and spatial evolution characteristics of the surface soil moisture among different data sets and driving factors of the long-term temporal and spatial evolution characteristics of the surface soil moisture; the atmospheric physical mechanism behind the global surface soil moisture change is clarified, and the response of the global surface soil moisture change to artificial climate change and climate internal change rate is revealed through maximum covariance analysis; according to the method, the space-time evolution characteristics and climate driving factors of the surface soil moisture are determined, so that a scientific basis is provided for improving the quality of a soil moisture data set and improving the prediction capability of cascade extreme weather events, and scientific support is provided for retarding global warming and promoting the realization of a global carbon neutralization target.

A system and method for architecturally designing and manufacturing building structures customized for environments vulnerable to extreme weather events or other natural phenomena. The method includes determining an environmental susceptibility of a proposed building location, designing building components factoring in geographical and climate-related features, manufacturing the building components, and assembling the building structure in a manner better configured to resist extreme weather or natural events.

The invention discloses an alpine grassland livestock capacity monitoring and predicting method and system based on climate change. The alpine grassland livestock capacity monitoring and predicting method based on climate change comprises the following steps: 1) modeling data preprocessing: acquiring grassland type data and meteorological factor data, and carrying out space-time mask fusion, year / month scale space-time alignment and dynamic standardization to obtain grassland type standardized data; 2) feature screening and XGBoost modeling: performing feature importance screening and multi-parameter grid search optimization on grassland type standardized data by using a random forest model, and establishing and verifying a model; 3) preprocessing future CMIP6 climate factor data; and (4) predicting the livestock carrying capacity by using the results of the steps (2) and (3). According to the method, the potential influence of the climate change on the alpine grassland ecosystem can be evaluated on the regional scale, the variation trend of the future livestock capacity is accurately predicted, and a scientific basis is provided for formulating pasturing area management, ecological restoration and climate adaptability strategies.