105 results about "Climate change" patented technology

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment monitoring method and system based on remote sensing image processing, and the method comprises the following steps: collecting remote sensing image data and climate factor data of a target region; performing spatial gridding and standardization processing on the data; establishing a nonlinear regression model to analyze a relation between climate factors and vegetation indexes; extracting spatio-temporal topological characteristics of the climate factor data and identifying a change mode; long-term prediction of regional vegetation productivity is carried out through time sequence model training; the regional ecological environment is analyzed and evaluated based on the prediction result, and a scientific basis is provided for ecological environment monitoring; the system comprises a data acquisition module, a data processing module, a nonlinear regression modeling module, a spatio-temporal topology analysis module, a time sequence prediction module and an ecological evaluation module. According to the method, the problems of small data coverage, low prediction precision and insufficient capture of nonlinear influence of climate change in the existing method are solved.

The invention discloses a vegetation-submerged embankment coupled tough coast protection method and system and a storage medium, and the method comprises the steps: firstly, determining a research region, and systematically collecting the near-shore terrain, water and sand power conditions, vegetation parameters and other data; secondly, constructing a water-sand-vegetation coupling numerical simulation model based on the collected data, and performing precision verification on the model; then selecting a suitable vegetation type in combination with environmental characteristics of the research area, carrying out artificial seeding and planting work in a vegetation potential habitat of the tidal flat, and determining a design elevation and an offshore distance of the submerged dike; and finally, evaluating the coast protection effect under the synergistic effect of the submerged embankment and the vegetation, and quantitatively analyzing the improvement degree of the coast protection effect on wave attenuation and beach stability. A sustainable tough coast protection system can be formed under the climate change background through a collaborative protection mechanism integrating natural vegetation and an artificial submerged embankment. The method has the advantages of low cost, remarkable wave reduction effect and the like, and the capability of coping with climate change challenges and comprehensive protection of coastal zones can be effectively improved.

The invention discloses an SSP scene reservoir group flood control scheduling method considering hydrological forecast uncertainty, and the method comprises the steps: obtaining SSP climate scene meteorological data to drive an SWAT model to generate daily runoff, and deducing and designing a flood hydrograph; a flow error sequence is calculated based on historical simulation and measured data, optimal distribution is optimized through fitting of a multi-probability distribution model, and a 95% confidence interval is generated through Monte Carlo simulation to correct design flood; and constructing an optimization model with the goal of minimizing the highest water level of flood regulation, bringing in various constraints, solving an optimal scheduling process by adopting a discrete differential dynamic programming algorithm, and finally refining an adaptive scheduling rule. The method solves the problems that prediction uncertainty is not considered and solving efficiency is low in the prior art, flood control safety and dispatching adaptability of the reservoir group are improved, and the method is suitable for flood control dispatching of the reservoir group under climate change.

The invention discloses a soil improvement measure decision-making method and system based on multi-modal deep learning. According to the method, firstly, soil state parameter change data and crop growth data of a target planting site are acquired, and a preliminary soil improvement scheme is established by combining the two data; then, a multi-modal deep learning network model is constructed, climate change characteristics and soil conditioner content change characteristics after improvement measures are implemented are fused and analyzed, and data characteristics of influences of climate changes on the conditioner are extracted; and based on the influence data, further identifying the failure characteristics of the modifier, including the failure propagation path and the dose change characteristics of the modifier. And finally, performing dynamic compensation on the original soil improvement measures according to the failure characteristics to form a refined compensation scheme. According to the method, intelligent adjustment and dynamic optimization of the soil improvement strategy can be realized, and the method has relatively high agricultural application value and popularization prospect.

The invention relates to the related technical field of climate monitoring, in particular to a freezing circle climate change monitoring method and system suitable for an Asian alpine region, and the method comprises the steps: configuring a monitoring network, receiving a frozen soil multi-source monitoring data set, constructing a frozen soil and climate coupling response model, embedding a region climate assessment engine, and generating a frozen soil degradation risk grade distribution map. And judging whether risk reminding is triggered or not according to the dynamic frozen soil degradation threshold value, and if the risk reminding is triggered, marking an abnormal region in the frozen soil degradation risk level distribution diagram. The technical problems that Asian alpine region freezing circle climate change monitoring abnormity reminding mostly depends on static data, the dynamic evolution process of frozen soil cannot be accurately captured, and the frozen soil degradation risk identification precision is limited are solved, a frozen soil and climate coupling response model constructed based on multi-source data is achieved, and the frozen soil degradation risk identification accuracy is improved in combination with a dynamic degradation threshold value set according to the frozen soil type. And risk reminding is triggered in time and an abnormal region is marked, so that the frozen soil degradation risk identification precision is improved.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.

The invention discloses a lake and reservoir future carbon income and expenditure scenario prediction accounting method based on climate change influence, and relates to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: S1, collecting climate data, social economic data and lake and reservoir ecological data of a target lake and reservoir and a surrounding region, and carrying out the cleaning and missing value supplementation of the data; according to the method, the social economy-climate coupling model is constructed, social economy factors such as population growth and economic development and climate change factors are cooperatively incorporated into lake and reservoir carbon income and expenditure prediction, comprehensive simulation of lake and reservoir carbon income and expenditure multi-driving factors is realized, the fitting degree of a prediction result and an actual situation is improved, and the prediction efficiency is improved. By setting multiple groups of social economy-climate scene combinations, lake and reservoir carbon income and expenditure prediction results under different development paths are output, a carbon income and expenditure accounting sub-model based on a process mechanism is embedded, and model parameters are verified and optimized in combination with historical data, so that the accuracy and reliability of lake and reservoir carbon income and expenditure prediction accounting are further improved.

The invention relates to the technical field of water and soil loss treatment, and discloses a water and soil loss treatment method and system based on a wind erosion area, and the system comprises a multi-dimensional monitoring module and an intelligent management module. According to the system, environment data and climate data of all areas are obtained through a multi-dimensional monitoring module and are classified to form a data set, an intelligent management module analyzes the water and soil loss degree of each area, corresponding earth surface exposure difference and erosion coefficient are generated, dynamic quantitative indexes are synthesized, and the water and soil loss degree is calculated. And the intelligent management module analyzes the climate change trend of each region, generates a corresponding wind power fluctuation index and a monitoring data set, quantifies the wind power fluctuation intensity, pays attention to individual differentiation, and obtains the corresponding wind power fluctuation index and monitoring data set. And the intelligent management module evaluates the water and soil loss degree, the wind power change degree and the climate change degree, outputs corresponding treatment decisions, realizes partitioned policy application, establishes an emergency protection mechanism, and is high in resource allocation efficiency and good in dynamic treatment effect.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses an elastic city rainwater garden layout optimization method and system self-adaptive to climate change, and relates to the technical field of sponge city drainage optimization. Multi-dimensional data are collected in real time through an ultrasonic liquid level sensor, an electromagnetic flowmeter and a meteorological radar, a physical model is constructed, and a weighted flow value is generated in combination with LSTM; intelligent prediction under the constraint of the physical law is achieved, threshold matching and deviation correction are conducted based on the weighted flow value, and the pipe diameter adjusting amount is calculated; and constructing a three-color matrix execution feedback strategy containing prediction precision, drainage efficiency and equipment loss. The problems that a traditional scheme is poor in dynamic adaptability, single in model and free of closed-loop feedback are solved, and the elasticity of a drainage system to cope with climate changes is improved.

The invention discloses a tough city evaluation method based on climate and biodiversity coupling and application. The evaluation method comprises the following steps: S1, determining a biodiversity index of a city to be analyzed; s2, based on a weight prediction model, determining a climate partition weight coefficient of the biodiversity index in a future time period; s3, an index evaluation value BRI is obtained through calculation by means of the biodiversity index and the climate partition weight coefficient, and the larger the index evaluation value BRI is, the higher the city toughness is; according to the method, the climatic factors and the biodiversity are subjected to coupling analysis, the weight prediction model is established to predict the biodiversity index in the future time period, the toughness of the urban biodiversity can be quantified, the limitation of static evaluation of an original evaluation method is solved, accurate evaluation of climatic partition adaptation is realized, and the evaluation efficiency is improved. A tough city evaluation system with better ecological adaptability is constructed, and a scientific tool is provided for global cities to cope with climate changes.

The invention discloses a storm surge coupling numerical simulation method and system based on climate change, and relates to the technical field of numerical simulation, and the method comprises the steps: determining the size of a regional grid according to a pre-obtained regional feature, and carrying out the data reconstruction processing of the obtained heterogeneous spatial data based on the size of the regional grid, and obtaining a comprehensive heterogeneous data set; constructing a comprehensive heterogeneous model, performing model coupling to obtain a comprehensive coupling model, and performing friction coefficient adjustment to obtain a storm surge coupling numerical simulation model; performing storm surge simulation on the comprehensive observation data acquired in real time, and evaluating and optimizing the storm surge coupling numerical simulation model to obtain a storm surge simulation model for storm surge coupling numerical simulation under climate change. Through the comprehensive coupling model, the limitation of traditional single water adding simulation is broken through, and the simulation capacity of extreme water level, offshore water adding and composite disaster processes is remarkably improved.

The invention relates to an ocean three-dimensional temperature field reconstruction method based on Stacking ensemble learning, and belongs to the field of ocean data processing and climate change research. According to the method, a high-precision temperature field reconstruction model is constructed by fusing multi-source sea surface observation data and historical thermohaline observation data. The method comprises the following specific steps: firstly, carrying out data preprocessing so as to integrate observation data from different sources; then, multiple base models (such as random forest, linear regression and the like) are adopted to carry out Stacking integration, and an Optuna hyper-parameter optimization technology is utilized to improve the model performance; and finally, carrying out system evaluation on the model, and verifying the reconstruction precision and robustness of the model at different depths. By means of the method, the reconstruction precision of the ocean three-dimensional temperature field can be remarkably improved, and important data support is provided for ocean dynamics, climate change evaluation and environment monitoring.

The invention relates to the technical field of climate change prediction, in particular to a climate change prediction method and system based on artificial intelligence, and the method comprises the following steps: S1, multi-source climate data space-time alignment processing: obtaining satellite remote sensing data, ground observation station data and ocean buoy data, generating a standardized space-time matrix covering the globe through a space-time interpolation algorithm; s2, multi-scale climate feature collaborative extraction: outputting global scale prediction data by a global branch, and outputting regional scale prediction data by a regional branch; and S3, cross-scale dynamic coupling prediction: inputting the global scale prediction data into a cross-scale coupling layer to generate a region correction vector, and outputting a final coupling prediction result after fusing the global scale prediction data with the region scale prediction data. According to the invention, through climate data standardization processing, double-branch multi-scale feature extraction and cross-scale dynamic coupling prediction, the accuracy and regional response capability of climate change modeling are improved.

The invention discloses a corn planting suitability comprehensive evaluation method which comprises the following steps: step 1, data collection and index system construction: a system collects multi-source information of meteorological data, crop growth data and historical disaster data, and a comprehensive corn planting suitability evaluation index system is constructed, the method comprises the following steps of 1, selecting a corn growth period, 2, dividing the growth period: determining four key stages of the corn growth period in combination with related data and local production practice experience, 3, constructing a comprehensive evaluation model, 4, evaluating the planting potential and carrying out weighting processing, 5, carrying out comprehensive evaluation and zoning, and 6, carrying out comprehensive evaluation and zoning. According to the method, the blank of the existing research in comprehensive evaluation of the corn planting suitability is filled, a new thought and method are provided for coping with agricultural risks caused by climate changes, a scientific regionalization reference is provided for agricultural decision makers by constructing a perfect corn planting suitability evaluation system, and the corn planting suitability evaluation method has a wide application prospect. The guarantee of global grain safety and the sustainable development of an agricultural ecosystem are facilitated.

A system for measuring greenhouse gases and automatic generation of carbon credits in forest areas, based on real-time data, which allows online analysis, generating automatic certification of carbon credits. The system presents a microprocessor unit (UP) that receives signals from sensors measurement of gases (Sg) analyzed in a chromatography unit High Efficiency Liquid (HPLC) and rainfall sensors (Sp). The data is processed in the local processing unit (UP) and transmitted to a remote processing unit (UPr) through the communication module (MC), using transmission protocols of data. The processed data generates credit certificates for authenticated and authorized by the UNFCCC (Convention-United Nations Framework on Climate Change), ensuring its internationally recognized validity.

The present application relates to the technical field of climate change warning, and discloses a mangrove blue carbon ecological risk identification and warning method and system for climate change, comprising the following steps: acquiring time series of global land surface characteristic satellite leaf area index data and environmental factors, performing detrending, deseasonalization and standardization processing to extract abnormal sequences; determining climate oscillation windows and neutral periods according to multivariate El Nino-Southern Oscillation index and dipole mode index, and calculating the mean value of the environmental factors in the neutral period as a reference; inputting the environmental factors into a long short-term memory network to extract attention weights, calculating the factor difference between the mean value of the climate oscillation window and the reference, mapping the factor difference by using a nonlinear saturation kernel function, combining the weights to obtain contribution amplitude, and selecting a dominant risk factor by comparing the direction; processing the absolute value of the leaf area index anomaly by using an S function to output risk intensity, and predicting future leaf area index anomaly based on the dominant risk factor to generate a warning evaluation.

The invention discloses a system and a method for predicting and early warning the ecological risk of river and lake water caused by climate change in the technical field of water ecological environment monitoring, and the system comprises a stand column, the stand column is provided with a central control device, a meteorological detection device, an alarm and a water ecological detection device, and the central control device is communicated with a detection probe through a cable; the cable is provided with a floating piece and a storage box, and the storage box is internally provided with a storage assembly; when the change of the water level of the river and lake is detected, the central control equipment controls the storage assembly to work, so that an external cable enters the storage box or the cable in the storage box is released to the outer side, the detection probe cannot be exposed out of the water surface, meanwhile, it is ensured that the detection probe cannot be separated from the located area, and it is ensured that the detection probe is always in water; the water ecological parameter acquisition is more accurate and reliable, and the stable operation of the prediction and early warning system is ensured.

A kind of high-strength road of water resources adjustment system in response to climate change, including the structure system template with hollow unit body is combined into underground structure space by pouring and solidifying concrete slurry, and then is paved as road or pavement, the hollow unit body is combined by at least one piece of structure template and multiple side plates;The upper surface of the template is provided with a plate, and a through hole and at least one pipe are provided on the plate, pour concrete slurry after the structure system template and the side plate are combined and paved, and form an underground structure space with high support strength after solidification;Therefore, replace the drainage system infrastructure such as the traditional road drain and drain cover, and use the reserved hole or drill hole of the road, or the water permeable pavement, or the water storage and drainage system of the rainwater collected from the roadside drainage hole and stored in the underground space, effectively store water to prevent regional waterlogging, and provide larger drainage space system, when no drain is set, the urban renewal appearance of the road can be planned to be wider, safer and more beautiful, and as the best method of water resources adjustment, it can also prevent mosquito breeding and foul odor in traditional drain.

Systems, methods, and computer-readable storage media for forecasting the impact of climate change, and more specifically to the impact on water quality and / or quantity. The system receives, from a plurality of sensors within a predefined geographic area, environmental data. The system normalizes the environmental data and executes an artificial intelligence algorithm, where inputs to the artificial intelligence algorithm include the normalized environmental data, and outputs of the artificial intelligence algorithm include environmental risks, consequences, and probabilities associated with at least one environmental event. The system then modifies a planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

The present invention discloses an existing structure strength detection device under climate change, and relates to the technical field of strength detection, the existing structure strength detection device comprises a first carrier plate, the right side of the first carrier plate is fixedly provided with a connection seat, the bottom of the connection seat is fixedly provided with a first U-shaped seat, and the top of the first U-shaped seat is provided with a driving assembly; a supporting rod is fixed to the outer wall of the first rotating rod, a fourth carrying rod is fixed to the left side of the supporting rod, an impact ball is fixed to the left end of the fourth carrying rod, a third carrying rod is fixed to the right side of the supporting rod, and an iron block is fixed to the right end of the third carrying rod. The impact ball is used for impacting the concrete structure under the climate change, and then the detection assembly is used for detecting the concrete structure before and after the impact, so that the change condition of the strength of the concrete structure under the climate change is obtained, and the concrete structure can be correspondingly maintained subsequently.

The invention discloses a climatic change response type modular micro-landscape self-adaptive monitoring system, which comprises a micro-landscape module, a climatic change monitoring module, a data processing module and a self-adaptive adjustment module, the micro-landscape module is made of a sustainable material subjected to multi-dimensional testing and is integrated with climatic change sensitive type indicative organisms; the climatic biological monitoring module is used for collecting climatic environment parameter data and indicative biological quantitative characteristic data; the data processing module is used for receiving climate environment parameter data and indicative biological quantitative characteristic data acquired by the climate biological monitoring module, predicting climate trend and micro-landscape growth state pre-judgment through a CNN-LSTM network, and outputting an optimization scheme in combination with a genetic algorithm; and the adaptive module dynamically adjusts the module structure and generates environmental parameters, thereby realizing accurate response to climate change and micro-landscape ecological efficiency optimization.

The present invention discloses a lightweight keel partition wall system for coping with changes in roof structure caused by climate change, relating to the field of building construction technology, including a steel plate top plate, and two top keels are fixedly installed on the lower side of the steel plate top plate by bolts; the present invention symmetrically opens two movable grooves on the inner side of the L-shaped angle code, and connects the vertical keel and the L-shaped angle code by bolts, and the bolts are screwed until the vertical keel can move up and down along the two movable grooves without the risk of falling off. Through this connection method, the vertical keel can slide vertically to eliminate deformation caused by temperature; the L-shaped angle code can be easily engaged by the engagement of the slider and the slide groove, and a plurality of extrusion springs are fixed between the two limit blocks and the installation groove. By utilizing the elastic potential energy of the plurality of extrusion springs, the L-shaped angle code can have a certain lateral adjustment space on the inner side of the installation groove, so that the vertical keel can slide horizontally, further eliminating deformation caused by temperature.

The present application embodiment discloses a quantitative assessment method for the impact of climate change and human activities on terrestrial water reserves, which relates to the field of geographic information technology. The method includes: obtaining first TWSA data and climate-driven remote sensing data related to climate factors; reconstructing the TWSA data in combination with a gradient boosting regression model to output second climate-driven TWSA data; verifying the second TWSA data, and if the verification passes, extracting third TWSA data related to human activity factors through a separation method; calculating the component contribution rates of the second TWSA data and the third TWSA data respectively, and determining the main factors driving changes in terrestrial water reserves. The present application embodiment not only comprehensively considers the direct driving effect of climate factors on TWSA data, but also combines the dynamic relationship between climate factors and TWSA data, which can provide a scientific basis for quantifying the relative contributions of climate factors and human activity factors.

The invention provides an exploration area prediction method and device based on multistage sublithofacies paleogeography, and the corresponding method comprises the steps: constructing a lithofacies paleogeography evolution process in space according to the construction activity property of a same-sedimentary-period area; wherein the space level comprises regional multi-plate blocks, basins in the plates and structure-lithofacies zones in the basins; in time, the lithofacies paleogeography evolution process is restrained according to global sea level change, tectonic settlement, material source supply and climate change; wherein the time level comprises a second-level sequence, a third-level sequence and a fourth-level sequence; and generating a lithofacies paleogeographic map according to the constrained lithofacies paleogeographic evolution process, and predicting a favorable exploration area according to the lithofacies paleogeographic map. According to the method, the development and configuration relation of reservoir forming elements such as hydrocarbon source rocks, reservoir bodies and cap strata in the petroliferous basin is well revealed, so that the oil and gas exploration field and direction are explained, and a basis is provided for optimizing zones and targets.

The present application relates to a kind of based on multi-source data and machine learning ground observation evapotranspiration complete sequence acquisition method and system.The steps of the method include: preparing high-quality evapotranspiration data and time-continuous reference variable;Training machine learning model, construct the nonlinear relationship of evapotranspiration and reference variable, and evaluate the performance of machine learning model;Using the machine learning model trained completely to interpolate the evapotranspiration of missing time, produce time-continuous evapotranspiration data.The present application effectively solves the problem of how to obtain more real day-scale data, can provide high-quality continuous true value for ground verification;At the same time, it solves the problem of how to obtain long-time complete sequence data, can provide powerful data support for climate change analysis.

The present application belongs to the technical field of surface water heat flux inversion, in particular to a multi-temporal surface water heat flux collaborative inversion method, device and medium. The method comprises: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature; realizing multi-temporal remote sensing surface temperature collaborative decomposition; solving the joint balance equation of multi-temporal water heat flux and its components; and inverting multi-temporal surface sensible heat flux, water heat flux and its components. The present application realizes multi-temporal surface temperature joint decomposition based on surface temperature and vegetation coverage time series information, thereby proposing a new idea of multi-temporal surface water heat flux joint cutting sharing reference temperature, establishing multi-temporal balance equation considering surface water heat flux and its components, overcoming the deficiency that current remote sensing model is only suitable for single temporal surface water heat flux inversion, improving the efficiency of surface water heat flux remote sensing estimation, and providing technical services for climate change, agricultural production, ecological construction, water resources management and the like.

The invention relates to a light energy battery technology based on ferroelectric polarization. The invention belongs to the technical field of renewable energy sources, new energy sources and clean energy sources, and belongs to national key industries. The technical problems to be solved are that the conversion efficiency of a silicon electric p-n junction solar cell is relatively low, and a photovoltaic power generation system of the silicon electric p-n junction solar cell has intermittency, volatility and uncertainty. The technical scheme for solving the problem is characterized in that ferroelectric photovoltaic positive and negative polarization is opposite in conductive property, generated positive and negative photoelectric conversion batteries form a battery unit, and formed positive and negative magnetic fields are combined into a unified magnetic field. The conversion efficiency of the battery unit is improved, all-optical and all-time fixed mode power generation is realized, and the problems of intermittency, volatility and uncertainty are avoided. The solar power generation device is mainly used for solar power generation to cope with climate changes. In the field of aerospace, energy is provided for space stations and satellites. The invention relates to the field of military weapons, and relates to cold light missiles made of energy power and cold light materials to prevent heat tracking.

The invention discloses a method and system for quantizing the influence of temperature increase on farmland emission reduction potential, and relates to the technical field of crossing of agricultural ecology and climate change, and the method comprises the following steps: S1, collecting multi-dimensional temperature increase characteristics and basic data, dividing farmland regions and crop growth periods, extracting temperature increase characteristic parameters, and calculating the temperature increase characteristic parameters; greenhouse gas emission, soil, crop and meteorological basic data are synchronously collected; s2, contrast case matching and coupling model construction: matching a same-period contrast case based on a random forest algorithm, measuring and calculating a temperature increase response coefficient, and introducing a time-dependent attenuation coefficient and a region regulation factor to construct a multi-factor coupling quantification model; s3, emission reduction potential dynamic quantification and scene prediction; according to the method and the system for quantifying the influence of the temperature increase on the farmland emission reduction potential, the dynamic influence of the temperature increase on the farmland emission reduction potential is accurately captured, the shortages of the time effect and the regional suitability in the prior art are effectively made up, and the evaluation precision is remarkably improved.

A sea ice habitat restoration system is configured to rapidly extract pack ice and raft it onto level ice to establish disturbance corridors that augment the ecological carrying capacity and resistance to continuing climate change. The deployable system is configured to be mounted on an icebreaking ship and includes a inclined-plane ice ramp supported by an ice rafting device. The system applies a hogging moment to the extracted sea ice and laterally rafts it with minimal damage, facilitating the restoration of sea ice habitats of threatened ice-obligate and ice-associated species. This ship-mounted version produces significantly greater volumes, additional vertically and horizontally arranged layers, and more widely distributed composite masses of freeze-bonded rafted ice over the course of a w inter compared to the singly rafted ice passively produced by the moored version referenced herein.