Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions
- URL: http://arxiv.org/abs/2409.08765v1
- Date: Fri, 13 Sep 2024 12:12:26 GMT
- Title: Cross-Country Comparative Analysis of Climate Resilience and Localized Mapping in Data-Sparse Regions
- Authors: Ronald Katende,
- Abstract summary: Agriculture is the most vulnerable to climate change in low-income countries (LICs)
This paper introduces a framework for cross-country comparative analysis of sectoral climate resilience.
The study identifies shared vulnerabilities and adaptation strategies across LICs, enabling more effective policy design.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limited insights into broader cross-country patterns of adaptation and vulnerability. This paper addresses these gaps by introducing a framework for cross-country comparative analysis of sectoral climate resilience using meta-analysis and cross-country panel data techniques. The study identifies shared vulnerabilities and adaptation strategies across LICs, enabling more effective policy design. Additionally, a novel localized climate-agriculture mapping technique is developed, integrating sparse agricultural data with high-resolution satellite imagery to generate fine-grained maps of agricultural productivity under climate stress. Spatial interpolation methods, such as kriging, are used to address data gaps, providing detailed insights into regional agricultural productivity and resilience. The findings offer policymakers tools to prioritize climate adaptation efforts and optimize resource allocation both regionally and nationally.
Related papers
- Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.
Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.
Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
arXiv Detail & Related papers (2024-10-14T17:22:55Z) - Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks.
We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events.
Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
arXiv Detail & Related papers (2024-09-11T19:31:56Z) - Identifying Climate Targets in National Laws and Policies using Machine Learning [0.0]
We present an approach for extracting mentions of climate targets from national laws and policies.
We create an expert-annotated dataset identifying three categories of target ('Net Zero', 'Reduction' and 'Other')
We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features.
arXiv Detail & Related papers (2024-04-03T15:55:27Z) - Cross Domain Early Crop Mapping using CropSTGAN [12.271756709807898]
This paper introduces the Crop Mapping Spectral-temporal Generative Adrial Neural Network (CropSTGAN)
CropSTGAN learns to transform the target domain's spectral features to those of the source domain, effectively bridging large dissimilarities.
In experiments, CropSTGAN is benchmarked against various state-of-the-art (SOTA) methods.
arXiv Detail & Related papers (2024-01-15T00:27:41Z) - Analyzing Regional Impacts of Climate Change using Natural Language
Processing Techniques [0.9387233631570752]
We use BERT (Bidirectional Representations from Transformers) for Named Entity Recognition (NER) to identify specific geographies within the climate literature.
We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area.
These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies.
arXiv Detail & Related papers (2024-01-11T16:44:59Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - Deep generative model super-resolves spatially correlated multiregional
climate data [5.678539713361703]
We show an adversarial network-based machine learning enables us to correctly reconstruct the inter-regional spatial correlations in downscaling.
The proposed method has a potential application to the inter-regionally consistent assessment of the climate change impact.
We present the outcomes of another variant of the deep generative model-based downscaling approach in which the low-resolution precipitation field is substituted with the pressure field.
arXiv Detail & Related papers (2022-09-26T05:45:16Z) - Jalisco's multiclass land cover analysis and classification using a
novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis.
In this work, we combine three real-world open data sources to obtain 13 channels.
Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
arXiv Detail & Related papers (2022-01-26T14:58:51Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z) - Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies.
This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events.
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
arXiv Detail & Related papers (2020-04-11T19:49:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.