Tag: Climate Change Downscaling Techniques

Abstract

A fragile terrain makes a mountainous watershed like Pare in India’s eastern Himalayas extremely sensitive to climate change. However, this watershed has a great deal of potential for the development of water resources; thus, hydrological investigation and impact assessments in light of climate change are essential. Using the soil and water assessment tool (SWAT), the current study examined how discharge and soil moisture in the Pare River of Arunachal Pradesh would change in response to climate change. The projected streamflow was compared with the baseline projection during 1976–2005. The future precipitation scenarios predicted that there will be a decrease in rainy episodes and an increase in dry days in the Pare watershed. This meant that more extreme occurrences would occur in the future. The SWAT model’s performances during calibration and validation were deemed satisfactory based on the Nash–Sutcliffe efficiency, p-factor, and r-factor. It was discovered that SWAT slightly underestimated the discharge. The findings revealed that discharge would continue to rise as time progressed from the near to the far-future. Variations in discharge showed shrinkage in high flow days with increased flood amplitude, which might result in major flooding in low-lying areas downstream and significant soil erosion from the upland areas. It was predicted that the surface runoff component would increase significantly in the future, possibly leading to frequent flash floods and soil erosion. However, soil moisture in the Pare watershed would remain more or less the same throughout this century. Even if future streamflow was predicted to rise, worry would always persist due to its unequal distribution. The region's water managers may set guidelines for water and soil conservation measures to cope up with these changes. More studies may be conducted to recommend actions in the study region by local authorities and managers associated with various soil and water sectors.

Abstract

A fragile terrain makes a mountainous watershed like Pare in India’s eastern Himalayas extremely sensitive to climate change. However, this watershed has a great deal of potential for the development of water resources; thus, hydrological investigation and impact assessments in light of climate change are essential. Using the soil and water assessment tool (SWAT), the current study examined how discharge and soil moisture in the Pare River of Arunachal Pradesh would change in response to climate change. The projected streamflow was compared with the baseline projection during 1976–2005. The future precipitation scenarios predicted that there will be a decrease in rainy episodes and an increase in dry days in the Pare watershed. This meant that more extreme occurrences would occur in the future. The SWAT model’s performances during calibration and validation were deemed satisfactory based on the Nash–Sutcliffe efficiency, p-factor, and r-factor. It was discovered that SWAT slightly underestimated the discharge. The findings revealed that discharge would continue to rise as time progressed from the near to the far-future. Variations in discharge showed shrinkage in high flow days with increased flood amplitude, which might result in major flooding in low-lying areas downstream and significant soil erosion from the upland areas. It was predicted that the surface runoff component would increase significantly in the future, possibly leading to frequent flash floods and soil erosion. However, soil moisture in the Pare watershed would remain more or less the same throughout this century. Even if future streamflow was predicted to rise, worry would always persist due to its unequal distribution. The region's water managers may set guidelines for water and soil conservation measures to cope up with these changes. More studies may be conducted to recommend actions in the study region by local authorities and managers associated with various soil and water sectors.

Abstract

Groundwater (GW) has been prominent source of freshwater for sustainable growth of agriculture, water management and urban/industrial purposes. The overexploitation of the water source leads to large variation of groundwater level (GWL) in the Indian sub-continent. The GWL mostly fluctuates via several factors like; groundwater extraction, precipitation, soil moisture, evaporation, etc., and prediction of GWL by collecting these factor for large geographical region is a challenging task. In the study, deep learning (DL) approach, namely Convolution Neural Network-Long short term memory (ConvLSTM) model has been implemented for prediction of the GWL. The model is designed based on the U-Net framework with the integration LSTM unit, to process the spatiotemporal information induced by the GWL factors between the years 2005–2017. The assessment of the GW in North-West India (NWI) has been carried out using several aforementioned hydrological parameters, selected based on correlation. In addition, in-situ groundwater has been used to get GW fluctuation scenarios (i.e., categorised into four cycles PrePre, PrePost, PostPre, and PostPost) w.r.t monsoon season to predict the difference (Δh) in GWL. The proposed model has been tested w.r.t Artificial neural network (ANN) and Convolution neural network (CNN) and cross-validate using several geo-locations information of NWI. The ConvLSTM has outperformed based on overall root means square (RMSE) error of 0.1099, 0.1082, 0.1005 and 0.0957 for each cycle i.e. PrePre, PrePost, PostPre, and PostPost, respectively, compared to ANN and CNN.

Abstract

Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world’s coral reefs. This prognosis is primarily based on a small subset of available models that apply similar ‘excess heat’ threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. ‘Excess heat’ models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field’s ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.

Abstract

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

Abstract

With an increasing focus on improving localized understanding of weather and climate phenomena and the computation cost involved in high-resolution modelling, spatial downscaling of data has proven to be a viable alternative method to obtain high-resolution climate data. Historically, several statistical and dynamical downscaling techniques have been employed to enhance coarse-resolution data to a finer grid scale. However, these conventional methods are either inefficient in capturing several finer scale details or are resource-heavy for recursive applications. Image Super-Resolution (SR) is a computer vision concept of using grid-based approaches to enhance the resolution of an image, analogous to spatial downscaling. In this study, we explore and compare the performance of different image super-resolution methods in downscaling the Indian Meteorological Department’s (IMD) gridded rainfall data from a low spatial resolution (1.0°) to a high resolution (0.25°). The SR methods considered include a fully connected autoencoder, four traditional convolutional neural networks, and two residual neural networks. The primary objective of this study was to make an initial assessment of the performance of such methods in downscaling gridded rainfall data over the Indian region. Furthermore, the resultant downscaled products have been compared using different objective metrics and analytical comparisons with ground truth data. One of the key findings of this study is that the residual learning-based neural networks demonstrated better performance in creating perceptually realistic rainfall maps (proven both quantitatively and qualitatively), closely followed by the traditional convolutional neural networks and the autoencoder.

Abstract

High-resolution integration of large-scale and long-term anthropogenic nutrient discharge data is crucial for understanding the spatiotemporal evolution of pollution and identifying intervention points for pollution mitigation. Here, we establish the MEANS-ST1.0 dataset, which has a high spatiotemporal resolution and encompasses anthropogenic nutrient discharge data collected in China from 1980 to 2020. The dataset includes five components, namely, urban residential, rural residential, industrial, crop farming, and livestock farming, with a spatial resolution of 1 km and a temporal resolution of monthly. The data are available in three formats, namely, GeoTIFF, NetCDF and Excel, catering to GIS users, researchers and policymakers in various application scenarios, such as visualization and modelling. Additionally, rigorous quality control was performed on the dataset, and its reliability was confirmed through cross-scale validation and literature comparisons at the national and regional levels. These data offer valuable insights for further modelling the interactions between humans and the environment and the construction of a digital Earth.

Abstract

According to the IPCC, most regions worldwide will be gradually exposed to the amplification of the duration, frequency, and intensity of extreme climatic events, and the effects that extreme events can cause on human well-being and the economy. This study aims to develop linear regression models to estimate the soy and maize yields from extreme climatic and bioclimatic indices in three geographical subregions of Latin America (Mexico, Brazil, and Argentina) between 1979 and 2005. We used daily datasets from observations (CPC), reanalysis (ERA5), and regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to investigate the impact of extreme events of temperature and precipitation on maize and soy yields over the CORDEX Central America and South America domains. We first assessed the RCMs’ performance in reproducing extreme indices by comparing them against observations. The validation process evidenced the need for applying bias correction techniques to simulate daily precipitation and temperature for a better performance of the indices. The results show a higher correlation between the daily temperature range (DTR), cold nights and warm nights for soy production in Argentina (R2: − 0.74, − 0.80 and 0.75, respectively) and Mexico (R2: − 0.80, − 0.81, 0.70) for maize. Regionally, the linear model (simulated with observed data) using these indices presented an agreement with observed yield data in Mexico and Brazil, with explained variances exceeding 70% for maize in these subregions, while Argentina presented a better performance for soy yield. An intriguing finding was the superior performance of linear models when used with CPC-corrected RCM data compared to ERA5. Taken together, our results highlight the capabilities and constraints of linear models as valuable tools for developing adaptation and mitigation strategies, enabling precise yield forecasting, and informing policy decisions.

Abstract

Agriculture and weather are intrinsically linked. Variations in the weather patterns due to climate change pose a foremost risk to agricultural production and food security. The IPCC (Intergovernmental Panel on Climate Change) propagates adaptation to tackle the irreversible climate change impact and its associated risks. The Hague Conference on Agriculture, Food Security, and Climate Change in 2010 gave the concept of climate-smart agriculture (CSA) as an adaptation measure to enhance food security by raising productivity, developing resilience systems to adjust to climate change, and dropping GHG (greenhouse gases) emissions. This study systematically reviews the literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to understand the different practices followed by the farmers and the factors that determine the CSA adaptation. Most importantly, it examines the role of extension services in adaptation. The results show that the adapted practices among the different study areas can be broadly categorised into resilient technologies, conservation technologies, management technologies, diversification of income security, and risk mitigation strategies. The paper finds that the CSA adaption achieves the intended benefits with possible trade-offs and is determined through the socio-economic, institutional, behavioural factors and the land’s physical characteristics. The critical evaluation of different extension systems exhibits the importance of varying field schools to promote the CSAPs. The study also emphasises developing networks among the different stakeholders, particularly between formal extension and informal extensions such as NGOs (non-governmental organisations), farmer groups, and private players, and the inclusion of ICTs (information and communication technologies) for the holistic extension systems and effective delivery to the farmers’ CSA adaptation.

Abstract

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions.