Month: October 2024

Abstract

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m³/m³ and a coefficient of determination (R²) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Abstract

Coastal zones are crucial ecosystems supporting significant biodiversity and pertinent socio-economic activities. However, anthropogenic development contributes to socio-environmental complexities, particularly public water supply threats caused by climate change. This research presents a case study on the north-western coast of Yucatan, Mexico, which models potential saltwater intrusion in groundwater for multiple projections of sea level rise and recharge change due to climate change and its implications for the public water supply of the regional population and ecosystem. For this purpose, a previously calibrated and validated numerical model is employed, adapting its boundary conditions, keeping its calibrated hydrogeologic parameters, and considering the 2040 and 2100 climate change projections. Simulation results show that under these projections, significant saltwater intrusion may occur, reducing freshwater thickness due to increased salinity in groundwater and a loss of freshwater sources resulting from brackish-saline wedge intrusion. These scenarios are of particular concern as freshwater in this coastal region is the main source for public water supply and for freshwater input in coastal ecosystems. Moreover, this study underscores the susceptibility of karstic aquifers to salinization, especially in the face of rising sea levels, given their unique hydrogeological characteristics and substantial responsiveness to marine forcings. In spite of the uncertainties in global climate change predictions, this study enhances our understanding of the dynamics of these unique aquifers, and presents future saltwater intrusion projections that offer valuable technical insights to design and implement pertinent and resilient coastal aquifer management strategies.

Abstract

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China’s specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

Abstract

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Abstract

The study provides a detailed analysis of the climate change projections in mean and extreme precipitation across the Dinaric Alps and the Adriatic coastal area until the end of the 21st century. It uses simulations from the extensive EURO-CORDEX regional climate model ensemble to study precipitation changes considering three greenhouse gas concentration scenarios. Additionally, the performance and systematic errors of historical simulations are evaluated. The ensemble demonstrates good skill in representing spatial variability and seasonal variations of mean and extreme precipitation. However, biases are evident and substantial across the Dinaric Alps, predominantly wet in winter and autumn, with the exceptions of dry biases in summer. The ensemble overestimates the frequency of heavy and extreme events. Regardless of these inconsistencies, projections clearly suggest a change in precipitation character with an overall intensification and a decrease in wet-day frequency, resulting in a mean precipitation winter increase over northern lowlands, summer decrease across southern parts, and spring and autumn zero-change zone across the Dinaric Alps. Extreme precipitation events are expected to intensify and become more frequent during winter and autumn with robust signals over the lowlands. During summer, the ensemble shows substantial uncertainties, but an intensification nonetheless within a smaller number of extreme events. Overall, the study identifies more consistency in the direction of change than magnitude in individual simulations, with the strongest consensus on precipitation intensification. Limitations include low station density in the observational dataset and an incomplete ensemble size, however, findings align with previous research and observed trends.

Abstract

Decadal climate prediction project (DCPP) hindcasts/predictions from Coupled Model Intercomparison Project phase-6 (CMIP6) provide vital information on the near-future climate up to a decade. Coarse-resolution climate models however fail to accurately represent the regional climatic features thereby limiting the prediction skill. In this study, DCPP models maximum and minimum surface temperatures (T_max and T_min) hindcasts are downscaled and bias-corrected (DBC) over the Indian region to examine the characteristics of heat/cold waves for 1–10 lead years. It is found that the DBC T_max (T_min) captures the heat (cold) waves intensity, frequency, and spatial distribution over India quite effectively. After DBC, representation of extreme thresholds of T_max (T_min) over India has improved by 82(90)%. Further, the areal extent of extremely high (low) temperatures associated with the large and small area heat (cold) waves are well characterized after DBC up to 10-year lead. Importantly, DBC product showed superior skills in capturing the regional temperature peaks associated with large and small area heatwave/cold wave days and is limited before DBC. After DBC, the temperature extremes (both warm and cold) display enhanced intensity with increased (decreased) mean T_max (T_min) by ~ 0.6 °C (0.3 °C) in the recent decade (2017–2026) compared to the previous decade (2007–2016) during April to June (November to February). The present analysis of DCPP models opens the door for the near future or decadal prediction of temperature extremes by demonstrating the increased prediction ability across India for T_max and T_min following DBC. The study has important implications for decision-making and may help different stakeholders, policymakers, and disaster managers.