Depiction of Drought Over Africa in the Light of Changing Climate from CMIP6 Models
Abstract
Drought stands as a prevalent weather-related natural climatic disaster on a global scale. This study employs twenty GCMs from CMIP6 to investigate hydrological drought characteristics (frequency, duration, and intensity) across Africa under four SSPs (shared socioeconomic pathway) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 spanning three designated periods: 2021–2040 (near-term), 2041–2060 (mid-term), and 2081–2100 (long-term). The (SPEI) at a 12-month scale and employs bias correction on the multi-model ensemble mean extracted from GCMs of the CMIP6 project via cumulative distribution function (CDF). Mann–Kendall approach was implemented for trend and the SPEI-12-based drought trend from 1979 to 2014 displays a negative trajectory of − 0.05/a for Africa, with similar figures for specific regions: Northern Africa (NAF) at − 0.06/a, Sahara (SAH) at − 0.05/a, Western Africa (WAF) at − 0.01/a, Central Africa (CAF) at − 0.02/a, Eastern Africa (EAF) at − 0.06/a, and Southern Africa (SAF) at − 0.03/a. Notably, the severe drought characteristics are manifested as a 6-month duration, 12 occurrences, and an intensity of − 1.9 events over Africa. The highest duration and frequency are observed over NAF at 7.5-month and 15.1 occurrences, respectively, while the most extreme intensity of − 2.3 events is noted over SAH. In addition, future droughts are expected to be lower frequency, extended duration, and more intense. Extreme scenarios indicate values of − 2.1, − 2.2, and − 2.6 events for Africa in the periods 2021–2040, 2041–2060, and 2081–2100, respectively, compared to − 1.99 in the historical period. The intensity of the drought is expected to increase across all sub-regions, with SAH and NAF projecting the worst conditions with − 2.4, − 2.8, and − 3.0 events respectively for SAH and − 2.2, − 2.7, and − 3.0 events for NAF. Africa’s droughts are predicted to last 6, 8, and 13 months, significantly longer than the usual 4-month period. As the historical events of 17 times is anticipated to drop to 6, 5, and 3 times, respectively. The insights derived from this study could provide a foundational basis for devising effective drought adaptation strategies in Africa. Given the anticipated severity of drought events, prioritizing plans to cope with such challenges is of paramount importance.
Novel dual-image encryption scheme based on memristive cellular neural network and K-means alogrithm
Abstract
In this paper, we propose a lossless encryption method based on dual images. A cosine perturbation is added to the generalized voltage-controlled memristor, thus constituting a generalized voltage-controlled cosine memristor. And the characteristics of the memristor are analyzed and introduced into a four-dimensional cellular neural network, thus constituting the memristor-based four-dimensional cellular neural network (M4DCNN). The parameters of the M4DCNN are adjusted to achieve a hyperchaotic state for the diffusion and scrambling stages of the image. On the key, the partial key is obtained by K-means algorithm to make the key more random. In the scrambling phase, perturbed logistic chaotic map is introduced in this paper, which makes the image pixels better satisfy the avalanche effect. Through security analysis and comparison with other encryption methods, it is found that the encryption method shows to have good encryption effect in various indexes such as key sensitivity, key space, pixel correlation, information entropy, resistance to shear attack, resistance to noise attack, UACI and NPCR analysis.
The AfCFTA Digital Protocol – IISD Reporting Services
The AfCFTA Digital Protocol IISD Reporting Services
Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques
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.
Saltwater intrusion simulations in coastal karstic aquifers related to climate change scenarios
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.
HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China
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.