Search Results (360)

The validation of climate models is important for ensuring accurate climate variability over a given region. This study evaluates the performance of multiple global climate model simulations from the Coupled Model Intercomparison Project Phases 5 and 6 and the downscaled regional climate model simulations from the Coordinated Regional Climate Downscaling Experiment against gridded observational data from the Climatic Research Unit gridded data during the historic period 1981–2000. Spatial analysis using monthly bias maps and statistical metrics (i.e., correlation coefficient, standard deviation, and centred root-mean-squared error) were employed to assess the model outputs’ ability to reproduce monthly temperature and precipitation patterns over South Africa. The results indicate an improvement in CMIP6 and CORDEX model simulation outputs compared to their CMIP5 predecessors, with reduced biases and enhanced correlation. The study underscores the importance of model selection for regional climate analysis and highlights a need for further model development to capture complex physical processes. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R² of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions. Full article

Leveraging multi-source remote sensing datasets and dynamic groundwater monitoring well observations, this study explores the multiscale spatiotemporal linkages of groundwater storage changes and land deformation in North China Plain (NCP) after the South-to-North Water Diversion Project (SNWDP). Firstly, we employed Gravity Recovery and Climate Experiment (GRACE) and interferometric synthetic aperture radar (InSAR) technology to estimate groundwater storage (GWS) and land deformation. Secondly and significantly, we proposed a novel GRACE statistical downscaling algorithm that integrates a weight allocation strategy and GWS estimation applied with InSAR technology. Finally, the downscaled results were employed to analyze spatial differences in land deformation across typical ground fissure areas. The results indicate that (1) between 2018 and 2021, groundwater storage in the NCP exhibited a declining trend, with an average reduction of −3.81 ± 0.53 km³/a and a maximum land deformation rate of −177 mm/a; (2) the downscaled groundwater storage anomalies (GWSA) showed high correlation with in situ measurements (R = 0.75, RMSE = 2.91 cm); and (3) in the Shunyi fissure area, groundwater storage on the northern side increased continuously, with a maximum growth rate of 28 mm/a, resulting in surface uplift exceeding 70 mm. Full article

Climate change substantially affects water resources and agriculture, highlighting the critical importance of assessing land suitability for surface irrigation. This study was initiated with the objective of assessing the present and future land suitability for surface irrigation in the Gardulla Zone of Southern Ethiopia, utilizing meteorological, topography, soil, land cover, and proximity data. The analytic hierarchy process and weighted overlay analysis were employed to assign factor weights, while future climate projections were downscaled via a statistical downscaling model (SDSM4.2) under the shared socio-economic pathways (i.e., SSP2-4.5 and SSP5-8.5) scenarios. Irrigation suitability mapping was performed via inverse distance-weighted interpolation. The results revealed that 8% of the area is highly suitable, 54.3% is moderately suitable, 30% is marginally suitable, and 2.3% is unsuitable under current climate conditions. In the future periods, under both SSP scenarios, highly suitable land increases (up to 9.7% and 10.3% by 2050s and 10.8% and 13.5% by the 2080s under SSP2-4.5 and SSP5-8.5, respectively), whereas unsuitable land decreases (down to 0.6% by 2080s under SSP5.8.5). In terms of area, highly to moderately suitable land expanded by 1357.6–6867.7 ha, depending on the scenario and timeframe. The study concludes that climate change is expected to affect the suitability of land for surface irrigation potential in the study area and similar hydroclimatic settings, highlighting the need for forward-looking policies and adaptation options. Therefore, it is recommended to promote climate-smart irrigation systems by integrating site-specific suitability mapping into regional land-use planning and prioritizing investment in small-scale, community-managed surface irrigation schemes that reduce water losses and ensure long-term agricultural sustainability. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km²) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article

Detection and analysis of hydroclimatic trends are crucial for quantifying climate change, global warming, and their potential impacts. This study investigates hydroclimatic trends over the South African Sugar Belt (SASB) under a changing climate using nonparametric and innovative trend detection techniques for the periods 1980–2022, 2025–2050, and 2050–2080. Statistical tests, including the original and modified Mann–Kendall test, sequential Mann–Kendall test, and Innovative Trend Analysis were performed to detect trends and changes in hydroclimatic variables over the SASB’s dryland and irrigated regions. An 18-month low-pass filter was applied to 19 GCMs of the CMIP6, which were downscaled to a local setting. The results indicate contrasting rainfall trends: a positive trend in the dryland region and a negative trend in the irrigated region from 1980 to 2022. Under low- (SSP2–4.5) and high-emission (SSP5–8.5) scenarios, both regions exhibited a significant drying trend from 1980 to 2080, with the irrigated region drying and warming faster than the dryland region. Mann–Kendall tests and Innovative Trend Analysis revealed robust positive trends in surface air temperatures across the SASB, with even stronger trends projected for the future, potentially promoting water loss in the area. Compound dry–hot events were also projected to cause significant socioeconomic impacts in the near and distant future. Future studies can explore nonparametric and monotonic trend detection and analysis for water quality parameters in the SASB under a changing climate. Full article

This study assesses the use of Quantile-Mapping methods for bias correction and downscaling in climate change studies. A “Perfect Model Experiment” is conducted using high-resolution climate simulations as pseudo-references and coarser versions as biased data. The focus is on European daily temperature and precipitation under the RCP 8.5 scenario. Six methods are tested: an empirical Quantile-Mapping approach, the “Cumulative Distribution Function—transform” (CDF-t) method, and four CDF-t variants with different parameters. Their performance is evaluated based on univariate and multivariate properties over the calibration period (1981–2010) and a future period (2071–2100). The results show that while Quantile Mapping and CDF-t perform similarly during calibration, significant differences arise in future projections. Quantile Mapping exhibits biases in the means, standard deviations, and extremes, failing to capture the climate change signal. CDF-t and its variants show smaller biases, with one variant proving particularly robust. The choice of discretization parameter in CDF-t is crucial, as the low number of bins increases the biases. This study concludes that Quantile Mapping is not appropriate for adjustments in a climate change context, whereas CDF-t, especially a variant that stabilizes extremes, offers a more reliable alternative. Full article

Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process. Full article

Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM_2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM_2.5 are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM_2.5 are available through networks of monitors; however, these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM_2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here, we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods, and evaluation via cross-validation that is implemented in the software. We also provide a case study of estimating PM_2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. Full article

Water temperature is a fundamental parameter influencing a range of biotic and abiotic processes occurring within various components of the hydrosphere. This study presents a multi-step, data-driven predictive modeling framework to estimate water temperatures for the period 2021–2100 in three aquatic environments in Central Europe: the Odra River, the Szczecin Lagoon, and the Baltic Sea. The framework integrates Bayesian Model Averaging (BMA), Random Sample Consensus (RANSAC) regression, Gradient Boosting Regressor (GBR), and Random Forest (RF) machine learning models. To assess the performance of the models, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) were used. The results showed that the application of statistical downscaling methods improved the prediction of air temperatures with respect to the BMA. Moreover, the RF method was used to predict water temperature. The best model performance was obtained for the Baltic Sea and the lowest for the Odra River. Under the SSP2-4.5 and SSP5-8.5 scenario-based simulations, projected air temperature increases in the period 2021–2100 could range from 1.5 °C to 1.7 °C and 4.7 to 5.1 °C. In contrast, the increase in water temperatures by 2100 will be between 1.2 °C and 1.6 °C (SSP2-4.5 scenario) and between 3.5 °C and 4.9 °C (SSP5-8.5). Full article

Soil moisture (SM) estimates are essential for drought monitoring, hydrological modeling, and climate resilience planning applications. While satellite and model-derived SM products effectively capture SM dynamics, their coarse spatial resolutions (~10–36 km) hinder their ability to represent SM variability in heterogeneous landscapes influenced by local factors. This study proposes a novel downscaling framework that employs an Artificial Neural Network (ANN) on a cloud-computing platform to improve the spatial resolution and representation of multi-source SM datasets. A data analysis was conducted by integrating Google Earth Engine (GEE) with the computing capabilities of the python language through Google Colab. The framework downscaled Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5-Land), and Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) at 500 m for Kenya, East Africa. This was achieved by leveraging ten input variables comprising elevation, slope, surface albedo, vegetation, soil texture, land surface temperatures (day and night), evapotranspiration, and geolocations. The coarse SM datasets exhibited spatiotemporal consistency, with a standard deviation below 0.15 m³/m³, capturing over 95% of the variability in the original data. Validation against in situ SM data at the station confirmed the framework’s reliability, achieving an average UbRMSE of less than 0.04 m³/m³ and a correlation coefficient (r) over 0.52 for each downscaled dataset. Overall, the framework improved significantly in r values from 0.48 to 0.64 for SMAP, 0.47 to 0.63 for ERA5-Land, and 0.60 to 0.69 for FLDAS. Moreover, the performance of FLDAS and its downscaled version across all climate zone is consistent. Despite the uncertainties among the datasets, the framework effectively improved the representation of SM variability spatiotemporally. These results demonstrate the framework’s potential as a reliable tool for enhancing SM applications, particularly in regions with complex environmental conditions. Full article