Climate change impacts on mesophotic regions of the Great Barrier Reef.

Climate change projections for coral reefs are founded exclusively on sea surface temperatures (SST). While SST projections are relevant for the shallowest reefs, neglecting ocean stratification overlooks the striking differences in temperature experienced by deeper reefs for all or part of the year. Density stratification creates a buoyancy barrier partitioning the upper and lower parts of the water column. Here, we mechanistically downscale climate models and quantify patterns of thermal stratification above mesophotic corals (depth 30 to 50 m) of the Great Barrier Reef (GBR). Stratification insulates many offshore regions of the GBR from heatwaves at the surface. However, this protection is lost once global average temperatures exceed ~3 °C above preindustrial, after which mesophotic temperatures surpass a recognized threshold of 30 °C for coral mortality. Bottom temperatures on the GBR (30 to 50 m) from 2050 to 2060 are estimated to increase by ~0.5 to 1 °C under lower climate emissions (SSP1-1.9) and ~1.2 to 1.7 °C under higher climate emissions (SSP5-8.5). In short, mesophotic coral reefs are also threatened by climate change and research might prioritize the sensitivity of such corals to stress.

Overcoming Downscaling Limitations in Organic Semiconductors: Strategies and Progress.

Organic semiconductors have great potential to revolutionize electronics by enabling flexible and eco-friendly manufacturing of electronic devices on plastic film substrates. Recent research and development led to the creation of printed displays, radio-frequency identification tags, smart labels, and sensors based on organic electronics. Over the last 3 decades, significant progress has been made in realizing electronic devices with unprecedented features, such as wearable sensors, disposable electronics, and foldable displays, through the exploitation of desirable characteristics in organic electronics. Neverthless, the down-scalability of organic electronic devices remains a crucial consideration. To address this, efforts are extensively explored. It is of utmost importance to further develop these alternative patterning methods to overcome the downscaling challenge. This review comprehensively discusses the efforts and strategies aimed at overcoming the limitations of downscaling in organic semiconductors, with a particular focus on four main areas: 1) lithography-compatible organic semiconductors, 2) fine patterning of printing methods, 3) organic material deposition on pre-fabricated devices, and 4) vertical-channeled organic electronics. By discussing these areas, the full potential of organic semiconductors can be unlocked, and the field of flexible and sustainable electronics can be advanced.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment.

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 µg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 µg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 µg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia.

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (-32.2%) and SSP 4.5 (-88.8%) for 2024-2049, with further decreases expected for 2050-2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata.

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (Topt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, Topt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40-60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Ultramicroextraction as a miniaturization of the already miniaturized. A step toward nanoextraction and beyond.

Miniaturization of the analytical process has been a widespread trend, and the sample preparation stage is not exempted from this downscaling. Since the introduction of microextraction techniques as miniaturization of classical extraction techniques, they have become one of the strengths in this field. However, some of the original approaches to these techniques did not fully cover all the current principles of Green Analytical Chemistry. For this reason, during the last years, much emphasis has been placed on reducing/eliminating toxic reagents, reducing the amount of the extraction phase, and searching for new greener, and more selective extractant materials. On the other hand, even though high accomplishments have been achieved, the same attention has not always been paid to reducing the amount of sample, which is essential when treating low-availability samples such as biological samples, or in developing portable devices. In this review, we intend to give the readership an overview of the advances toward further miniaturization of microextraction techniques. Finally, a brief reflection is made on the terminology used, or that should, in our opinion, be used to term these new generation of miniaturized microextraction approaches. To this regard, the term, 'ultramicroextraction' is proposed to refer to those approaches beyond microextraction.

Adversarial super-resolution of climatological wind and solar data.

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a [Formula: see text] resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change's Fifth Assessment Report.

Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea.

As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.

Using Downscaled GRACE Mascon Data to Assess Total Water Storage in Mississippi Alluvial Plain Aquifer.

The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R2 for each model was 0.88. Large R2 and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.

Analysis of Long Time Series of Summer Surface Urban Heat Island under the Missing-Filled Satellite Data Scenario.

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect.

Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods.

The Urmia lake in north-west Iran has dried up to perilously low levels in the past two decades. In this study, we investigate the drivers behind the decline in lake water level with the help of in-situ and remote sensing data. We use total water storage (TWS) changes from the gravity recovery and climate experiment (GRACE) satellite mission. TWS from GRACE includes all the water storage compartments in a column and is the only remote sensing product that can help in estimating groundwater storage (GWS) changes. The coarse spatial (approx. 300 km) resolution of GRACE does not allow us to identify local changes that may have led to the Urmia lake disaster. In this study, we tackle the poor resolution of the GRACE data by employing three machine learning (ML) methods including random forest (RF), support vector regression (SVR) and multi-layer perceptron (MLP). The methods predict the groundwater storage anomaly (GWSA), derived from GRACE, as a function of hydro-climatic variables such as precipitation, evapotranspiration, land surface temperature (LST) and normalized difference vegetation index (NDVI) on a finer scale of 0.25° × 0.25°. We found that i) The RF model exhibited highest R (0.98), highest NSE (0.96) and lowest RMSE (18.36 mm) values. ii) The RF downscaled data indicated that the exploitation of groundwater resources in the aquifers is the main driver of groundwater storage and changes in the regional ecosystem, which has been corroborated by few other studies as well. The impact of precipitation and evapotranspiration on the GWSA was found to be rather weak, indicating that the anthropogenic derivers had the most significant impact on the GWSA changes. iii) We generally observed a significant negative trend in GWSA, having also significant positive correlations with the well data. However, over regions with dam construction significant negative correlations were found.

Impact of climate change induced future rainfall variation on dynamics of arid-humid zone transition in the western province of India.

The transition of the Earth's climate from one zone to another is one of the major causes behind biodiversity loss, rural-urban migration, and increasing food crises. The rising rate of arid-humid zone transition due to climate change has been substantially visible in the last few decades. However, the precise quantification of the climate change-induced rainfall variation on the climate zone transition still remained a challenge. To solve the issue, the Representative Grid Location-Multivariate Adaptive Regression Spline (RGL-MARS) downscaling algorithm was coupled with the Koppen climate classification scheme to project future changes in various climate zones for the study area. It was observed that the performance of the model was better for the humid clusters compared to the arid clusters. It was noticed that, by the end of the 21st century, the arid region would increase marginally and the humid region would rise by 24.28-36.09% for the western province of India. In contrast, the area of the semi-arid and semi-humid regions would decline for the study area. It was observed that there would be an extensive conversion of semi-humid to humid zone in the peripheral region of the Arabian sea due to the strengthening of land-sea thermal contrast caused by climate change. Similarly, semi-arid to arid zone conversion would also increase due to the inflow of dry air from the Arabian region. The current research would be helpful for the researchers and policymakers to take appropriate measures to reduce the rate of climate zone transition, thereby developing the socioeconomic status of the rural and urban populations.

Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan.

Climate change has a significant impact on the intensity and spread of dengue outbreaks. The objective of this study is to assess the number of dengue transmission suitable days (DTSD) in Pakistan for the baseline (1976-2005) and future (2006-2035, 2041-2070, and 2071-2099) periods under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Moreover, potential spatiotemporal shift and future hotspots of DTSD due to climate change were also identified. The analysis is based on fourteen CMIP5 models that have been downscaled and bias-corrected with quantile delta mapping technique, which addresses data stationarity constraints while preserving future climate signal. The results show a higher DTSD during the monsoon season in the baseline in the study area except for Sindh (SN) and South Punjab (SP). In future periods, there is a temporal shift (extension) towards pre- and post-monsoon. During the baseline period, the top ten hotspot cities with a higher frequency of DTSD are Karachi, Hyderabad, Sialkot, Jhelum, Lahore, Islamabad, Balakot, Peshawar, Kohat, and Faisalabad. However, as a result of climate change, there is an elevation-dependent shift in DTSD to high-altitude cities, e.g. in the 2020s, Kotli, Muzaffarabad, and Drosh; in the 2050s, Garhi Dopatta, Quetta, and Zhob; and in the 2080s, Chitral and Bunji. Karachi, Islamabad, and Balakot will remain highly vulnerable to dengue outbreaks for all the future periods of the twenty-first century. Our findings also indicate that DTSD would spread across Pakistan, particularly in areas where we have never seen dengue infections previously. The good news is that the DTSD in current hotspot cities is projected to decrease in the future due to climate change. There is also a temporal shift in the region during the post- and pre-monsoon season, which provides suitable breeding conditions for dengue mosquitos due to freshwater; therefore, local authorities need to take adaption and mitigation actions.

Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data.

Soil moisture (SM) at the interface between the land surface and atmosphere is one of the major environmental parameters which plays an important role in hydrological applications. In this article, the SM measured by Soil Moisture Active Passive (SMAP) is downscaled from 3- to 1-km spatial resolution. The main purpose is to evaluate the performance of two different downscaling methods over a variety of climatic conditions and land cover types. These two methods, based on regression and artificial neural network (ANN), are used for enabling us to cross-validate and reliably interpret the obtained results since the number of ground measurements is not sufficient for accuracy assessment. These methods are applied over four different case studies; one is located in the USA, i.e., state of Utah (semi-arid), and the remaining three are located in Iran, i.e., Fars (arid and semi-arid), Yazd (hyper-arid), and Golestan (humid). In both methods, different combinations of input features correlated with SM including land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperatures in horizontal and vertical polarizations (TBH and TBV), shortwave infrared (SWIR), and digital elevation model (DEM) are used. It is found that the DEM does not add extra information in downscaling. The reason is due to high correlation between topography and LST. Moreover, SWIR is most likely able to model only large-scale variations of SM. The downscaled SM products are then compared to 1-km resolution SMAP SM extracted from Sentinel-1 for the study areas in Iran and in situ measurements in Utah. Both methods produce results which are considerably consistent except that the regression method adds more spatial details in the downscaled SM. The achievements illustrate that the performance of both downscaling methods is higher in areas with more homogeneous climatic conditions, i.e., Yazd and Golestan. The best evaluation metrics including correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE) for Yazd and Golestan are R = 0.89, RMSE = 0.025 m3/m3, and MAPE = 21.13% and R = 0.93, RMSE = 0.044 m3/m3, and MAPE = 21.95%, respectively. Moreover, large model biases are associated with dense vegetated areas and high altitudes. The best downscaling accuracy in both methods over all study areas belongs to bare soil and flat regions.

Impacts of climate change on water footprint components of rainfed and irrigated wheat in a semi-arid environment.

Climate change is one of the biggest environmental challenges that significantly impact water resources and the quantity and quality of agricultural products. Assessment of these impacts during the historical period and under future climate is essential for achieving a sustainable agricultural system in the face of climate change threats and water scarcity. In this research, we evaluated the yield and water footprint of rainfed and irrigated wheat during the historical period (1986-2015) and two future periods (2016 to 2055) in a semi-arid environment in Fars province, Iran. The future climate data was selected from the CanESM2 model outputs (bias-corrected and downscaled using the SDSM model) under the RCP4.5 scenario, and the yield projection was made using the AquaCrop model. Our result showed that for both irrigated and rainfed wheat, the yield significantly increases in southern parts of the study area in future climates, primarily because of an increase in effective precipitation. Other regions will experience a marginal yield decrease or no yield changes (in the case of irrigated wheat). Our assessments of the water footprint of wheat production showed a significant reduction in green and blue water footprints in the southern regions. In other regions, various patterns emerged for irrigated and rainfed wheat, but an overall increase was observed. The southern regions of the study area will be more suitable for wheat production owing to the higher yield and lower water footprint.

Implication of climate change on crop water requirement in the semi-arid region of Western Maharashtra, India.

Climate change and human activities have massively impacted the hydrological cycle. Thus, it is of the greatest concern to examine the effect of climate change on water management, especially at the regional level, to understand the possible future shifts in water supply and water-related crises and support regional water management. Fortunately, there is a high degree of ambiguity in determining the effect of climate change on water requirements. In this paper, the statistical downscaling (SDSM) model is applied to simulate the potential impact of climate on crop water requirements (CWR) by downscaling ET0 in the region of Western Maharashtra, India, for the future periods, viz., the 2030s, 2050s, and 2080s, across three meteorological stations (Pune, Rahuri, and Solapur). Four crops, i.e., cotton, soybean, onion, and sugarcane, were selected during the analysis. The Penman-Monteith equation calculates reference crop evapotranspiration (ET0). Furthermore, in conjunction with the crop coefficient (Kc) equation, it calculates crop evapotranspiration (ETc)/CWR. The predictor variables were extracted from the National Centre for Environmental Prediction (NCEP) reanalysis dataset for 1961-2000 and the HadCM3 for 1961-2099 under the H3A2 and H3B2 scenarios. The results indicated by SDSM profound good applicability in downscaling due to satisfactory performance during calibration and validation for all three stations. The projected ET0 indicated an increase in mean annual ET0 compared to the present condition during the 2030s, 2050s, and 2080s. The ET0 would increase for all months (in summer, winter, and pre-monsoon seasons) and decrease from June to September (monsoon season). The estimated future CWR shows variation in the range for cotton (- 0.97 to 2.48%), soybean (- 2.09 to 1.63%), onion (0.49 to 4.62%), and sugarcane (0.05 to 2.86%). The significance of this research lies in its contribution to understanding the potential impacts of climate change at a regional level. This study provides valuable insights into the expected changes in water demand for key crops. The research also manifests implementing an identical methodology for downscaling other environmental parameters using a similar approach.

The importance of 1.5°C warming for the Great Barrier Reef.

Tropical coral reefs are among the most sensitive ecosystems to climate change and will benefit from the more ambitious aims of the United Nations Framework Convention on Climate Change's Paris Agreement, which proposed to limit global warming to 1.5° rather than 2°C above pre-industrial levels. Only in the latest Intergovernmental Panel on Climate Change focussed assessment, the Coupled Model Intercomparison Project phase 6 (CMIP6), have climate models been used to investigate the 1.5° warming scenario directly. Here, we combine the most recent model updates from CMIP6 with a semi-dynamic downscaling to evaluate the difference between the 1.5 and 2°C global warming targets on coral thermal stress metrics for the Great Barrier Reef (GBR). By ~2080, severe bleaching events are expected to occur annually under intensifying emissions (shared socioeconomic pathway SSP5-8.5). Adherence to 2° warming (SSP1-2.6) halves this frequency but the main benefit of confining warming to 1.5° (SSP1-1.9) is that bleaching events are reduced further to 3 events per decade. Attaining low emissions of 1.5° is also paramount to prevent the mean magnitude of thermal stress from stabilizing close to a critical thermal threshold (8 Degree Heating Weeks). Thermal stress under the more pessimistic pathways SSP3-7.0 and SSP5-8.5 is three to fourfold higher than the present day, with grave implications for future reef ecosystem health. As global warming continues, our projections also indicate more regional warming in the central and southern GBR than the far north and northern GBR.

A simulation study of disaggregation regression for spatial disease mapping.

Disaggregation regression has become an important tool in spatial disease mapping for making fine-scale predictions of disease risk from aggregated response data. By including high resolution covariate information and modeling the data generating process on a fine scale, it is hoped that these models can accurately learn the relationships between covariates and response at a fine spatial scale. However, validating these high resolution predictions can be a challenge, as often there is no data observed at this spatial scale. In this study, disaggregation regression was performed on simulated data in various settings and the resulting fine-scale predictions are compared to the simulated ground truth. Performance was investigated with varying numbers of data points, sizes of aggregated areas and levels of model misspecification. The effectiveness of cross validation on the aggregate level as a measure of fine-scale predictive performance was also investigated. Predictive performance improved as the number of observations increased and as the size of the aggregated areas decreased. When the model was well-specified, fine-scale predictions were accurate even with small numbers of observations and large aggregated areas. Under model misspecification predictive performance was significantly worse for large aggregated areas but remained high when response data was aggregated over smaller regions. Cross-validation correlation on the aggregate level was a moderately good predictor of fine-scale predictive performance. While these simulations are unlikely to capture the nuances of real-life response data, this study gives insight into the effectiveness of disaggregation regression in different contexts.

Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990-2019: A Space-Time Bayesian Neural Network Downscaler.

Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O3 concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R2 = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R2 = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R2 = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O3 are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O3 are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O3 tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O3 exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O3 database with multiple metrics fills in the literature gap for long-term surface O3 exposure tracing.