Tag: Climate Change Downscaling Techniques

Abstract

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene’s horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene’s perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.

Abstract

The impact of climate change on the hydrological cycle has spurred extensive research, particularly regarding potential evapotranspiration (PET), a crucial variable linking water, energy, carbon cycles, and ecosystem services. PET estimation usually relies on in situ weather station data, but data scarcity in regions like Nigeria’s Ogun-Osun Basin poses challenges. With few in situ ET monitoring stations, researchers have turned to alternative PET sources, such as satellite and reanalysis products. In this study, we evaluated four PET products in the Ogun-Osun Basin: Global Land Evaporation Amsterdam Model (GLEAM), hourly potential evapotranspiration (hPET), amine early warning systems network (NET) Land Data Assimilation System (FLDAS), and Global Land Data Assimilation System (GLDAS). We assessed monthly and annual timescales using statistical indicators such as the Pearson correlation coefficient (PCC/r), mean absolute error (M.A.E.), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The results showed that hPET outperformed other PET datasets at the monthly scale, with the highest correlation, lowest errors, and minimal bias values (P.C.C. = 0.80, RMSE = 25.55, PBIAS = 13.62%). GLDAS dataset showed closer performance to the hPET dataset (P.C.C. = 0.61, RMSE = 94.76, PBIAS = 71.1%) and GLEAM (P.C.C. = 0.12, RMSE = 64.67, PBIAS = 73.52%). Moreover, the FLDAS dataset performed least compared to other assessed PET datasets. hPET’s overall better performance was further certified at the annual scale, again outperforming the other products across all performance indicators (PCC = 0.34, M.A.E. = 258.10, RMSE = 263.05). The performance of the other products was quite poor, but the GLEAM product came closest to hPET compared to the other assessed products (P.C.C. =  − 0.20, M.A.E. – 711.57, RMSE = 716.97). Overall, the hPET dominated all statistical indicators at both timescales, making it the best PET product among the ones evaluated by this study. The findings indicate that hPET is a reliable alternative source of PET data, which can greatly support future hydrological research and modelling in the Ogun-Osun Basin.

Abstract

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM₁₀, PM_2.5, and SO₂ are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly.

Abstract

In the era of Anthropocene climate that the world is currently experiencing, accurate climate models that exhibit minimal uncertainties for precise estimation of the sporadic extreme climate anomalies is urgently needed. To address this gap, the present study quantified the added value in the recently released NEX-GDDP-CMIP6 precipitation models as compared to their native CMIP6 models over 9 climatic zones in Africa in order to identify the best performing models with minimal biases. Accordingly, 22 NEX-GDDP-CMIP6 precipitation models and similar number for native CMIP6 precipitation models were evaluated with respect to two observational products (CHIRPS and CPC). With robust statistical techniques employed, the results showed that at annual and seasonal scales, the NEX-GDDP-CMIP6 GCMs and their multi-model ensemble (MME) reproduced a coherent spatial pattern of precipitation to the observed better than the native CMIP6 GCMs. The NEX-GDDP-CMIP6 GCMs and their MME also exhibited a stronger spatial pattern with higher correlation coefficients, lower mean bias and root mean square error recorded, than in the CMIP6 GCMs. The differences and improvements exhibited by the NEX-GDDP-CMIP6 GCMs, highlight the significance of the improved bias correction method and finer spatial resolution of 0.25*0.25 which characterize the newly published NEX-GDDP-CMIP6 GCMs. The Taylor Skill Score and the Interannual Variability Scores were used to rank the NEX-GDDP-CMIP6 GCMs after evaluation and the results confirmed they were better than the native CMIP6 GCMs in simulating daily precipitation over diverse climate zones of Africa. It is recommended that new future projections of precipitation under whatever scenario (SSPs) or region should adopt this better improved dataset.

Abstract

Climate change, whose negative impacts are becoming increasingly apparent as a result of human actions, intensifies the drought problems to dangerous levels. The development of local-scale drought projections is crucial to take necessary precautions for potential risks and possible effects of drought. In this study, drought analysis was conducted in the Upper Kızılırmak Basin using the standard precipitation index (SPI) method for the near future (2020–2049), mid-century (2050–2074), and late century (2075–2099). The precipitation data required for the SPI were gathered from the data sets developed for the SSP climate change scenarios of the four chosen global climate models. Precipitation data has been made more convenient for local analysis studies with the statistical downscaling method. Forecasts have been created for the temporal variation and spatial distribution of drought events. The study findings indicate that, under the SSP 2-4.5 scenario, drought-related effects of climate change will decrease until 2100. On the other hand, the number and severity of drought events, as well as the duration of dry periods, will increase until 2100 under the SSP 5-8.5 scenario. According to the SSP 5-8.5 scenario, consisting of the most pessimistic forecasts, moderate drought will last 0–60 months, severe drought will last 0–30 months, and extreme drought will last 0–20 months in different regions of the area in the late century. The spatial distribution of droughts will differ based on the SPI index and climate change scenarios. Comparison of SPI and CZI data showed that both indices are effective in meteorological drought analyses.

Abstract

This study investigates the possible effects of climate change on temperature and precipitation variables in the Eastern Black Sea Basin, Türkiye’s wettest and flood-prone region. The outputs of three GCMs under historical, RCP4.5, and RCP8.5 scenarios were downscaled to regional scale using the multivariate adaptive regression splines method. The future monthly temperature and precipitation for 12 stations in the basin were projected for three periods: the 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100). In addition to relative changes, high and low groups and intra-period trends were analyzed for the first time using innovative methods. For the pessimistic scenario, an increase of 3.5 °C in the interior and 3.0 °C in the coastal areas of the basin is projected. For the optimistic scenario, these values are expected to be 2.5 and 2.0 °C, respectively. A decrease in precipitation is projected for the interior region, and a significant increase is expected for the eastern and coastal areas of the basin, especially in spring. This result indicates that floods will occur frequently coastal areas of the basin in the coming periods. Also, although the monotonic trends of temperatures during periods are higher than precipitation in interior regions, these regions may have more uncertainty as their trends are in different directions of low and high groups of different scenarios and GCMs and contribute to all trends, especially precipitation.

Abstract

This study investigates the possible effects of climate change on temperature and precipitation variables in the Eastern Black Sea Basin, Türkiye’s wettest and flood-prone region. The outputs of three GCMs under historical, RCP4.5, and RCP8.5 scenarios were downscaled to regional scale using the multivariate adaptive regression splines method. The future monthly temperature and precipitation for 12 stations in the basin were projected for three periods: the 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100). In addition to relative changes, high and low groups and intra-period trends were analyzed for the first time using innovative methods. For the pessimistic scenario, an increase of 3.5 °C in the interior and 3.0 °C in the coastal areas of the basin is projected. For the optimistic scenario, these values are expected to be 2.5 and 2.0 °C, respectively. A decrease in precipitation is projected for the interior region, and a significant increase is expected for the eastern and coastal areas of the basin, especially in spring. This result indicates that floods will occur frequently coastal areas of the basin in the coming periods. Also, although the monotonic trends of temperatures during periods are higher than precipitation in interior regions, these regions may have more uncertainty as their trends are in different directions of low and high groups of different scenarios and GCMs and contribute to all trends, especially precipitation.

Abstract

Planktonic organisms including phyto-, zoo-, and mixoplankton are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand these changes. Advances in imaging technology have enabled novel possibilities to study plankton populations, but the manual classification of images is time consuming and expert-based, making such an approach unsuitable for large-scale application and urging for automatic solutions for the analysis, especially recognizing the plankton species from images. Despite the extensive research done on automatic plankton recognition, the latest cutting-edge methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for operational use. Then, we provide a detailed description of solutions found in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. Many important challenges remain unsolved including the following: (1) the domain shift between the datasets hindering the development of an imaging instrument independent plankton recognition system, (2) the difficulty to identify and process the images of previously unseen classes and non-plankton particles, and (3) the uncertainty in expert annotations that affects the training of the machine learning models. To build harmonized instrument and location agnostic methods for operational purposes these challenges should be addressed in future research.

Abstract

Accuracy in the global climate model (GCM) projections is essential for developing reliable impact mitigation strategies. The conventional bias correction methods used to improve this accuracy often fail to capture the extremes, specifically for precipitation, due to the generic correction application to whole data. Given the importance of understanding future extreme precipitation behavior for disaster mitigation, we propose Extremes-Weighted Empirical Quantile Mapping (EW-EQM) bias correction with a specific emphasis on extremes. The EW-EQM applies separate EQM correction to threshold-exceeded extremes and frequency-adjusted non-extreme precipitation occurrences. The bias correction results demonstrated using station-observed precipitation records at 945 locations in the Mid-Atlantic region of the United States, and five Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs demonstrate the strength of EW-EQM to improve the bias correction abilities of extreme precipitation occurrences. The spatial median of Root Mean Square error between observed and bias-corrected extreme precipitation was mostly less than 6 mm for EW-EQM across GCMs, while EQM and Power Transformation had a median higher than 12 mm. Further, future bias-corrected precipitation series for 2021–2050 under SSP245 indicate a 0–10% increase in total annual precipitation and a 10% decrease to 25% increase in mean annual maximum precipitation in the region. The improved bias correction of extremes could be significant in climate change impact mitigation decisions such as flood management.