Tag: Climate Change Downscaling Techniques

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.

Abstract

The water scarcity risk induced by climate change is contributing to a sequence of hydrological and socioeconomic impacts. Certain numbers of related impacts are locked in already and are expected to be much greater in the future. So, there is still a lack of understanding of its dynamics, origin, propagation, and the mutual interaction of its drivers. In recent years, several model-based approaches have been introduced to tackle the complexity, dynamics, and uncertainty of water scarcity specifically. However, the coupled modeling while addressing different aspects of the risk of water scarcity under the climate change scenarios has been rarely done. For bridging this gap, in this research, the combination of complementary System Dynamics modeling and Bayesian Network was applied to Qazvin Plain in Iran with five AOGCM models under two Shared Socioeconomic Pathways (SSP) scenarios (126 and 585). Key findings of this research show: 1) Baseline risk assessment indicates a low probability of water scarcity; however, in the future 30-year time horizon with continuous change in hazard, vulnerability, and exposure for SSP126, the risk fell in the extreme category with an average probability of 41%. Under SSP585, the risk varies between extreme and high categories with an average probability of 47%. 2) Economic development, particularly regional gross domestic product (RGDP) in 2045–2054 in SSP585 can diminish the negative projected consequences of climate change and therefore investments in adaptation policies could offset negative consequences, highlighting the role of economic growth in climate resilience. 3) It is projected that crop yield and income will receive the largest negative effects due to cutting back the agriculture area. 4) Considering the interplay of climate change, economic development, and water extraction policies is essential for the design, operation, and management of water-related activities. The proposed integrated methodology provides a comprehensive framework for understanding climate change-induced water scarcity risks, their drivers, and potential consequences. This approach facilitates adaptive decision-making to address the evolving challenges posed by climate change.

Abstract

Northeast China is an important base for grain production, dominated by rain-fed agriculture that relies on green water. However, in the context of global climate change, rising regional temperatures, changing precipitation patterns, and increasing drought frequency pose threats and challenges to agricultural green water security. This study provides a detailed assessment of the spatiotemporal characteristics and development trends of green water security risks in the Northeast region under the base period (2001–2020) and the future (2031–2090) climate change scenarios (SSP245 and SSP585) using the green water scarcity (GWS) index based on raster-scale crop spatial distribution data, Delta downscaling bias-corrected ERA5 data, and CMIP6 multimodal data. During the base period, the green water risk-free zone for dry crops is mainly distributed in the center and east of the Northeast region (72.4% of the total area), the low-risk zone is primarily located in the center (14.0%), and the medium-risk (8.3%) and high-risk (5.3%) zones are mostly in the west. Under SSP245 and SSP585 future climate change scenarios, the green water security risk shows an overall expansion from the west to the center and east, with the low-risk zone increasing to 21.6% and 23.8%, the medium-risk zone increasing to 16.0% and 17.9%, and the high-risk zone increasing to 6.9% and 6.8%, respectively. Considering dry crops with GWS greater than 0.1 as in need of irrigation, the irrigated area increases from 27.6% (base period) to 44.5% (SSP245) and 48.6% (SSP585), with corresponding increases in irrigation water requirement (IWR) of 4.64 and 5.92 billion m³, respectively, which further exacerbates conflicts between supply and demand of agricultural water resources. In response to agricultural green water security risks, coping strategies such as evapotranspiration (ET)-based water resource management for dry crops and deficit irrigation are proposed. The results of this study can provide scientific basis and decision support for the development of Northeast irrigated agriculture and the construction planning of the national water network.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.

Abstract

In 2022, a catastrophic flood triggered by the extreme precipitation in Sind Province, Pakistan. To better understand the comprehensive response of water vapor, rainfall, topography, and flood, the source of water vapor for the flood was calculated by the NCAR Command Language (NCL) application. Simultaneously, the Global Precipitation Measurement (GPM) data was collected from NASA for overlay analysis with water vapor observations. In addition, a digital elevation model (DEM) was also obtained to analyze the impact of topography on flood inundation. Importantly, multi Sentinel-1 data was used to monitor the long-term changes in flood inundation area. The extreme precipitation is dominated by water vapor continue transferred by southwest monsoon, especially impacted by the occurrence of cyclone. Simultaneously, influenced by the steep terrain that located in the north and west of Pakistan, the extreme precipitation first occurred in Islamabad and its adjacent area, subsequently in Punjab Province, and finally concentrated in Sind Province. The surface runoff induced by rainstorm converged in the junction of Sind and Punjab Province with the pattern of fire hose effect. Subsequently, the flood in Indus River in the Sind Province overflow into the low-lying area along the bank of Indus River due to the terrain of Indus River in these regions has the characteristics of over ground river, and the flood flow capacity is lower than that in northern of Pakistan. In addition, the long-term changes in the flood inundation area can be summarized into four stages: increase slowly period (In June), increase slightly period (In July), increase rapidly period (Between August and the beginning of September), rapidly decline period (After September 15, 2022). Importantly, a conceptual model of disaster caused by the fire pipe effect is summarized based on the comprehensive response of water vapor, rainfall, and topography.