Tag: Climate Change Downscaling Techniques

Abstract

Providing accurate and reliable rainfall data that can be used and applied in various climate and hydrological studies is essential. This paper aims to assess the accuracy of monthly rainfall data for satellites (climate engine, NASA-POWER) and corresponding data for ground stations, and through spatial mapping and linear measurement of rainfall indicators in Duhok Governorate in northern Iraq. The data evaluation process included the use of some statistical and cartographic methods available within the Jeffrey Amazing Statistics Program (JASP) and Origin Pro software, the evaluation and statistical analysis were conducted during the period from 2003 to 2022. The analysis results indicate that the relationship of ground stations with the climate engine recorded good values ​​(Pbias = 1.24, NSE = 0.93, R = 0.97, Slope = 0.99, KGE = 0.72). However, these values are lower when compared with NASA-POWER​​ (Pbias = 14.09, NSE = 0.55, R = 0.94, Slope = 0.34, KGE = -12.9). Both results indicate a positive relationship between the satellite and the ground data, and in general, all climate stations recorded a high correlation factor in monthly rainfall forecasting. Furthermore, the climate engine’s data were characterized by high rainfall accuracy and quality and were relatively consistent with the observed scale data (ground stations). This study provides new ideas about the methods for selecting rainfall products for climatic and hydrological studies.

Abstract

Climate change brings a range of challenges and opportunities to shrimp fisheries globally. The case of the Colombian Pacific Ocean (CPO) is notable due the crucial role of shrimps in the economy, supporting livelihoods for numerous families. However, the potential impacts of climate change on the distribution of shrimps loom large, making it urgent to scrutinize the prospective alterations that might unfurl across the CPO. Employing the Species Distribution Modeling approach under Global Circulation Model scenarios, we predicted the current and future potential distributions of five commercially important shrimps (Litopenaeus occidentalis, Xiphopenaeus riveti, Solenocera agassizii, Penaeus brevirostris, and Penaeus californiensis) based on an annual cycle, and considering the decades 2030 and 2050 under the Shared Socioeconomic Pathways SSP 2.6, SSP 4.5, SSP 7.0, and SSP 8.5. The Bathymetric Projection Method was utilized to obtain spatiotemporal ocean bottom predictors, giving the models more realism for reliable habitat predictions. Six spatiotemporal attributes were computed to gauge the changes in these distributions: area, depth range, spatial aggregation, percentage suitability change, gain or loss of areas, and seasonality. L. occidentalis and X. riveti exhibited favorable shifts during the initial semester for both decades and all scenarios, but unfavorable changes during the latter half of the year, primarily influenced by projected modifications in bottom salinity and bottom temperature. Conversely, for S. agassizii, P. brevirostris, and P. californiensis, predominantly negative changes surfaced across all months, decades, and scenarios, primarily driven by precipitation. These changes pose both threats and opportunities to shrimp fisheries in the CPO. However, their effects are not uniform across space and time. Instead, they form a mosaic of complex interactions that merit careful consideration when seeking practical solutions. These findings hold potential utility for informed decision-making, climate change mitigation, and adaptive strategies within the context of shrimp fisheries management in the CPO.

Abstract

The Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) was developed and implemented operationally at Environment and Climate Change Canada (ECCC) to support a variety of critical marine applications. These include support for ice services, search and rescue, environmental emergency response and maritime safety. CIOPS-E uses a 1/36° horizontal grid (~ 2 km) to simulate sea ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). Forcing at lateral open boundaries is taken from ECCC’s data assimilative Regional Ice-Ocean Prediction System (RIOPS). A spectral nudging method is applied offshore to keep mesoscale features consistent with RIOPS. Over the continental shelf and GSL, the CIOPS-E solution is free to evolve according to the model dynamics. Overall, CIOPS-E significantly improves the representation of tidal and sub-tidal water levels compared to ECCC’s lower resolution systems: RIOPS (~ 6 km) and the Regional Marine Prediction System – GSL (RMPS-GSL, 5 km). Improvements in the GSL are due to the higher resolution and a better representation of bathymetry, boundary forcing and dynamics in the upper St. Lawrence Estuary. Sea surface temperatures show persistent summertime cold bias, larger in CIOPS-E than in RIOPS, as the latter is constrained by observations. The seasonal cycle of sea ice extent and volume, unconstrained in CIOPS-E, compares well with observational estimates, RIOPS and RMPS-GSL. A greater number of fine-scale features are found in CIOPS-E with narrow leads and more intense ice convergence zones, compared to both RIOPS and RMPS-GSL.

Abstract

The European North-West shelf seas (NWS) support economic interests and provide environmental services to adjacent countries. Expansion of offshore activities, such as renewable energy infrastructure, aquaculture, and growth of international shipping, will place increasingly complex demands on the marine environment over the coming decades. Skilful forecasting of NWS properties on seasonal timescales will help to effectively manage these activities. Here we quantify the skill of an operational large-ensemble ocean-atmosphere coupled global forecasting system (GloSea), as well as benchmark persistence forecasts, for predictions of NWS sea surface temperature (SST) at 2–4 months lead time in winter and summer. We identify sources of and limits to SST predictability, considering what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. GloSea outperforms simple persistence forecasts by adding information about atmospheric variability, but only to a modest extent as persistence of anomalies in the initial conditions contributes substantially to predictability. Where persistence is low – for example in seasonally stratified regions – GloSea forecasts show lower skill. GloSea skill can be degraded by model deficiencies in the relatively coarse global ocean component, which lacks dynamic tides and subsequently fails to robustly represent local circulation and mixing. However, “atmospheric mode matched” tests show potential for improving prediction skill of currently low performing regions if atmospheric circulation forecasts can be improved. This underlines the importance of coupled atmosphere-ocean model development for NWS seasonal forecasting applications.

Abstract

In the context of changes in global climate and land uses, biodiversity patterns and plant species distributions have been significantly affected. Soil salinization is a growing problem, particularly in the arid areas of Northwest China. Halophytes are ideal for restoring soil salinization because of their adaptability to salt stress. In this study, we collected the current and future bioclimatic data released by the WorldClim database, along with soil data from the Harmonized World Soil Database (v1.2) and A Big Earth Data Platform for Three Poles. Using the maximum entropy (MaxEnt) model, the potential suitable habitats of six halophytic plant species (Halostachys caspica (Bieb.) C. A. Mey., Halogeton glomeratus (Bieb.) C. A. Mey., Kalidium foliatum (Pall.) Moq., Halocnemum strobilaceum (Pall.) Bieb., Salicornia europaea L., and Suaeda salsa (L.) Pall.) were assessed under the current climate conditions (average for 1970–2000) and future (2050s, 2070s, and 2090s) climate scenarios (SSP245 and SSP585, where SSP is the Shared Socio-economic Pathway). The results revealed that all six halophytic plant species exhibited the area under the receiver operating characteristic curve values higher than 0.80 based on the MaxEnt model, indicating the excellent performance of the MaxEnt model. The suitability of the six halophytic plant species significantly varied across regions in the arid areas of Northwest China. Under different future climate change scenarios, the suitable habitat areas for the six halophytic plant species are expected to increase or decrease to varying degrees. As global warming progresses, the suitable habitat areas of K. foliatum, S. salsa, and H. strobilaceum exhibited an increasing trend. In contrast, the suitable habitat areas of H. glomeratus, S. europaea, and H. caspica showed an opposite trend. Furthermore, considering the ongoing global warming trend, the centroids of the suitable habitat areas for various halophytic plant species would migrate to different degrees, and four halophytic plant species, namely, S. salsa, H. strobilaceum, H. gbmeratus, and H. capsica, would migrate to higher latitudes. Temperature, precipitation, and soil factors affected the possible distribution ranges of these six halophytic plant species. Among them, precipitation seasonality (coefficient of variation), precipitation of the warmest quarter, mean temperature of the warmest quarter, and exchangeable Na⁺ significantly affected the distribution of halophytic plant species. Our findings are critical to comprehending and predicting the impact of climate change on ecosystems. The findings of this study hold significant theoretical and practical implications for the management of soil salinization and for the utilization, protection, and management of halophytes in the arid areas of Northwest China.

Abstract

The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

Abstract

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Abstract

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Abstract

In the light of observed variability in precipitation patterns, there is a growing need for comprehensive data mining of regularly updated rainfall recording databases. Therefore, an analysis of heavy rainfall and hyetographs was conducted using a 30-year high-resolution dataset from 100 rain gauges across Poland, covering 31 646 rainfall events. Distributions of rainfall depths, durations, and intensities were explored, and maxima were compared to global records. Spatial analysis revealed significant variations in the frequency, depths, and durations of extreme rainfall across different regions. Cluster analysis determined model hyetographs for each station. The likelihood of regions belonging to clusters with three to five model hyetographs was assessed using Indicator Kriging. Findings underscore the importance of using local, characteristics rainfalls in hydrodynamic modelling of drainage systems and future rainfall scenarios. These results provide a foundational step towards understanding and monitoring the impacts of climate change on rainfall characteristics, especially extremes, in future decades.

Abstract

It is widely predicted that climate change’s adverse effects will intensify in the future, and along with inadequate agricultural practices, settlement development, and other anthropic activities, could contribute to rapid wetland degradation and thus exert significant negative effects on local communities. This study sought to develop an approach based on the Integrated Water Resource Management (IWRM) in the Ruzizi Plain, eastern Democratic Republic of Congo (DRC), where adverse effects of the climate change are increasingly recurrent. Initially, we analyzed the trends of climate data for the last three decades (1990–2022). Subsequently, the Water Evaluation and Planning (WEAP) approach was employed on two contrasting watersheds to estimate current and future water demands in the region and how local wetlands could serve as reservoirs to meeting water demands. Results indicate that the Ruzizi Plain is facing escalating water challenges owing to climate change, rapid population growth, and evolving land-use patterns. These factors are expected to affect water quality and quantity, and thus, increase pressure on wetland ecosystems. The analysis of past data shows recurrence of dry years (SPI ≤  − 1.5), reduced daily low-intensity rainfall (Pmm < 10 mm), and a significant increase in extreme rainfall events (Pmm ≥ 25 mm). The WEAP outcomes revealed significant variations in future water availability, demand, and potential stressors across watersheds. Cropland and livestock are the main water consumers in rural wetlands, while households, cropland (at a lesser extent), and other urban uses exert significant water demands on wetlands located in urban environments. Of three test scenarios, the one presenting wetlands as water reservoirs seemed promising than those considered optimal (based on policies regulating water use) and rational (stationary inputs but with a decrease in daily allocation). These findings highlight the impact of climate change in the Ruzizi plain, emphasizing the urgency of implementing adaptive measures. This study advocates for the necessity of the IWRM approach to enhance water resilience, fostering sustainable development and wetland preservation under changing climate.