Tag: Climate Change Downscaling Techniques

Abstract

Soil erosion is very hazardous to the global ecosystem. Government aided soil erosion control schemes happen dilatorily with minimal resources. Recognition and identifying the scale and the area of eroded land can be extremely time-consuming and difficult as well. To overcome this problem, a real-time Soil erosion detection system is introduced. The real-time part has been implemented using satellite imagery with the use of RUSLE modelling considering various factors. This was generated with the help of Google Earth Engine (GEE) interface. The RUSLE model offers a straightforward approach to assess soil erosion. By using remote sensing data and GIS, RUSLE effectively evaluates erosion. Researchers have developed various equations to model the five factors of the RUSLE model, considering the diverse variations in the soil erosion process. The system also includes the analysis of satellite imagery with a mapped view of soil erosion. Here, the Unet (EfficientNetb3) model is used giving optimal accuracy for the detection of soil erosion.

Abstract

Impact-based nowcasting systems at detailed scales, to the street level, have become essential in flood risk management. This is achieved by focusing on predicting the impacts of flood events rather than merely forecasting weather conditions. This approach leverages advancements in 2D hydrodynamic modelling, high-performance computing (HPC), and detailed rainfall forecasting to improve the precision of early warning systems. However, its real-world implementation is hindered by challenges such as the coarse temporal resolution of weather forecasts and inherent modelling uncertainties. This study investigates the uncertainties and challenges associated with impact-based nowcasting systems, using the Mandra town (Greece) as a case study. We demonstrate the feasibility of applying a comprehensive framework that integrates 2D hydrodynamic modelling, HPC, and temporally disaggregated rainfall forecasting. Our findings show that the Alternating Block Method (ABM) effectively captures storm dynamics, mitigating significant underestimations that arise from coarser forecast inputs. Additionally, we assess various flood impact indices to manage modelling uncertainties. Our results highlight that similarities exist in the flood indices when storms are mild with short return periods. However, discrepancies between indices increase with storms of longer return periods, underscoring the critical need for careful index selection. This research provides new insights into enhancing flood nowcasting accuracy and effectiveness, particularly in small to medium-sized catchments. Moreover, it offers evidence that the scientific community along with the stakeholders such as Civil Protection, local governments, and others should focus orient their efforts on more reliable flood indices, as the discrepancies between the methodologies investigated increase with the severity of the events.

Abstract

Reference evapotranspiration (ET0), as one of the main components of the hydrological cycle, plays an important role in water resources management and agricultural planning. This study was conducted with the aim of predicting the temporal and spatial changes of ET0 in the Gorganroud watershed in northern Iran. The minimum and maximum temperatures were predicted using the output of five CMIP6 climate models under two climate scenarios of SSP2-4.5 and SSP5-8.5 for the historical base (1985–2014), near future (2025–2054) and far future (2071–2100) periods. The bias correction of the simulation data was performed using the linear scaling method. To reduce the uncertainty of climate models, a multi-model ensemble based on the application of Bayesian Model Averaging (BMA) was created and the reference evapotranspiration was calculated using the Hargreaves-Samani method. The results showed that under the SSP2-4.5 scenario, the minimum and maximum temperatures will increase by 1.65 and 1.8 ºC, respectively, whereas under the SSP5-8.5 scenario, the minimum and maximum temperatures will increase by 2.5 and 2.7 ºC, respectively. Similarly, the projections show that the reference evapotranspiration will increase on seasonal and annual scales in the future climate compared to the base period. The largest increase in ET0 is estimated to be 12.4% under the SSP5-8.5 scenario in the period 2071–2100 compared to the base period. The largest increase in evapotranspiration is in summer with values of 5.8–8% and 7.8–13.3% for the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Analysis of the zonation of changes in evapotranspiration showed that most of the changes occur in the eastern regions and at Gharehbil and Cheshmehkhan stations. Our results indicate that future climate change will cause a significant increase in ET0 at high altitudes.

Abstract

As a result of economic development, population growth, accelerated urbanization and the frequent occurrence of extreme weather events, the contradiction between the supply and demand for water resources between regions has become increasingly acute. In order to solve the problem of regional water shortage and irrational utilization, the optimal allocation of water resources has become one of the research hotspots in recent years. In this study, firstly a multi-objective integrated allocation model of regional water resources is constructed by introducing social, economic, and environmental objective functions to address the complex uncertainties in the water resources system. Secondly, the standard whale algorithm is optimized and improved by introducing chaotic population initialization, chaotic convergence factor, adaptive Lévy flight and improved positive cosine mechanism. The model parameters, including the 2025 water resource demand and supply, pollutant discharge content, and unit water supply cost coefficients, are set by consulting the Shanxi Water Resources Bulletin 2022, the Shanxi Provincial Department of Water Resources, and the Report on the Work of the Shanxi Provincial Government 2023. Subsequently, the improved whale algorithm is utilized for the optimization of the predicted water resources for various target years in the future in the lower reaches of the Fen River in Shanxi Province, China. This ultimately yields optimized allocation results independently from both supply and demand sides. The experimental results demonstrate that the framework for water resource optimization using the improved whale algorithm is feasible, providing a reference scheme for regional multi-objective water resource optimization. Finally, the proposed policy recommendations emphasize the necessity of strengthening water diversion planning and management, promoting virtual water and water-saving initiatives, and highlighting water recycling and environmental protection in order to ensure the sustainable allocation of water resources in the downstream Fen River basin.

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.