Tag: Climate Change Downscaling Techniques

Abstract

According to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristics such as precipitation, air temperature, humidity, wind, sunshine, cloud cover, and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from the Ethiopian Meteorological Institute. Download canESM2 (Canadian Second Generation Earth System Model). The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. The SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987 to 2018 was considered as a base period, whereas the period from 2019 to 2100 was considered as future periods. Historically, from 1987 to 2018, there was an overall increase in the mean annual minimum and maximum temperatures by 0.016 °C and 0.048 °C, respectively, with a little decrease in the average annual rainfall (up to 0.685 mm). The highest increment of maximum temperature recorded in October month up to + 2.7 °C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for the RCP4.5 scenario and 66% (2073–2100) for the RCP4.5 (representative concentration pathway 4.5) scenario in the Belg (February to May). Precipitation decreases in the Kiremt (June to September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategies.

Abstract

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting.

Abstract

Evaluating physical properties and mechanical parameters of rock slopes and their spatial variability is challenging, particularly at locations inaccessible for fieldwork. This obstacle can be bypassed by acquiring spatially-distributed field data indirectly. InfraRed Thermography (IRT) has emerged as a promising technology to statistically infer rock properties and inform slope stability models. Here, we explore the use of Cooling Rate Indices (CRIs) to quantify the thermal response of a granodiorite rock wall within the recently established Požáry Test Site in Czechia. We observe distinct cooling patterns across different segments of the wall, compatible with the different degrees of weathering evaluated in the laboratory and suggested by IRT observations of cored samples. Our findings support previous examinations of the efficacy of this method and unveil correlations between cooling phases in the field and in the laboratory. We discuss the scale-dependency of the Informative Time Window (ITW) of the CRIs, noting that it may serve as a reference for conducting systematic IRT field surveys. We contend that our approach not only represents a viable and scientifically robust strategy for characterising rock slopes but also holds the potential for identifying unstable areas.

Abstract

Information regarding the uncertainty associated with weather forecasts, particularly when they are related to a localized area at convective scales, can certainly play a crucial role in enhancing decision-making. In this study, we discuss and evaluate a short-range forecast (0–75 h) from of a regional ensemble prediction system (NEPS-R) running operationally at the National Centre for Medium Range Weather Forecasting (NCMRWF). NEPS-R operates at a convective scale (~ 4 km) with 11 perturbed ensemble members and a control run. We assess the performance of the NEPS-R in comparison to its coarser-resolution global counterpart (NEPS-G), which is also operational. NEPS-R relies on initial and boundary conditions provided by NEPS-G. The NEPS-G produces valuable forecast products and is capable of predicting weather patterns and events at a spatial resolution of 12 km. The objective of this study is to investigate areas where NEPS-R forecasts could add value to the short-range forecasts of NEPS-G. Verification is conducted for the period from 1st August to 30th September 2019, covering the summer monsoon over a domain encompassing India and its neighboring regions, using the same ensemble size (11 members). In addition to standard verification metrics, fraction skill scores, and potential economic values are used as the evaluation measures for the ensemble prediction systems (EPSs). Near-surface variables such as precipitation and zonal wind at 850 hPa (U850) are considered in this study. The results suggest that, in some cases, such as extreme precipitation, there is a benefit in using regional EPS forecast. State-of-the-art probabilistic measures indicate that the regional EPS has reduced under-dispersion in the case of precipitation compared to the global EPS. The global EPS tends to provide higher skill scores for U850 forecasts, whereas the regional EPS outperforms the global EPS for heavy precipitation events (> 65 mm/day). There are instances when the regional EPS can provide a useful forecast for cases, including moderate rainfall, and can add more value to the global EPS forecast products. The investigation of diurnal variations in precipitation forecasts reveals that although both models struggle to predict the correct timing, the time phase and peaks in precipitation in the convection-permitting regional model are closer to the observations.

Abstract

Studying the pressing impacts of climate change on runoff is vital for the sustainable functioning of society and ecosystems. In Senegal, there is insufficient consideration given to the magnitude of the decrease in water resources caused by climate change and the potential impact of this decrease on both society and the environment. The objective of this study was to evaluate the hydrological effects of climate change in the Aga-Foua-Djilas basin by employing CWatM hydrological models inside the frameworks of ISIMIP and HMF-WA. Over the historical period (1981–2019) in the Aga-Foua-Djilas basin, the analysis of all hydrological parameters indicates positive trends, although not statistically significant (except for runoff). Over the future period, unlike temperatures and PET, which show an upward trend in all scenarios, precipitation and runoff show downward trends, which are more significant under SSP 585. For precipitation, Kendall’s Tau shows a downward trend of − 0.157 mm/yr, − 0.321 mm/yr, and − 0.472 mm/yr under SSP 126, SSP 370 and SSP 585, respectively. For runoff, the trends are negative and of the order of − 0.207 m³/s/yr, − 0.44 m³/s/yr, and − 0.565 m³/s/yr, respectively, under SSP 126, SSP 370 and SSP 585 with CwatM and − 0.248 m³/s/yr (SSP 126), − 0.389 m³/s/yr (SSP 245) and − 0.579 m³/s/yr (SSP585) with HMF-WA. Compared with the decrease in precipitation toward the end of the century, the decrease in runoff noted for the distant future (2081–2100) will be of the order of − 32.8% (SSP 126), − 80.8% (SSP 370) and − 94.6% (SSP 585) with CwatM and − 22.3% (SSP 126), − 19.6% (SSP 245) and − 50.9% (SSP 585) with HMF-WA. This study could help policymakers and stakeholders to develop adaptation strategies for the Aga-Foua-Djilas basin.

Abstract

For managing water resources and operating reservoirs in dynamic contexts, accurate hydrological forecasting is essential. However, it is difficult to track complex hydrological time series with highly non-linear and non-stationary characteristics. The intricacy of the issue is further increased by the risk and uncertainty that are brought about by the dependence of several factors on the hydrological system’s output. To hydrologically model river outflows, a hybrid GARCH time series model technique has been applied in this study. To improve the precision of the proposed model estimation, this hybrid model employs a controllable fuzzy logic system to explore the impact of various input variables and an Archimedean detail function to account for the uncertainty in the dependence of the variables. The prediction error in this model is minimized by utilizing weighting factors and problem analysis parameters that are calculated using the water cycle algorithm. It was found that the minimum root-mean-square error values for the training and testing modeling stages are RMSE = 1.89 m and 1.92 m, respectively, by looking at the hydrological modeling results for a watershed of the Karaj dam. For extended lead (i.e., a 6-month rainfall lag), the weakest forecasting capacity was found. The modeling of the copula function using a higher percentage of answers in the confidence band and a smaller bandwidth resulted in less uncertainty for the estimation of the suggested model, according to the uncertainty analysis.

Abstract

Cities and other human settlements are major contributors to climate change and are highly vulnerable to its impacts. They are also uniquely positioned to reduce greenhouse gas emissions and lead adaptation efforts. These compound challenges and opportunities require a comprehensive perspective on the public policy of human settlements. Drawing on core literature that has driven debate around cities and climate over recent decades, we put forward a set of boundary objects that can be applied to connect the knowledge of epistemic communities and support an integrated urbanism. We then use these boundary objects to develop the Goals-Intervention-Stakeholder-Enablers (GISE) framework for a public policy of human settlements that is both place-specific and provides insights and tools useful for climate action in cities and other human settlements worldwide. Using examples from Berlin, we apply this framework to show that climate mitigation and adaptation, public health, and well-being goals are closely linked and mutually supportive when a comprehensive approach to urban public policy is applied.

Abstract

Scots pine is of greatest importance in northern Germany regarding its cultivation area and expected capability to perform in climate change. However, pine predominantly occurs in monocultures. Therefore, future pine forestry depends on an adaptation to climate change while improving ecological and economic forest functions. Yet future development of pine remains uncertain due to leeway in silvicultural guidelines and future climate. This study questions: (i) what is the range of future pine shares under climate change and different silvicultural management in northern Germany, (ii) how will the current stands develop and (iii) what is the range of uncertainty arising from climate models and silvicultural options? To answer these issues we (i) selected forest development types site- and climate-sensitively to either minimize or to maximize pine shares, (ii) simulated four, now practiced forest management scenarios for 50 years based on the German National Forest Inventory and (iii) analyzed the differences, to be interpreted as uncertainty. Novel to our approach is the site- and climate-sensitive selection of forest development types on large scales which emphasizes the contrasts of the different management guidelines. The results show that growing stock and cultivation area will decrease even if pine is promoted in forestry. The predicted restoration rate ranges from 50 to 72% depending on scenario and previous thinning regime. In conclusion, under the given management concepts and considering today’s high proportion of old pine, restoration is alarmingly slow. Amid the rapidly changing climate, we recommend to further adjust the management guidelines to accelerate forest restoration.

Abstract

This study investigates the potential impacts of future climate change on river water quality in Ardak Watershed, Northeast Iran, and proposes adaptation strategies to mitigate adverse effects. The SWAT model is calibrated and verified by Monthly water quality sampling and flow measurements. The premium SWAT-CUP model was utilized for sensitivity analysis and parameter adjustment to simulate runoff, sediment, nitrate, mineral phosphorus, and dissolved oxygen. Future catchment temperature and precipitation were projected using CMhyd statistical downscaling by incorporating four CMIP6 models under SSP scenarios for the near (2025–2049), intermediate (2050–2074), and far (2075–2099) future. The Mianmorgh River experienced increased levels of various pollutants in winter, summer, and autumn but decreased in spring for future periods. In the Abghad River, pollutant levels are expected to increase from late autumn to late winter and decrease in other months. Nitrate increased from the late summer to late winter, then decreased throughout the year. Three adaptation strategies were proposed: reducing rural swage pollutants, creating pasture on 5% of unvegetated land, and combining both. The SWAT model showed responsiveness to the mix scenario, with average reductions of 4—4.5% for suspended solids, 23—16% for inorganic phosphorus, and 16—20% for nitrate for the first strategy. The results revealed that climate change can significantly affect water quality, but its adverse effects can be mitigated with suitable actions. Combined adaptation strategies effectively reduced suspended solids and mineral phosphorus and removed pollutants. Therefore, implementing a combination of effective strategies is more beneficial than individual approaches.

Abstract

As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced impressive generative results by applying deep generative models to create synthetic data and by performing an adversarial training process. Nevertheless, numerous issues related to the instability of training need to be urgently addressed. Evolutionary computation (EC), using the corresponding paradigm of biological evolution, overcomes these problems and improves evolutionary-based GANs’ ability to deal with real-world applications. Therefore, this paper presents a systematic literature survey combining EC and GANs. First, the basic theories of GANs and EC are analyzed and summarized. Second, to provide readers with a comprehensive view, this paper outlines the recent advances in combining EC and GANs after detailed classification and introduces each of them. These classifications include evolutionary GANs and their variants, GANs with evolutionary strategies and differential evolution, GANs combined with neuroevolution, evolutionary GANs related to different optimization problems, and applications of evolutionary GANs. Detailed information on the evaluation metrics, network structures, and comparisons of these models is presented in several tables. Finally, future directions and possible perspectives for further development are discussed.