Tag: Climate Change Downscaling Techniques

Abstract

Purpose

Reducing the environmental impacts of food systems has become a growing concern for public authorities. This study aims to adapt the territorial LCA framework (T-LCA) to local food system assessment to identify territorial hotspots of a food system in relation to its land use functions. To achieve this goal, the T-LCA must be enhanced by overcoming previously exposed limitations.

Methods

Deriving from the T-LCA framework, the methodology used in this paper assesses all territorial food-producing, processing, and consuming activities. The methodological developments suggest addressing its three principal methodological limitations by (i) using agricultural statistics to estimate the local consumption and thus account for intra-territorial flows, (ii) proposing novel agri-food land use functions related to a local food system, and (iii) developing a simplified framework for sensitivity analysis (SA) through detection of the most uncertain and influential data followed by a once-at-a-time (OAT) approach to improve the uncertainty related to the substantial number of data involved in meso-level LCAs. The methodology is applied to a case study in France using the Environmental Footprint (EF) 3.0 method.

Results and discussion

The results indicate that intra-territorial flow analysis effectively distinguishes between local and imported flows, identifying their primary environmental hotspots. Despite the significant impact of imported flows, export-oriented livestock production emerges as the principal hotspot of the studied food system. Integrating agri-food land use functions into LCA is crucial for linking the activities with higher environmental impact contributions and their territorial functions. This is the case of animal husbandry which is the main environmental hotspot and one of the principal local economic activities. Finally, the sensitivity analysis reveals a low sensitivity of the overall results to the most influential and uncertain parameters.

Conclusions

These findings confirm the interest in further developing territorial LCA methodologies and adapting them to various contexts to determine the principal environmental burdens of local systems and improve territorial land planning. This study also proposes various research perspectives to confirm and enhance the robustness of T-LCA frameworks, including the development of regional life cycle inventories.

Abstract

The Gabès region in southeastern Tunisia faces significant water stress due to limited water resources, with groundwater being the primary source for various human activities. The ongoing and future effects of global warming and climate change, characterized by rising temperatures and fluctuations in rainfall patterns, are set to exacerbate water scarcity and degrade the quality of this region's water resources. This study aimed to assess the impact of global warming and climate change on natural groundwater recharge in an unconfined aquifer system within the Gabès region utilizing the Markov chains methodology. To do so, future climatic parameters specific to the study area were projected by downscaling a general circulation model (GCM). Over the temporal period from 2020 to 2100, various climate change scenarios, including A1, A2, and B2, were employed to estimate the evolution of groundwater levels throughout the region. Upon analyzing historical data and employing the Markov chain method to forecast future scenarios, the downscaling modeling approach proved to be effective in predicting climate parameters such as average temperature, precipitation, and wind speed for the designated time frame. The results were visually represented as piezometric maps representing the different scenarios. It is important to note that there is predicted to be a general decline in precipitation, with an anticipated average decrease of 40 mm/year by the 2100s, particularly when considering potential increases in water abstraction rates. Consequently, groundwater recharge is expected to decrease, leading to a noticeable drawdown in groundwater levels across all three scenarios. This study sheds light on the critical implications of climate change for the Gabès region, highlighting the urgent need for sustainable water resource management and conservation measures to mitigate the adverse effects of decreasing groundwater availability.

Abstract

The Gabès region in southeastern Tunisia faces significant water stress due to limited water resources, with groundwater being the primary source for various human activities. The ongoing and future effects of global warming and climate change, characterized by rising temperatures and fluctuations in rainfall patterns, are set to exacerbate water scarcity and degrade the quality of this region's water resources. This study aimed to assess the impact of global warming and climate change on natural groundwater recharge in an unconfined aquifer system within the Gabès region utilizing the Markov chains methodology. To do so, future climatic parameters specific to the study area were projected by downscaling a general circulation model (GCM). Over the temporal period from 2020 to 2100, various climate change scenarios, including A1, A2, and B2, were employed to estimate the evolution of groundwater levels throughout the region. Upon analyzing historical data and employing the Markov chain method to forecast future scenarios, the downscaling modeling approach proved to be effective in predicting climate parameters such as average temperature, precipitation, and wind speed for the designated time frame. The results were visually represented as piezometric maps representing the different scenarios. It is important to note that there is predicted to be a general decline in precipitation, with an anticipated average decrease of 40 mm/year by the 2100s, particularly when considering potential increases in water abstraction rates. Consequently, groundwater recharge is expected to decrease, leading to a noticeable drawdown in groundwater levels across all three scenarios. This study sheds light on the critical implications of climate change for the Gabès region, highlighting the urgent need for sustainable water resource management and conservation measures to mitigate the adverse effects of decreasing groundwater availability.

Abstract

Grain aeration is an environmentally friendly technique commonly used to preserve the grains of cereals. However, this technique could be disrupted by climate change, due to anomalies of the air temperature and relative humidity, which would impact its effectiveness, especially in hot climate regions, such as northern Tunisia. This study examines the future potential of grain aeration over a 30-year projection period (2041–2070), comparing it with the current state (2015–2020) and focusing on the critical storage period (from July to October) in northwestern Tunisia. To assess the effectiveness of the aeration technique, we used the global climate models CNRM-CM5.1 and ESM2M, in combination with the regional climate model SMHI, considering two projection scenarios RCP4.5 and RCP8.5 to predict ambient air temperature and relative humidity. The grain aeration simulator AERO was coupled with two process control strategies, AERO2 and the timer controller, to predict grain moisture content, temperature, and suitable aeration times. The results show that the current state, as well as projections based on the RCP4.5 and RCP8.5 scenarios, allow for safe grain storage with moisture levels between 12% and 13% relative humidity throughout the storage period. However, simulations indicate that climate change could reduce the favorable aeration hours, with an average decrease of 23.44 h compared with the current situation with high variability of suitable hours, reflecting the warming trend and prolonged warm periods. This reduction varies significantly due to global warming and the extended warm period. During the 2645 h of storage, the projection shows an average standard deviation of 34.12 h. However, reality indicates an average standard deviation of less than 1. The impact of climate change will reduce the use of ambient air aeration, necessitating careful short-term and long-term planning. To prevent the deterioration of grain quality during storage, it is essential to incorporate the climate change component into expert grain stock management systems and harness the potential of sunlight for grain preservation through chilling aeration.

Graphical abstract

Abstract

Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks.

Abstract

Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks.

Abstract

To adapt to Earth’s rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day.

Abstract

To adapt to Earth’s rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day.

Abstract

Xinjiang Uygur Autonomous Region is a typical inland arid region in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibits distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person’s correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data are slightly lower than the best precipitation product (CMFD), the overall performance of DBMA is more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrate the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. Our results showed that the proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid regions, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.