Theoretical and Applied Climatology最新文献

This study aimed to assess the risk of heat stress conditions for dairy cattle in the Tigray regional state of Ethiopia under historical and future climatic conditions. The daily thermal heat index (THI) was computed for each of the 14 weather stations after quality control of the maximum and minimum temperature datasets. The calculations were performed for the historical period (1980–2023) and two future climate periods (mid-term: 2040–2069 and end-term: 2070–2099) using an ensemble of 20 global circulation models under two representative concentration pathways (RCP 4.5 and 8.5). During the historical period, the frequency of severe heat stress was 3.4% (13 days/year), predominantly occurring in the western corner of the region (39.5% of days/year). The frequency of projected severe heat stress days across the region is expected to increase to 5.4% (mid-term) and 6% (end-term) under the RCP 4.5 emission scenario. Under the RCP 8.5 scenario, the frequency is expected to rise to 6.2% (mid-term) and 9.4% (end-term). On average, there were 6–9 consecutive severe heat stress days in both the historical and future climate periods. It is crucial to emphasize that the mapping of heat stress risk in dairy cattle was carried out using THI thresholds developed elsewhere. However, it is imperative to underscore the significance of conducting local experiments to determine context-specific thresholds.

Gridded climate datasets such as satellite and reanalysis products have biases related to the methods used to develop them. This study aims to improve the hourly rainfall distribution of the WFDE5 reanalysis over all grid boxes in Benin, based on bias adjustment through Quantile Mapping (QM). The bias adjusted product (called NEW) was evaluated on an hourly scale, using the percentage and quantity of precipitation events per modality class ([0–1), [1–2), [2–3), [3–4), [4–5) and ≥ 5 mm/h) and the relative mean absolute error (RMAE). On a daily scale, evaluation was based on Pearson correlation and RMAE values using nine extreme precipitation indices. The mean absolute error (MAE) and Mann Kendall trend are sometimes shown. Our result showed that assimilated rainfall from WFDE5 performed well at seasonal scales (RMAE < 15%) but underperformed at the hourly scale (RMAE sometimes > 400%). NEW offers RMAE values generally < 100%, i.e. an almost four-fold reduction in bias. The QM method improves the rainfall distribution, particularly with regard to the percentage of rainfall between 0 and 1 mm/h (WFDE5 = 95.40%, Obs = 97.87% and NEW = 97.83% at the Nalohou station) and the cumulative rainfall quantity greater than 5 mm/h (WFDE5 = 131 mm/year vs. Obs = 880 mm/year and NEW = 790 mm/year at the Nalohou station). The bias adjustment also significantly improved the description of climate extremes in Benin, particularly in terms of bias. At Cotonou station, WFDE5 was associated with an average RMAE of 61% for the nine indices, compared to 33% for NEW. Finally, NEW presents mean values closer to observation data, and can be used for hydrological impact studies in Benin.

Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.

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.

The protection of heritage from a changing climate has been of increasing interest over the last few decades, which creates a need for a systematic approach to the impacts of climate on tangible and intangible heritage. We present heritage climatology as an applied, interdisciplinary field of science that examines aspects of climate that affect heritage and provides data, statistics, well-tuned climate parameters and projections that can aid interpreting past changes and future management of heritage. It must consider the impact of extreme events, cyclic processes and the gradual accumulation of damage. Climate threats to heritage need to be represented at the appropriate temporal and spatial scales, and transferred using dose–response functions such that they can be interpreted in terms of management decisions yet be resistant to errors from both the representation of the climate threat and its translation into policy.

Over the last 20 years, several record-breaking events linked to high air temperatures have enhanced the attention over extreme heat phenomena, especially during summer. Both 2022 and 2023 have been exceptional years in this regard. Heat waves, in particular, are a major natural hazard for the most fragile groups of the population. In order to address the points of impact, adaptation and future predictions, a quantitative analysis of all their specific aspects is necessary. These include the monitoring of their changes over time. This study consists of a comprehensive analysis at national level of several air temperature-extremes indices over Italy, including those specifically related to heat waves. The dataset is based upon a set of high quality daily maximum and minimum air temperature records, from more than 250 ground-based weather stations. The time frame considered ranges from 1991 to 2020, providing information about the present Italian climate. Two analyses are reported. One is targeted to find local features and is based on single stations data. The second one looks for a global signal related to heat extremes. The double level of the information (local and global) can be very useful in the comparison of results. The two methods are in agreement in describing an increasing presence of heat extremes, in terms of overall frequency, duration and intensity. Some areas appear as hot spots for the observed tendencies.

Part of the gaseous carbon dioxide (CO₂) produced in karst soils / epikarst is transported into underground cavities / caves during the growing season by advective flux, diffusive flux, and flux associated with degassing of seeping water. In dynamic caves, accumulated CO₂ is released into the outside atmosphere during the autumn-winter period through advective flux associated with ventilation of the cave in the upward airflow mode. This case study from the Moravian Karst (MK) showed that the net weight of CO₂ released annually from the Sloup-Šošůvka Caves (total volume of 131,580 m³ and a total area of 17,950 m²) into the external atmosphere was 348 kg. Extrapolating this value to all known MK caves (area about 352,080 m²) yielded a total of CO₂ flux of 6820 kg yr⁻¹. This flux is representing only 0.024‰ of the annual soil respiration from entire MK area (about 2.81 × 10⁸ kg CO₂ yr⁻¹).

Pre-harvest yield forecasting is important for the sustainability of agricultural companies, enabling more sustainable economic decision-making. In the present study, we propose an approach based on the sum of degree days of the corn crop related to the dates of satellite images to organize the data of two crops generating predictive models with the k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost). The field study was carried out in a commercial area during the 2017/18 and 2018/19 harvests. Spectral data were obtained from Sentinel-2 satellite images. After the correction and processing of the images, the values ​​of the spectral bands and the vegetation indices were obtained. For the development of the models, the images obtained throughout the cycle were divided into three classes of the mean weeks before harvest (WBH) from different degree-days (GD) during the cycle, in this study we adopted 12 combinations of data inputs to develop the models. In yield forecasting, we were able to forecast approximately 30 to 70 days before harvest (500 to 900 degree-days before harvest), in addition, the most accurate models were when the data used as driven variables were the spectral bands of the red, blue, green and nir collected from 800 to 1200 degree-days of the culture (WBH4). For the models developed, combined with WBH for yield forecast, it was possible to forecast yield with an average error of 0.503 t ha-1, and the greatest precision and accuracy occurred with the use of all variables RGB e Near-infrared.

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.

Utilizing a decade-long observation from the spaceborne dual-frequency precipitation radar, this study investigates the seasonal variations and microphysical characteristics of precipitation in North China. The results elucidate that the mean storm top height (STH) attains its zenith during the summer, potentially linked to the pronounced strong evaporation and convection. An upsurge in STH is frequently correlated with the evolution of precipitation particles of greater dimensions for both convective and stratiform precipitation. This investigation further discerns that the predominant microphysical mechanisms underlying precipitation during the process from altitudes of 3 km to 1 km exhibit significant seasonal variations and are contingent upon the precipitation types. Collision and coalescence processes are identified as the predominant contributors to precipitation formation, whereas evaporative processes and particle size sorting are less significant. For the coalescence process, the lowest is from summer (29.24%) to autumn (38.01%), spring (50.84%) and winter (58.43%). Additionally, this study observes that the altitude of the melting layer in North China(3–4 km) is relatively lower than that in East China and Yangtze-Huai River Valley region(4.5 km), which may be ascribed to the higher latitude, resulting in comparatively lower temperatures aloft and thus a reduced height for the melting layer.