Theoretical and Applied Climatology最新文献

Knowing the number of workable days (NWD) with agricultural machinery during a crop is crucial to mitigate structural soil degradation in conditions of insufficient moisture. Although soil moisture is the most important, field planning often only involves precipitation. This study aimed to quantify NWD for the main sugarcane producing municipalities in Brazil across different seasons, considering the water balance. Two criteria were adopted to determine NWD: one includes days suitable for agricultural activities with daily precipitation less than 5 mm and soil water content between 40 and 90% of available water capacity, while the other only integrates precipitation (< 5 mm). Thirty years of daily climate data were collected for Brazilian locations to calculate the water balance. Cluster analysis was applied to group similar localities based on meteorological and water balance components. The study began with a statistical analysis of variability between groups and intragroup of meteorological and water balance elements. Subsequently, NWD maps were created for the past 30 years (characterization) and the past 10 years (planning), both for dry and rainy periods. A comparative analysis of the two criteria to account NWD was conducted. Over the last decade, significant drought trends led to an approximately 10-day increase in NWD nationwide during both dry and rainy seasons. The NWD criteria choice significantly impacted results, depending on the region of the country, reaching differences of up to 60 days within a total period of 90 days. The criterion considering soil water content tended to underestimate workable days but closely aligned with agricultural reality.

The Mediterranean Coast in Egypt has witnessed a significant change in climate over the past two decades. However, relying solely on prognoses without applying rigorous statistical tests may lead to unreliable results. This research aimed to investigate the historical performance of the rainfall data trend and its change through the time and identify the change points along the Mediterranean coast area of Egypt in order to gain comprehensive insights into future changes. Thus, four tests were applied on the Global Precipitation Climatology Centre (GPCC) data with spatiotemporal resolution (0.25o, Month) to identify abrupt and continuous trends. The applied tests classified into two: parametric and non-parametric tests. Non-parametric tests, such as Mann–Kendall and Sen’s slope tests, were employed to assess trends in the data, while the Pettit test was used as a change point test. On the other hand, the parametric test employed the Buishand test to detect change points. The GPCC rainfall time series last version is available from 1900 until 2019, where those 119 years of time span are divided into three periods; (1900–1940), (1941–1980) and (1981–2019). The research offers a rigorous approach to understanding past trends and identifying change points, revealing decreasing trends in rainfall during 1900–1940 and 1981–2019. January and March had the highest decreases in these periods. 69% of stations showed a significant decrease in annual rainfall, mainly along the Mediterranean coast. Change points were identified in 1931 (delta region) and 1999 (Sinai), with no significant change in the West delta.

The terrestrial ecosystem stores a huge amount of carbon in biomass and acts as a major carbon sink. Gross primary productivity (GPP) measures the carbon assimilation rate in terrestrial ecosystems. Accurate quantification and spatiotemporal analysis of GPP have become the essential indicators of various management, policy developments, and restoration activities in recent decades with the installation of new ground stations, development of robust models, and use of Earth Observation satellite data. The model-estimated and satellite data-derived GPP values greatly differ from ground observations due to model structure and approach, input driving data, coefficients and parameters, and various assumptions. Consequently, considerable ambiguity prevails among datasets and their benchmarking. Moreover, the productivity of ecosystems is regulated by physiological traits coupled with the local environmental conditions. This review provides an overview of the environmental and anthropogenic variables that regulate productivity and pose challenges in GPP estimation and evaluation of the available GPP products. It also evaluates the various available GPP datasets/ products and estimation methods/ models and compares the ecosystem productivity in broad natural and human-modified ecosystems in South Asia. Moreover, this study includes a case study on evaluating five globally available GPP products with variable spatiotemporal resolutions, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation Photosynthesis Model (VPM), the Global Land Surface Satellite (GLASS), Global OCO-2-based SIF product (GOSIF), and the Penman-Monteith-Leuning (PML) in three major land cover type of South Asia (forest, cropland, and grassland) comparing with eddy covariance (EC) flux tower data. Results showed a better performance of GOSIF and GLASS data than other GPP products. The study aims to provide an overview of the prevailing environmental conditions and carbon sequestration in different ecosystems and assist in developing sustainable landscape management strategies to reduce carbon emissions and mitigate climate change impact.

Reference Evapotranspiration (RET) is one of the fundamental variables for water resource management and finds applications in various fields such as irrigation sciences, environmental studies, and hydrology. However, its calculation can be complex, leading to research delays or reliance on outdated datasets, which may introduce errors in the results. Therefore, in this study, we developed an online software named GEE RET for calculating RET and accessing meteorological data based on Google Earth Engine. The aim of GEE RET was firstly to create a simple and efficient tool for calculating and accessing RET and meteorological data globally and online; secondly, to access long-term RET in the absence of meteorological data; and thirdly, to generate RET in areas where weather stations are not available. In GEE RET, the complex steps of calculating RET are performed through cloud processing, and users can access these data simply by specifying their desired area. Additionally, GEE RET provides access to reference and actual evapotranspiration from the WaPOR dataset. Achieving these goals assists researchers in easily accessing meteorological variables and RET at any point on Earth without the need for learning various sciences such as remote sensing or programming and without the need for processing large volumes of data, while also facilitating the use of up-to-date data. This software is freely accessible on the website [https://rahmanbarideh.users.earthengine.app/view/ret].

Bangladesh, a sub-tropical monsoon climate with low-lying areas, is very susceptible to the impacts of climate change. However, there has been a shortage of studies about the periodicity and projected changes in extreme temperature in this area, which is a crucial part of adapting to climate change. A study employed a multimodal ensemble (MME) mean of 13 bias-corrected CMIP6 GCMs to fill this knowledge gap. The purpose of this study was to project changes in 8 extreme temperature indices (ETIs) across Bangladesh for the near future (2021–2060) and far future (2061–2100) under two different Shared Socioeconomic Pathways (SSPs): medium (SSP2-4.5) and high (SSP5-8.5) scenarios. The research analyzed the average spatiotemporal changes by considering the reference period from 1995 to 2014 for each indicator in future periods. The results indicate that Bangladesh is projected to see a rise in average annual temperature in the 21st century, aligning with the global average. Warm days (TX90p) and nights (TN90p) were projected to increase, while cold days (TX10p) and nights (TN10p) were expected to decrease across the country for both the near (2021–2060) and far future (2061–2100). The projected highest increase in TX90p and TN90p was 6.90 days/decade in the northeast, and the highest decrease in TX10p and TN10p was 6.22 days/decade in the southwest. The study revealed a higher rise in TN90p than TX90p, indicating a faster decline in cold extremes than a rise in hot extremes. The rising temperature would cause an increase in the spell duration index (WSDI) and growing degree day (GDD) by 5–6 and 6–7 days/decade, respectively. Therefore, immediate measures must be taken to mitigate the detrimental effects of extreme temperatures, leading to heat stress. To reduce the effects on agriculture, ecosystems, human health, and biodiversity, policymakers and stakeholders must understand these anticipated changes and adopt appropriate actions.

In Indian cities, where streets are the only affordable public space, thermal-climatic conditions have a significant impact on pedestrian activity and comfort. However, narratives are insufficient on pedestrian risk assessment in asymmetrical urban settings. Therefore, current study investigates the potential of thermal stress mitigation in the context of human biometeorological assessment in asymmetrical urban settings of Bhopal city. It initiates with the selection of a commercial street in tropical climate of Bhopal (Koppen climatic classification, Aw) with the measurement of its metrological and morphological attributes. Furthermore, it leads to an assessment of thermal stress utilizing physical survey and Envi-met simulations including the identification of critical spots. Finally, development of iterated scenario considering one major and local street with five varied street sections in symmetrical/asymmetrical condition for the EW, NWSE and NS orientation. The efficiency of mitigation measures in cooling the outdoor stress area was analysed by using Universal Thermal Climate Index (UTCI) along with mean radiant temperature (MRT) from a spatiotemporal perspective. The highest stress reduction was observed in higher asymmetrical section while lowest was recorded by lower symmetrical section. However, it is recommended to integrate high asymmetrical sections in complex urban area which can provide a better reduction (average UTCI by 3 °C, average MRT by 7 °C) to outdoor stress due to their ability of regulating efficient wind flow and shielding radiation. The evidence-based selection of street orientation and openness can adopt to optimize the urban configuration in similar climates to improve streetscape and activities from an environmental quality perspective.

Forests in Brazil play a crucial role in maintaining ecological balance and the environment, but this has been threatened by deforestation, forest fires, and the effects of climate change. Among these, forest fire has happened frequently in areas where there have been changes in land use for activities of agriculture and livestock farming, that motivate the destruction of forests particularly in biomes like the Amazon and Cerrado. In those biomes, the forest fires initiated to clear land for pasture are the most worrying because promote ecological and socioeconomic consequences, contributing to greenhouse gas emissions and impacts the regional flora and biogeochemical cycles. As a strategy to understand and identify areas at risk of forest fires, this study aimed to develop a risk zoning framework for fire hotspots in the biomes of Brazilians. This framework combines multiple variables, incorporating factors like physical terrain, land use, and climatic data, to assess the potential fire risk. The areas with greater fire risk are located in the Caatinga, Cerrado, and Pantanal biomes, in which the physical and climate variables influence directly in incidence and propagation of fire. In the Amazon biome there is a fire risk, some possibly intentional, but can be regulated by elevated precipitation in the region. The identification of areas at high fire risk allows the implementation of proactive strategies for fire prevention for safeguarding Brazil’s biomes and ecosystems, which are integral to the environment and biodiversity.

Urban heat islands, exacerbated by climate change, have become a pressing issue as summer temperatures rise. This study uses data mining techniques to classify the thermal impact of tree shade on building façades in the urban area of a tropical city. Our objective was to develop models to assist stakeholders and policymakers in forecasting the thermal impact of solar orientation and tree shade on building façades in the urban areas of a tropical city. Minimum and maximum infrared surface temperatures were registered in health clinics’ façades in Teresina, Brazil. Random forest methodology was applied to develop classifying models. This technique, known for its robust classification and prediction of categorical variables, offers a significant advantage over other modeling methods. Key input variables included façade infrared surface temperature, solar orientation, environmental temperature, relative humidity, and the extent of tree shade. Critical attributes were identified as solar orientation (North, South, East, and West), tree shade, and façade temperature (maximum and minimum). Two tree-ensemble models were selected for an accuracy rate of 88% and Kappa (κ) = 0.86. The models indicate that tree-ensemble methods can accurately classify and predict the thermal impact of tree shade on building façades. Additionally, the method effectively identified and ranked the factors influencing thermal impact, providing users with reliable predictive capabilities.

The present study analyzed sensitivity of flow parameters using SWAT and effect of climate change on surface water resources, considering uncertainty with Monte Carlo. For this purpose, output of nine-model related to fifth-climate change-report during baseline period 1971–2000 was weighted. Using Monte Carlo, 100 samples of probabilistic distribution of basin temperature and rainfall were generated. LARS-WG model was used for downscaling, then temperature and precipitation were calculated under RCP2.6 and RCP8.5 for future periods 2040–2069 and 2070–2099. SWAT was calibrated using observational-data and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) (NCEP CFSR) global climate datasets and sensitivity of parameters affecting flow was analyzed. Results showed that observed-data had better performance than NCEP CFSR. Finally, future runoff was calculated under RCP2.6 and RCP8.5 for 2040–2069 and 2070–2099. Results showed that average annual runoff decreased by 84, 80, 82 and 80%, for 2040–2069 (RCP2.6 and RCP8.5) and for 2070–2099 (RCP2.6 and RCP8.5) relative to baseline, respectively.

Precipitation records in North Patagonia (Argentina) are scarce, which hinders climate research. This research aims to assess the performance of four novel local monthly datasets together with three commonly used global datasets for North Patagonia to evaluate the advantages and disadvantages of each one. First, four different local observed-interpolated datasets were built, two using the Angular Distance Weighted (ADW) method and two using ordinary Kriging. In addition, the global datasets CRU, ERA5-Land, and GPCC were evaluated. To assess the performance of the precipitation datasets, four metrics were used to evaluate the systematic errors (bias), the mean errors, the representation of time variations, and the representation of the probability density function. The ERA5-Land with a correction factor stands out as the best global dataset and it also presents the overall best representation of the probability density function (PDF). The built dataset with ADW using a precipitation index presents the overall best performance, especially in representing the time variations. Even though ADW presents an overall better performance, ERA5-Land with a correction factor presents a better performance in terms of errors in the southern region (south of 40°S). The novel dataset is freely available through the link provided in the conclusions section.