Theoretical and Applied Climatology最新文献

The simulation of rainfall interception by vegetation is essential to water resource management, considering both changing land use and climate change effects. In the rainfall interception models, the evaporation rate is frequently estimated by means of the Penman-Monteith method, but the Priestley-Taylor equation appears as a promising approach with fewer input requirements. In this study these both formulations were evaluated with the sparse Gash model with variable parametrization for estimating rainfall interception by four tree species in a Brazilian dry tropical forest. The Penman-Monteith equation was used with the canopy resistance set to zero, and the momentum method was applied for estimating the aerodynamic resistance. The Priestley-Taylor formulation was tested with the proportional coefficients (α) of 1.26 and 1.34. The results of rainfall predictions were compared with the measurements by statistical indicators, which pointed slightly favorable to Penman-Monteith method. The Priestley-Taylor with α = 1.26 resulted in predictions better than with α = 1.34. Most of the simulations were classified as good (CMRE varying from 5.5 − 9.3%). The Priestley-Taylor method can be used for estimating the evaporation rate in simulations based on the sparse Gash model with variable parametrization in the studied dry tropical forest, under situations with restrictions of micrometeorological measurements or minimal processing time requirement.

A comprehensive analysis of regional climate changes is essential in arid and semi-arid regions to optimize water resources management. This research aims to evaluate the changes in temperature and precipitation across the Mujib Basin in Jordan, using the most recent Coupled Model Inter-comparison Project Phase 6 (CMIP6) model. Firstly, the performance of six CMIP6 general circulation models (GCMs) to reproduce historical temperature and precipitation from 1985 to 2014 was evaluated using observed climate data. The most suitable GCM was then bias-corrected using the linear scaling approach. The findings demonstrate that the EC-Earth3–Veg model could reasonably simulate the historical climate pattern of the Mujib Basin, with coefficient of determination (R²) values of 0.90, 0.83, and 0.65 for monthly Tmin, Tmax, and precipitation, respectively. Under both the SSP2-4.5 and SSP5-8.5 scenarios, Tmax is projected to increase by 1.4 to 3.9 °C and 1.6 to 6.8 °C, respectively, whereas Tmin increases from 1.4 to 3.4 °C and 1.6 to 6.4 °C. Furthermore, precipitation is projected to decrease by 4.61–23.2% at the end of the 21st century. These findings could help policymakers in formulating better adaptation strategies to reduce the impact of climate change in Jordan This is a crucial step toward becoming a climate-resilient nation.

Snowfall in the Ohio River Valley, USA, presents a relatively unique challenge due to the large gradient of event frequency and magnitude, and subsequent levels of preparation within local communities. Even relatively small magnitude events can cause widespread impacts due to available infrastructure. Here we present a climatology of snowfall conditions and events over a 74-year period using a network of daily observational stations across the region. Snowfall totals and event frequencies both exhibit a southwest to northeast gradient of increasing snowfall, where the majority of snowfall (> 80%) occurs during the core winter months of December through February. There is a clear influence of Lake Erie on snowfall conditions in the northeast corner of the domain, where snowfall frequency, totals, and trends are substantially higher within the lake belt relative to areas further inland. Over time, snowfall significantly increased downwind of Lake Erie by as much as 42%, while significant decreases of over 55% occurred in central Tennessee and eastern Ohio. Intra-seasonally, snowfall totals trended significantly less during November and March for much of the domain, suggesting a compression of the snowfall season to more core winter months. Trends in snowfall frequency were apparent for many sub-regions, however evidence here suggests the trends in snowfall totals were primarily driven by trends in snowfall magnitude per event.

Precipitation plays an important role in the water resources system in Tarim Basin (TB), the most arid region in China. However, the source of water vapor for precipitation and its linkages with climate circulations still keep mysterious. Based on the observed precipitation of 21 stations from 1961 to 2020 in TB and the NCAR/NCEP reanalysis data, the temporal and spatial variations of summer precipitation in TB and their linkages with the westerly, Asian summer monsoons and extratropical circulation are investigated. The results show that: (a) the summer precipitation in TB changes consistently at all 21 stations, and exhibits an increasing trend (4.1 mm/decade) from 1961 to 2020 with large interannual fluctuations; (b) although the water vapor in TB mainly comes from the westerly circulation, the water vapor from the edges of Asians summer monsoons also plays an important role. It is unexpectedly found that the summer precipitation in TB is negatively correlated with the Westerly Index (WI). When the upstream westerly wind weakens, the northeast wind from the eastern boundary and the southwest wind from the southern boundary strengthen, which bring more water vapor from the edges of East Asian and South Asian summer monsoons respectively, leading to abundant precipitation in TB; (c) when the Eastern Atlantic/Western Russian pattern (EA/WR) is in extreme negative phase, there is an anticyclone anomaly near 55°N, 55°E, which weakens the upstream westerly wind and reduces the water vapor input of western boundary into TB. However, the water vapor input from the edges of the Eastern and Southern Asian summer monsoon strengthens, facilitating abundant precipitation in TB. This study provides a new insight into the joint influence of westerly, monsoons and extratropical circulation on summer precipitation in TB, which is meaningful for understanding the mechanism of change in summer precipitation and the trend projection of future water resources.

A statistical downscaling of wind and wave regimes is presented. The study is around the Cuban archipelago for the mid-term (2031–2060) and the long-term (2061–2090) with respect to the historical period 1976–2005. A multimodel ensemble of CMIP5 models under the RCP4.5 and the RCP8.5 scenarios is used. Projections of the wind and wave regimes are projected through the BIAS correction (delta and empirical quantile mapping), and multiple regression with a determination coefficient of 88.3%, a residual standard deviation of 0.11, and a square mean error of 0.29. According to the statistical downscaling, the mean annual wind speed and the wave height showed significant changes in the western part of the Cuban archipelago. The extreme indicators of climate change referred to significant wave height show similarity in the representation of the future Cuban marine climate, which would have the most accentuated changes on the north coast of the central and eastern regions.

Submesoscale motions may substantially influence similarity relationships within the Stable Boundary Layer (SBL), leading to considerable uncertainty in these relationships. Therefore, we conducted a comparison of similarity relationships within the SBL in the Taklimakan Desert before and after the removal of submesoscale motions, aiming to gain deeper insights into the impacts of submesoscale motions on the similarity relationships. We introduced a method utilizing Discrete Wavelet Transform with orthogonal wavelets to identify and filter out submesoscale motions. By investigating nocturnal observations from June 29 to July 31, 2021, daily from 22:00 to 07:00 local time, we tested and confirmed that submesoscale motions indeed exert a substantial influence on similarity relationships in different ways. After removing submesoscale motions, dimensionless wind velocity standard deviations become more consistent across different averaging periods, with notably higher Correlation Coefficients and lower Root Mean Square Errors. This highlights the effectiveness of the method in eliminating submesoscale motions. Submesoscale motions themselves do not exert a direct and significant influence on the flux–profile relationship for wind speed. It seems the enhanced turbulence intermittency induced by episodic submesoscale motions results in notable deviations from the Businger-Dyer relationship within the strong stable regime. The influence of submesoscale motions on intermittency appears more pronounced as stability increases. Submesoscale motions significantly influence the relationship between turbulence intensity and wind speed. The episodic submesoscale motions appear to be the direct cause for the presence of moderate turbulence intensity at low wind speeds. Horizontal wind velocity variances are mainly influenced by submesoscale motions, while vertical wind variance is predominantly associated with small-scale turbulence. These findings may contribute to a more accurate understanding of the impacts of submesoscale motions on similarity relationships in the SBL and provide genuine and stable similarity relationships of small-scale turbulence for SBL modeling.

The fundamental exchange of water for carbon lays the groundwork for understanding the interplay between carbon and water cycles in terrestrial ecosystems, providing valuable insights into global water and carbon balances and vegetation growth. Inherent water use efficiency (IWUE) was used as a study framework of the diurnal patterns and degree of coupling of carbon and water exchange to investigate the net ecosystem carbon exchange (NEE) responses of three water regime potato cropping systems [full-irrigation (FI), deficit-irrigation (DI), and rainfed (RF)] in Cundinamarca, Colombia. The eddy covariance method was used to determine CO₂ and water fluxes, surface resistances, and the omega decoupling factor (Ω). Additionally, leaf area index (LAI), and specific leaf area (SLA) were assessed to determine the canopy influence on carbon and water exchange. The highest carbon sink activity (NEE = -311.96 ± 12.82 g C m⁻²) at FI, is primarily attributed to a larger canopy with high autotrophic activity and low internal resistance. This supported a highly coupled and synchronized exchange between evapotranspiration (ET) and gross primary production (GPP), as reflected in the highest IWUE (4.7 mg C kPa s⁻¹ kg⁻¹ H₂O). In contrast, the lower sink capacity at DI (NEE = − 17.3 ± 4.6 g C m⁻²) and the net carbon source activity from RF (NEE = 187.21 ± 3.84 g C m⁻²) were related to a smaller leaf area available for water and carbon exchange, resulting in lower IWUE (2.3 and 1.01 mg C kPa s⁻¹ kg⁻¹ H₂O, respectively) and a decoupled and desynchronized gas exchange caused by unbalanced restrictions on ET and GPP fluxes. These results provide new information on carbon–water interactions in potatoes and improve the understanding of carbon sequestration and drought effects on potato sink activity.

Spring frost, is one of the important phenomena that damage agricultural production in cold areas. Predicting the occurrence of frost events can be valuable for managing and mitigating frost risk in orchards. In this study, copula models were applied to calculate the joint bivariate return period of frost event in both historical (1984–2014) and future (2023–2053) periods in Chadegan’s almond orchard. For the future period, a combination of 10 general circulation models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) under three Shared Socioeconomic Pathway scenarios (SSPs) SSP1-2.6, SSP2-4.5 and SSP5-8.8 was employed using a weighting approach. The results indicated that the Generalized Pareto (GP) and Inverse Gaussian were the best marginal distribution functions of the severity (S) and duration (D), respectively. The Frank copula best explained the relationship between severity and duration of frost event. According to the joint bivariate return period of frost event, the extreme frost occurred more frequently in the future period under three SSPs compared to the historical period. In both historical and future periods, in “AND” mode, the frost event with S ≥ 6 ̊C and D ≥ 4 ̊C days, would be more likely to return in 64.71 years and about 14 years, respectively. In "OR" mode, the joint bivariate return period of mentioned frost event increase slightly in future period (3 years for SSPs) compared to the historical (1.54 years). This probabilistic assessment was pointed as a strong toll for predicting the return period of frost event in Chadegan.

This study investigates the spatial patterns and variabilities of Seasonal Rainfall Onset Day (OD), Cessation Day (CD), Seasonal Length (SL), and Number of Rainy Days (RD) in Rwanda for the long rain season (LR) of March–April-May (MAM) and the short rain season (SR) of September–October-November–December (SOND). Data used, provided by the Rwanda Meteorology Agency, consisted of a time series of gridded rainfall and temperature from 1983 to 2021. The northern, western, and southwestern regions experience earlier OD than the remaining parts of the country, [mid-February, early March] for LR and [early September, mid-October] for SR. The entire eastern region experiences later OD ([mid-March, end March]) during LR. During SR, the central east and the southeastern regions experience later OD ([mid-October, end November]). During LR and SR, the mean SL and mean RD are highest in the northwestern and southwestern regions and lowest in the central-eastern and southeastern regions. In those regions, the mean SL and mean RD are higher during SR ([81, 116], [49, 74] days) than during LR ([77, 99], [46, 68] days). In the remaining parts of the country, they are lower during SR ([46, 81], [24, 49] days) than during LR ([55, 77], [24, 46] days). The temporal variability (coefficient of variation) is relatively high in different places. During LR, for OD ([21.5, 34] %) over the northwest, central plateau, and eastern regions, for SL ([22.5, 35] %) over the northern and eastern regions, and for RD ([24.5, 32] over the eastern region. During SR, for SL ([23, 31] %) over the southcentral and the central plateau regions, and for RD ([25.5, 38] %) over the northern, western, southern, and central plateau regions. The seasonal length and the number of rainy days are strongly dependent to rainfall intensity, but more dependent in short rains seasons. An investigation done El Nino and the Indian Ocean Dipole indicates that they may have an influence on the studied rainfall characteristics in Rwanda. Results from this study are important, as the country’s economy remains dependent on rain-fed agriculture. They will help farmers, policy and decision-making for appropriate adaptation and mitigation strategies and policies.

Climate change leads to changes in climatic variables, with rainfall being one of them. Changes in rainfall influence rainfall erosivity and subsequently erosion rates. This study analysed rainfall data from 1901 to 2017 in Odisha, focusing on different agro-climatic zones to discern annual rainfall pattern, its spatial variation, and trend, particularly concerning the rainfall erosivity factor and its impact on soil erosion and agricultural productivity. Notably, the Eastern Ghats Highland region received the highest average annual rainfall of 1578.5 mm, while the Western Undulating Zone received the lowest (1308.4 mm). The rainfall distribution showed spatial variability largely influenced by topography, with areas experiencing orographic lifting receiving higher rainfall. The study observed significant trend in annual rainfall, noting a maximum decline of 1.2 mm yr⁻¹ in the North Western Plateau, Western Central Table Land, and Western Undulating Zone, whereas the East and South Eastern Plain, Mid Central Table Land, North Eastern Coastal Plain, North Eastern Ghats, and South Eastern Ghats exhibited a noteworthy increase in annual rainfall (0 to 3.9 mm yr⁻¹). The decline in rainfall can result in the drying up of water bodies and reduced soil water availability to crop, thereby influencing agricultural production. On the other hand, areas with increased rainfall, may face extreme events which can aggravate soil erosion and thereby loss of soil fertility. Considering the scarcity of pluviographic data in countries like India, Modified Fournier Index (MFI) may be considered as one of the useful methods to capture rainfall’s aggressiveness towards soil erosion through rainfall erosivity (R-factor). Therefore, to evaluate potential soil erosion levels, the Modified Fournier Index method was employed, revealing varying degrees of soil erosiveness across different regions. The Eastern Ghats Highlands exhibited the highest erosion potential. The R-factor, aligned with these spatial patterns, with the Eastern Ghats Highland (12,965.4 MJ mm ha⁻¹ h⁻¹ yr⁻¹) and South Eastern Ghats (12,242.3 MJ mm ha⁻¹ h⁻¹ yr⁻¹) regions displaying the highest R-factor values. Furthermore, the research identified areas prone to soil erosion by overlaying R-factor, slope, and land use maps, highlighting vulnerable regions such as Eastern Ghats Highlands, North Eastern Ghats, South Eastern Ghats, and Western Undulating Zone. This comprehensive analysis allows for informed prioritization of conservation efforts and the implementation of appropriate measures like strip cropping of finger millet with groundnut, intercropping finger millet with hedgerows of Gliricidia and Leucaena, bio-engineering measures such as earthen or stone bunds with broom grass in arable land and growing of aromatic grasses like lemon and citronella grass, construction of staggered trenches in non-ar