Daily Temperature Data Research Articles

Variation characteristics of extreme climate events in Southwest China from 1961 to 2017

Climate change is increasing the intensity of extreme climate events. Significant impacts of extreme climate events on human society and ecosystem have occurred in many places of the world, for example, Southwest China (SWC). In this study, the daily temperature and precipitation data from 438 meteorological stations are used to analyze the variation characteristics of extreme climate events in the SWC from 1961 to 2017. The annual extreme warm events show a significant increasing trend at 99% confidence level at most stations, and a few stations with a decreasing trend are mainly located in the southern Sichuan Province, the northern Yunnan Province and the western Guizhou Province. Meanwhile, the annual extreme cold events show a significant decreasing trend at 99% confidence level at most stations, and a few stations with an increasing trend are mainly distributed in the Sichuan Basin. Both the annual extreme heavy precipitation indexes and rainstorm indexes show nonsignificant increasing trends, but they differ greatly in the spatial distribution. These indexes in the western Tibet, Chongqing and most parts of Guizhou show significant increasing trends at 95% confidence level, while those in the central Sichuan and southeastern Yunnan show significant decreasing trends. The percentage of extreme heavy precipitation shows a significant increasing trend at 99% confidence level, especially in the northeastern Sichuan, the central-eastern Guizhou and the central Yunnan. Overall, under the background of global warming, the extreme warm events in SWC increase significantly from 1961 to 2017, and the extreme cold events decrease significantly. The variation trends of extreme precipitation events differ greatly in different regions, and the percentage of extreme heavy precipitation increases significantly.

Re-defining the Climatic Zones over Nigeria

Aims: This study aimed at identifying climatic zones over Nigeria.
 Duration of Study: Daily air temperature data for the 36 states and FCT in Nigeria were obtained from National Aeronautic and Space Administration (NASA) for the period of thirty-seven (37) years (1984-2020).
 Method: Several hierarchical clustering procedures–single linkages, complete linkage, average distance within clusters, average distance between clusters, centroid clustering, median linkage and Ward’s method were used in this study.
 Results: Based on the findings, median linkage, complete linkage and Ward’s clustering method solutions seems more realistic. The application of cluster analysis, revealed five climate zones (cluster) over Nigeria. Cluster 1 covers Plateau state only while cluster 2 covers three south-west states and one north-west state (Kaduna). Cluster 3 covers all the states in south-east, south-south (except Cross River), North-central (except Kogi and Kwara), Bauchi in North-west and ogun and Oyo in South-west. Cluster 4 covers one state in south-south (Cross River), South-south (Lagos), two states in North-central (Kogi and Kwara), three states in North-east (Adamawa, Gombe and Taraba) and four states in North-west (Jigawa, Kano, Kastina and Zamfara). Lastly cluster 5 covers two states in North-east (Borno and Yobe) and North-west (Kebbi and Sokoto).
 Conclusion: Our findings clearly show that cluster analysis can be applied to identify similar weather/climate state from air temperature data.

Research on thermal infrared anomaly characteristics of moderate strong earthquakes in northeast China

In this article, the daily brightness temperature data from January 2006 to May 2020 of China’s geostationary meteorological satellite FY-2E/G were used to identify the brightness temperature differences before deep and shallow earthquakes in the study area using wavelet transform and the relative wavelet power spectrum (RWPS) methods. The objective was to explore the characteristics of thermal infrared (TIR) radiation anomaly changes before deep and shallow earthquakes in Northeast China by carrying out anomaly extraction and data analysis. The research has shown that five significant earthquakes experienced TIR radiation anomalies in the vicinity of the epicenter approximately 1–2 months before the event. The amplitude of the anomaly ranged from seven to twenty times higher than average, and the anomaly lasted about 3 months. The infrared radiation anomaly characteristics before the earthquake were especially significant in the case of two earthquakes in the Songyuan area. From the research, it was concluded that the TIR radiation anomaly could act as a short-term precursor for earthquake prediction. The method employed in this study would provide great support for predicting deep and shallow earthquakes in Northeast China using satellite thermal infrared technology.

Global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019: a multi-country time-series study

The global spatiotemporal pattern of mortality risk and burden attributable to tropical cyclones is unclear. We aimed to evaluate the global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019. The wind speed associated with cyclones from 1980 to 2019 was estimated globally through a parametric wind field model at a grid resolution of 0·5° × 0·5°. A total of 341 locations with daily mortality and temperature data from 14 countries that experienced at least one tropical cyclone day (a day with maximum sustained wind speed associated with cyclones ≥17·5 m/s) during the study period were included. A conditional quasi-Poisson regression with distributed lag non-linear model was applied to assess the tropical cyclone-mortality association. A meta-regression model was fitted to evaluate potential contributing factors and estimate grid cell-specific tropical cyclone effects. Tropical cyclone exposure was associated with an overall 6% (95% CI 4-8) increase in mortality in the first 2 weeks following exposure. Globally, an estimate of 97 430 excess deaths (95% empirical CI [eCI] 71 651-126 438) per decade were observed over the 2 weeks following exposure to tropical cyclones, accounting for 20·7 (95% eCI 15·2-26·9) excess deaths per 100 000 residents (excess death rate) and 3·3 (95% eCI 2·4-4·3) excess deaths per 1000 deaths (excess death ratio) over 1980-2019. The mortality burden exhibited substantial temporal and spatial variation. East Asia and south Asia had the highest number of excess deaths during 1980-2019: 28 744 (95% eCI 16 863-42 188) and 27 267 (21 157-34 058) excess deaths per decade, respectively. In contrast, the regions with the highest excess death ratios and rates were southeast Asia and Latin America and the Caribbean. From 1980-99 to 2000-19, marked increases in tropical cyclone-related excess death numbers were observed globally, especially for Latin America and the Caribbean and south Asia. Grid cell-level and country-level results revealed further heterogeneous spatiotemporal patterns such as the high and increasing tropical cyclone-related mortality burden in Caribbean countries or regions. Globally, short-term exposure to tropical cyclones was associated with a significant mortality burden, with highly heterogeneous spatiotemporal patterns. In-depth exploration of tropical cyclone epidemiology for those countries and regions estimated to have the highest and increasing tropical cyclone-related mortality burdens is urgently needed to help inform the development of targeted actions against the increasing adverse health impacts of tropical cyclones under a changing climate. Australian Research Council and Australian National Health and Medical Research Council.

Future Scenarios of Bioclimatic Viticulture Indices in the Eastern Mediterranean: Insights into Sustainable Vineyard Management in a Changing Climate

The evaluation of bioclimatic viticulture indices (BVIs) zones, similar to any other crop, necessitates a comprehensive understanding of the spatial variability of climate data. This study focuses on assessing the suitability of BVIs in the Jabal Al Arab region, a significant viticulture area in the Eastern Mediterranean. The aim is to analyze four temperature-based bioclimatic indices and the hydrothermal coefficient (HTC) to map their patterns and spatial variation across the region under climate change scenarios. Daily temperature data from 15 meteorological stations and 57 rain gauges spanning 1984–2014 were utilized, along with downscaled future scenarios (the Representative Concentration Pathways (RCPs) based on the second generation Canadian Earth System Model (CanESM2)) for 2016–2100. Additionally, statistical analysis and hybrid interpolation (regression-kriging) were employed to accurately map the BVIs throughout the region. The results reveal substantial spatial variability in Jabal Al Arab’s climate, with heat accumulation and the hydrothermal index during the growing season significantly influenced by elevation and distance to the seacoast. Additionally, the viticulture zones vary based on the specific index used and the projected future climate scenarios compared to the current climate. Climate change projections indicate a trend toward warmer conditions in the future. Under the RCP scenarios, the region can be categorized into up to three bioclimatic classes for certain indices, in contrast to the current climate with six classes. These findings offer valuable insights into viticulture suitability within each climatic region and facilitate the identification of homogeneous zones. By employing consistent bioclimatic indices and advanced hybrid interpolation techniques, this study enables meaningful comparisons of Jabal Al Arab with other viticulture regions worldwide. Such information is crucial for selecting suitable grapevine varieties and assessing the potential for grape production in the future.

Variations of Extreme Temperature Event Indices in Six Temperature Zones in China from 1961 to 2020

In this study, eight extreme temperature event indices were calculated based on daily maximum, minimum, and mean temperature data recorded at 699 National Reference Stations in China during 1961–2020. The yearly change of mean temperature and the magnitude, frequency, and duration of extreme temperature events in six temperature zones were evaluated. All temperature zones had a trend of an increase in mean temperature (rate: 2.1–3.3 °C per 10 years), and the warming was more significant in the warm temperate zone and the Qinghai–Tibet Plateau zone (QPZ). For extreme temperature events, the extreme maximum and minimum temperatures in most temperature zones showed significant trends of increase, and the rates of increase were greater in the northern zones and QPZ. The rate of increase in extreme minimum temperature was substantially (up to three times) higher than the rate of increase in extreme maximum temperature in the same temperature zone; however, the finding was the opposite for the cold temperate zone (CTZ), which is the northernmost region of China. The rate of increase in extreme maximum temperatures was the greatest (0.35 °C per 10 years), whereas the rate of increase in extreme minimum temperatures was the smallest (0.17 °C per 10 years). The number of warm days/nights and the warm spell duration index also showed significant trends of increase that were most obvious in the southern zones and QPZ. In the tropical zone (TZ), which is the southernmost part of mainland China, the number of warm nights was only 15.3 days in 1961–1970, whereas it increased to 61.9 days in 2011–2020 (an increase of 303.9%). The rate of increase in warm nights in TZ (8.8 days per 10 years) was four times that in CTZ (2.2 days per 10 years). The number of cold days/nights and the cold spell duration index showed significant trends of decrease, with the greatest rates of reduction in QPZ and TZ. In evaluating the frequency of extreme temperature events, the amplitude of warming of the night index was found to be greater than that of the day index. In evaluating the duration of extreme temperature events, the variation of the cold index was found to be greater than that of the warm index. The notable asymmetries found in the variations of the minimum/maximum temperatures, day/night indices, and cold/warm spell durations in China are direct manifestations of global warming.

The Impact of Extreme Temperatures on Grain Production in the Huaihe River Basin

The Huaihe River Basin is an important major grain producing area in China, maintaining the stability of grain output is of great significance to food security. However, with global warming, the frequency of extreme temperature events has increased, and the frequent meteorological disasters have brought great challenges to the stable supply of food production. In this paper, the impact of extreme temperature on grain production in the Huaihe River Basin was analyzed by using the daily temperature and agricultural production related data of 28 prefecture-level cities in the Huaihe River Ecological Economic Belt from 2001 to 2020. The following conclusions are drawn: 1) The influence coefficient of extreme high temperature on grain production in the Huaihe River Basin is -0.021, extreme low temperature has no significant impact on food production. 2) The effects of extreme temperature on food production in different regions in the river basin were heterogeneous, but the overall impact was negative. The influence coefficients of extremely high temperature days on the eastern region and Midwest region were -0.035 and -0.052, respectively, while the influence coefficients of extremely low temperature days on the eastern region were -0.413. The results showed that the increase in the number of extremely high temperature days would reduce production in the eastern region andMidwest region of the basin, while the northern region was not significantly affected by extreme heat. The increase in the number of extremely low temperature days will reduce grain production in the eastern region, and the impact on the Midwest region and the northern region will not be significant. Based on the above conclusions, three suggestions are put forward to maintain the safety of food production in the Huaihe River Basin: 1) enhance the ability to detect climate change in the basin; 2) Protect the stability of grain cultivated land in the river basin; 3) Innovative breeding of climate-resilient food crop seeds.

Trends and impacts of climate change on crop production in Burkina Faso

Abstract Understanding past climate trends and their impacts in the Sahel region is fundamental for climate change (CC) adaptation and mitigation. This study analyses climate trends from 1961 to 2020 in three climatic zones in Burkina Faso and the impacts of CC on five major crops production. Long time series of daily rainfall and temperature data from National Meteorology Agency for the period 1961 to 2020 has been compiled. Crop production data (1984–2020) were retrieved from the agriculture department. Climate temporal variations in each climatic zone were analyzed using extreme climate indices and principal component analysis. Linear regression was used to assess climate impacts on crop production. The results showed a high rainfall variability and changes in temperature extremes in the three zones. The climate window, 1991–2020, was hotter than 1961–1990, while the last decade (2011–2020) was the wettest. Most climate indices (67%) showed significant correlations with crop yields. Dry spells, cool days, cold nights, average daily wet days and rainfall intensity showed positive and negative effects on maize, cowpea, millet and sorghum yields. This study highlights the importance of climate-smart policy promoting drought-resistant and short-duration varieties in addressing the adverse effects of CC on crop production.

An empirical model to calculate snow depth from daily snow water equivalent: SWE2HS 1.0

Abstract. Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R2) of 0.97, and bias of −0.3 cm; it is able to achieve RMSE of 20.5 cm, R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R2 of 0.97, and bias of −0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.

Comparison of bias correction methods for climate change projections in the lower Shivaliks of Punjab

Abstract The study evaluates the performance of bias correction techniques by dividing the observed climate data period into calibration and validation sets. For this purpose, the daily data of temperature, rainfall, and solar radiation from 2010–2095 for lower Shivaliks of Punjab (Ballowal Saunkhri) were downloaded from Marksim weather generators using outputs of CSIRO-Mk3-6-0 climate model under four RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). The bias correction of model data (temperature, rainfall, and solar radiation) was done by developing correction functions (using a model and observed data from 2010 to 2015) from different bias correction methods (difference method, Leander and Buishand method, modified difference method, linear scaling, variance scaling, and quantile mapping). The corrected model data for the year 2016–2020 were validated against the observed data. The difference method was found to be best for bias correction due to low error and high efficiency. The corrected future model data (2021–2095) analysis on an annual and seasonal basis predicted a rise in maximum temperature and minimum temperature by 1.3–2.8 °C and 0.5–3.0 °C, respectively, under different scenarios. The study predicted more increase in rainfall and solar radiation under RCP 8.5 followed by RCP 6.0, RCP 4.5, and RCP 2.6 scenarios.

Gender inequalities in heat-related mortality in the Czech Republic

It is acknowledged that climate change exacerbates social inequalities, and women have been reported as more vulnerable to heat than men in many studies in Europe, including the Czech Republic. This study aimed at investigating the associations between daily temperature and mortality in the Czech Republic in the light of a sex and gender perspective, taking into account other factors such as age and marital status. Daily mean temperature and individual mortality data recorded during the five warmest months of the year (from May to September) over the period 1995–2019 were used to fit a quasi-Poisson regression model, which included a distributed lag non-linear model (DLNM) to account for the delayed and non-linear effects of temperature on mortality. The heat-related mortality risks obtained in each population group were expressed in terms of risk at the 99th percentile of summer temperature relative to the minimum mortality temperature. Women were found generally more at risk to die because of heat than men, and the difference was larger among people over 85 years old. Risks among married people were lower than risks among single, divorced, and widowed people, while risks in divorced women were significantly higher than in divorced men. This is a novel finding which highlights the potential role of gender inequalities in heat-related mortality. Our study underlines the relevance of including a sex and gender dimension in the analysis of the impacts of heat on the population and advocates the development of gender-based adaptation policies to extreme heat.

Ambient cold exposure amplifies the effect of ambient PM1 on blood pressure and hypertensive disorders of pregnancy among Chinese pregnant women: A nationwide cohort study

BackgroundLittle evidence exists regarding the combined effect between ambient temperature and air pollution exposure on maternal blood pressure (BP) and hypertensive disorders of pregnancy (HDP). ObjectivesTo assess effect modification by temperature exposure on the PM1-BP/HDP associations among Chinese pregnant women based on a nationwide study. MethodsWe conducted a cross-sectional country-based population study in China, enrolling 86,005 participants from November 2017 to December 2021. BP was measured with standardized sphygmomanometers. HDP was defined according to the American College of Obstetricians and Gynecologists' recommendations. Daily temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. PM1 concentrations were evaluated using generalized additive model. Generalized linear mixed models were used to examine the health effects, controlling for multiple covariates. We also performed a series of stratified and sensitivity analyses. ResultsThe pro-hypertensive effect of PM1 was observed in the first trimester. Cold exposure amplifies the first-trimester PM1-BP/HDP associations, with adjusted estimate (aβ) for systolic blood pressure (SBP) of 3.038 (95 % CI: 2.320–3.755), aβ for diastolic blood pressure (DBP) of 2.189 (95 % CI: 1.503–2.875), and aOR for HDP of 1.392 (95 % CI: 1.160–1.670). Pregnant women who were educated longer than 17 years or living in urban areas appeared to be more vulnerable to the modification in the first trimester. These findings remained robust after sensitivity analyses. ConclusionsFirst trimester maybe the critical exposure window for the PM1-BP/HDP associations among Chinese pregnant women. Cold exposure amplifies the associations, and those with higher education level or living in urban areas appeared to be more vulnerable.

A descriptive study of weaned piglet transport practices in Alberta, Ontario, and Saskatchewan, Canada between 2014 and 2018.

Canadian transport practices for shipments of newly weaned piglets are not well-described despite documentation requirements for those conducting the movement of these animals. The objective of this study was to describe the characteristics of weaned piglet transport events that occurred between 2014 and 2018 using records provided by five Canadian swine companies. Following cleaning and validation, the dataset included records from 6203 transport events involving the transport of approximately 6.9 million piglets (5.7kg, 4.1-7.9kg) from 62 origin sites in Alberta, Ontario, or Saskatchewan, Canada. This represents approximately 4.7% of the piglets estimated to have been weaned in Canada between 2014 and 2018, and 1.7% of sow farms in Canada according to 2016 National census data. Most transport events ended at farms in Canada (71.3%), while the remaining delivered piglets to one of eight American states. The predominant trailer types used were Straightdeck (51.4%) and Potbelly (36.6%), but this did not reflect the number of piglets transported as Potbelly trailers have greater load capacity. Transport events most frequently involved loading piglets from one origin barn and delivering them to a single destination barn (78.1%). Only transport events involving export to the United States picked up piglets from, or delivered them to, more than one farm site. Most transport events had very short trip distances (median distance: 48.0km; IQR: 497.0), but a marked range was observed (1.8-2931.2km). Average daily temperature data matched to the transport records by origin and destination location demonstrated ambient environmental conditions during these transport events ranged from -30.3-28.7°C. Overall, less than 10% of transport events had mortality occur. Comparable with other observational studies documenting weaned piglet mortality, the average in-transit mortality rate observed over the multiple seasons, companies, trip distances, and other characteristics in this dataset was 0.027%. However, instances of mortality over 1% did sporadically occur and could translate to considerable losses given the large load sizes common for piglets of this age and size (median load size: 1105 piglets; IQR: 1036 piglets). These data provide a better understanding of the interconnectedness of the Canadian swine industry as well as common transport practices which may inform future research on disease transmission in swine transport networks, or piglet welfare during transport. Additionally, variables that were not present in this dataset that would further strengthen these types of investigations are highlighted (e.g., space allowance).

Fractional coefficient for estimating physiologically meaningful and diurnal transpiration cycle weighted VPD from daily ambient environmental data

Appropriate representation of environmental conditions via meaningful metrics is critical for their effectiveness in modeling crop physiological and resource use processes. Vapor pressure deficit (VPD) is a primary determinant of crop water use efficiency, commonly reported as daily average. However, VPD can vary substantially during the day in relation to diurnal cycle of transpiration, in which case daily average VPD may not suffice. In such scenarios, integrating VPD over the course of diurnal transpiration cycle into a weighted VPD (VPDw) can be physiologically meaningful as a metric. The goal of this research is to characterize the fractional coefficient (Frac) that can be used to effectively calculate VPDw from daily temperature and relative humidity data without the need for sub-daily environmental data. Site-specific and seasonal estimates of Frac, which represent the relative positioning of VPDw to diurnal extremes of saturation vapor pressure are currently lacking for semi-arid regions. To address this, we quantified Frac at 101 sites in the U.S. Central Plains using long-term (1982–2019) hourly and daily radiation and VPD datasets. Frac varied substantially across the region, with growing season mean values ranging from 0.59 to 0.92. Summer months had the highest Frac values with relatively lower uncertainty as compared to lower Frac during winter. Local observations of Frac deviated significantly from previously assumed stationary estimates implying that site-specific and month-specific Frac may be necessary for robust water use estimations. VPDw measured at an experimental site in Central U.S. Plains explained greater variance in daytime crop transpiration than daily mean VPD in maize, soybean, and sorghum canopies. Site-specific Frac was also assessed to perform better than commonly used Frac estimate of 0.75 to explain day-to-day variation in transpiration, demonstrating effective use of long-term mean Frac values presented here to estimate VPDw using daily datasets.

Intraseasonal variations in winter surface air temperature over China and its prediction skill in ECMWF System5

AbstractBased on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well.

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

Examining trends in temperature and precipitation mean/extremes over Gandaki Province, Nepal

Abstract Despite the increased frequency of extreme climate events including their significance in Nepal's socio-economy, climate studies have seldom considered extremes, and even fewer have considered them in combination with temperature and precipitation. This study aimed at examining the trend of climate variables in Gandaki Province, Nepal. Daily temperature and precipitation data of five stations between 1990 and 2020 were analyzed. Modified Mann–Kendall and Sen's slope methods were used to detect trend and magnitude. The Mann–Whitney–Pettitt test was used to detect abrupt changes, and the Pearson correlation coefficient was used to find the correlation. The result showed an increasing trend and a significant abrupt change in the maximum temperature for all stations. A decreasing trend in the minimum temperature was observed in the Himalayas and the Hill region, whereas an increasing trend was seen in Siwalik and Terai regions. The Jomsom station, however, behaved differently by showing an increasing trend in precipitation and the number of rainy days. The majority of the temperature indices showed an increasing trend unlike precipitation indices, which showed a mixed result. The maximum five-day precipitation and consecutive dry days showed a significant positive correlation with altitude. The results indicate an increase in the frequency and intensity of extreme climate conditions in Gandaki Province.

Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Accurate estimation of potential evapotranspiration (ET0) is important for the sound design of irrigation schedules, management of water resources, assessment of hydrological drought, and research on atmospheric variations. The present study proposed a novel deep learning (DL) approach for daily ET0 estimations with limited daily climate data: HS- LSTM. This approach was constructed based on a classic ET0 model and a long short-term memory neural network (LSTM). Specifically, the Hargreaves-Samani (HS) model was employed as the classic model, and the predictors were restricted to the daily maximum and minimum air temperature data. Ground truth data for ET0 were employed to train, validate, and test the models. Traditional machine learning (ML) algorithms comprising adaptive neuro-fuzzy inference system (ANFIS), genetic programming (GP), multi-gene genetic programming (MGGP), and one-dimensional CNN (1D-CNN), as well as the HS-ML models (HS-ANFIS, HS-GP, HS-MGGP, HS-1D-CNN), were also established and assessed for daily ET0 estimations. Compared to the other tested approaches, the errors of the HS-LSTM technique significantly decreased, demonstrating that the novel HS-LSTM approach significantly outperformed the other techniques beyond the study area (in Songliao Basin, Northeast China, which is a semi-humid zone with temperate continental climate). The developed models can then be used to estimate future ET0 with only air temperature forecasts, which can be readily obtained from public weather forecasts. The present study provides a new and promising strategy that can provide more accurate estimations of daily ET0 with limited meteorological data, along with significant implications for enhancing atmospheric research.

Extreme Heat Events over Southeast Europe Based on NEX-GDDP Ensemble: Present Climate Evaluation and Future Projections

Southeast Europe is considered one of the most vulnerable regions in the context of climate change, and projected future summer warming is expected to exceed global rates significantly. Despite the importance of this problem, there have been few studies that utilized Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Circulation Models (GCMs) and the multi-model ensemble approach to examine extreme heat events on a regional scale. Additionally, the NEX-GDDP dataset, successfully applied in other parts of the world to assess extreme heat, has not yet been utilized for Southeast Europe. This study aims to fill that gap, presenting the time evolution and spatial distribution of extreme heat events in Southeast Europe for the historical period 1950–2005 and for the expected future climate up to the end of the 21st century based on the NEX-GDDP dataset. In order to leverage the strengths of the multi-model ensemble approach, a set of purpose-tailored indicators, such as the annual number of hot days, the maximum number of consecutive hot days, and hot spell duration at different thresholds, is computed by the daily maximum temperature data from all datasets, produced by NEX-GDDP (21 for the historical period and 42 for the future period). The E-OBS dataset is used as a reference for evaluating the NEX-GDDP’s capability to simulate the features of the observed historical extreme heat events. The results show that the multi-model ensemble can satisfactorily capture the occurrence of extreme heat events in the historical period, and therefore it is reasonable to assume that the NEX-GDDP dataset has the potential to reproduce such extremes in the projected future. The study provides clear evidence that the persistence and spatial extent of extreme heat will increase significantly. Some indicators that were not relevant for the historical period due to the high-temperature threshold will become helpful in assessing extreme heat in Southeast Europe in the latter part of the century. Thus, under the RCP8.5 scenario, the area-averaged duration of hot spells at 32 °C and 34 °C will increase from near zero in 1976–2005 to 60 and 45 days, respectively, by the end of the century. The indicators used in the study may be helpful for decision-makers to implement climate change mitigation strategies and actions adequately. The findings are consistent with general tendencies in maximum temperatures considered in our previous works but also with the outcomes of recent studies dedicated to the future climate of the region.

Climate Potential for Apple Growing in Norway—Part 1: Zoning of Areas with Heat Conditions Favorable for Apple Growing under Observed Climate Change

Agricultural production is already, and obviously, affected by climate change. Adapting to climate change includes reducing future risks to ensure yield quality and quantity and considers seizing any potential opportunities induced by climate change. In higher latitude areas, such as Norway, cold climate limits the cultivation of fruits. An increase in temperature offers more favorable conditions for fruit production. In this study, using available phenological observations (full blooming) and harvest dates, and meteorological data from the experimental orchard of NIBIO Ullensvang, the minimum heat requirements for growing different apple varieties are determined. Those criteria are used for zoning of the areas with heat favorable conditions for apple growing. Data on six varieties were used, with lower and higher requirements for heat for fruit development (Discovery, Gravenstein, Summerred, Aroma, Rubinstep, and Elstar). High resolution daily temperature data were generated and used for zoning of the areas with heat favorable conditions for apple growing within the selected domain, which includes Western Norway, Southern Norway, Eastern Norway, and the western part of Trøndelag, Mid-Norway. Dynamics of the change in such surfaces was assessed for the period of 1961–2020. The total surface with favorable heat conditions for growing the varieties with lesser requirement for heat increased three times during this period. The growing of more heat-demanding varieties increased from near zero to about 2.5% of the studied land surface. In the period of 2011–2020, surface area with favorable heat conditions for apple growing was almost 27,000 km2, and a surface area of about 4600 km2 can sustain growing of more heat-demanding varieties. The presented results show the increasing potential of the climate of Norway for apple cultivation and highlight the importance of implementation of fruit production planned according to climate change trends, including the assessment of potential risks from climate hazards. However, the methodology for determining heat requirements can be improved by using phenological ripening dates if available, rather than harvest dates which are impacted by human decision. Zoning of areas with the potential of sustainable apple growing requires the use of future climate change assessments and information on land-related features.