Double increase in precipitation extremes across China in a 1.5 °C/2.0 °C warmer climate

Spatial-temporal variation of extreme precipitation in the Yellow-Huai-Hai-Yangtze Basin of China

Climate warming leads to frequent extreme precipitation events, which is a prominent manifestation of the variation of the global water cycle. In this study, data from 1842 meteorological stations in the Huang-Huai-Hai-Yangtze River Basin and 7 climate models of CMIP6 were used to obtain the historical and future precipitation data using the Anusplin interpolation, BMA method, and a non-stationary deviation correction technique. The temporal and spatial variations of extreme precipitation in the four basins were analysed from 1960 to 2100. The correlation between extreme precipitation indices and their relationship with geographical factors was also analysed. The result of the study indicates that: (1) in the historical period, CDD and R99pTOT showed an upward trend, with growth rates of 14.14% and 4.78%, respectively. PRCPTOT showed a downward trend, with a decreasing rate of 9.72%. Other indices showed minimal change. (2) Based on SSP1-2.6, the intensity, frequency, and duration of extreme precipitation changed by approximately 5% at SSP3-7.0 and 10% at SSP5-8.5. The sensitivity to climate change was found to be highest in spring and autumn. The drought risk decreased, while the flood risk increased in spring. The drought risk increased in autumn and winter, and the flood risk increased in the alpine climate area of the plateau in summer. (3) Extreme precipitation index is significantly correlated with PRCPTOT in the future period. Different atmospheric circulation factors significantly affected different extreme precipitation indices of FMB. (4) CDD, CWD, R95pD, R99pD, and PRCPTOT are affected by latitude. On the other hand, RX1day and RX5day are affected by longitude. The extreme precipitation index is significantly correlated with geographical factors, and areas above 3000 m above sea level are more sensitive to climate change.

Future Projection of Extreme Precipitation Indices over the Qilian Mountains under Global Warming

The Qilian Mountains are a climate-sensitive area in northwest China, and extreme precipitation events have an important impact on its ecological environment. Therefore, considering the global warming scenario, it is highly important to project the extreme precipitation indices over the Qilian Mountains in the future. This study is based on three CMIP6 models (CESM2, EC-Earth3, and KACE-1-0-G). A bias correction algorithm (QDM) was used to correct the precipitation outputs of the models. The eight extreme precipitation indices over the Qilian Mountains during the historical period and in the future were calculated using meteorological software (ClimPACT2), and the performance of the CMIP6 models to simulate the extreme precipitation indices of the Qilian Mountains in the historical period was evaluated. Results revealed that: (1) The corrected CMIP6 models could simulate the changes in extreme precipitation indices over the Qilian Mountains in the historical period relatively well, and the corrected CESM2 displayed better simulation as compared to the other two CMIP6 models. The CMIP6 models performed well while simulating R10mm (CC is higher than 0.71) and PRCPTOT (CC is higher than 0.84). (2) The changes in the eight extreme precipitation indices were greater with the enhancement of the SSP scenario. The growth rate of precipitation in the Qilian Mountains during the 21st century under SSP585 is significantly higher than the other two SSP scenarios. The increment of precipitation in the Qilian Mountains mainly comes from the increase in heavy precipitation. (3) The Qilian Mountains will become wetter in the 21st century, especially in the central and eastern regions. The largest increase in precipitation intensity will be observed in the western Qilian Mountains. Additionally, total precipitation will also increase in the middle and end of the 21st century under SSP585. Furthermore, the precipitation increment of the Qilian Mountains will increase with the altitude in the middle and end of the 21st century. This study aims to provide a reference for the changes in extreme precipitation events, glacier mass balance, and water resources in the Qilian Mountains during the 21st century.

Association between sequential extreme precipitation-heatwaves events and hospitalizations for schizophrenia: The damage amplification effects of sequential extremes

OBJECTIVES

In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss.

METHODS

A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events.

RESULTS

EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)).

CONCLUSIONS

Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.

Machine learning-based assessment of long-term climate variability of Kerala

Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann-Kendall test (MK), three modified Mann-Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons.

Performance Evaluation of Different Combined Drainage Forms on Flooding and Waterlogging Removal

Farmland in southern China is prone to flooding and waterlogging alternation after short-term heavy rainfall. Single drainage form cannot meet the requirements of the farmland flooding and waterlogging removal. Drainage measures and layout forms should be explored to alleviate flooding and waterlogging threat and improve crop yield. So, based on an indoor sand tank experiment, this paper presents a combined drainage form: conventional subsurface drainage as an auxiliary drainage measure and is alternatively combined with open ditch (OD), filter drainage (FD), conventional (CD) and improved subsurface drainage (ID), respectively, under equal and unequal drain depth. The performance of different combined drainage forms and the effect of auxiliary drainage measures are discussed for stable ponding and receding water. During the experiment, two factors—drainage measure and drain depth—are considered. The results indicate that compared with the conventional subsurface drainage alone, the flow rates of the open-ditch, thin-improved, and thick-improved subsurface drainage combined with conventional subsurface drainage can be increased by 22.4–32.3%, 10.6–16.2%, and 29.8–32%, respectively, under equal drain depth in stable ponding water. Among the four combined drainage forms, the flow rate of shallow–deep combination is 8.1–17.1% higher than that of the shallow–shallow combination. Compared with a single drainage form, the flow rates of the combined drainage have the same change characteristics over time. Additionally, the use of auxiliary, conventional, subsurface drainage can improve the flooding and waterlogging removal efficiency in farmland. For the combined drainage forms, the contribution degree of the open-ditch and thin-improved subsurface drainage is 51.3–56.7%, while the thick-improved subsurface drainage is approximately 61.0%, under equal drain depth conditions in the flooding removal process. Moreover, open-ditch and thick-improved subsurface drainage combined with conventional subsurface drainage have significant advantages in flooding and waterlogging removal, which were 11.5–38.1% and 37.1–48.6% faster than conventional subsurface drainage in flooding removal time, 14.3–157.1% and 14.3–44.4% faster than conventional subsurface drainage in the waterlogging removal time. The synergistic application of shallow–deep and medium–medium combinations can be carried out by exploiting the advantages of each drainage measure. The experimental flow rate observation is in good agreement with the theoretically calculated value, with a relative error of less than 5%. These research findings could provide technical support for the increased application of combined drainage forms in areas prone to flooding and waterlogging.

Integrated analyses of fecal indicator bacteria, microbial source tracking markers, and pathogens for Southeast Asian beach water quality assessment

The degradation of coastal water quality from fecal pollution poses a health risk to visitors at recreational beaches. Fecal indicator bacteria (FIB) are a proxy for fecal pollution; however the accuracy of their representation of fecal pollution health risks at recreational beaches impacted by non-point sources is disputed due to non-human derivation. This study aimed to investigate the relationship between FIB and a range of culturable and molecular-based microbial source tracking (MST) markers and pathogenic bacteria, and physicochemical parameters and rainfall. Forty-two marine water samples were collected from seven sampling stations during six events at two tourist beaches in Thailand. Both beaches were contaminated with fecal pollution as evident from the GenBac3 marker at 88%-100% detection and up to 8.71 log₁₀ copies/100 mL. The human-specific MST marker human polyomaviruses JC and BK (HPyVs) at up to 4.33 log₁₀ copies/100 mL with 92%-94% positive detection indicated that human sewage was likely the main contamination source. CrAssphage showed lower frequencies and concentrations; its correlations with the FIB group (i.e., total coliforms, fecal coliforms, and enterococci) and GenBac3 diminished its use as a human-specific MST marker for coastal water. Human-specific culturable AIM06 and SR14 bacteriophages and general fecal indicator coliphages also showed less sensitivity than the human-specific molecular assays. The applicability of the GenBac3 endpoint PCR assay as a lower-cost prescreening step prior to the GenBac3 qPCR assay was supported by its 100% positive predictive value, but its limited negative predictive values required subsequent qPCR confirmation. Human enteric adenovirus and Vibrio cholerae were not found in any of the samples. The HPyVs related to Vibrio parahaemolyticus, Vibrio vulnificus, and 5-d rainfall records, all of which were more prevalent and concentrated during the wet season. More monitoring is therefore recommended during wet periods. Temporal differences but no spatial differences were observed, suggesting the need for a sentinel site at each beach for routine monitoring. The exceedance of FIB water quality standards did not indicate increased prevalence or concentrations of the HPyVs or Vibrio spp. pathogen group, so the utility of FIB as an indicator of health risks at tropical beaches maybe challenged. Accurate assessment of fecal pollution by incorporating MST markers could lead to developing a more effective water quality monitoring plan to better protect human health risks in tropical recreational beaches.

Changes in precipitation extremes in the Yangtze River Basin during 1960-2019 and the association with global warming, ENSO, and local effects

Extreme precipitation events can pose great risks to natural ecosystems and human society. Investigating past changes in the frequency, intensity, and duration of such events and understanding the possible driving factors are critical for reliable projections of future changes and for informing adaptation strategies planning. Here we analyze trends in a complete list of extreme precipitation indices (EPIs) over the Yangtze River Basin (YRB) during the period of 1960-2019. Also, we examine the possible influences of global warming, ENSO, and local effects on the spatiotemporal variability of the EPIs. Our results show that average and extreme precipitation intensities, and the frequency of extreme heavy precipitation in the YRB have significantly increased, while precipitation frequency and maximum duration of wet spells have significantly decreased. A regional difference in trend occurrence and magnitude is also observed, showing the intensity and frequency of precipitation extremes over the Middle and Lower reaches are more likely to increase and increase faster, compared with those of the Upper reach of the YRB. Furthermore, our correlation analysis shows global warming, ENSO, and local effects all are significant driving factors that control the spatiotemporal variability of precipitation extremes over the YRB. Global warming tends to enhance the frequency and intensity of precipitation extremes. The La Niña phase of ENSO often corresponds to an increase of frequency and intensity of precipitation extremes in the current year, but a decrease of frequency and intensity in the coming year. Local warming mainly exerts a reducing effect on precipitation extremes, which is likely a response to the significant decrease of relative humidity in the YRB. Our findings highlight the need for a systematic approach to examine global, regional, and local drivers of trends in precipitation extremes in the YRB, and contribute to the understanding of precipitation changes in this region.

Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China

Given the fact that researchers require more specific spatial rainfall information for storm flood calculation, hydrological risk assessment, and water budget estimates, there is a growing need to analyze the spatial heterogeneity of rainfall accurately. This paper provides insight into rainfall spatial heterogeneity in urban areas based on statistical analysis methods. An ensemble of short-duration (3-h) extreme rainfall events for four megacities in China are extracted from a high-resolution gridded rainfall dataset (resolution of 30 min in time, 0.1° × 0.1° in space). Under the heterogeneity framework using Moran’s I, LISA (Local Indicators of Spatial Association), and semi-variance, the multi-scale spatial variability of extreme rainfall is identified and assessed in Shanghai (SH), Beijing (BJ), Guangzhou (GZ), and Shenzhen (SZ). The results show that there is a pronounced spatial heterogeneity of short-duration extreme rainfall in the four cities. Heterogeneous characteristics of rainfall within location, range, and directions are closely linked to the different urban growth in four cities. The results also suggest that the spatial distribution of rainfall cannot be neglected in the design storm in urban areas. This paper constitutes a useful contribution to quantifying the degree of spatial heterogeneity and supports an improved understanding of rainfall/flood frequency analysis in megacities.

Assessing Hydrological Vulnerability to Future Droughts in a Mediterranean Watershed: Combined Indices-Based and Distributed Modeling Approaches

Understanding the spatiotemporal distribution of future droughts is essential for effective water resource management, especially in the Mediterranean region where water resources are expected to be scarcer in the future. In this study, we combined meteorological and hydrological drought indices with the Soil and Water Assessment Tool (SWAT) model to predict future dry years during two periods (2035–2050and 2085–2100) in a typical Mediterranean watershed in Northern Morocco, namely, Bouregreg watershed. The developed methodology was then used to evaluate drought impact on annual water yields and to identify the most vulnerable sub-basins within the study watershed. Two emission scenarios (RCP4.5 and RCP8.5) of a downscaled global circulation model were used to force the calibrated SWAT model. Results indicated that Bouregreg watershed will experience several dry years with higher frequency especially at the end of current century. Significant decreases of annual water yields were simulated during dry years, ranging from −45.6% to −76.7% under RCP4.5, and from −66.7% to −95.6% under RCP8.5, compared to baseline. Overall, hydrologic systems in sub-basins under the ocean or high-altitude influence appear to be more resilient to drought. The combination of drought indices and the semi-distributed model offer a comprehensive tool to understand potential future droughts in Bouregreg watershed.