Heavy Precipitation Research Articles

Multimodel regional frequency analysis of CMIP extreme precipitation

Abstract A recurrent question in climate risk analysis is determining how climate change will affect
heavy precipitation patterns.
Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters.
In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to diffrent forcing effects.
Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence.
It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence.
We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs.

As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project.
We combine the climatologically consistent subregions identified for all GCMs.
This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence.
Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.

Quantifying the Role of Orographic Processes in Producing Extreme Precipitation: A Case Study of an Atmospheric River Associated With Storm Bronagh

AbstractThis study explores the mechanisms contributing to heavy precipitation associated with landfalling atmospheric rivers (ARs) and orography, focusing on a high‐profile case study of an extratropical cyclone, storm Bronagh, which caused flooding and travel disruption in the UK. While prior research has established the connection between ARs, orography, and precipitation, the specific mechanisms leading to intense rainfall remain unclear. A novel methodology is introduced that quantifies the relative contribution to the total precipitation from cyclone‐induced processes and orographic processes using modifications to the Met Office Unified Model orography and microphysics parameterization scheme. Results show that the majority of storm Bronagh 's rainfall over land is attributed to cyclone‐related processes (frontal ascent and embedded convection). However, interaction of the AR associated with storm Bronagh and orography focuses more of the rain over the hills, enhancing precipitation in this region by a further . Further analysis reveals that the seeder‐feeder mechanism plays a predominant role in orographically enhanced rainfall. Accretion and riming processes occur as rain from the cyclone induced (seeder) cloud falls through the orographically induced (feeder) cloud enhancing precipitation rates. The feeder cloud formation is related to forced ascent of air within the AR over the orography. In conclusion, this study sheds light on the intricate interplay between ARs, orography, and cyclone precipitation during the landfall of extratropical cyclones. By separating the mechanisms behind heavy rainfall in the context of storm Bronagh, we quantify for the first time the effect of the seeder‐feeder effect at different heights over orography.

Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations

AbstractIn this study, we evaluate mesoscale convective system (MCS) simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using PyFLEXTRKR algorithm, which defines MCSs based on both cloud top brightness temperature (Tb) and surface precipitation. Results from using only Tb to define MCSs are also discussed to understand the impact of different MCS tracking algorithms on MCS evaluation and provide additional insights into model errors in simulating MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in tropical and extratropical regions. This is mainly attributed to the underestimated MCS genesis and underestimated precipitation intensity in EAMv2. Comparing the two MCS tracking methods, simulated MCS precipitation is increased if MCSs are defined with only cloud top Tb. The Tb‐based MCS tracking method, however, includes cloud systems with very weak precipitation. This illustrates the model issues in simulating heavy precipitation even though the convective cloud shield is overall well simulated from the moist convective processes. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. The new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are not significantly improved. This suggests that simulating MCSs still remain a challenge for the next version of E3SM.

Microphysical Characteristics of Monsoon Precipitation over Yangtze-and-Huai River Basin and South China: A Comparative Study from GPM DPR Observation

It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h−1) than over YHRB (2.27 mm/h, 3.22 h−1) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation.

Characterizing the uncertainty of CMORPH products for estimating orographic precipitation over Northern California

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations.

Land degradation vulnerability mapping using geospatial techniques: a case study of Nandakini River basin, NW Himalaya, India

ABSTRACT Land degradation is a significant global environmental issue, and its prevention has become a serious concern of the twenty-first century, particularly in the context of anthropogenic impact and climate change. It is crucial to map and assess the grade of land degradation to determine vulnerable areas. The Indian Himalayan region faces heightened risks with its steep terrain, active tectonics, heavy precipitation, and human interventions. This study focuses on the Nandakini Watershed. Employing AHP technique, the study calculates the Land Degradation Vulnerability Index (LDVI) using slope, terrain ruggedness, rainfall, temperature, LULC, soil depth, and organic carbon content. The results indicate that the upper region is highly vulnerable, and a higher slope influences the runoff in the area, followed by factors like NDVI and rainfall, collectively contributing to 62% of the overall index. In this context, the upstream regions of the watershed display a high LDVI, while most of the area falls within the low LDVI category. This indicates that approximately 2.10% of the study area is highly susceptible to soil erosion. RUSLE-based estimates have been used to further validate the LDVI result. The integrated LDVI offers a valuable tool for prioritising soil conservation and disaster mitigation.

Future changes in extremes across China based on NEX-GDDP-CMIP6 models

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

Amplified precipitation extremes since 21st century in the Beijing-Tianjin-Hebei urban agglomeration, China

The Beijing-Tianjin-Hebei urban agglomeration(BTH) has successively witnessed the extraordinary precipitation extremes (PEs) with huge economic losses and death-toll in the recent decade. To timely and comprehensively understand the PEs in the urban agglomeration, we investigate the characteristic and mechanism of PEs variation based on six extreme precipitation indices (EPIs) including maximum daily precipitation(Rx1day), maximum consecutive 5-day precipitation(Rx5day), total precipitation with daily precipitation more than the 95th percentile (R95P), average daily precipitation on wet days (SDII), heavy precipitation days(R25) and very heavy precipitation days(R50). Our results suggest that the PEs of summertime over the BTH has significantly amplified since 21st century. During 2001–2023, the Rx1day, Rx5day, R95p, SDII, R25 and R50 significantly increased at a rate of 13.5 mm/10a, 26.3 mm/10a, 49.4 mm/10a, 2.2 mm/10a, 0.78d/10a and 0.46d/10a, respectively. The average contribution of urbanization to the increased EPIs is estimated by 21 %. The strengthened East Asian Summer Monsoon, intensified and northward extended West Pacific Subtropical High may increase occurrence and severity of PEs in the era of rapid global warming. Three case studies of PEs in 2012, 2016 and 2023 verify our finding. We hope this study can help policy makers to shape strategies to mitigate or reduce societal impact of PEs under global warming crisis and rapid urbanization.

Recent and forecasted increases in coccidioidomycosis incidence in California linked to hydroclimatic swings.

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhalation of Coccidioides spp., fungi found primarily in soils of the southwestern United States. Prior work showed that coccidioidomycosis cases in California sharply increase by nearly 2-fold following wet winters that occur one- and two-years following drought. Statewide drought between 2020-2022 followed by heavy precipitation during the 2022-2023 winter raised concerns over potential increases in coccidioidomycosis cases in the fall of 2023, prompting California Department of Public Health (CDPH) to issue public health alerts. As anticipated, California saw a near record number of cases in 2023, with 9,054 provisional cases reported. During the 2023-2024 California wet season, precipitation was 115% the long-term average, furthering concerns about continued high coccidioidomycosis risk. We developed an ensemble model to forecast coccidioidomycosis cases in California in 2024-2025. Using this model, we predicted a total of 11,846 cases (90% PI: 10,056-14,094) in California between April 1, 2023, and March 31, 2024, encompassing the preliminary state report of 10,593. Our model forecasted 12,244 cases statewide between April 1, 2024, and March 31, 2025 - a 62% increase over the cases reported during the same period two years prior, and on par with the high incidence seen in 2023. The Southern San Joaquin Valley (5,398 cases, 90% PI: 4,556-6,442), Southern Coast (3,322, 90% PI: 2,694-3,961), and Central Coast (1,207 cases, 90% PI: 867-1,585) regions are expected to see the largest number of infections. Our model forecasts that disease incidence will exhibit pronounced seasonality, particularly in endemic regions, with cases rising in June and peaking in November at 1,411 (90% PI: 815-2,172) cases statewide - 98% higher than the peak two years prior (714) and nearly as high as the peak in 2023 (1,462). Near-term forecasts have the potential to inform public health messaging to enhance provider and patient awareness, encourage risk reduction practices, and improve recognition and management of coccidioidomycosis.

Statistical studies of the characteristics and key influencing factors of sediment changes in the upper Yangtze River over the past 60 years

Sediments in the upper Yangtze River have significantly changed over the past 60 years. The characteristics and causes of these sediment changes must be determined in the upcoming phases of the comprehensive management of the Yangtze River Basin. Based on sediment observation data from 1960 to 2020, this study examined the trends and sudden changes in the sediments in the mainstream and its significant tributaries in the upper Yangtze River. The relative contributions of precipitation change and various human activities to sediment load changes are quantified. The relationships between sediment variations and the construction and large dam projects, soil and water conservation measures, and earthquake disasters are established. The results indicate that there was a significant decline in sediment load with main changes occurring around the 1985s and 2010s. Multiple double mass curves are used to quantify the relative contributions of precipitation change and human activities to sediment load changes. The relative contribution of dams to the decrease in sediment load ranges from 53 % to 74 %. Precipitation change serves a significant role in the observed changes, accounting for a range of 10 % to 27 %. Soil conservation measures take some time to show their impact on sediment reduction. These measures work by stabilizing the soil and improving hydrological conditions. On average, they contribute between 7 % and 20 % towards reducing sediment. In recent times, there has been a noticeable rise in sediment load in rivers like the Jialing and Min Rivers. This unexpected increase could potentially be linked to a combination of factors including earthquakes, heavy precipitation, and localized sediment scouring. The results of this study offer valuable insights into the variations in river sediment load, aiding in the improved management of water resources and sediment in large dams. These findings provide helpful insights for dam management.

Mesoscale air motion and thermodynamics predict heavy hourly U.S. precipitation

Predicting heavy precipitation remains scientifically challenging. Here we combine Atmospheric Infrared Sounder (AIRS) temperature and moisture soundings and weather forecast winds to predict the formation of thermodynamic conditions favourable for convection in the hours following satellite overpasses. Here we treat AIRS retrievals as air parcels that are moved adiabatically to generate time-varying fields. Over much of the Central-Eastern Continental U.S. during the non-winter months of 2019–2020, our derived convective available potential energy alone predicts intense precipitation. For hourly precipitation above the all-hours 99.9th percentile, performance is marginally lower than forecasts from a convection permitting model, but similar to the ERA5 reanalysis and substantially better than using the original AIRS soundings. Our results illustrate how mesoscale advection is a major contributor to developing heavy precipitation in the region. Enhancing the full AIRS record as described here would provide an alternative approach to quantify multi-decade trends in heavy precipitation risk.

Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events.

Long-term meteorological characteristics and extreme climate indices over Tirupati: a rapidly developing tropical city

Tirupati’s climate has undergone significant changes in both temperature and precipitation patterns. While there has been a consistent increase in rainfall during the southwest monsoon, there is a concerning long-term trend of a decrease in total annual precipitation over the last 30 years. The city has experienced a rise in wet days during both the southwest and northeast monsoons, yet a recent decrease over the past three decades. Heavy precipitation events, particularly during the southwest monsoon, have shown a positive trend, whereas there have been no significant changes in heavy rainfall days during the northeast monsoon. Temperature trends reveal that there has been a warming scenario, with a significant positive trend in annual maximum temperatures and a consistent annual rise in mean minimum temperatures. A substantial decrease in cold and very cold days, especially during the last 30 years, suggests a broader warming trend impacting seasonal temperature variations in Tirupati. These findings highlight the complex interplay of monsoons, temperature variations, and changing precipitation patterns in Tirupati's climate over the years.

The driving of North American climate extremes by North Pacific stationary-transient wave interference

Wave interference between transient waves and climatological stationary waves is a useful framework for diagnosing the magnitude of stationary waves. Here, we find that the wave interference over the North Pacific Ocean is an important driver of North American wintertime cold and heavy precipitation extremes in the present climate, but that this relationship is projected to weaken under increasing greenhouse gas emissions. When daily circulation anomalies are in-phase with the climatological mean state, the anomalous transport of heat and moisture causes the enhanced occurrence of cold extremes across the continental U.S. and a significant decrease of heavy precipitation extremes in the western U.S. In a future emissions scenario, the climatological stationary wave over the eastern North Pacific weakens and shifts spatially, which alters and generally weakens the relationship between wave interference and North American climate extremes. Our results underscore that the prediction of changes in regional wave interference is pivotal for understanding the future regional climate variability.

Remote Sensing-Based Analysis of Precipitation Events: Spatiotemporal Characterization across China

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

An increase in the spatial extent of European floods over the last 70 years

Abstract. Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator of potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify flood characteristics and, in particular, changes in flood extents over large regions. Here, we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) to identify large spatiotemporally connected flood events. Our identified spatiotemporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern and southwestern Europe. Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities to combine long-term consistent regional runoff simulations with a spatiotemporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The detected change in flood extent should be considered in risk assessments as it may challenge flood control and water resource management.

Development of Polar Lows in Future Climate Scenarios over the Barents Sea

Abstract Polar lows (PLs) are intense mesoscale cyclones that form over polar oceans during colder months. Characterized by high wind speeds and heavy precipitation, they profoundly impact coastal communities, shipping, and offshore activities. Amid the substantial environmental changes in polar regions due to global warming, PLs are expected to undergo noteworthy transformations. In this study, we investigate the response of PL development in the Barents Sea to climate warming based on two representative PLs. Sensitivity experiments were conducted including the PLs in the present climate and the PLs in a pseudo–global warming scenario projected by the late twenty-first century for Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0 scenarios from phase 6 of the Coupled Model Intercomparison Project (CMIP6). In both warming climate scenarios, there is an anticipated decrease in PL intensity, in terms of the maximum surface wind speed and minimum sea level pressure. Despite the foreseen increase in latent heat release in the future climate, contributing to the enhancement of PL intensity, other primary factors such as decreased baroclinic instability, heightened atmospheric static stability, and reduced overall surface heat fluxes play pivotal roles in the overall decrease in PL intensity in the Barents Sea under warming conditions. The augmentation of surface latent heat flux, however, results in increased precipitation associated with PLs by enhancing the latent heat release. Furthermore, the regional steering flow shifts in the warming climate can influence the trajectory of PLs during their development. Significance Statement Global warming is anticipated to impact cyclone systems worldwide. Polar lows (PLs), intense mesocyclones in polar regions with potential socioeconomic and human life implications, pose uncertainties regarding intensity changes in a warming climate. In this study, we aimed to better understand how PLs over the Barents Sea will respond to the environmental changes in future climate conditions [Shared Socioeconomic Pathway (SSP) 2-4.5 and SSP 3-7.0] by the end of the twenty-first century. Our results find that the intensity of PLs is expected to decrease in the future while there is an expected increase in precipitation associated with PLs in the warming climate. These findings aim to contribute valuable insights for disaster management strategies in the face of evolving climate scenarios.

Adsorption characteristics and mechanism analysis of heavy metal Zn2+by cement-soil and alkali activated slag-bentonite-soil

With the increasing emphasis on low-carbon environmental protection, alkali activated cementitious materials are gradually being used as a substitute for cement in vertical cutoff walls for polluted sites. The composition of barrier materials significantly impacts the adsorption of heavy metals, thereby affecting the service life of cutoff walls. However, the adsorption characteristics and mechanisms of traditional cement-soil (CS) and alkali activated slag-soil (AASS) for heavy metals are still unclear. This study investigates the adsorption characteristics of Zn2+ using different mix ratios of barrier materials. Various mix proportions were designed, and qualitative and quantitative analyses were conducted using X-ray diffraction and thermogravimetric tests, respectively. The research results indicate that the adsorption of Zn2+ by barrier materials prepared from CS and slag-bentonite-soil (AASBS) is mainly chemical adsorption. The adsorption rate of heavy metals can be enhanced by increasing the cement content in the CS mix ratio. This is primarily because an increase in OH- in the hydration products encourages the formation of heavy metal precipitation. The generation of C-S-H and ettringite in the hydration products and ion exchange of heavy metals improve the adsorption of heavy metals. Increasing slag and alkali activator dosage, along with decreasing alkali activator modulus, promotes C-A-S-H generation and increases OH-, enhancing heavy metal precipitation. Increasing the content of bentonite in the AASBS mixture promotes the generation of C-A-S-H, and the interlayer structure of bentonite promotes the ion exchange of heavy metals. The findings of the study may offer a theoretical foundation for the installation of AASBS vertical cutoff walls in contaminated areas.

Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP

The southeastern region of Nigeria is susceptible to flood disasters primarily triggered by extreme precipitation with localized impacts. This study uses the output of High-Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate seasonal dependent changes in precipitation extremes in the near future (2031–2050) in the Anambra-Imo River Basin, in the southeastern region of Nigeria. Evaluating the models against observation for the 1995–2014 period, it is found that models reproduced the spatial pattern of the observed annual precipitation extremes over the river basin. Results show that in the near future, annual precipitation extremes will be characterized by a robust increase in annual total precipitation amount (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation (R10mm). The models project a significant increase in PRCPTOT, RX5day, R10mm, and wet-day intensity (SDII) for the June–July–August (JJA) and September–October–November (SON) seasons. The results demonstrate a robust and higher magnitude increase in precipitation extremes during the SON season. Specifically, PRCPTOT, RX5day, R10mm and SDII are projected to increase by up to 46 mm, 24 mm, 1.2 days and 2.4 mm/day, respectively. Whereas during the March–April-May (MAM) season, the HighResMIP suggests that PRCPTOT, R10mm, and SDII will marginally increase over the eastern part of the Anambra-Imo River Basin. Besides, the December–January–February (DJF) season will be characterized by a marginal increase in the precipitation extremes, especially over the southern fringes of the river basin. We note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes during the JJA and SON, potentially exacerbating flash flooding in the river basin. Hence, the results of this study may be vital for near-term socio-economic planning and policy decisions that will minimize the impact of flood disasters in the Anambra-Imo River Basin.

Diurnal Variation in Summer Precipitation and the Characteristics of Precipitation Events in the Western Tarim Basin, China

The Tarim Basin in the western part of Northwest China (NWC) is the largest inland basin in the world and one of the most arid regions in the middle latitudes. In recent years, heavy precipitation events have occurred frequently in this region, especially in the western Tarim Basin (WTB), due to the climate change. Based on the hourly precipitation data from 2010 to 2022, the diurnal variation in summer precipitation and the characteristics of precipitation events with different durations in WTB have been analyzed. The results mainly show that (1) the diurnal variations in the precipitation amount (PA), precipitation frequency (PF) and precipitation intensity (PI) mainly present a unimodal pattern, but the times of maximum value do not coincide. The peak value of PA and PF appears between 01:00 and 03:00 BJT (Beijing Time), while the valley value appears around 18:00 BJT, yet the peak value of PI appears between 20:00 and 23:00 BJT with no obvious valley value. (2) There are some differences in the diurnal variation characteristics of precipitation among different summer months and different regions. (3) During the past decade, the precipitation structure in WTB has been continuously adjusted, and short-duration- and long-duration-precipitation-dominant periods appear alternately. On the whole, short-duration precipitation has been more frequent in summer, accounting for 70% of the total precipitation events and 40% of the total accumulated precipitation amount. These results can help us to better understand the refined physical characteristics of precipitation events and enhance our understanding of the local climate in the WTB under the background of climate change.