Seasonal Scales Research Articles

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

AbstractUsing the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

Evaluation of the PM2.5 concentrations in South America: Climatological patterns and trend analysis

This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022. The bilinear interpolation method was applied to the data resampling from 0.75° × 0.75°–0.25° × 0.25° spatial resolution, and subsequently, the climatological analysis was performed on seasonal and annual scales of PM2.5 concentrations for the 20-year series. The assessment comprised three main stages: 1) descriptive statistics, 2) seasonal and annual climatology patterns, and 3) trend analysis (Mann-Kendall - MK and Pettitt tests). Results indicated that the largest annual PM2.5 concentrations were found in the Amazon region and northern Paraguay due the El-Niño South Oscillation events (2003–2005, 2007, 2010, 2020, and 2022). The annual average PM2.5 concentrations exceeded the WHO recommended limit (5 μg m−3), ranging from 15.57 μg m−3 (1999) and 22.74 μg m−3 (2007 and 2010). The highest PM2.5 concentrations were observed in the Amazon and Chile (Santiago), reaching 160.4 μg m−3 annually and 177.8 μg m−3 seasonally (spring). Elevated seasonal PM2.5 concentrations were attributed to increased deforestation and wildfires associated with meteorological systems such as the Bolivian high and subtropical jets during dry periods (winter and spring). The MK test revealed a significant reduction in PM2.5 concentrations between 2003 and 2022, observed in Argentina (north), Brazil (arc of reforestation), Bolivia (south), and Chile (north). Reduction values varied between 0.5 and 10 μg m−3.year−1 annually and during winter, attributed to no-tillage practices in regions cultivating commodities. The Pettitt test also identified significant structural changes occurring in 2006–2008 and 2010–2011, aligning with areas highlighted in the MK test. This study provides valuable insights for air quality and environmental monitoring managers to mitigate PM2.5 pollution in SA.

Hydrographic shifts in coastal waters reflect climate-driven changes in hydrological regimes across Northwestern Patagonia

Climate-driven changes in freshwater inputs have been shown to affect the structure and function of coastal ecosystems. We evaluated changes in the influence of river runoff on coastal systems of Northwestern Patagonia (NWP) over recent decades (1993–2021) by combined analysis of long-term streamflow time series, hydrological simulation, satellite-derived and reanalysis data on sea surface conditions (temperature, turbidity, and salinity). Significant decreases in minimum streamflow across a zone spanning six major river basins were evident at weekly, monthly, and seasonal scales. These changes have been most pronounced in mixed-regime northern basins (e.g., Puelo River) but appear to be progressing southward to rivers characterised by a nival regime. In the adjacent two-layer inner sea, reduced freshwater input corresponds with a shallower halocline and increased surface temperatures across northern Patagonia. Our results underscore the rapidly evolving influence of rivers on adjacent estuarine and coastal waters in NWP. We highlight the need for cross-ecosystem observation, forecasting, mitigation and adaptation strategies in a changing climate, together with corresponding adaptive basin management of systems that supply runoff to the coastal marine waters.

Impact of El Niño-Southern Oscillation on Dust Variability during the Spring Season over the Arabian Peninsula

This study investigates the dust aerosol optical depth (DAOD) variability over the Arabian Peninsula (AP) in the spring season, a region profoundly affected by dust activity due to its desert terrain. Employing the MERRA-2 DAOD reanalysis dataset for the period 1981–2022, a significant trend in DAOD is noted in the spring season compared to the other seasons. The leading Empirical Orthogonal Function (EOF) explains 67% of the total DAOD variance during the spring season, particularly over the central and northeastern parts of AP. The analysis reveals the strengthening of upper-level divergence over the western Pacific, favoring mid-tropospheric positive geopotential height anomalies over the AP, leading to warm and drier surface conditions and increased DAOD. A statistically significant negative relationship (correlation = −0.32, at 95% confidence level) is noted between DAOD over AP and the El Niño-Southern Oscillation (ENSO), suggesting that La Niña conditions may favor higher dust concentrations over the AP region and vice versa during El Niño phase. The high (low) DAOD over the region corresponds to mid-tropospheric positive (negative) geopotential height anomalies through strengthening (weakening) of the upper-level divergence (convergence) over the western Pacific during the La Niña (El Niño) phase. This study shows that ENSO could be a possible precursor to predicting dust variability on a seasonal time scale.

Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

Drying and wetting trend in Xinjiang and related circulations background over the past 60 years

The study of climate variability and trend provides important evidence for water resource management strategies in Xinjiang. The scientific community has systematically studied the ‘warming and wetting’ process in the Xinjiang region. However, significant controversy persists regarding whether this trend has stagnated, and there remains a gap in understanding the decadal climate variability across different seasons within the subregions of Xinjiang. Here, we analyzed the spatiotemporal characteristics of seasonal-to-annual climate variations in Southern and Northern Xinjiang over the past 60 years by the Standardized Precipitation Evapotranspiration Index (SPEI-3) and identified the change points of mean and trend changes in the SPEI series and further explored the atmospheric circulation patterns before and after the transition from dry/wet conditions. Specifically, although the mean change point test indicated a wetter climate after 1986 at both the annual and seasonal scales, the trend analysis showed a shift from wet to dry from 1996, which has persisted until the present. There were significant interdecadal features in the spatial distribution of SPEI. Xinjiang exhibited a predominantly dry pattern from 1961 to 1980, which changed to a wetter pattern from 1981 to 2000, Xinjiang experienced its wettest decade from 1991 to 2000, and the whole region returned to a drier pattern from 2001 to 2020. From the annual and seasonal variation, the rate of change is greater in the south, with stronger change amplitude in summer and autumn and a higher frequency of anomalous SPEI. The results of the atmospheric circulation analysis demonstrated that the net water vapor flux in the Xinjiang region increased by about 8.30 g·kg−1·m·s−1 in the period 1987–2020 compared to 1961–1986 due to the combined interaction of high-, mid-, and low-latitude systems with the Central Asian low-pressure system. The results indicated a sharp increase in aridification in Xinjiang, providing valuable insights for disaster prevention, mitigation, and risk management related to regional drought hazards.

Spatiotemporal variations of permafrost extent in Mongolia during 1950–2022

Permafrost in Mongolia is located in the transition zone between high-latitude and high-altitude permafrost regions of the Northern Hemisphere, with large temperature differences and complex subsurface characteristics. In this study, the reliability of the skin temperature data from the ERA5-Land product covering Mongolia is assessed via site observations. The ERA5-Land skin temperature dataset shows a cold bias, which is more pronounced in the cold season. Following calibration based on elevation differences, significant improvements are observed at both the annual scale (92 % improvement in RMSE (root mean square error) and 98 % improvement in MBE (mean bias error)) and the seasonal scale (78 % improvement in RMSE and 82 % improvement in MBE). Additionally, the spatial variations in the surface freezing index (SFI) and surface thawing index (STI) are most pronounced in the central and northeastern Mongolia. The SFI exhibits a significant decreasing trend of 7.16 °C·d/year, while the STI shows a significant increasing trend of 4.49 °C·d/year. Furthermore, the permafrost extent in Mongolia is simulated from 1950 to 2022 using the frost number (Fn) model and the temperature on top of permafrost (TTOP) model. The validated results indicate that the accuracy of the Fn model is relatively high, with an overall accuracy of 0.9 and a Kappa coefficient of 0.47. The permafrost extent in Mongolia has declined from 734.7 × 103 km2 in the 1950 s to 480.1 × 103 km2 in the 2010 s, with a prominent decrease of 3.2 × 103 km2/decade after 1994. According to the variations in permafrost extent during past 72 years, the Hovsgol and Khentii Mountain ranges have experienced significant permafrost degradation.

Analysis of sea surface temperature pattern, variability and their teleconnection with rainfall dynamics over the Gulf of Guinea

Spatial pattern and variability of sea surface temperature SST and their teleconnections with rainfall dynamics in the Gulf of Guinea (GOG) were examined in this study. SST and rainfall data of 50 years (1970–2022) were obtained from ERA5 and NOAA CPC at 0.25° x × 0.25° and 0.5° × 0.5° spatial resolution from longitudes 10°W and 8°W, 6oW, 4oW, and 2oW and latitudes 15°N, 5oN, 3oN, 15oS, 5oS, and 3oS, distributed along the Gulf of Guinea (GOG) respectively. Analysis was further carried out on twelve rainfall gridded stations distributed along the Gulf of Guinea (GoG) for the characterization of the Rainfall-SST teleconnection across the region while the relationship between the rainfall-SST anomalies, seasonal, inter-annual, and decadal scales was carried out using correlation analyses and composites. Interpolation of the meteorological variables was carried out using Inverse Distance Weight (IDW) from the ArcGIS Spatial Analyst Tool, Ferret, and CDO which were further employed to generate the seasonal and decadal rainfall and SST maps and statistical analysis of the study area. The result of the decadal and seasonal analysis of SST variability from 1970–1980,1980–1990, 1990–2000,2000–2010,2010–2020 and 2022 indicate that SST was highest from 2010 to 2020 at 28.91 °C and fluctuated between (28.49 °C) in the 1970–1980 and (28.08 °C) for the 1980–1990 decade While the seasonal pattern of SST showed marked variability with March–April and May(MAM) recording 29.34 °C with the lowest being in June-July–August(JJA) at 28.7 °C. In terms of decadal analysis of rainfall, the period 2010–2020 recorded the highest amount of rainfall along the coast (3,145.5 mm-3,928.3 mm while 1970–1980 recorded the lowest amount of rainfall (2,650–3.310 mm. To investigate the teleconnection between of SST and rainfall dynamics, statistical analysis was used where the SST values were plotted against seasonal rainfall in 11 stations namely Abidjan, Banjul, Accra, Guinea, Conakry, Cotonou, Dakar Doula, Freetown, Lagos, Lome, and Monrovia. The outcome of the statistical analysis and Standardized Anomaly Index used indicate that Banjul, Cotonou, Dakar, and Doula exhibited statistically insignificant correlation at 0.05 confidence level while Abidjan, Accra, Lagos, Lome, Freetown, and Monrovia showed positive and statistically significant correlation. The spatial pattern of seasonal rainfall climatology categorized into DJF, MAM, JJA, and SON reveals that JJA and SON produced 80% of rainfall in the Coastal GOG followed by MAM. The study affirmed that warm and cold tongues exist in the GOG alongside positive teleconnection and that the spatial variability of SST observed in this study corresponds positively with the decadal and seasonal variability of rainfall.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

Spatial and seasonal dynamics of zooplankton functional groups and their relationship with environmental factors in urban rivers in Haikou City, China

The urban rivers, including Changwang, Meishe, and Wuyuan in Haikou City, China, are vital water resources. Despite the rivers experiencing anthropogenic impacts, limited studies have investigated their water quality and ecological community structure. This study assessed zooplankton functional groups' spatial and seasonal dynamics relative to environmental factors in the three urban rivers. The samples were collected and analyzed seasonally between March and December 2019. The environmental factors varied significantly at spatial and seasonal scales (p < 0.05). Meishe and Wuyuan Rivers had higher electrical conductivity, salinity, TN, and TP than Changwang, which had low turbidity. Seasonally, higher TP, TN, NH3-N, and CODMn levels were found in the spring, while summer and autumn experienced high temperatures. Additionally, spring and summer had high Chl-a levels. Likewise, spring had the lowest heavy metal concentrations, while nearly all metals were elevated in winter and summer. Generally, the average metal content followed the order of Mn > Zn > Cu > Ni > As > Cr > Co > Pb > Cd. The environmental parameters were within class III of the China standard for surface waters (GB 3838–2002), suggesting negligible pollution. This study identified 159 zooplankton species, including 62 Rotifers, 50 Protozoans, 31 Cladocerans, and 16 Copepods. They were classified into 11 functional groups, dominated by Rotifers filter feeders (RF), Rotifers carnivores (RC), and small copepods and cladocerans filter feeders (SCF), followed by Middle copepods and cladocera carnivores (MCC) and Middle copepods and cladocerans filter feeders (MCF). Analysis of similarity (ANOSIM) revealed seasonal differences (p < 0.001) in zooplankton functional groups, with RF and RC most dominant in summer, autumn, and winter, while SCF, MCF, and MCC were dominant in spring. Hence, the redundancy analysis (RDA) and Pearson Correlation analysis revealed that temperature, Chl-a, TP, salinity, CODMn, NH3-N, TN, As, Co, and Cd were the main environmental factors influencing zooplankton functional groups. This study helps understand urban rivers' water quality and ecological community structure, bridging the knowledge gap. The findings are essential for water quality monitoring and management.

Dispersal limitation determines the ecological processes that regulate the seasonal assembly of bacterial communities in a subtropical river.

Bacteria play a crucial role in pollutant degradation, biogeochemical cycling, and energy flow within river ecosystems. However, the underlying mechanisms governing bacterial community assembly and their response to environmental factors at seasonal scales in subtropical rivers remain poorly understood. In this study, we conducted 16S rRNA gene amplicon sequencing on water samples from the Liuxi River to investigate the composition, assembly processes, and co-occurrence relationships of bacterial communities during the wet season and dry season. The results demonstrated that seasonal differences in hydrochemistry significantly influenced the composition of bacterial communities. A more heterogeneous community structure and increased alpha diversity were observed during the dry season. Water temperature emerged as the primary driver for seasonal changes in bacterial communities. Dispersal limitation predominantly governed community assembly, however, during the dry season, its contribution increased due to decreased immigration rates. Co-occurrence network analysis reveals that mutualism played a prevailing role in shaping bacterial community structure. Compared to the wet season, the network of bacterial communities exhibited higher modularity, competition, and keystone species during the dry season, resulting in a more stable community structure. Although keystone species displayed distinct seasonal variations, Proteobacteria and Actinobacteria were consistently abundant keystone species maintaining network structure in both seasons. Our findings provide insights into how bacterial communities respond to seasonal environmental changes, uncovering underlying mechanisms governing community assembly in subtropical rivers, which are crucial for the effective management and conservation of riverine ecosystems.

River Flashiness in Great Britain: A Spatio-Temporal Analysis

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index (RBI¯) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly RBI¯, the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter.

Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model

AbstractMonthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed.

Oceanographic Characteristics in the Three International Indonesian Archipelago Sea Lanes (IASLs) Region: Implications for Underwater Acoustics System

Graphical Abstract Highlight Research The IASL-1 entry portal in the southern and northern regions shows the emergent SOFAR channels. The shadow zone and the existence of a SOFAR deep sound channel in the IASL-2 and IASL-3 routes can be triggered by the emergent “saddle” SVP pattern. The variability seasonally and interannually due to variations in seawater properties stratification plays an important role in SOFAR channel appearances in IASLs. The diverse oceanographic characteristics of IASLs necessitate the implementation of sustainable marine geospatial data. Abstract The Indonesian Maritime Continent (IMC) provides three international sea lanes, known as the Indonesian Archipelago Sea Lanes (IASLs), allowing ships to navigate across territorial waters between the Pacific and Indian Oceans and vice versa. Gaining knowledge about the distinct oceanographic characteristics of the three IASLs can offer valuable insight into maritime safety and sustainable marine resource management. This study aims to review oceanographic characteristics in IASL regions from available past studies to provide a comprehensive insight into the processes and dynamical oceanography in the IASL region and its implications for underwater acoustic patterns. The results of research found that the IASL-1 route is characterized by a shallow shelf passage with homogeneous sound velocity profile (SVP) but a deep and narrow entry portal in the southern and northern SOFAR channels. Seasonal reversal monsoonal wind-driven current dominates the circulation. The IASL-2 and IASL-3 routes convey a deep and narrow passage with complexity of sea-air interactions; they vary on seasonal and interannual time scales. These IASLs emerge with the “saddle” SVP, which can trigger the shadow zone and the existence of a SOFAR deep sound channel that varies seasonally and interannually due to variations in seawater properties stratification. The diverse oceanographic characteristics discussed above significantly influence the underwater object detection equipment, the planning time, and the strategies for underwater defense systems. Due to these implications, it is necessary to utilize marine geospatial database. Subsequently, these data may be utilized to facilitate policy-making and provide approximations for marine activities and management along the IASLs.

Accelerated Atmospheric to Hydrological Spread of Drought in the Yangtze River Basin under Climate

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought.

Where Dust Comes From: Global Assessment of Dust Source Attributions With AeroCom Models

AbstractThe source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long‐range transport. Recently, a multi‐model experiment, namely the AeroCom‐III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East‐ and West‐Africa, Middle East, Central‐ and East‐Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bodélé and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter‐model differences are significant with the global model dust diversity in 30%–50%, but the differences in regional and seasonal scales are even larger. The multi‐model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models.

Bacterial ecology and evolution converge on seasonal and decadal scales.

Ecology and evolution are often viewed as distinct processes, which interact on contemporary time scales in microbiomes. To observe these processes in a natural system, we collected a two-decade, 471-sample freshwater lake time series, creating the longest metagenome dataset to date. Among 2,855 species-representative genomes, diverse species and strains followed cyclical seasonal patterns, and one in five species experienced decadal shifts in strain composition. The most globally abundant freshwater bacterium had constant species-level abundance, but environmental extremes appeared to trigger a shift in strain composition and positive selection of amino acid and nucleic acid metabolism genes. These genes identify organic nitrogen compounds as potential drivers of freshwater responses to global change. Seasonal and long-term strain dynamics could be regarded as ecological processes or equivalently as evolutionary change. Rather than as distinct processes that interact, we propose a conceptualization where ecology and evolution converge along a continuum to better describe change in diverse microbial communities.

Statistical assessment of uncertainty and performance of multiple satellite-based precipitation products in capturing extreme precipitation events across Punjab Province, Pakistan

ABSTRACT The changing climate poses a significant danger to both environment and humans. Precipitation stands out as a critical factor for impacting hydrology, ecology, agriculture, and vegetation as well as crucial for maintaining the atmospheric equilibrium. This research examined four satellite-based precipitation products spanning from 2010 to 2018, focusing on assessing the uncertainty associated with these products in Punjab Province, Pakistan. Additionally, a comparative analysis of multi-satellite-derived precipitation data was conducted for the region. Various evaluation metrics, including CC, RMSE, Bias, rBias, and POD, were employed to gauge the performance of these datasets throughout the study area. Our research yielded the following findings: (1) datasets CHIRP and SM2RAIN exhibited the capability of capturing temporal changes observed in precipitation throughout the study area. (2) All satellite-derived datasets displayed superior performance on a monthly basis compared to daily timeframes. (3) On a seasonal scale, CHIRP and SM2RAIN exhibited superior precipitation detection capabilities compared to PERSIANN-CCS and PERSIANN-CDR. (4) CHIRP and SM2RAIN exhibited superior performance across all seasons when compared to PERSIANN-CCS and PERSIANN-CDR. Nonetheless, all products exhibited reasonably accurate detection of light to moderate precipitation events. This study serves to establish a foundation for effective monitoring, mitigation, and decision-making processes.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.