Monsoon Season Research Articles

A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow

The detrimental impacts of fine particulate matter (PM2.5) on human health, climate, ecosystems, crops, and building materials are well-established. However, there remain unresolved inquiries regarding the precise location of the sources of PM2.5. This study is the first attempt to use a calibrated sensors-based ambient air quality monitoring network (SAAQM network) and regulatory government monitors to train micro-satellite images for high spatial-resolution air pollution field determination of PM2.5 in Lucknow, Uttar Pradesh, India. A hybrid approach is developed to integrate three different datasets that include microsatellite images, PM2.5 ground measurements, and supporting information (meteorological parameters and geographical coordinates), to be fed into a Random Trees-Random Forest- Convolutional Neural Network (RT-RF-CNN) joint model to estimate PM2.5 concentrations at a sub-km level. The RT-RF-CNN joint model can derive PM2.5 concentrations at a spatial resolution of 500 m with statistically significant indicators such as spatial r of 0.9, a low root-mean-square error of 26.9 μg/m3 and a mean absolute error of 17.2 μg/m3. Based on our approach, the PM2.5 prediction maps using micro-satellite images (spatial resolution of 3m/pixel) and RT-RF-CNN joint model were generated for each day throughout the study period (December 2021–December 2022). The inter-grid comparison of these maps revealed the intra-urban local hotspots and coolspots at a fine-granular level seasonally, monthly, and daily. It is observed that the monsoon season has the highest number of coolspots (67%), while winter (0.1%), post-monsoon (0.5%) and summer (11%) have fewer. It is noted that the high temporal-spatial information of PM2.5 estimates from our integrated approach is not achievable by ground-based measurements and other existing satellite-based estimates alone. The findings of this study have potential applications on a diverse array, encompassing near real-time daily PM2.5 predicted maps, specific air pollution hotspot identification, PM2.5 exposure assessment at the neighbourhood level, and integration of remote sensing-based micro-satellite images and ground-based measurements.

Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka

ABSTRACT Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.

Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine

Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping.

Seasonal Variations in Physico- chemical Parameters of the Sediment of Thengapatnam Estuary, South-coastal Region of India

The tremendous biodiversity and ecological processes that depend on freshwater and marine environments are essential to human survival. The physicochemical parameters in both surface waters and sediments are important to understand estuarine dynamics. The present study was carried out to determine how the seasonal variation will affect the physicochemical parameters of sediments of five stations in Thengapatnam estuary. Sediment samples were collected for a period of one year from June 2014-May 2015 from five different sampling stations. The parameters such as pH, temperature, Nitrogen, Phosphorous, Potassium, Copper, Zinc, Iron and Manganese were analysed using standard procedures. The result expose that, soil sediment have high amount of potassium in all five stations. Sediment temperature varied from 25.7- 30.1°C. Hydrogen ion concentration ranged between 6.9 to 8.3, concentrations of nutrients viz. nitrate (2.4 – 6.4 mg/100g), phosphate (1.5 - 15.25 mg/100g) potassium (138 – 560 ppm) and concentration of metals like iron, zinc and manganese were found to be higher during monsoon season and lower during premonsoon seasons The overall findings highlight the need for a scientific solution to reduce the use of harmful chemicals and heavy metal contamination from the river system in order to preserve the biodiversity of freshwater and marine ecosystems.

Estimation of Surface and Groundwater Interaction by Stable Isotopic Techniques – A Case Study of Chengalpattu District, OMR Region

Isotopes are atoms of an element having the same atomic number but different mass numbers. Isotopes in hydrology and water resources are used for identifying its occurrence, movement, residence times, recharge, and discharge process. Stable isotopes of hydrogen(δ2H) and oxygen(δ2O) are used for identifying the surface and groundwater interactions as they constitute hydrogen and oxygen. In this study oxygen and hydrogen stable isotopes are used to identify surface and groundwater interaction in Old Mahabalipuram Road (OMR) regions of Chengalpattu district. The precipitation, lake, surface, and groundwater were collected during pre-monsoon, monsoon, and post-monsoon seasons. The collected sample is analyzed for stable isotopic compositions of oxygen and hydrogen seasonal-wise. The measured stable isotopic compositions during pre-monsoon season of stable oxygen are -4.29 to -2.00 and stable hydrogen are -29.39 to -24.67. The isotopic compositions during monsoon season range from -4.72 to -4.00 and for hydrogen ranges from -29.39 to -23.50. During monsoon season the depletion of isotopic composition is seen and the enrichment of isotopic composition is observed during pre-monsoon season. The variation in stable isotopic composition of oxygen and hydrogen are observed. A Groundwater Water Meteoric Water Line (GMWL) is developed for the study area, and it is compared with a Local Meteoric Water Line (LMWL) for better interpretation of the results. A slight deviation is observed from that of GMWL to LMWL mostly due to isotopic depletion and evaporation effects. From the analysis, a good correlation exists between precipitation and surface water in the study area indicating about recharge mechanism existing in the study area. The groundwater recharge is observed during monsoon seasons and discharge is more towards the pre-monsoon seasons.

PROTEIN ELECTROPHORESIS AND REFERENCE INTERVALS OF SERUM PROTEINS OF A CAPTIVE POPULATION OF ROTI ISLAND SNAKE-NECKED TURTLES (CHELODINA MCCORDI).

Protein electrophoresis (PEP) is an important tool in mammals to characterize specific dysproteinemias and detect acute and chronic inflammatory responses. In reptiles, PEP is the gold standard method for globulin fraction determination and albumin measurement. In this study, preliminary reference intervals were established for serum PEP in 22 clinically healthy adult Roti Island snake-necked turtles (Chelodina mccordi), a critically endangered species, kept in captivity and sampled over two monsoon seasons. The species has a prominent prealbumin fraction and β-globulins were the dominant globulin fraction. Significant differences between females and males were found in prealbumin (P < 0.01), albumin (P = 0.02), α1-globulin (P = 0.05) and γ-globulin (P = 0.01). Gravid females had significantly lower total protein (P < 0.01), prealbumin (P < 0.01), albumin (P < 0.01) and albumin:globulin ratio (P = 0.01). These preliminary reference intervals should aid in clinical investigation in this species as well as further research studies seeking to understand the application of PEP in reptilian species.

Seasonal Variability of Pesticide Residues in Vellode Bird Sanctuary: Implications for Ecosystem Health

This research provides a comprehensive examination of the seasonal fluctuations in pesticide residues within the Vellode Bird Sanctuary, with a particular emphasis on the detection and quantification of organochlorines, organophosphates, synthetic pyrethroids, and herbicides in the sanctuary's aquatic environment. The pesticide analysis was conducted from July 2019 to May 2020. This study period spanned three seasons: monsoon, winter, and summer. Gas Chromatography-Mass Spectrometry (GC-MS) from Perkin Elmer was used to analyze the pesticide residues in lake water. The pesticide analysis showed that aldrin, α-cypermethrin, α-HCH, β-HCH, bifenthrin, butachlor, chlorpyrifos, dimethoate, endosulfan-α, γ-HCH, pendimethalin, phorate, profenophos, p,p'-DDT, and quinalphos were recorded at higher levels during the monsoon season. The values of pesticide residues in the winter and summer seasons were below the level of detectable quantification (less than 0.5 μg/l). The study shows that increased pesticide concentrations during the monsoon are due to runoff from nearby agricultural fields caused by heavy rainfall. In contrast, lower pesticide levels in winter and summer are likely due to reduced pesticide use and rainfall. These findings highlight the significant impact of seasonal variations on pesticide contamination, with greater ecological risks during the monsoon. The sanctuary's aquatic ecosystem is particularly vulnerable during periods of high rainfall, emphasizing the need for targeted conservation and management strategies.

Nursery Habitat Requirements for the Blue Swimming Crab: Implications for Larval Development

Overfishing causes a significant decline in blue swimming crab (Portunus pelagicus) populations, an essential marine resource for Thailand. This research aimed to identify suitable nursery areas for this species throughout the four distinct monsoon seasons. This is the first comprehensive study of habitat characteristics: water temperature, pH, salinity, dissolved oxygen, marine sediment, seagrass beds, and suitability assessment of nursery areas, using a combined approach of Analytic Hierarchy Process and Geographic Information Systems. Our findings reveal that highly suitable nursery habitats comprise a small percentage of the total study area, varying with the monsoon season: 1.66% in the Northeast Monsoon (NEMS), 1.00% in the NEMS transition (NEMST), 1.11% in the Southwest Monsoon (SWMS), and 1.97% in the SWMS transition (SWMST). These areas are concentrated along the coastlines of Sed Island, Phum Riang, Takrob, Tha Thong, and Donsak. Sed Island was designated as a fishery refuge zone in 2022. The identified nursery areas coincide with existing crab banks, highlighting the model's accuracy. Conversely, unsuitable areas were predominantly in shellfish farming zones and low-salinity estuaries. By identifying suitable nursery grounds for the blue swimming crab, this study offers valuable information that can support Thailand's sustainable management and conservation efforts for this species. Doi: 10.28991/HEF-2024-05-03-01 Full Text: PDF

Adapting to Change: The Impact of Climatic Shifts on Tourism Destinations and Tourist Mobility in Northern Pakistan

Pakistan aims to showcase its authenticity, pristine nature, and unique cultural heritage from its northern valleys. Mountain tourism in Pakistan is popular during the summer, with tourists enjoying pleasant weather and scenic landscapes. The northern valleys of KP, GB, and AJK are favored destinations, as visitors generally have positive impressions of their interactions with the mountains and local people. Pakistan, an agricultural country, has tourism as a growing sector with significant economic potential. However, both agriculture and tourism are heavily affected by climate change. However, in the past decade, climate change has led to increased natural hazards such as floods, rockfalls, debris flows, rockslides, and avalanches specifically in northern areas of Pakistan. These events frequently cause road blockages and trap tourists, especially during the monsoon season. Such extreme conditions not only endanger local communities but also disrupt transportation infrastructure and damage tourist facilities. The resulting communication network disruptions and road blockages increase tourists' anxiety and fear, leading to unpleasant experiences. This study examines how climate change affects tourist mobility and hinders the tourism industry. It explores how increasing natural hazards, such as floods and landslides, disrupt travel, damage infrastructure, and create safety concerns, ultimately impacting tourist experiences and industry operations.

GIS-Based Mapping of the Water Quality and Geochemical Assessment of the Ionic Behavior in the Groundwater Aquifers of Middle Ganga Basin, Patna, India

The study implemented Geographic Information System (GIS) techniques and multivariate hydrogeochemical analysis to evaluate the spatial-temporal and seasonal variation in the groundwater quality of Patna, India. For this purpose, sixty groundwater samples were collected and analyzed for major anions and cations during the pre-monsoon, monsoon, and post-monsoon seasons of 2019-2020. The physicochemical parameters such as pH, EC (Electrical Conductivity), TDS (Total Dissolved Solids), TH (Total Hardness), Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42- were considered to evaluate the water quality index. The result revealed degradation in groundwater quality from pre-monsoon (49.21) to post-monsoon (74.48). EC, TDS, TH, Mg2+, Na+, Ca2+, K+, and HCO3- ions were found accountable for high WQI values at various sampling sites during different seasons. Spatial maps showed that 45 % of the sampling stations exhibited poor quality in all three seasons, where the eastern part of the studied region was revealed to be the most affected area. The application of multivariate statistical methods and hydrogeochemical investigation has clearly defined the dominant role of the weathering process, and reverse ion exchange mechanism in controlling the aquifer’s ionic chemistry. Moreover, poor seepage system, and waste leachate from the surface have been found as the main cause of high levels of Na+, K+, and Cl- in the eastern part of Patna.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of &gt;90% and &lt;1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Ectoparasitic infestations among patients attending the dermatology OPD: A hospital-based study

Background: Ectoparasites are organisms that live in the skin for varying lengths of time and can cause disease. These infestations are highly prevalent in poor and underdeveloped areas. There is a paucity of studies regarding their prevalence in rural areas like the upper Karnali region of Nepal. We conducted a hospital-based study to know the burden of these infestations in the area. Methods: A hospital-based descriptive study was done from March 2023 to February 2024. Data were collected from medical records of the dermatology outpatient department (OPD) and all the clinically diagnosed cases of ectoparasitic infestation were studied. Data were presented as frequency and percentage; seasonal pattern of the diseases was also reported. Results: A total of 6,698 patients attended dermatology OPD during the study period, among whom 1340 had ectoparasitic infestations, resulting in a proportion of 20.01% in this group. Two infestations were found, namely, scabies and pediculosis capitis, with a proportion of 18.36% and 1.65%, respectively. Females represented almost half of the patients with ectoparasitic infestations (49.63%), but about 98% with pediculosis. Ectoparasitic infestation was most common in children aged 0–9 years, i.e., 31.31% and pediculosis was most common in children of 10–19 years (31.81%). Ectoparasitic infestations (41.94%), both scabies (41.7%) and pediculosis (44.55%) were most common during the monsoon season. Conclusion: The ectoparasitic infestations were common the patients presenting to the hospital. Scabies was the most common ectoparasitic infestation and a major public health problem.

Analysing Sea Surface Temperature and Chlorophyll-a Distribution Along Visakhapatnam Coast Using MODIS Data

Satellite images along with Geographic Information Systems (GIS) are indispensable instruments for the identification, monitoring, and analysis of environmental transformations and management. The primary focus of this study is to investigate the relationship between Sea Surface Temperature (SST) and Chlorophyll-a concentration in the Bay of Bengal along the Visakhapatnam Coast by utilizing MODIS data. The research utilized remote sensing methodologies to accumulate data on SST and Chlorophyll-a concentration from the MODIS Aqua satellite, which provided daily observations for the period of January to December 2022. The investigation has unveiled important findings indicating a strong correlation between SST and Chlorophyll-a concentration, in general, higher SST levels are found to be linked with lower Chlorophyll-a concentrations, and conversely. The majority of months demonstrated notable determination coefficient (r2), except July, August, September, and October, wherein weaker correlations were observed due to increased precipitation levels in these specific months. Specifically, the determination coefficients were as follows: r2 = -0.58, -0.38, -0.35, -0.61, 0.16, -0.25, -0.06, and -0.59, with the exceptions being July (r2 = 0.01), August (r2 = 0.19), September (r2 = 0.33), and October (r2 = 0.24). Additionally, seasonal fluctuations were evident, with increased SST levels and reduced Chlorophyll-a concentrations during the summer season, and decreased SST levels and heightened Chlorophyll-a concentrations during the monsoon period. To effectively conclude, extensive research utilizing MODIS data has revealed significant trends and relationships between SST and Chlorophyll-a concentrations over the Bay of Bengal along the Visakhapatnam Coast. SST has been observed to increase significantly over recent decades, while Chlorophyll-a concentrations peak during the northeast monsoon season and decline during the southwest monsoon. A robust inverse correlation exists between SST and Chlorophyll-a concentrations, indicating that elevated SSTs are associated with decreased Chlorophyll-a levels, with exceptions during periods of heightened precipitation. Seasonal fluctuations further emphasize the dynamic nature of these parameters, with implications for marine ecosystem management and conservation efforts in the face of climate change, overfishing, and pollution. Remote sensing remains a crucial tool for monitoring these variables and understanding the marine ecosystem's dynamics in the Bay of Bengal.

Comparative study using spectroscopic and mineralogical fingerprinting for suspended sediment source apportionment in a river–reservoir system

AbstractThe need to control soil erosion has received increasing attention, but quantitative data on the sources of suspended sediment in many river–reservoir systems is still lacking. The goal of this research was to compare the application of spectroscopic [mid‐infrared (MIR)] and mineralogical [X‐ray diffraction (XRD)] fingerprints for assessing relative sediment source contributions from different land use groups (agricultural lands, forests and human settlements) in the Konar–Damodar river–reservoir system in India. Source apportionment was estimated using partial least square (PLS) regression for spectroscopic tracers (MIR) and the Bayesian MixSIAR model for mineralogical tracers. Both methods identified differences between the pre‐ and post‐monsoon sediment contributions of forests (overall contribution bounds of ~35–43%). During monsoon seasons, both fingerprinting methods indicated agricultural land use as the primary source of suspended sediment. Although there were some temporal variations in the predicted contributions of the land use sources, the MIR‐PLS and mineralogical–MixSIAR methods produced comparable ranges. The respective variations in contributions, using MIR‐PLS and mineralogical–MixSIAR, were ~31 to 66% compared with ~36 to 61% for agricultural lands, ~21 to 43% compared with ~15 to 39% for forests and ~16 to 37% compared with ~19 to 32% for human settlements.

Clinical burden of Plasmodium infection and associated risk factors among the local population of Chakwal, Punjab, Pakistan (2019–2023)

BackgroundThis study aimed to determine the clinical burden, spatial dynamics, and associated risk factors of Plasmodium infection among the natives of Chakwal, Punjab Pakistan from 2019 to 2023 to guide targeted screening and treatment interventions.MethodA community-based cross-sectional study was conducted using primary and secondary data sources. Participants were recruited using a multi-stage cluster sampling by taking informed consent for the primary data sources. Whereas a secondary dataset of Plasmodium infection was obtained from the database of surveyed hospitals after ethical consideration. All the participants living in the study area for at least 3 months were included, while patients with chronic illness were excluded. Sociodemographic and environmental data were collected using a structured questionnaire. Plasmodium infection was diagnosed using microscopy, rapid diagnostic tests (RDTs), and nested polymerase chain reaction (PCR) as the reference standard. A Zero-inflated negative binomial logistic regression model and interpolation of malaria distribution to environmental variables were employed to determine the association and spatiotemporal patterns of malaria in Chakwal.ResultsAmong the 2457 participants, the prevalence of Plasmodium vivax infection (99%) was significantly higher than P. falciparum (0.8%) and mixed infection (0.2%). Females had a higher infection rate than males and the infection rate was higher in adults (21–40 years) and children (0–20 years). Age category (61–100), P. vivax species, temperature, rainfall, monsoon season, and mixed infection factors were significantly associated (p < 0.05) with increased Plasmodium infection risk. Geospatial mapping indicated potential malaria hotspots in Sarkal Kasar, Chak Baili, Mulkwal, Chohan, and other close areas of the Potohar region.ConclusionThe clinical burden of P. vivax was the highest during monsoon season in Chakwal. Age, gender, climatic factors, and mixed infection were significant risk factors for malaria transmission in this region. Targeted screening and treatment strategies should prioritize identified hotspots and high-risk groups to effectively control malaria in Potohar region of Pakistan.

Assessment of Drainage Capacity of a Surface Drain in Rajshahi, Bangladesh

One of the most important components of a community's sustainable environment system is its drainage capacity. The drainage capacity of a community is essential to maintaining environmental resilience, effective water management, and encouraging a healthy urban lifestyle. The surface drains in Rajshahi City receive a mix of waste, including sanitary wastewater, stormwater runoff, and solid waste from roadsides and households. To improve the drainage facilities of Rajshahi, some new drains have been constructed. This study considered a recently constructed drain in the Rajshahi City Corporation (RCC), running from Talaimari to Chowddopai. The primary objective of this study is to assess the drainage capacity of this drain during both dry and monsoon seasons, ensuring its capability to contain the generated waste. Additionally, the investigation encompasses observing the sludge depth, and clear space of the drain. A field survey was conducted, employing a measuring rod and scale to precisely measure the depth, width, and sludge accumulation. A map was created in ArcGIS providing all the data collected from field surveys. By examining these parameters, this study seeks to contribute valuable information for enhancing the resilience and functionality of the drainage system, thereby promoting a sustainable urban environment.

Exploring climate shifts in the Ganga-Brahmaputra basin based on rainfall and temperature variability.

Climate change has a significant impact on the Ganga-Brahmaputra (GB) basin, the major food belt of India, which frequently experiences flooding and varied incidences of drought. The current study examines the changing trend of rainfall and temperature in the GB basin over a period of 30years to identify areas at risk with an emphasis on the Paris Agreement's mandate to keep increasing temperatures below 2°C. The maximum temperature anomaly in the middle Ganga plains recorded an increase of more than 1.5°Cyear-1 in 1999, 2005, and 2009. Some extreme events were observed in the Brahmaputra basin during 1999, 2009, and 2010, where a prominent temperature increase of 1.5°Cyear-1 was observed. The minimum temperature revealed an increasing trend for the G-B basin with an anomalous increase of 0.04 to 0.06°Cyear-1. The rainfall variability across the Ganga basin shows a rising tendency over the lower Ganga region while the Brahmaputra basin showed a downward trend. To identify the statistical relation between the Global climatic oscillations and regional climate, Standardized Precipitation Index (SPI) and Niño 3.4 were used. The wet and dry period estimation shows a rise in flood conditions in the Ganga basin whereas, in the Brahmaputra basin, an increase in drought frequency was observed. The correlation based on Niño 3.4 and SPI3 presents a negative relation for the monsoon season in the G-B basin revealing a situation of drought occurrence (SPI3 below 0) with increased Nino 3.4 values (El Niño above + 0.4C).

WorldSeasons: a seasonal classification system interpolating biome classifications within the year for better temporal aggregation in climate science

We present a seasonal classification system to improve the temporal framing of comparative scientific analysis. Research often uses yearly aggregates to understand inherently seasonal phenomena like harvests, monsoons, and droughts. This obscures important trends across time and differences through space by including redundant data. Our classification system allows for a more targeted approach. We split global land into four principal climate zones: desert, arctic and high montane, tropical, and temperate. A cluster analysis with zone-specific variables and weighting splits each month of the year into discrete seasons based on the monthly climate. We expect the data will be able to answer global comparative analysis questions like: are global winters less icy than before? Are wildfires more frequent now in the dry season? How severe are monsoon season flooding events? This is a natural extension of the historical concept of biomes, made possible by recent advances in climate data availability and artificial intelligence.

Expert-based prior uncertainty analysis of gridded water balance components: Application to the irrigated Hindon River Basin, India

Study regionHindon River Basin, North India. Study focusAccurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the regionGrid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R2 above 0.8) than ET fluxes (R2 less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

Trajectory of microplastic particles with 2-dimensional hydrodynamic modelling approach at Pekalongan waters, Central Java, Indonesia.

This research aims to understand the extent of microplastic contamination in Pekalongan waters, Central Java, and its potential impact on fishing grounds, aligning with Indonesia's National Action Plan for Handling Marine Debris 2018-2025. The study employs a 2D hydrodynamics modelling approach with Mike 21 Software to map the spatial distribution of microplastic movement concerning fishing areas during the west and east monsoon seasons. The results showed that microplastic particles follow tidal currents in Pekalongan waters, with their movement influenced by factors such as current, wind, and tidal conditions. The trajectory of microplastics entering fishing ground areas poses potential contamination risk for fish caught by fishermen, threatening the health of marine ecosystems and the stability of their structure and function.