Seasonal Rainfall Research Articles

Water Resources Potential Assessment during Dry Season in Indonesia: A Systematic Literature Review

The dry season in Indonesia has been studied in the context of its impact on rainfall extremes, with research indicating that the dry season rainfall extremes are strongly affected by IOD and ENSO (El Niño-Southern Oscillation). The impact of dry season in 2023 based on the data from the Meteorological, Climatological, and Geophysical Agency (BMKG ) mentioned that this dry season started from march to September 2023. Java Island, whose land area is only 138,379 km² or 7 (seven) percent of Indonesia's area, has a very important position because it is crowded by 65 percent of Indonesia's population or an average of 864 people / km² and contributes to GNP of 60 percent. This article aims to review the water resources in Java Island in Indonesia during the dry season in 2017 to 2023. That causes the lack of water resources has become the main problem in some area in Java Island in Indonesia. This research result assesses the water resources during the dry season, the technique, the method, and the findings. There are 287 articles found only from Scopus sources, 40 article reviewed, and only 5 articles have been matched to the topic by using Prisma P. process. Finally, the result shows that one aspect that remains unclear about Indonesia's dry season is how exactly climate change influences its duration and intensity. While it's known that Indonesia undergoes a dry season, the specific impacts of climate change on factors like rainfall patterns, temperature, and the occurrence of extreme weather events are not fully grasped. This lack of understanding presents challenges for planning agriculture, managing water resources, and conserving ecosystems in the region.

Using the Observed Variations of the Start Date of the Rainy Season over Central America for Its Reliable Seasonal Outlook

Abstract We introduce a simple method to define the start and the end of the rainiest part of the year as the first and the last day of the year when the daily rain rate is more or less than the annual mean climatological rain rate for a region or at a given grid point of the rainfall analysis, respectively. A novelty of this work is the adoption of a perturbation technique to generate a total of 1001 ensemble members to account for observational and analysis uncertainties. This allows for a probabilistic estimate of the start and retreat dates of the rainy season at the granularity of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), version 6, rainfall analysis over Central America. The seasonal cycle of the IMERG rainfall analysis is also found to verify with in situ observations in the region. Many large-scale climate drivers affect regional rainfall, often with complex interactions that affect the onset date, retreat date, and magnitude of the seasonal rainfall cycle, making it difficult to predict the length or total quantity of seasonal rainfall using climate drivers alone. Once an onset date is established, however, this metric alone can be more indicative of both the length and total seasonal rainfall anomaly than predicting how the climate drivers will interact to affect the quantity and duration of upcoming seasonal rainfall. The local relationships of the start date with seasonal length and rainfall anomaly are leveraged to produce effective seasonal outlooks of the rainy season for the region by just monitoring the start date variations. Significance Statement The start date and retreat date of the rainy season in Central America are defined every year, precisely to a specific date from our proposed definition of it. This is possible because the region exhibits a strong seasonality of the rainfall. As a result, the year-to-year (interannual) variation of the seasonal rainfall during the rainy season is also determined by the variations in the length of the season that are usually overlooked in fixed calendar seasons. We define the start or retreat date of the rainy season as the first or the last day of the year when the daily rain rate exceeds or falls below the average daily rainfall from 2001 to 2022, respectively. We also find that both start and retreat date variations independently influence the length and total seasonal rainfall of the rainy season. Consequently, these relationships are leveraged to provide an outlook of the forthcoming rainy season from the diagnosis of the variations of its onset date, which is shown to be an effective predictor with significant useful seasonal prediction skills.

A New Paradigm for Active–Break Cycle in the Indian Summer Monsoon

Abstract A multichannel singular spectrum analysis of 121 years of daily rainfall over India and 43 years of outgoing longwave radiation and horizontal wind has revealed two dominant intraseasonal oscillations (ISOs) that describe the space–time variability of monsoon rainfall. The two nonlinear oscillations are not perfectly periodic but exhibit broadband spectra centered at 45 and 28 days. These modes have coherent and near-regular spatial structure and temporal variations. This study posits that the two nonlinear oscillations provide a consistent and comprehensive framework to study the variability and predictability of intraseasonal rainfall variations. The active and break cycles of monsoon rainfall over India have been traditionally defined by using an arbitrarily chosen duration of persistence of arbitrarily chosen amount of rainfall averaged over an arbitrarily chosen area. This study shows that the phases of the two objectively derived modes of variability provide an objective method for defining the active and break cycles. The two ISOs are further shown to make negligible contribution to the seasonal mean rainfall and its interannual variability. This study proposes that the investigations of the origins of these nonlinear modes, and modulations of their amplitudes and phases, provide a new paradigm for future research on validation and predictability of intraseasonal variations in climate models.

Influence of fungicide on soybean seed quality from delayed harvest and environment

AbstractField studies in 2019–2020 evaluated the influence of fungicide application on seed quality from delayed harvest (approx. 20, 30, and 44 days after optimum harvest timing, i.e., 13% seed moisture). Treatments included nofungicide, pydiflumetofen plus difenoconazole (13.7 fl oz/acre Miravis Top, Syngenta), or mefentrifluconazole plus pyraclostrobin plus fluxapyroxad (8 fl oz/acre Revytek, BASF). Effect of environment was investigated in both field (natural rainfall events) and environmentally controlled growth chambers (79°F or 90°F with 30% or 100% relative humidity and exposed for 48 or 96 h) for potential impacts on soybean [Glycine max (L.) Merr.] seed quality. Seed quality was based on a rating scale of 1 to 10 with 1 being seeds in good condition and 10 being seeds in poor condition, based on USDA reference images. Fungicide application had no effect on seed quality from delayed harvest or a saturated environment (100% relative humidity). Delaying harvest beyond approximately 20 days past optimum timing can result in reduced seed quality regardless of fungicide application (1.0 to 2.0 vs 4.0 to 8.1 rating). In addition, seedpod exposure averaged across temperature and relative humidity environments for as little as 96 h after optimum harvest timing can result in deteriorating seed quality issues (3.2 vs 1.4 rating) regardless of fungicide application. Results indicate that soybean harvest delayed 20 days after optimum timing and subjected to seasonal rainfall events or seedpods exposed to completely saturated conditions for 96 h associated with a tropical weather event will result in soybean seed quality deterioration regardless of fungicide application.

Cascading Landslide: Kinematic and Finite Element Method Analysis through Remote Sensing Techniques

Cascading landslides are specific multi-hazard events in which a primary movement triggers successive landslide processes. Areas with dynamic and quickly changing environments are more prone to this type of phenomena. Both the kind and the evolution velocity of a landslide depends on the materials involved. Indeed, rockfalls are generated when rocks fall from a very steep slope, while debris flow and/or mudslides are generated by fine materials like silt and clay after strong water imbibition. These events can amplify the damage caused by the initial trigger and propagate instability along a slope, often resulting in significant environmental and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici Mountains along the border of the Abruzzo and Lazio regions (Italy), serves as a notable example of the complexities and devastating consequences associated with such events. In March 2021, a substantial debris flow event obstructed the Liri River, marking the latest step in a series of landslide events. Conventional techniques such as geomorphological observations and geological surveys may not provide exhaustive information to explain the landslide phenomena in progress. For this reason, UAV image acquisition, InSAR interferometry, and pixel offset analysis can be used to improve the knowledge of the mechanism and kinematics of landslide events. In this work, the interferometric data ranged from 3 January 2020 to 24 March 2023, while the pixel offset data covered the period from 2016 to 2022. The choice of such an extensive data window provided comprehensive insight into the investigated events, including the possibility of identifying other unrecorded events and aiding in the development of more effective mitigation strategies. Furthermore, to supplement the analysis, a specific finite element method for slope stability analysis was used to reconstruct the deep geometry of the system, emphasizing the effect of groundwater-level flow on slope stability. All of the findings indicate that major landslide activities were concentrated during the heavy rainfall season, with movements ranging from several centimeters per year. These results were consistent with numerical analyses, which showed that the potential slip surface became significantly more unstable when the water table was elevated.

Understanding the drivers of the catchment hydrological cycle of the Jonkershoek Valley catchment, South Africa

Understanding trends in hydro-climate variables and the impacts of land use on the streamflow is crucial for the development of appropriate catchment water management strategies. Jonkershoek (146 km2) is an important headwater catchment in the Table Mountain Group (TMG) geological region, contributing flow to the Cape Winelands District Municipality in the Western Cape. This study analyses hydro-climate variables at annual, monthly, and seasonal scales using an integrated approach composed of statistical homogeneity testing for abrupt changes, Mann-Kendall tests for trend analysis, and the Indicators of Hydrological Alteration (IHA) tool for streamflow alterations. Analyses were conducted for three headwater sub-catchments within the Jonkershoek value, each with different land use history.Homogeneity test of rainfall and streamflow data spanning from 1946 to 2019 identified gradual downwards change points for annual rainfall and streamflow across the sub-catchments. Moreover, the change points for streamflow were inconsistent with those of rainfall. The identified change points in streamflow were consistent with the timing of afforestation activities in Tierkloof, whereas in Bosboukloof and Langrivier they could be attributed to earlier climatic variability. Furthermore, Mann-Kendall test detected significant (p < 0.05) decreasing trends for both annual and seasonal rainfall which coincided with most of the streamflow trends. Trends were strongest for the winter season suggesting a possible shift in climate patterns which influence winter rainfall. Winter streamflows declined by 15.5%–39.5%. Analysis of hydrological flow indices indicated significant decrease in 1-,7- and 30-day annual maximum extremes during afforestation which were attributed to high evapotranspiration rates of pines. The opposite was observed during clearfelling period in Bosboukloof. The median monthly flow also showed a decrease for winter months. This shows that climate variability and land-use change by afforestation have major impacts on streamflow. The findings of this study are important to inform policymakers on the impacts of climate change and land use, allowing pro-active mitigation and adaptation.

Determining dengue infection risk in the Colombo district of Sri Lanka by inferencing the genetic parameters of Aedes mosquitoes

BackgroundFor decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes.MethodsIn this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes.ResultsOur research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease.ConclusionOur research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

Submersible solar powered pump system design for sustainable agriculture using

Current climate change has many implications for people's livelihoods, one of which is the lack of seasonal rainfall, leading to drought, which in turn reduces agricultural yields. Therefore, people have to plant dry season crops, and in the dry season, crops have to rely on water from rivers and streams. But some areas where there is no stagnant water or stagnant water are very dry. So, our team did experimental research and studied the feasibility of using solar groundwater pumping systems in agriculture. It will cover the design, control, and know the strengths and weaknesses of the system, and its suitability to be used in our country, especially in the eight Northern provinces. In this research, we designed and used a Submersible solar-powered pump system, after installation, the system will work automatically without the need to turn it on and off. The panel size used is 330 watts, two panels serial connection for a total of 660 watts; the Submersible solar-powered pump is 400 watts, 48 voltages, Automatic control system, Groundwater depth 68 meters, the diameter of the surface pipe 0.1 meters, The diameter of the suction pipe has a cross-sectional area of 0.025 meters. Experimentation shows the water pump deep at 30 m can absorb more water than 25 m deep. The capacity of the system to be used for agriculture in one day can use 6.13 m3 of water, which is enough for cultivation in an area of 8,000 m2 or more, depending on the type of crop grown. The power generated per day is 3,142 W and is used at 3.14 kWh per day as well or 94.24kWh per month which is sufficient for the use of the system. In cash compared to the electricity consumption from the Electricity du Laos is 39,000 LAK per month. The maximum amount of water collected is 6.13 m3 per day or 184 m3 per month, which is 646,644 LAK per month compared to the water supply system, in the analysis of the cost of installation is 31 months or 2 years and 7 months.

Spatio-Temporal Mechanisms of Rainfall in Kalaburagi District: An Analysis of Trends and Variability

The study used data from 11 taluks to analyse the changes and variability in annual and seasonal rainfall in the Kalaburagi district between 2016 and 2022. Sen Slope and Mann-Kendall analyses were used to evaluate trends, while the coefficient of variation was used to measure rainfall variability. The mean rainfall was calculated using simple mathematical formulas. The findings showed minimal seasonal and yearly variability, with the exception of the southwest monsoon season. The district as a whole as well as all 11 taluks consistently saw a rise in rainfall, according to annual Sen slope research. However, during the northeast monsoon season, negative trends were observed in Aland and Sedam Taluks. In the Mann-Kendall test, the district did not exhibit any general pattern; nevertheless, several taluks, such as Yadrami and Kamalapur, demonstrated remarkable trends in the southwest and northeast seasons, respectively. Due to geographical and climatic fluctuations, the district of Kalaburagi experienced an annual mean precipitation of 797 mm. Higher mean rainfall was recorded in Sedam, Kalagi, Chincholi, and Kamalapur, while lower mean rainfall was recorded in Jevaragi and Yadrami Taluks. In the water-scarce Kalaburagi district, this work is important for policymakers addressing climate change and water resource management.

ACCESS-S2 seasonal forecasts of rainfall and the SAM–rainfall relationship during the grain growing season in south-west Western Australia

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.

From crisis to opportunity: climate change benefits livestock production in Somalia

Abstract While livelihoods of Somalian livestock smallholders rely heavily on seasonal climate conditions, little is known of long-term implications of the changing climate for this nation. Here, we quantify implications of the changing climate on the productivity and profitability of livestock smallholders across a rainfall gradient in northwestern Somalia. Using the Sustainable Grazing Systems (SGS) model, we explore 80 future climate realisations, with global climate model projections including low- and high-impact socio-economic pathways (SSP245 and SSP585), two climate horizons (2040 and 2080) and four case study farm regions. In general, future seasonal and annual rainfall and temperature relative to the baseline period (1981–2020) increased for most regions. Mean annual temperatures increased by 9%–14%, while cumulative annual precipitation increased by 37%–57% from mid to late century, respectively. Grassland production increased with later climate horizons, as higher average annual rainfall together with elevated atmospheric carbon dioxide drove up growth rates in spring and autumn. Under the low emissions scenario (SSP245), changes in farm profit were modest or positive, ranging from negative 4% in Berbera–20% plus in Sheikh. Under the higher emissions scenario (SSP585), farm profits were higher, ranging from 23% to 42% above baseline profits, largely due to greater pasture production and lower requirements for supplementary feed. We conclude that future climates will benefit the productivity and profitability of smallholder farmers in Somalia, although more agile farm management will be required to cope with increased seasonal climate variability.

Conservation agriculture practices: Adaptation and yield

Abstract Conservation agriculture (CA) has been promoted in sub-Saharan Africa (SSA) to increase crop productivity and for climate change adaptation. CA is the simultaneous application of the three principles: no-till, mulch cover, and crop diversification. The potential benefits are largely linked to moisture conservation of crop residues, reduced run-off and erosion, increased infiltration, and reduced evaporative losses. This study uses a review of recent literature in SSA under rain-fed conditions to synthesize evidence of the effect of CA on yield and climate change adaptation. Web of Science and Google Scholar were used for literature searches. Crop productivity results in the literature suggest that CA increases yield in certain circumstances such as well-drained soils and moderate rainfall, and that poorly drained soils in combination with excessive rains lead to depressed yields. The yield benefits reported range from as low as 4% and as high as 16%, with negative effects also reported. Stability analysis used as a proxy for adaptation revealed only a marginal benefit of CA above conventional practices suggesting the significant effect of seasonal rainfall on crop productivity. The results suggest the need to target CA practices to different agroecologies and other pragmatic local agronomic practices that may be required in cases of excessive rainfall and extended mid-season dry spells. The benefits of CA reported are largely plot level, and only a few studies consider the whole farm, especially within the holistic livelihood framework. In addition, adoption of CA remains low among smallholder farmers, and the widespread benefits of the practices cannot be realized at multiple scales.

Spatiotemporal variability and trends of intra-seasonal rainfall and temperature in the drought-prone districts of Northwestern Ethiopia

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.

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.

Effects of the El Niño-Southern Oscillation and seasonal weather conditions on Aedes aegypti infestation in the State of São Paulo (Brazil): A Bayesian spatio-temporal study.

Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008-2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial.

Physical-chemical characterisation of an alum-based water treatment sludge in different raw water turbidity scenarios

Characterisation of the water treatment sludge (WTS) generated in drinking water treatment plants (DWTPs) is crucial to define alternatives for its adequate management, including potential reuse options. To define these alternatives, it is necessary to evaluate rainfall seasonality effect on WTS production and its physical and chemical characteristics. This study assessed the production and characterisation of four types of alum-based WTS. The WTS was generated in a pilot-scale system from different raw water turbidities (i.e., low: <5 NTU, medium: 5–10 NTU, high: ≥10 NTU, and very high turbidity: ∼300 NTU) and coagulant doses. To estimate WTS production, mathematical models based on variables such as raw water turbidity, coagulant dosage, and organic matter removed were used. The WTS characterisations included physical (solids and particle size distribution), chemical (metallic oxides, pH, mineral phases), and surface properties (functional groups and zero-charge point pH). The modified Kawamura model presented the best fit (R2 = 1.0, RMSE = 0.1062 and the lower Akaike Information Criterion) for the estimation of WTS production, indicating that at the DWTPs, it is possible to make sludge production projections using only two simple variables: coagulant dose and the raw water turbidity. The four types of WTS consist mainly of amorphous materials (45–65 %), featuring some mineral phases and exhibiting high contents of Al (Al2O3: 30–34 %), Si (SiO2: 21–26 %) and Fe (Fe2O3: 11–13 %). Nevertheless, very high turbidity WTS shows variations in its characteristics, notably a heightened content of clays. As a result of the high concentrations of Al and Fe, the WTS has the potential to be used as coagulants or for the recovery of coagulants, especially low turbidity WTS, which is produced from water with low turbidity and organic matter. The presence of aluminium–silicate clays and the surface functional groups of the silica network suggest that WTS, particularly very high turbidity WTS, also has the potential to be raw materials for generating adsorbents. The potential applications of WTS in coagulation and adsorption can be leveraged in wastewater treatment, promoting the circular economy in the water sector.

Heat and moisture transport characteristics in permafrost embankment under seasonal rainfall

Heat and moisture transport characteristics in permafrost embankment under seasonal rainfall

Detectable use of ENSO information on crop production in Southern Africa

People have known that El Niño events are associated with low rainfall in Southern Africa for a century, and seasonal rainfall forecasts are now available in agricultural advisories for farmers. While there is abundant theory as to how farmers might (or should) use seasonal rainfall information on their farms, little is known about whether this information has been widely used or has had widespread benefit. In this study, we use subnational data on cropping area and yield to see if we can detect any macro-level patterns in agricultural choices or outcomes that are related to knowledge of the El Niño Southern Oscillation or seasonal forecast information in Southern Africa. We find that in Lesotho and parts of South Africa, planted area of maize and sorghum is reduced when there is a dry start to the season and an El Niño event is apparent at the time of planting. Similarly, we find that in both Lesotho and most provinces of South Africa, drought years associated with El Niño have worse yields than drought years that are not associated with El Niño (controlling for rainfall). This association could indicate that people are discouraged during El Niño years by the potential for drought, and they might be reducing cropping area, reducing agricultural investments, or turning to other income-generating activities. We are unable to detect a relationship between yields and the accuracy of seasonal rainfall forecasts, therefore we are unable to observe any additional yield benefit when more accurate seasonal forecast information is available.

Surface water dynamics of Lake Chad Basin (Sahelian Africa) based on daily temporal resolution Earth observation time series

ABSTRACT Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km2. The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km² between 2005 and 2012, from 2016 to 2019), with less than 350 km2 lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km2, except during drought years (2006–2007), specifically between the days of the year to approximately 66, and days 301–365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1–59 and 305–365/6, and between days 60 and 304, respectively.

Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model

The study used the DSSAT model to assess potential soybean yields in different regions of India and validated it under diverse agroecological conditions. The average simulated yield under irrigated conditions was 3794 kg ha−1 relative to the simulated average rainfed yield of 2446 kg ha−1, showing a 35.52% reduction in grain yield due to adverse moisture conditions under rainfed conditions. Relative to simulated yield, the average observed (actual) rainfed yield across 43 districts of India was 1025 kg ha−1, which was 2769 and 1421 kg ha−1 lower than irrigated and rainfed potential yield, respectively. A significant positive correlation was observed between simulated water non-limited yield and solar radiation (R2 = 0.55, p ≤ 0.05). The simulated rainfed grain yield (R2 = 0.66, p ≤ 0.05) had a significant, positive, and curvilinear relationship with growing season rainfall. On the other hand, the actual yield (R2 = 0.008) showed a non-significant relationship with mean crop seasonal rainfall across locations. The gap between simulated yield under irrigated and rainfed conditions is huge at locations with low seasonal rainfall and narrows with increasing rainfall. In addition, the gap between actual yield and simulated yield under rainfed conditions was larger, even in high seasonal rainfall areas. The yield gap under rainfed conditions is due to the non-adoption of improved crop management practices and could be reduced with proper interventions. This includes adapting drought-resistant varieties, conserving rainwater, changing land configuration, and adopting waterlogging-tolerant varieties using improved technology to reduce the soybean yield gap.