Seasonal Variability Research Articles

Employing stable isotopes to reveal temporal trajectories of water travelling through the soil–plant-atmosphere continuum

The spatiotemporal patterns of water travelling through the soil–plant-atmosphere continuum (SPAC) have received much attention due to the frequency of extreme weather and water scarcity. However, the interconnections and transboundary transport of specific compartments within the hydrologic cycle are poorly understood. We portrayed the propagation paths of water isotope signals by analyzing isotopes of precipitation, soil water, and xylem water. The isotope sine curves analysis method was improved to quantify water transfer efficiency, mean transmission time from precipitation to soil (MTT1), mean transmission time from soil to trees (MTT2), mean residence time within soil (MRTSW), and mean residence time within tree xylems (MRTXY). The temporal trajectory of water traveling through SPAC was depicted, and its influencing factors and intrinsic mechanisms were explored. Our results showed that (1) water isotopes were blocked when crossing water pools (precipitation, soil water, and xylem water) and the transfer efficiency was progressively weaker (85.5 %→13.5 %). (2) The MTT1 was significantly and positively correlated with soil porosity (Spearman correlation coefficient, 0.551). Its spatiotemporal pattern (0.8–15.9d) indicated that the transport and mixing of soil water involved two modes: displacement flow and bypass flow. The MTT2 (−13.2d–4.3d) of Platycadus orientalis was in general smaller than that of Quercus variabilis (−4.7d–11.5d). (3) The MRT (35.2d–343.8d) was influenced by a combination of soil physicochemical properties, root distribution, and tree morphological traits. Our study shows that connectivity between pools is better reflected with transfer efficiency. Comparing MTT and MRT of P. orientalis and Q. variabilis, tree hydrodynamic processes were quantified, and P. orientalis was more sensitive to seasonal moisture variability. Additionally, our study improved the understanding of the “black box” within the hydrological interface of plant-soil interactions.

Sugarcane Yellow Leaf Virus – A Silent Threat?

Abstract Sugarcane yellow leaf virus (SCYLV) is the causal agent of yellow leaf disease, a significant threat to global sugarcane production. SCYLV is disseminated via infected planting material and transmitted by aphids, primarily by Melanaphis sacchari (Zehntner). The disease’s impact varies depending on the sugarcane cultivar, with some varieties showing little to no foliar symptoms, yet experiencing severe yield losses. Reliable diagnostic methods, including serological and molecular techniques, are crucial for accurate detection, though these methods vary in sensitivity. Advances in molecular techniques have revealed ten major SCYLV genotypes grouped into three phylogroups, further complicating detection efforts. Improved understanding of SCYLV’s distribution within the plant and its seasonal variability are yet to be understood. Management strategies focus on breeding SCYLV-resistant varieties, but this is challenging due to the virus’s variable impact on yield and the often asymptomatic nature of epidemics. Efforts to develop SCYLV-free propagative material have been implemented, but the virus can still spread via aphid vectors from neighbouring fields. Ongoing research aims to identify resistant cultivars, optimize diagnostic techniques, and better understand the epidemiology of SCYLV to mitigate its impact on sugarcane yield and quality. Information © The Authors 2024

Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient

ABSTRACTIn the context of rising global temperatures and their impact on weather patterns and water cycles, understanding the relationship between vegetation and hydroclimatic forcing is critical. Riparian forests are highly vulnerable to hydroclimatic variability, which can significantly affect water availability in the soil on which they primarily depend. Along large rivers, hydroclimatic forcings can vary, resulting in different vegetative responses depending on the local climatic context and site conditions. To explore this, we studied riparian forest greenness along a 512‐km river corridor with a 3° latitudinal gradient, analysing the relative contributions of climate (latitude, season, temperature, precipitation) and local hydrological conditions (groundwater). Here, we show that riparian forests along a latitudinal gradient respond differently to hydroclimatic controls, with vegetative dynamics that can be attenuated or accentuated by local site conditions. We combined Sentinel‐2 satellite Normalised Difference Vegetation Index (NDVI) data over seven years (2016–2022) with hydroclimatic data to examine riparian forest greenness responses to latitudinal, seasonal and interannual hydroclimatic variability. We found contrasting hydroclimatic controls across the latitudinal gradient, with the northernmost sites predominantly controlled by temperature, while those further south are limited by water availability. In addition, we identified temperature as the primary driver of NDVI throughout the growing season, either positively or negatively. Late season precipitation and high phreatic water availability positively influenced NDVI, emphasising the role of local conditions in governing riparian forest resilience. This study enhances understanding of climate controls on riparian tree greenness, which is critical for designing effective landscape‐scale riparian ecosystem management and restoration strategies.

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Coral Sr/Ca-SST calibration: SST records from Kenyan corals

To better understand the seasonal variability in SST in Kenyan coral reef ecosystems, Sr/Ca ratios have been measured in Kenyan coral skeletons as proxies of SST, and a new Sr/Ca = f(SST) calibration was established: Sr/Ca = − 0.053 (± 0.006) *SST + 10.43 (± 0.16). The slope was within the margins of − 0.04 and − 0.08 mmolmol−1 °C of previously published Sr/Ca-SST calibration equations. A reliability test revealed that the present calibration equation reproduces a previously published calibration equation developed for Kenyan corals. This calibration was applied to corals collected in reef ecosystems under different natural and anthropogenic influences: Tana Delta Ziwayu Island, Watamu National Marine Park, and Shimon Mupunguti Marine Reserve. Using the developed Sr/Ca-thermometry, the reconstructed Sr/Ca-SST revealed that reefs from the southern basin of the Kenyan Indian Ocean are exposed to warmer SSTs than reefs in the northern Basin are. Additionally, the results revealed that the seasonal variability on SST increased moving toward the southern basin of the Kenyan Indian Ocean. We use the local instrument SST as an SST reference, which is aligned against the Sr/Ca seasonal signals recorded in SMR3-18 (the nearest coral site), and the intensity of the third global coral bleaching (April and May 2016) is affirmed.

Evaluating water quality of rock glacier outflows in the Western Alps, Italy: a regional perspective.

Intact rock glaciers (RG) are considered valuable water storage because containing permafrost ice volumes. The hydrological relevance of RG is forecasted to increase with respect to glaciers under climate change scenarios, as well as RG's role as water resources in alpine basins for multiple uses. Besides the assessment of water amount stored in intact rock glaciers, the evaluation of water quality is of primary importance. Here, we present the results of a chemical survey performed on five outflows from intact RG in 2020-2023 in the Piedmont region, Western Alps, Italy, along a latitudinal gradient and in different geological settings. The survey aimed to assess the water quality of RG outflows based on chemical indicators (major ions, nutrients, trace metals). Sampling and analyses were performed according to standard methods for freshwater samples, paying specific attention to the analytical quality and consistency of the data. We considered seasonal and interannual variability of the main chemical variables and the possible effects of RG outflows on the chemistry of lakes and ponds located in proximity to the RG. All the investigated sites were characterized by low to moderate ion content, low nutrients, and trace metals close to or below the detection limit, indicating a good water quality status. Results suggested lithology as the main factor affecting the chemical composition of RG outflows. The results of this study indicate it is advisable to develop shared protocols and joint monitoring programs for data collecting at RG outflow sites all over the Alps, possibly integrating chemical and biological indicators, with the final aim of monitoring the water quality of these valuable resources and its temporal evolution under climate change.

Evaluation of CMIP5 and CMIP6 Models Based on Weather Types Applied to the South Atlantic Ocean

ABSTRACTChanges in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios.

Methane in the Water Column of the Gdansk Deep (Baltic Sea): Seasonal and Vertical Variability

The study of the vertical distribution of methane dissolved in water and related parameters (water temperature and salinity, dissolved oxygen concentration) was carried out in 2021–2023 at the offshore carbon supersite Rosyanka in the Gdansk Deep of the Baltic Sea. Measurements with such frequency (a total of 16 surveys) were carried out in the region for the first time. Methane concentrations varied over a fairly wide range (0.000–1.122 μmol/L), and increased with depth, which is a typical distribution for the Baltic Sea and is associated with the vertical stratification of the water column. Single maximum values were characteristic of the layer extending from the bottom to the upper boundary of the halocline, which indicates the flow of methane from bottom sediments into the water column. In the near-surface layer (5–15 m), a weakly pronounced peak in methane concentrations was observed, which is a manifestation of the “oceanic methane paradox”. No pronounced seasonality was detected in the vertical distribution of dissolved methane; the correlation between temperature, salinity, oxygen, and methane content turned out to be low.

Spatial and seasonal fluctuations in fresh submarine groundwater discharge revealed by marine continuous resistivity profiling

Submarine Groundwater Discharge (SGD) is a major pathway for the discharge of fresh and saline groundwater and associated dissolved compounds into marine environments. However, assessing SGD processes in coastal aquifers is challenging due to inaccessibility, dynamic conditions, complex subsurface geology, and the need for long-term monitoring to capture temporal and spatial variations in SGD rates accurately. This study employs marine continuous resistivity profiling (MCRP) as a main method to assess the presence of freshwater or brackish SGD offshore and to examine its potential seasonal variations. The method has been applied in the coastal alluvial aquifer of Maresme (Spain) and validated with other methods to trace SGD, including salinity profiles, Ra isotopes, and piezometric levels. Several MCRP transects of 700 m long, perpendicular to the coastline, were performed in a coastal marine area to obtain electrical resistivity data of the seabed covering an area of 3 km2. The data was acquired in two field campaigns with contrasting hydrological conditions (dry and wet seasons). The MCRP results allow the identification of areas of fresh SGD in marine sediments, with a clear seasonal variability that indicates a higher discharge of fresh groundwater in the wet season.

Seasonal and Interannual Variations in Sea Ice Thickness in the Weddell Sea, Antarctica (2019–2022) Using ICESat-2

The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness.

Seasonal Variability of Human Enteric Viruses Discovered in Food Production Mussels (Mytilus galloprovincialis) Farmed in the Central Adriatic Sea (Italy).

Among the different naked and quasi-enveloped viruses, the hepatitis A virus (HAV), hepatitis E virus (HEV), and norovirus genogroups I and II (NoV GI and NoV GII) are considered the main microbiological noxae involved in foodborne outbreaks. Mussels can harbor pathogens in their tissues. In addition to epidemiological attention, marine water temperature changes are considered a crucial variable influencing viral loads. This study aimed to biomolecularly screen 1775 farmed mussels (Mytilus galloprovincialis) for viral ribonucleic acid (RNA) sequence detection (belonging to the HAV, HEV, and NoV GI and GII genogroups) in three different sampling periods (spring, summer, and winter), with the mussels collected from three farms located in the Central Adriatic Sea (Italy). The results showed that 10.42% of the screened animals harbored at least one type of pathogen RNA, more specifically, 5.35% HEV, 4.51% NoV GI, and 0.56% HAV. The highest genetic equivalent (GE) amounts were majorly observed in the winter season (NoV GI 1.0 × 103 GE/g and HEV 1.0 × 102 GE/g), resulting in statistical differences when compared to summer and spring (p-value: <0.001). The original data obtained serve to bring scientific attention to the possible influence of environmental and climatic aspects on viral loads, highlighting the crucial role played by biomolecular assays as preventive medicine tools.

Scabies in 604 Patients: A Glimpse into the Disease Burden and Its Associated Mortality in Hong Kong.

Scabies is a worldwide parasitic dermatosis with a significant health burden on the young and the elderly. Statistics about the prevalence of scabies in Hong Kong are not available. This is a retrospective study of patients from a regional hospital cluster in Hong Kong with microscopy-documented Sarcoptes scabiei infestations from January 2018 to December 2022. The condition was categorised into classical scabies and crusted scabies upon clinical presentation. Demographic data, comorbid diseases, mobility and residential status, seasonal variability, secondary bacterial infection, treatment and outcomes were described. These were compared between classic and crusted scabies. In total, 604 patients were identified, representing 51.65 per 100,000 discharged patients during the study period. The median age was 84 years and 54.5% were male. The majority (506 or 83.8%) came from residential care homes for the elderly. The mean time from admission to diagnosis was 8.8 days for community-acquired infestation. There were 564 and 40 cases of classic and crusted scabies, respectively. The two groups of patients were comparable in terms of residence in elderly homes, co-existing chronic illnesses, mobility, and time from admission to diagnosis. Forty-five (7.5%) patients had positive blood cultures temporally associated with scabies. Patients with crusted scabies were at higher risk for bacteraemia (7/40 versus 38/564, p = 0.022). Permethrin and benzyl benzoate were the most popular treatment regimens, with treatment failure observed in 59/397 (14.4%) and 18/173 (10.4%), respectively. There were 172 (28.5%) mortalities within 30 days of scabies diagnosis. Thus, the burden of scabies infestation is significant in Hong Kong. Hospitalised patients diagnosed with scabies are mainly senior citizens living in residential care homes for the elderly, suggesting reservoirs of S. scabiei in the community. Of concern, bacteraemic illnesses are common and significant mortality is temporarily associated with infestation. With a rising elderly population, there is a pressing need to understand and control scabies in Hong Kong. Our study did not find that common medical illness, besides immunosuppressive therapy, predisposed patients to crusted scabies. The crusted form of scabies was associated with a higher risk of bacteraemia. The current study provides a better perspective of the disease load of scabies in Hong Kong.

Analysing the accessibility of a selected southern Baltic Sea location with relevance to conducting surveys

The article presents the accessibility conditions for carrying out research work for an exemplary site in the southern Baltic Sea region. The basic parameter of the state of the marine environment was the significant wave height determined at a frequency of one hour based on the ERA 5 model data covering the period between 1940 and 2022. A statistical analysis of the variability of this height on a monthly basis, taking into account its conversion into the time of potential availability in conditions enabling research, showed its strong seasonal variability in the form of differences in the number of hours that can be used for operating purposes. This number, depending on the research vessel capabilities, may even be an order of magnitude higher in the summer months than in winter. Additionally, it was found that the variability of the significant wave height has not changed substantially over the last 82 years.

Прогноз толщины ледяного покрова в прибрежных областях Карского и Охотского морей

Based on the CICE viscoplastic sea ice model, the ice cover of the Gulf of Ob and the northwestern Okhotsk Sea was simulated for the winter seasons of 2021/2022, 2022/2023, and 2023/2024. The description of the seasonal variability of sea ice characteristics in these areas and examples of their numerical modeling are given. Using atmospheric forcing based on the WRF-ARW nonhydrostatic atmospheric model, the sea ice thickness in the northwestern Okhotsk Sea was predicted for April 2024. A comparison of the forecasts with the ice maps of the Hydrometcentre of Russia and sea ice thickness measurements at the Ayan and Bolshoy Shantar stations gave satisfactory results.

HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution

Abstract. Surface albedo is an important parameter in radiative-transfer simulations of the Earth's system as it is fundamental for correctly calculating the energy budget of the planet. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on NASA's Terra and Aqua satellites continuously monitor daily and yearly changes in reflection at the planetary surface. The MODIS Surface Reflectance Black-Sky Albedo dataset (version 6.1 of MCD43D) provides detailed albedo maps for seven spectral bands in the visible and near-infrared range. These albedo maps allow us to classify different Lambertian surface types and their seasonal and yearly variability and change, albeit only into seven spectral bands. However, a complete set of albedo maps covering the entire wavelength range is required to simulate radiance spectra and correctly retrieve atmospheric and cloud properties from remote sensing observations of the Earth. We use a principal component analysis (PCA) regression algorithm to generate hyperspectral albedo maps of the Earth. By combining different datasets containing laboratory measurements of hyperspectral reflectance for various dry soils, vegetation surfaces, and mixtures of both, we reconstruct albedo maps across the entire wavelength range from 400 to 2500 nm. The PCA method is trained with a 10-year average of MODIS data for each day of the year. We obtain hyperspectral albedo maps with a spatial resolution of 0.05° in latitude and longitude, a spectral resolution of 10 nm, and a temporal resolution of 1 d (day). Using the hyperspectral albedo maps, we estimate the spectral profiles of different land surfaces, such as forests, deserts, cities, and icy surfaces, and study their seasonal variability. These albedo maps will enable us to refine calculations of the Earth's energy budget and its seasonal variability and improve climate simulations.

The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques

Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.

Applying a Comprehensive Model for Single-Ring Infiltration: Assessment of Temporal Changes in Saturated Hydraulic Conductivity and Physical Soil Properties

Modeling agricultural systems, from the point of view of saving and optimizing water, is a challenging task, because it may require multiple soil physical and hydraulic measurements to investigate the entire crop cycle. The Beerkan method was proposed as a quick and easy approach to estimate the saturated soil hydraulic conductivity, Ks. In this study, a new complete three-dimensional model for Beerkan experiments recently proposed was used. It consists of thirteen different calculation approaches that differ in estimating the macroscopic capillary length, initial (θi) and saturated (θs) soil water contents, use transient or steady-state infiltration data, and different fitting methods to transient data. A steady-state version of the simplified method based on a Beerkan infiltration run (SSBI) was used as the benchmark. Measurements were carried out on five sampling dates during a single growing season (from November to June) in a long-term experiment in which two soil management systems were compared, i.e., minimum tillage (MT) and no tillage (NT). The objectives of this work were (i) to test the proposed new model and calculation approaches under real field conditions, (ii) investigate the impact of MT and NT on soil properties, and (iii) obtain information on the seasonal variability of Ks and other main soil physical properties (θi, soil bulk density, ρb, and water retention curve) under MT and NT. The results showed that the model always overestimated Ks compared to SSBI. Indeed, the estimated Ks differed by a factor of 11 when the most data demanding (A1) approach was considered by a factor of 4–8, depending on the transient or steady-state phase use, when A3 was considered and by a practically negligible factor of 1.0–1.9 with A4. A relatively higher seasonal variability was detected for θi at the MT than NT system. Under both MT and NT, ρb did not change between November and April but increased significantly until the end of the season. The selected calculation approaches provided substantially coherent information on Ks seasonal evolution. Regardless of the approach, the results showed a temporal stability of Ks at least from early April to June under NT; conversely, the MT system was, overall, more affected by temporal changes with a relative stability at the beginning and middle of the season. These findings suggest that a common sampling time for determining Ks could be set at early spring. Soil management affected the soil properties, because the NT system was significantly wetter and more compact than MT on four out of five dates. However, only NT showed a significantly increasing correlation between Ks and the modal pore diameter, suggesting the presence of a relatively smaller and better interconnected pore network in the no-tilled soil. This study confirms the need to test infiltration models under real field conditions to evaluate their pros and cons. The Beerkan method was effective for intensive soil sampling and accurate field investigations on the temporal variability of Ks.

Analysis of seasonal rainfall variability with innovative graphical methods of Konya Closed Basin, Türkiye

This study looked into the variability of seasonal rainfall in the semi-arid Konya Closed Basin (KCB), a water-limited region with high exploitation demand for water resources due to agricultural activities. In the study, seasonal precipitation data of eleven stations in the basin for the period 1971–2020 were used and analyzed with four trend methods (Şen-ITA, CWTSD, IPTA, and TPSC). Visual and statistical findings from the analyses showed generally increasing trends in winter and summer precipitation and decreasing trends in spring precipitation in the basin. In autumn precipitation, according to Şen-ITA, there is an increasing trend in seven stations and a decreasing trend in four stations, while according to CWTSD, there is an increasing trend in six stations and a decreasing trend in five stations. Based on the arithmetic mean IPTA graphs, Winter-Spring transitions in all stations occur from a rising trend region to a falling trend region, but Spring-Summer transitions take the reverse direction. In the standard deviation IPTA graphs, similar transitions in the trend regions are predominant. To the mean and standard deviation TPSC graphs, most of the stations generally have similar behavior. Spring-Summer transition arrows are dominant in Zone III, while Summer-Autumn and Autumn-Winter transitions are dominant in Zone I. Although Winter-Spring transitions are variable, they are more intense in Zone I and III. CWTSD and Şen-ITA methods are also consistent with the results obtained from IPTA graphs, both visually and numerically.

Analysis of spatio-temporal variability of temperature, relative humidity and temperature humidity index in Naivasha Sub-county, Kenya

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.

Analyses of sea surface chlorophyll a trends and variability from 1998 to 2020 in the German Bight (North Sea)

Abstract. Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a.