This paper examines the onset and cessation dates of the rainy season over Ghana using rain gauge data from the Ghana Meteorological Agency (GMet) over the period of 1970-2012. The onset and cessation dates were determined from cumulative curves using the number of rainy days and rainfall amount. In addition, the inter-annual variability of the onset and cessation dates for each climatic zone was assessed using wavelet analysis. A clear distinction between the rainfall characteristics and the length of the rainy season in the various climatic zones is discussed. The forest and coastal zones in the south had their rainfall onset from the second and third dekads of March. The onset dates of the transition zone were from the second dekad of March to the third dekad of April. Late onset, which starts from the second dekad of April to the first dekad of May, was associated with the savannah zone. The rainfall cessation dates in the forest zone were in the third dekad of October to the first dekad of November, and the length of the rainy season was within 225-240 days. The cessation dates of the coastal zone were within the second and third dekad of October, and the length of rainy season was within 210-220 days. Furthermore, the transition zone had cessation dates in the second to third dekad of October, and the length Climate 2015, 3 417 of the rainy season was within 170-225 days. Lastly, the savannah zone had cessation dates within the third dekad of September to the first dekad of October, and the length of rainy season was within 140-180 days. The bias in the rainfall onset, cessation and length of the rainy season was less than 10 days across the entire country, and the root mean square error (RMSE) was in the range of 5-25 days. These findings demonstrate that the onset derived from the cumulative rainfall amount and the rainy days are in consistent agreement. The wavelet power spectrum and its significant peaks showed evidence of variability in the rainfall onset and cessation dates across the country. The coastal and forest zones showed 2-8-and 2-4-year band variability in the onsets and cessations, whereas the onset and cessation variability of the transition and savannah zones were within 2-4 and 4-8 years. This result has adverse effects on rain-fed agricultural practices, disease control, water resource management, socioeconomic activities and food security in Ghana.

Land-atmosphere interactions play an important role for hot temperature extremes in Europe. Dry soils may amplify such extremes through feedbacks with evapotranspiration. While previous observational studies generally focused on the relationship between precipitation deficits and the number of hot days, we investigate here the influence of soil moisture (SM) on summer monthly maximum temperatures (TXx) using water balance model-based SM estimates (driven with observations) and temperature observations. Generalized extreme value distributions are fitted to TXx using SM as a covariate. We identify a negative relationship between SM and TXx, whereby a 100 mm decrease in model-based SM is associated with a 1.6 °C increase in TXx in southern-central and southeastern Europe. Dry SM conditions result in a 2-4 °C increase in the 20-year return value of TXx compared to wet conditions in these two regions. In contrast with SM impacts on the number of hot days (NHD), where low and high surface-moisture conditions lead to different variability, we find a mostly linear dependency of the 20-year return value on surface moisture conditions. We attribute this difference to the non-linear relationship between TXx and NHD that stems from the threshold-based calculation of NHD. Furthermore the employed SM data and the standardized precipitation index (SPI) are only weakly correlated in the investigated regions, highlighting the importance of evapotranspiration and runoff for resulting SM. Finally, in a case study for the hot 2003 summer we illustrate that if 2003 spring conditions in southern-central Europe had been as dry as in the more recent 2011 event, temperature extremes in summer would have been higher by about 1 °C, further enhancing the already extreme conditions which prevailed in that year

BackgroundA major health burden in Cameroon is malaria, a disease that is sensitive to climate, environment and socio-economic conditions, but whose precise relationship with these drivers is still uncertain. An improved understanding of the relationship between the disease and its drivers, and the ability to represent these relationships in dynamic disease models, would allow such models to contribute to health mitigation and adaptation planning. This work collects surveys of malaria parasite ratio and entomological inoculation rate and examines their relationship with temperature, rainfall, population density in Cameroon and uses this analysis to evaluate a climate sensitive mathematical model of malaria transmission.MethodsCo-located, climate and population data is compared to the results of 103 surveys of parasite ratio (PR) covering 18,011 people in Cameroon. A limited set of campaigns which collected year-long field-surveys of the entomological inoculation rate (EIR) are examined to determine the seasonality of disease transmission, three of the study locations are close to the Sanaga and Mefou rivers while others are not close to any permanent water feature. Climate-driven simulations of the VECTRI malaria model are evaluated with this analysis.ResultsThe analysis of the model results shows the PR peaking at temperatures of approximately 22 °C to 26 °C, in line with recent work that has suggested a cooler peak temperature relative to the established literature, and at precipitation rates at 7 mm day−1, somewhat higher than earlier estimates. The malaria model is able to reproduce this broad behaviour, although the peak occurs at slightly higher temperatures than observed, while the PR peaks at a much lower rainfall rate of 2 mm day−1. Transmission tends to be high in rural and peri-urban relative to urban centres in both model and observations, although the model is oversensitive to population which could be due to the neglect of population movements, and differences in hydrological conditions, housing quality and access to healthcare. The EIR follows the seasonal rainfall with a lag of 1 to 2 months, and is well reproduced by the model, while in three locations near permanent rivers the annual cycle of malaria transmission is out of phase with rainfall and the model fails.ConclusionMalaria prevalence is maximum at temperatures of 24 to 26 °C in Cameroon and rainfall rates of approximately 4 to 6 mm day−1. The broad relationships are reproduced in a malaria model although prevalence is highest at a lower rainfall maximum of 2 mm day−1. In locations far from water bodies malaria transmission seasonality closely follows that of rainfall with a lag of 1 to 2 months, also reproduced by the model, but in locations close to a seasonal river the seasonality of malaria transmission is reversed due to pooling in the transmission to the dry season, which the model fails to capture.

To quantify the potential impact of rotavirus vaccines and identify strategies to improve vaccine performance in Bangladesh, a better understanding of the drivers of pre-vaccination rotavirus patterns is required. We developed and fitted mathematical models to 23 years (1990–2012) of weekly rotavirus surveillance data from Dhaka with and without incorporating long-term and seasonal variation in the birth rate and meteorological factors. We performed external model validation using data between 2013 and 2019 from the regions of Dhaka and Matlab. The models showed good agreement with the observed age distribution of rotavirus cases and captured the observed shift in seasonal patterns of rotavirus hospitalizations from biannual to annual peaks. The declining long-term trend in the birth rate in Bangladesh was the key driver of the observed shift from biannual to annual winter rotavirus patterns. Meteorological indices were also important: a 1°C, 1% and 1 mm increase in diurnal temperature range, surface water presence and degree of wetness were associated with a 19%, 3.9% and 0.6% increase in the transmission rate, respectively. The model demonstrated reasonable predictions for both Dhaka and Matlab, and can be used to evaluate the impact of rotavirus vaccination in Bangladesh against changing patterns of disease incidence.

Daily observations of potential mosquito developmental habitats in a suburb of Kumasi in central Ghana reveal a strong variability in their water persistence times, which ranged between 11 and 81 days. The persistence of the ponds was strongly tied with rainfall, location and size of the puddles. A simple power-law relationship is found to fit the relationship between the average pond depth and area well. A prognostic water balance model is derived that describes the temporal evolution of the pond area and depth, incorporating the power-law geometrical relation. Pond area increases in response to rainfall, while evaporation and infiltration act as sink terms. Based on a range of evaluation metrics, the prognostic model is judged to provide a good representation of the pond coverage evolution at most sites. Finally, we demonstrate that the prognostic equation can be generalised and equally applied to a grid-cell to derive a fractional pond coverage, and thus can be implemented in spatially distributed models for relevant vector-borne diseases such as malaria.