Bayesian approach to Spatio-temporally Consistent Simulation of Daily Monsoon Rainfall over India

Simulation of rainfall over a region for long time-sequences can be very useful for planning and policy-making, especially in India where the economy is heavily reliant on monsoon rainfall. However, such simulations should be able to preserve the known spatial and temporal characteristics of rainfall over India. General Circulation Models (GCMs) are unable to do so, and various rainfall generators designed by hydrologists using stochastic processes like Gaussian Processes are also difficult to apply over the vast and highly diverse landscape of India. In this paper, we explore a series of Bayesian models based on conditional distributions of latent variables that describe weather conditions at specific locations and over the whole country. During parameter estimation from observed data, we use spatio-temporal smoothing using Markov Random Field so that the parameters learnt are spatially and temporally coherent. Also, we use a nonparametric spatial clustering based on Chinese Restaurant Process to identify homogeneous regions, which are utilized by some of the proposed models to improve spatial correlations of the simulated rainfall. The models are able to simulate daily rainfall across India for years, and can also utilize contextual information for conditional simulation. We use two datasets of different spatial resolutions over India, and focus on the period 2000-2015. We propose a large number of metrics to study the spatio-temporal properties of the simulations by the models, and compare them with the observed data to evaluate the strengths and weaknesses of the models

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Dengue in Bangladesh: assessment of the influence of climate and under-reporting in national incidence

Dengue occurs in many tropical countries, despite substantial effort to control the Aedes mosquitoes that transmit the virus. The majority of the burden occurs in the South-East Asian Region of the World Health Organization. Bangladesh is a lower-middle income country located in South Asia, with strong seasonal weather variation, heavy monsoon rainfall, and high population density. Dengue has been endemic in Bangladesh since an epidemic in 2000. The aim of my research was to investigate the influence of climate on dengue transmission in Bangladesh over the period January, 2000 - December, 2009. To achieve this aim, I conducted a series of studies integrating epidemiological and socio-environmental factors into a unified statistical modelling framework to better understand transmission dynamics. In a narrative review (Chapter 3), I discuss the emergence and establishment of dengue along with the possibility of future epidemics of severe dengue. Introduction of a dengue virus strain from neighbouring Thailand likely caused the first epidemic in 2000. Cessation of dichlorodiphenyltrichloroethane (DDT) spraying, climatic, socio-demographic, and lifestyle factors also contributed to epidemic transmission and endemic establishment of the virus. However, there has been a decline in reported case numbers following the largest epidemic in 2002, albeit with relatively greater case numbers in alternate years. This occurred despite the absence of significant additional control measures and no changes in the surveillance system having been introduced during the study period. The observed decline from 2002 may be an artefact of the national hospital-based passive surveillance system even though a real decline in incidence could plausibly have occurred due to increased prevalence of immunity, greater public awareness, and reduced mosquito breeding sites. From a temporal negative binomial generalised linear model (Chapter 4), developed using monthly dengue cases in Dhaka from January, 2000 - December, 2009, I identify that mean monthly temperature (coefficient estimate: 6.07; 95% confidence interval: 3.38, 8.67) and diurnal temperature range (coefficient estimate: 15.57; 95% confidence interval: 8.03, 22.85) influence dengue transmission, with significant interaction between the two (coefficient estimate: -0.56; 95% confidence interval: -0.81, -0.29), at a lag of one month in Dhaka, the capital city of Bangladesh where the highest number of cases were reported during the study period. In addition to mean monthly rainfall in the previous two months, dengue incidence is associated with sea surface temperature anomalies in the current and previous months through concomitant anomalies in the annual rainfall cycle. Population density is also significantly associated with increased dengue incidence in Dhaka. Chapter 5 reports an investigation into non-linear dengue–climate associations using the same dataset as used for the previous model in Chapter 4. A Bayesian semi-parametric thin-plate spline approach estimates that the optimal mean monthly temperature for dengue transmission in Dhaka is 29oC and that average monthly rainfall above 15mm decreases transmission. This study also reveals that between 2000 and 2009 only 2.8% (95% Bayesian credible interval 2.7- 2.8) of cases estimated to have occurred in Dhaka were reported through passive case detection. A Bayesian spatio-temporal model (Chapter 6), formulated using monthly dengue cases reported across the country from January, 2000 - December, 2009, identifies that the majority of dengue cases occur in southern Bangladesh with the highest in Dhaka (located almost in the middle of the country), accounting for 93.0% of estimated total cases across the country from 2000-2009. Around 61.0% of Bangladeshi districts are identified as affected with dengue virus during the high transmission season of August and September, contrasting with national surveillance data suggesting that only 42.0% of districts are affected. My thesis provides a better understanding of the dengue-climate relationships that will enable more accurate predictions of the likely impacts of changing climate on dengue risk. Knowledge about the extent of under-reporting will facilitate precise estimation of dengue burden which is vital to assess the risk of severe epidemics. These will help public health professionals to design interventions to strengthen the country’s capacity for prevention of severe dengue epidemics

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

A multivariate bias correction based on N-dimensional probability density function transform (MBCn) technique is applied to four different high-resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias-correction on the historical (1980–2005) period, the inter-variable relationships, and the measures of spatio-temporal consistency are investigated. The focus is on the discrepancies between the original and the bias-corrected results over five agro-ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

Multivariate bias‐correction of high‐resolution regional climate change simulations for West Africa: performance and climate change implications

A multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

Seasonality of Plasmodium falciparum transmission: a systematic review

This article is fully open access and the published version is available free of charge from the jounal website.http://www.malariajournal.com/content/14/1/343Background Although Plasmodium falciparum transmission frequently exhibits seasonal patterns, the drivers of malaria seasonality are often unclear. Given the massive variation in the landscape upon which transmission acts, intra-annual fluctuations are likely influenced by different factors in different settings. Further, the presence of potentially substantial inter-annual variation can mask seasonal patterns; it may be that a location has “strongly seasonal” transmission and yet no single season ever matches the mean, or synoptic, curve. Accurate accounting of seasonality can inform efficient malaria control and treatment strategies. In spite of the demonstrable importance of accurately capturing the seasonality of malaria, data required to describe these patterns is not universally accessible and as such localized and regional efforts at quantifying malaria seasonality are disjointed and not easily generalized. Methods The purpose of this review was to audit the literature on seasonality of P. falciparum and quantitatively summarize the collective findings. Six search terms were selected to systematically compile a list of papers relevant to the seasonality of P. falciparum transmission, and a questionnaire was developed to catalogue the manuscripts. Results and discussion 152 manuscripts were identified as relating to the seasonality of malaria transmission, deaths due to malaria or the population dynamics of mosquito vectors of malaria. Among these, there were 126 statistical analyses and 31 mechanistic analyses (some manuscripts did both). Discussion Identified relationships between temporal patterns in malaria and climatological drivers of malaria varied greatly across the globe, with different drivers appearing important in different locations. Although commonly studied drivers of malaria such as temperature and rainfall were often found to significantly influence transmission, the lags between a weather event and a resulting change in malaria transmission also varied greatly by location. Conclusions The contradicting results of studies using similar data and modelling approaches from similar locations as well as the confounding nature of climatological covariates underlines the importance of a multi-faceted modelling approach that attempts to capture seasonal patterns at both small and large spatial scales. Keywords: Plasmodium falciparum ; Seasonality; Climatic driversAcknowledgements This work was supported by the Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directory, Department of Homeland Security, and Fogarty International Center, National Institutes of Health. DLS is funded by a grant from the Bill & Melinda Gates Foundation (OPP1110495), which also supports RCR. PMA is grateful to the University of Utrecht for supporting him with The Belle van Zuylen Chair. PWG is a Career Development Fellow (K00669X) jointly funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and receives support from the Bill and Melinda Gates Foundation (OPP1068048, OPP1106023)

Potential for using climate forecasts in spatio-temporal prediction of dengue fever incidence in Malaysia

Dengue fever is a viral infection transmitted by the bite of female \textit{Aedes aegypti} mosquitoes. It is estimated that nearly 40\% of the world's population is now at risk from Dengue in over 100 endemic countries including Malaysia. Several studies in various countries in recent years have identified statistically significant links between Dengue incidence and climatic factors. There has been relatively little work on this issue in Malaysia, particularly on a national scale. This study attempts to fill that gap. The primary research question is `to what extent can climate variables be used to assist predictions of dengue fever incidence in Malaysia?'. The study proposes a potential framework of modelling spatio-temporal variation in dengue risk on a national scale in Malaysia using both climate and non-climate information. Early chapters set the scene by discussing Malaysia and Climate in Malaysia and reviewing previous work on dengue fever and dengue fever in Malaysia. Subsequent chapters focus on the analysis and modelling of annual dengue incidence rate (DIR) for the twelve states of Peninsular Malaysia for the period 1991 to 2009 and monthly DIR for the same states in the period 2001 to 2009. Exploratory analyses are presented which suggest possible relationships between annual and monthly DIR and climate and other factors. The variables that were considered included annual trend, in year seasonal effects, population, population density and lagged dengue incidence rate as well as climate factors such as average rainfall and temperature, number of rainy days, ENSO and lagged values of these climate variables. Findings include evidence of an increasing annual trend in DIR in all states of Malaysia and a strong in-year seasonal cycle in DIR with possible differences in this cycle in different geographical regions of Malaysia. High population density is found to be positively related to monthly DIR as is the DIR in the immediately preceding months. Relationships between monthly DIR and climate variables are generally quite weak, nevertheless some relationships may be able to be usefully incorporated into predictive models. These include average temperature and rainfall, number of rainy days and ENSO. However lagged values of these variables need to be considered for up to 6 months in the case of ENSO and from 1-3 months in the case of other variables. These exploratory findings are then more formally investigated using a framework where dengue counts are modelled using a negative binomial generalised linear model (GLM) with a population offset. This is subsequently extended to a negative binomial generalised additive model (GAM) which is able to deal more flexibly with non-linear relationships between the response and certain of the explanatory variables. The model successfully accounts for the large amount of overdispersion found in the observed dengue counts. Results indicated that there are statistically significant relationships with both climate and non-climate covariates using this modelling framework. More specifically, smooth functions of year and month differentiated by geographical areas of the country are significant in the model to allow for seasonality and annual trend. Other significant covariates included were mean rainfall at lag zero month and lag 3 months, mean temperature at lag zero month and lag 1 month, number of rainy days at lag zero month and lag 3 months, sea surface temperature at lag 6 months, interaction between mean temperature at lag 1 month and sea surface temperature at lag 6 months, dengue incidence rate at lag 3 months and population density. Three final competing models were selected as potential candidates upon which an early warning system for dengue in Malaysia might be able to be developed. The model fits for the whole data set were compared using simulation experiments to allow for both parameter and negative binomial model uncertainty and a single model preferred from the three models was identified. The `out of sample' predictive performance of this model was then compared and contrasted for different lead times by fitting the model to the first 7 years of the 9 years monthly data set covering 2001-2009 and then analysing predictions for the subsequent 2 years for lead time of 3, 6 12 and 24 months. Again simulation experiments were conducted to allow for both parameter and model uncertainty. Results were mixed. There does seem to be predictive potential for lead times of up to six months from the model in areas outside of the highly urbanised South Western states of Kuala Lumpur and Selangor and such a model may therefore possibly be useful as a basis for developing early warning systems for those areas. However, none of the models developed work well for Kuala Lumpur and Selangor where there are clearly more complex localised influences involved which need further study. This study is one of the first to look at potential climatic influences on dengue incidence on a nationwide scale in Malaysia. It is also one of the few studies worldwide to explore the use of generalised additive models in the spatio-temporal modelling of dengue incidence. Although, the results of the study show a mixed picture, hopefully the framework developed will be able to be used as a starting point to investigate further if climate information can valuably be incorporated in an early warning system for dengue in Malaysia.Ministry of Education Malaysia (MOE

Uncertainties in the Hydrological Modelling Using Remote Sensing Data over the Himalayan Region

Himalayas the “roof of the world” are the source of water supply for major South Asian Rivers and fulfill the demand of almost one sixth of world’s humanity. Hydrological modeling poses a big challenge for Himalayan River Basins due to complex topography, climatology and lack of quality input data. In this study, hydrological uncertainties arising due to remotely sensed inputs, input resolution and model structure has been highlighted for a Himalayan Gandak River Basin. Firstly, spatial input DEM (Digital Elevation Model) from two sources SRTM (Shuttle Radar Topography Mission) and ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) with resolutions 30m, 90m and 30m respectively has been evaluated for their delineation accuracy. The result reveals that SRTM 90m has best performance in terms of least area delineation error (13239.28 km2) and least stream network delineation error. The daily satellite precipitation estimates TRMM 3B42 V7 (Tropical Rainfall Monitoring Mission) and CMORPH (Climate Prediction Center MORPHing Technique) are evaluated for their feasibly over these terrains. Evaluation based on various scores related to visual verification method, Yes/no dichotomous, and continuous variable verification method reveal that TRMM 3B42 V7 has better scores than CMORPH. The effect of DEM resolution on the SWAT (Soil Water Assessment Tool) model outputs has been demonstrated using sixteen DEM grid sizes (40m-1000m). The analysis reveals that sediment and flow are greatly affected by the DEM resolutions (for DEMs>300m). The amount of total nitrogen (TN) and total phosphorous (TP) are found affected via slope and volume of flow for DEM grid size ≥150m. The T-test results are significant for SWAT outputs for grid size >500m at a yearly time step. The SWAT model is accessed for uncertainty during various hydrological processes modeling with different setups/structure. The results reflects that the use of elevation band modeling routine (with six to eight elevation bands) improves the streamflow statistics and water budgets from upstream to downstream gauging sites. Also, the SWAT model represents a consistent pattern of spatiotemporal snow cover dynamics when compared with MODIS data. At the end, the uncertainty in the stream flow simulation for TRMM 3B42 V7 for various rainfall intensity has been accessed with the statistics Percentage Bias (PBIAS) and RSR (RMSE-observations Standard Deviation Ratio). The results found that TRMM simulated streamflow is suitable for moderate (7.5 to 35.4 mm/day) to heavy rainfall intensities (35.5 to 124.4 mm/day). The finding of the present work can be useful for TRMM based studies for water resources management over the similar parts of the world

Assessing Predictive Performance: From Precipitation Forecasts over the Tropics to Receiver Operating Characteristic Curves and Back

Educated decision making involves two major ingredients: probabilistic forecasts for future events or quantities and an assessment of predictive performance. This thesis focuses on the latter topic and illustrates its importance and implications from both theoretical and applied perspectives. Receiver operating characteristic (ROC) curves are key tools for the assessment of predictions for binary events. Despite their popularity and ubiquitous use, the mathematical understanding of ROC curves is still incomplete. We establish the equivalence between ROC curves and cumulative distribution functions (CDFs) on the unit interval and elucidate the crucial role of concavity in interpreting and modeling ROC curves. Under this essential requirement, the classical binormal ROC model is strongly inhibited in its flexibility and we propose the novel beta ROC model as an alternative. For a class of models that includes the binormal and the beta model, we derive the large sample distribution of the minimum distance estimator. This allows for uncertainty quantification and statistical tests of goodness-of-fit or equal predictive ability. Turning to empirical examples, we analyze the suitability of both models and find empirical evidence for the increased flexibility of the beta model. A freely available software package called betaROC is currently prepared for release for the statistical programming language R. Throughout the tropics, probabilistic forecasts for accumulated precipitation are of economic importance. However, it is largely unknown how skillful current numerical weather prediction (NWP) models are at timescales of one to a few days. For the first time, we systematically assess the quality of nine global operational NWP ensembles for three regions in northern tropical Africa, and verify against station and satellite-based observations and for the monsoon seasons 2007-2014. All examined NWP models are uncalibrated and unreliable, in particular for high probabilities of precipitation, and underperform in the prediction of amount and occurrence of precipitation when compared to a climatological reference forecast. Statistical postprocessing corrects systematic deficiencies and realizes the full potential of ensemble forecasts. Postprocessed forecasts are calibrated and reliable and outperform raw ensemble forecasts in all regions and monsoon seasons. Disappointingly however, they have predictive performance only equal to the climatological reference. This assessment is robust and holds for all examined NWP models, all monsoon seasons, accumulation periods of 1 to 5 days, and station and spatially aggregated satellite-based observations. Arguably, it implies that current NWP ensembles cannot translate information about the atmospheric state into useful information regarding occurrence or amount of precipitation. We suspect convective parameterization as likely cause of the poor performance of NWP ensemble forecasts as it has been shown to be a first-order error source for the realistic representation of organized convection in NWP models. One may ask if the poor performance of NWP ensembles is exclusively confined to northern tropical Africa or if it applies to the tropics in general. In a comprehensive study, we assess the quality of two major NWP ensemble prediction systems (EPSs) for 1 to 5-day accumulated precipitation for ten climatic regions in the tropics and the period 2009-2017. In particular, we investigate their skill regarding the occurrence and amount of precipitation as well as the occurrence of extreme events. Both ensembles exhibit clear calibration problems and are unreliable and overconfident. Nevertheless, they are (slightly) skillful for most climates when compared to the climatological reference, except tropical and northern arid Africa and alpine climates. Statistical postprocessing corrects for the lack of calibration and reliability, and improves forecast quality. Postprocessed ensemble forecasts are skillful for most regions except the above mentioned ones. The lack of NWP forecast skill in tropical and northern arid Africa and alpine climates calls for alternative approaches for the prediction of precipitation. In a pilot study for northern tropical Africa, we investigate whether it is possible to construct skillful statistical models that rely on information about recent rainfall events. We focus on the prediction of the probability of precipitation and find clear evidence for its modulation by recent precipitation events. The spatio-temporal correlation of rainfall coincides with meteorological assumptions, is reasonably pronounced and stable, and allows to construct meaningful statistical forecasts. We construct logistic regression based forecasts that are reliable, have a higher resolution than the climatological reference forecast, and yield an average improvement of 20% for northern tropical Africa and the period 1998-2014