The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations

Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.

Assessing the uncertainty associated with intermittent rainfall fields

[1] In many practical situations where spatial rainfall estimates are needed, rainfall occurs as a spatially intermittent phenomenon. An efficient geostatistical method for rainfall estimation in the case of intermittency has previously been published and comprises the estimation of two independent components: a binary random function for modeling the intermittency and a continuous random function that models the rainfall inside the rainy areas. The final rainfall estimates are obtained as the product of the estimates of these two random functions. However the published approach does not contain a method for estimation of uncertainties. The contribution of this paper is the presentation of the indicator maximum likelihood estimator from which the local conditional distribution of the rainfall value at any location may be derived using an ensemble approach. From the conditional distribution, representations of uncertainty such as the estimation variance and confidence intervals can be obtained. An approximation to the variance can be calculated more simply by assuming rainfall intensity is independent of location within the rainy area. The methodology has been validated using simulated and real rainfall data sets. The results of these case studies show good agreement between predicted uncertainties and measured errors obtained from the validation data.

Statistical modelling of extreme rainfall in Madeira Island

Extreme rainfall events have triggered a significant number of flash floods in Madeira Island along its past and recent history. Madeira is a volcanic island where the spatial rainfall distribution is strongly affected by its rugged topography. In this thesis, annual maximum of daily rainfall data from 25 rain gauge stations located in Madeira Island were modelled by the generalised extreme value distribution. Also, the hypothesis of a Gumbel distribution was tested by two methods and the existence of a linear trend in both distributions parameters was analysed. Estimates for the 50– and 100–year return levels were also obtained. Still in an univariate context, the assumption that a distribution function belongs to the domain of attraction of an extreme value distribution for monthly maximum rainfall data was tested for the rainy season. The available data was then analysed in order to find the most suitable domain of attraction for the sampled distribution. In a different approach, a search for thresholds was also performed for daily rainfall values through a graphical analysis. In a multivariate context, a study was made on the dependence between extreme rainfall values from the considered stations based on Kendall’s τ measure. This study suggests the influence of factors such as altitude, slope orientation, distance between stations and their proximity of the sea on the spatial distribution of extreme rainfall. Groups of three pairwise associated stations were also obtained and an adjustment was made to a family of extreme value copulas involving the Marshall–Olkin family, whose parameters can be written as a function of Kendall’s τ association measures of the obtained pairs.

Spatial intra-annual variability of precipitation based on geostatistics. a case study for the Paraiba do Sul Basin, southeastern Brazil

The emphasis in this research is to evaluate the spatial distribution of the precipitation using a geostatistics approach. Seasonal time scales records considering DJF, MAM, JJA e SON periods performed the analysis. Procedures to evaluate the variogram selection and to produce kriging maps were performed in a GIS environment (ArcGIS®). The results showed that kriging method was very suitable to detect both large changes in the whole area as those local small and subtle changes. Kriging demonstrated be a powerful statistical interpolation method that might be very useful in regions with great complexity in climatology and geomorphology.

A space-time hybrid hourly rainfall model for derived flood frequency analysis

For derived flood frequency analysis based on hydrological modelling long continuous precipitation time series with high temporal resolution are needed. Often, the observation network with recording rainfall gauges is poor, especially regarding the limited length of the available rainfall time series. Stochastic precipitation synthesis is a good alternative either to extend or to regionalise rainfall series to provide adequate input for long-term rainfall-runoff modelling with subsequent estimation of design floods. Here, a new two step procedure for stochastic synthesis of continuous hourly space-time rainfall is proposed and tested for the extension of short observed precipitation time series. First, a single-site alternating renewal model is presented to simulate independent hourly precipitation time series for several locations. The alternating renewal model describes wet spell durations, dry spell durations and wet spell intensities using univariate frequency distributions separately for two seasons. The dependence between wet spell intensity and duration is accounted for by 2-copulas. For disaggregation of the wet spells into hourly intensities a predefined profile is used. In the second step a multi-site resampling procedure is applied on the synthetic point rainfall event series to reproduce the spatial dependence structure of rainfall. Resampling is carried out successively on all synthetic event series using simulated annealing with an objective function considering three bivariate spatial rainfall characteristics. In a case study synthetic precipitation is generated for some locations with short observation records in two mesoscale catchments of the Bode river basin located in northern Germany. The synthetic rainfall data are then applied for derived flood frequency analysis using the hydrological model HEC-HMS. The results show good performance in reproducing average and extreme rainfall characteristics as well as in reproducing observed flood frequencies. The presented model has the potential to be used for ungauged locations through regionalisation of the model parameters.

Climate information needs of Gascoyne-Murchison pastoralists: a representative study of the Western Australian grazing industry

The Gascoyne-Murchison region of Western Australia experiences an arid to semi-arid climate with a highly variable temporal and spatial rainfall distribution. The region has around 39.2 million hectares available for pastoral lease and supports predominantly catle and sheep grazing leases. In recent years a number of climate forecasting systems have been available offering rainfall probabilities with different lead times and a forecast period; however, the extent to which these systems are capable of fulfilling the requirements of the local pastoralists is still ambiguous. Issues can range from ensuring forecasts are issued with sufficient lead time to enable key planning or decisions to be revoked or altered, to ensuring forecast language is simple and clear, to negate possible misunderstandings in interpretation. A climate research project sought to provide an objective method to determine which available forecasting systems had the greatest forecasting skill at times of the year relevant to local property management. To aid this climate research project, the study reported here was undertaken with an overall objective of exploring local pastoralists' climate information needs. We also explored how well they understand common climate forecast terms such as 'mean', median' and 'probability', and how they interpret and apply forecast information to decisions. A stratified, proportional random sampling was used for the purpose of deriving the representative sample based on rainfall-enterprise combinations. In order to provide more time for decision-making than existing operational forecasts that are issued with zero lead time, pastoralists requested that forecasts be issued for May-July and January-March with lead times counting down from 4 to 0 months. We found forecasts of between 20 and 50 mm break-of-season or follow-up rainfall were likely to influence decisions. Eighty percent of pastoralists demonstrated in a test question that they had a poor technical understanding of how to interpret the standard wording of a probabilistic median rainfall forecast. this is worthy of further research to investigate whether inappropriate management decisions are being made because the forecasts are being misunderstood. We found more than half the respondents regularly access and use weather and climate forecasts or outlook information from a range of sources and almost three-quarters considered climate information or tools useful, with preferred methods for accessing this information by email, faxback service, internet and the Department of Agriculture Western Australia's Pastoral Memo. Despite differences in enterprise types and rainfall seasonality across the region we found seasonal climate forecasting needs were relatively consistent. It became clear that providing basic training and working with pastoralists to help them understand regional climatic drivers, climate terminology and jargon, and the best ways to apply the forecasts to enhance decision-making are important to improve their use of information. Consideration could also be given to engaging a range of producers to write the climate forecasts themselves in the language they use and understand, in consultation with the scientists who prepare the forecasts.

Seasonality of westerly moisture transport in the East Asian summer monsoon and its implications for interpreting precipitation δ18O

East Asian summer monsoon (EASM) rainfall impacts the world's most populous regions. Accurate EASM rainfall prediction necessitates robust paleoclimate reconstructions from proxy data and quantitative linkage to modern climatic conditions. Many precisely dated oxygen isotope records from Chinese stalagmites have been interpreted as directly reflecting past EASM rainfall amount variability, but recent research suggests that such records instead integrate multiple hydroclimatic processes. Using a Lagrangian precipitation moisture source diagnostic, we demonstrate that EASM rainfall is primarily derived from the Indian Ocean. Conversely, Pacific Ocean moisture export peaks during winter, and the moisture uptake area does not differ significantly between summer and winter and is thus a minor contributor to monsoonal precipitation. Our results are substantiated by an accurate reproduction of summer and winter spatial rainfall distributions across China. We also correlate modern EASM rainfall oxygen isotope ratios with instrumental rainfall amount and our moisture source data. This analysis reveals that the strength of the source effect is geographically variable, and differences in atmospheric moisture transport may significantly impact the isotopic signature of EASM rainfall at the Hulu, Dongge, and Wanxiang Cave sites. These results improve our ability to isolate the rainfall amount signal in paleomonsoon reconstructions and indicate that precipitation across central and eastern China will directly respond to variability in Indian Ocean moisture supply.

Precipitação sazonal sobre a Amazônia oriental no período chuvoso: observações e simulações regionais com o RegCM3

O presente trabalho apresenta uma contribuição aos estudos de modelagem climática com ênfase na variabilidade pluviométrica sazonal da Amazônia oriental, durante as estações de verão e outono (DJF e MAM). Baseado nos resultados das simulações regionais do RegCM3 para um período de 26 anos (1982/83 a 2007/08) e usando domínio em alta resolução espacial (30 Km) e dois diferentes esquemas de convecção (Grell e MIT), foi investigado o desempenho do modelo em simular a distribuição regional de precipitação sazonal na Amazônia oriental, com referência a um novo conjunto de dados observacional compilado com informações de uma ampla rede integrada de estações pluviométricas. As análises quantitativas evidenciaram que o RegCM3 apresenta erros sistemáticos, sobretudo aqueles relacionados com viés seco no Amapá e norte/nordeste do Pará usando ambos os esquemas Grell e MIT, os quais apontam que o modelo não reproduz as características da ZCIT sobre o Atlântico equatorial. As simulações usando MIT, também apresentaram viés úmido no sudoeste/sul/sudeste do Pará e norte do Tocantins. Além disso, através da técnica de composições, também foi investigado o desempenho do RegCM3 em reproduzir os padrões espaciais anômalos de precipitação sazonal em associação aos episódios ENOS, e as fases do gradiente térmico sobre o Atlântico intertropical. Os resultados demonstraram que o modelo conseguiu representar realisticamente bem o padrão espacial das anomalias pluviométricas acima (abaixo) do normal em grande parte da Amazônia oriental, durante os conhecidos cenários favoráveis, i.e., condições de La Niña e gradiente de aTSM para o Atlântico sul (desfavoráveis, i.e., El Niño e gradiente de aTSM para o Atlântico norte.

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

Estimation of Convective Extreme Events over Germany based on Large Scale Parameters

Strong convective events can produce extreme precipitation, hail, lightning or gusts, potentially inducing severe socio-economic impacts. These events have a relatively small spatial extension and, in most cases, a short lifetime. In this study, a model is developed for estimating convective extreme events based on large scale conditions. It is shown that strong convective events can be characterized by a Weibull distribution of radar-based rainfall with a low shape and high scale parameter value. A radius of 90km around a station reporting a convective situation turned out to be suitable. A methodology is developed to estimate the Weibull parameters and thus the occurrence probability of convective events from large scale atmospheric instability and enhanced near-surface humidity, which are usually found on a larger scale than the convective event itself. Here, the probability for the occurrence of extreme convective events is estimated from the KO-index indicating the stability, and relative humidity at 1000hPa. Both variables are computed from ERA-Interim reanalysis. In a first version of the methodology, these two variables are applied to estimate the spatial rainfall distribution and to estimate the occurrence of a convective event. The developed method shows significant skill in estimating the occurrence of convective events as observed at synoptic stations, lightning measurements, and severe weather reports. In order to take frontal influences into account, a scheme for the detection of atmospheric fronts is implemented. While generally higher instability is found in the vicinity of fronts, the skill of this approach is largely unchanged. Additional improvements were achieved by a bias-correction and the use of ERA-Interim precipitation. The resulting estimation method is applied to the ERA-Interim period (1979-2014) to establish a ranking of estimated convective extreme events. Two strong estimated events that reveal a frontal influence are analysed in detail. As a second application, the method is applied to GCM-based decadal predictions in the period 1979-2014, which were initialized every year. It is shown that decadal predictive skill for convective event frequencies over Germany is found for the first 3-4 years after the initialization.

Spatial and temporal effects of grazing management and rainfall on the vertebrate fauna of a tropical savanna

Grazing by domestic livestock is one of the most widespread uses of the rangelands of Australia. There is limited information on the effects of grazing by domestic livestock on the vertebrate fauna of Australia and the establishment of a long-term grazing experiment in north-eastern Queensland at Wambiana provided an opportunity to attempt an examination of the changes in vertebrate fauna as a consequence of the manipulation of stocking rates. The aim was to identify what the relative effects of vegetation type, stocking rate and other landscape-scale environmental factors were on the patterns recorded. Sixteen 1-ha sites were established within three replicated treatments (moderate, heavy and variable stocking rates). The sites were sampled in the wet and dry seasons in 1999-2000 (T-0) and again in 2003-04 (T-1). All paddocks of the treatments were burnt in 1999. Average annual rainfall declined markedly between the two sampling periods, which made interpretation of the data difficult. A total of 127 species of vertebrate fauna comprising five amphibian, 83 bird, 27 reptile and 12 mammal species were recorded. There was strong separation in faunal composition from T-0 to T-1 although changes in mean compositional dissimilarity between the grazing stocking rate treatments were less well defined. There was a relative change in abundance of 24 bird, four mammal and five reptile species from T-0 to T-1. The generalised linear modelling identified that, in the T-1 data, there was significant variation in the abundance of 16 species explained by the grazing and vegetation factors. This study demonstrated that vertebrate fauna assemblage did change and that these changes were attributable to the interplay between the stocking rates, the vegetation types on the sites surveyed, the burning of the experimental paddocks and the decrease in rainfall over the course of the two surveys. It is recommended that the experiment is sampled again but that the focus should be on a rapid survey of abundant taxa (i.e. birds and reptiles) to allow an increase in the frequency of sampling and replication of the data. This would help to articulate more clearly the trajectory of vertebrate change due to the relative effects of stocking rates compared with wider landscape environmental changes. Given the increasing focus on pastoral development in northern Australia, any opportunity to incorporate the collection of data on biodiversity into grazing manipulation experiments should be taken for the assessment of the effects of land management on faunal species.

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO2 fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO2) for two time horizons, namely 2020 and 2050. The potential benefits from CO2 fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from −5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from −5 to −30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO2 fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.

Large scale features and assessment of spatial scale correspondence between TMPA and IMD rainfall datasets over Indian landmass

Daily rainfall datasets of 10 years (1998-2007) of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) version 6 and India Meteorological Department (IMD) gridded rain gauge have been compared over the Indian landmass, both in large and small spatial scales. On the larger spatial scale, the pattern correlation between the two datasets on daily scales during individual years of the study period is ranging from 0.4 to 0.7. The correlation improved significantly (similar to 0.9) when the study was confined to specific wet and dry spells each of about 5-8 days. Wavelet analysis of intraseasonal oscillations (ISO) of the southwest monsoon rainfall show the percentage contribution of the major two modes (30-50 days and 10-20 days), to be ranging respectively between similar to 30-40% and 5-10% for the various years. Analysis of inter-annual variability shows the satellite data to be underestimating seasonal rainfall by similar to 110 mm during southwest monsoon and overestimating by similar to 150 mm during northeast monsoon season. At high spatio-temporal scales, viz., 1 degrees x1 degrees grid, TMPA data do not correspond to ground truth. We have proposed here a new analysis procedure to assess the minimum spatial scale at which the two datasets are compatible with each other. This has been done by studying the contribution to total seasonal rainfall from different rainfall rate windows (at 1 mm intervals) on different spatial scales (at daily time scale). The compatibility spatial scale is seen to be beyond 5 degrees x5 degrees average spatial scale over the Indian landmass. This will help to decide the usability of TMPA products, if averaged at appropriate spatial scales, for specific process studies, e.g., cloud scale, meso scale or synoptic scale.