Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

Flood modellling approaches for large lowland tropical catchments.

Flooding is increasing in tropical regions, where millions of people are at risk, and challenges exist in providing reliable predictions and warnings. This research responds to this challenge by identifying and applying physics-based and data-based hydrological modelling approaches for large-scale flood modelling in lowland tropical regions. First, a distributed hydrological model was developed to accurately represent catchment conditions and processes in the model. Second, empirical data from nested catchments were analysed using statistical scaling relationships to complement the accuracy of peak discharge estimates. Finally, the effects of uncertainty propagation and interactions were quantified to increase the reliability of model results. The research was conducted in the Grijalva catchment area (57 958 km²) southeast of Mexico. A large-scale model with a 2 x 2 km grid cell resolution was developed using the SHETRAN hydrological model and run enforced with 3-hour input rainfall data. Geostatistical techniques were used to quantify and reduce errors in input data, and all diverted flows were accounted for to optimise simulations. For the first time, the application of the Scaling theory of floods was applied in the study area to improve the estimation of peak discharge. A Monte Carlo technique was used to propagate and quantify rainfall and parameter uncertainties through a coupled hydrologic and hydraulic model and into model results. Although the model under-predicted the magnitude of peak discharge, calibration results showed satisfactory model performance (NSCE = 0.72, CC = 0.74, Bias = –0.44% and RMSE 139.56 mm) and validation results were good (NSCE = 0.56, CC = 0.60, Bias = –6.3% and RMSE 62.59 mm). A statistical log-log relationship between intercepts (α) and peak discharge, from the smallest nested catchment, was used to complement the simulation of peak discharge magnitudes. It was observed that given rainfall uncertainties of ±71%, ranging from 63 to 73%; the model generates discharge with uncertainties of ± 46%, ranging from 45 to 49% and errors of ±46% ranging from 45 to 46%. The propagated uncertainties resulted in flood inundation extents of ±4.34 km² varying from 1.66 to 7.02 km² Thus, flood modelling in large tropical regions can be achieved by optimally integrating several datasets with the best combination of the model parameter, input and output datasets based on uncertainty and error quantification and removal approaches.PhD in Water, including Desig

Spatio-temporal rainfall estimation and nowcasting for flash flood forecasting.

Thesis (Ph.D.Eng.)-University of KwaZulu-Natal, Durban, 2007.Floods cannot be prevented, but their devastating effects can be minimized if advance warning of the event is available. The South African Disaster Management Act (Act 57 of 2002) advocates a paradigm shift from the current "bucket and blanket brigade" response-based mind set to one where disaster prevention or mitigation are the preferred options. It is in the context of mitigating the effects of floods that the development and implementation of a reliable flood forecasting system has major significance. In the case of flash floods, a few hours lead time can afford disaster managers the opportunity to take steps which may significantly reduce loss of life and damage to property. The engineering challenges in developing and implementing such a system are numerous. In this thesis, the design and implement at ion of a flash flood forecasting system in South Africa is critically examined. The technical aspect s relating to spatio-temporal rainfall estimation and now casting are a key area in which new contributions are made. In particular, field and optical flow advection algorithms are adapted and refined to help predict future path s of storms; fast and pragmatic algorithms for combining rain gauge and remote sensing (rada r and satellite) estimates are re fined and validated; a two-dimensional adaptation of Empirical Mode Decomposition is devised to extract the temporally persistent structure embedded in rainfall fields. A second area of significant contribution relates to real-time fore cast updates, made in response to the most recent observed information. A number of techniques embedded in the rich Kalm an and adaptive filtering literature are adopted for this purpose. The work captures the current "state of play" in the South African context and hopes to provide a blueprint for future development of an essential tool for disaster management. There are a number of natural spin-offs from this work for related field s in water resources management

Advances in the space-time analysis of rainfall extremes

Statistical estimation of design rainfall is considered a consolidated topic in hydrology. However, extreme rainfalls and their consequences still constitute one of the most critical natural risks worldwide, particularly in urban environments. Additional efforts for improving the spatio-temporal analysis of extreme rainfalls are then required, particularly at the regional scale. In this work, a new set of data and techniques for improving the spatial statistical analysis of extreme rainfall is proposed. Italy is considered a challenging case study, due to its specific geographic and orographic settings, associated with recurring storm-induced disasters. At first, the rain-gauge data patchiness resulting from the evolution of the monitoring agencies and networks, is tackled with the "patched kriging" methodology. The technique, involving a sequential annual interpolation, provides complete annual maxima series consistent with the available data. This allows to extract all the information avaialble from the gauge records, considering also the information "hidden" in the shortest series, increasing the robustness of the results. Interpolation techniques, however, can only reflect the estimation variance determined by the spatial and temporal data resolution. Additional improvements can be obtained integrating the rain gauge information with remote sensing products, able to provide more details on the spatial structure of rainstorms. In this direction, a methodology aimed at maximizing the efficiency of weather radar when dealing with large rainfall intensities is developed. It consists in a quasi-real-time calibration procedure, adopting confined spatial and temporal domains for an adaptive estimation of the relation between radar reflectivity and rainfall rate. This allows one to follow the well-known spatio-temporal variability of the reflectivity-rainfall relation, making the technique suitable for a systematic operational use, regardless of the local conditions. The methodology, applied in a comprehensive case study reduces the bias and increases the accuracy of the radar-based estimations of severe rainfall intensities. The field of the satellite estimation of preciptation is then explored, by analyzing the ability of both the Tropical Rainfall Measurement Mission (TRMM) and the recently launched Global Precipitation Measurement (GPM) mission to help identifying the timing of severe rainfall events on wide spatial domains. For each considered product, the date of occurrence of the most intense annual daily records are identified and compared with the ones extracted from a global rain-gauge database. The timing information can help in tracking the pattern of deep convective systems and support the identification of localized rainfall system in poorly gauged areas. The last part of the work deals with the analysis of rainfall extremes at the country scale, with a particular focus on the most severe rainfall events occurred in Italy in the last century. Many of these events have been studied as individual case studies, due to the large recorded intensities and/or to their severe consequences, but they have been seldom expressly addressed as a definite population. To try to provide new insights in a data-drived approach, a comprehensive set of annual rainfall maxima has been compiled, collecting data from the different regional authorities in charge. The database represents the reference knowledge for extremes from 1 to 24 hours durations in Italy, and includes more than 4500 measuring points nationwide, with observation spanning the period 1916-2014. Exploratory statistical analyses for providing information on the climatology of extreme rainfall at the national scale are carried out and the stationarity in time of the highest quantiles is analysed by pooling up all the data for each duration together. The cumulative empirical distributions are explored looking for clues of the existence of a class of "super-extremes" with a peculiar statistical behavior. The analysis of the spatial the distribution of the records exceeding the 1/1000 overall empirical probability shows an interesting spatial clustering. However, once removed the influence of the uneven density of the rain gauge network in time and space, the spatial susceptibility to extraordinary events seems quite uniformly distributed at the country scale. The analyses carried out provide quantitative basis for improving the rainstorm estimation in gauged and ungauged locations, underlining the need of further research efforts for providing maps for hydrological design with uniform reliability at the various scales of technical interest

Climate change and impacts in the urban systems

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsUrban systems are not only major drivers of climate change, but also impact hotspots. The processes of global warming and urban population growth make our urban agglomerations vulnerable to chain reactions triggered by climate related hazards. Hence, the reliable and cost-effective assessment of future climate impact is of high importance. Two major approaches emerge from the literature: i) detailed spatially explicit assessments, and ii) more holistic approaches consistently assessing multiple cities. In this multidisciplinary thesis both approaches were addressed. Firstly, we discuss the underlying reasons and main challenges of the applicability of downscaling procedures of climate projections in the process of urban planning. While the climate community has invested significant effort to provide downscaling techniques yielding localised information on future climate extreme events, these methods are not widely exploited in the process of urban planning. The first part of this research attempts to help bridge the gap between the communities of urban planners and climatologists. First, we summarize the rationale for such cooperation, supporting the argument that the spatial scale represents an important linkage between urban and climate science in the process of designing an urban space. Secondly, we introduce the main families of downscaling techniques and their application on climate projections, also providing the references to profound studies in the field. Thirdly, special attention is given to previous works focused on the utilization of downscaled ensembles of climate simulations in urban agglomerations. Finally, we identify three major challenges of the wider utilization of climate projections and downscaling techniques, namely: (i) the scale mismatch between data needs and data availability, (ii) the terminology, and (iii) the IT bottleneck. The practical implications of these issues are discussed in the context of urban studies. The second part of this work is devoted to the assessment of impacts of extreme temperatures across the European capital cities. In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more vulnerable to extreme heat waves. In the period 1950-2015 the occurrence of extreme heat waves increased across European capitals. As an example, Moscow was hit by the strongest heat wave of the present era, killing more than ten thousand people. Here we focus on larger metropolitan areas of European capitals. By using an ensemble of eight EURO-CORDEX models under the RCP8.5 scenario, we calculate a suite of temperature based climate indices. We introduce a ranking procedure based on ensemble predictions using the mean of metropolitan grid cells for each capital, and socio-economic variables as a proxy to quantify the future impact. Results show that all the investigated European metropolitan areas will be more vulnerable to extreme heat in the coming decades. Based on the impact ranking, the results reveal that in near, but mainly in distant future, the extreme heat events in European capitals will be not exclusive to traditionally exposed areas such as the Mediterranean and the Iberian Peninsula. Cold waves will represent some threat in mid of the century, but they are projected to completely vanish by the end of this century. The ranking of European capitals based on their vulnerability to the extreme heat could be of paramount importance to the decision makers in order to mitigate the heat related mortality. Such a simplistic but descriptive multi-risk urban indicator has two major uses. Firstly, it communicates the risk associated with climate change locally and in a simple way. By allowing to illustratively relate to situations of other capitals, it may help to engage not only scientists, but also the decision makers and general public, in efforts to combat climate change. Secondly, such an indicator can serve as a basis to decision making on European level, assisting with prioritizing the investments and other efforts in the adaptation strategy. Finally, this study transparently communicates the magnitude of future heat, and as such contributes to raise awareness about heat waves, since they are still often not perceived as a serious risk. Another contribution of this work to communication of consequences of changing climate is represented by the MetroHeat web tool, which provides an open data climate service for visualising and interacting with extreme temperature indices and heat wave indicators for European capitals. The target audience comprises climate impact researchers, intermediate organisations, societal-end users, and the general public

Soil moisture droughts in Germany: retrospective analysis, parametric uncertainty, and monitoring

Droughts are worldwide the second most severe natural disaster beside floods. In Europe, droughts are the costliest natural disasters with average expenses of 621 million EUR per event. The last severe drought event took place in 2003. It induced an agro-economic loss of 1.5 billion EUR in Germany alone. Such economical losses emphasize the need of an operational system for monitoring agricultural droughts in order to mitigate their negative consequences. Observation-based monitoring of agricultural droughts, which are characterized by soil moisture deficits, is technically and economically not feasible on regional to national scales. Hydrologic modeling is the prime alternative to estimate soil moisture availability on large spatial domains. Such models are driven by meteorological observations and predict hydrological fluxes and states, such as soil moisture or evapotranspiration. Predictions of hydrologic models underlie several sources of uncertainties. These uncertainties arise from input data, model structure, initial conditions, and model parameters. The implications of parametric uncertainty to hydrologic predictions are analyzed herein. The main objective of this work is to develop a monitoring system for agricultural droughts in Germany. The development of such a system includes several challenges. First, a spatially continuous dataset of soil moisture for entire Germany is derived from modeling. The parametric uncertainty of such hydrologic predictions is taken into account. Second, the propagation of parametric uncertainty of soil moisture to the identification of drought characteristics is estimated in order to evaluate the uncertainty inherent to such a monitoring system. Third, an approach to reduce the parametric uncertainty by using satellite retrieved land surface temperature data is investigated. And forth, an operational system providing drought information in near-real time is developed and implemented