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

Advances in Modelling of Rainfall Fields

Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

Characteristics and trends of rainstorm activities and their impacts on seasonal vegetation variations in coastal China

Within the context of global climate changes, rainstorms have increasingly become one of the important ecological interferences limiting plant growth and population turnover. However, due to the significant spatial and temporal variability of rainstorms, determining their quantitative relationships with plant growth at some longer temporal and larger spatial scales is challenging. In this study, the relationships between rainstorm changes and vegetation activities are explored by analysing the spatial and temporal correlations between the normalized difference vegetation index (NDVI) and rainstorm frequency on the coast of China during the period of 1982–2015. Our results show that: (1) Both rainstorm frequency and vegetation activities tend to increase gradually over the study period. The impact of rainstorms on vegetation in the northern part of the study area is greater than that in the southern part. (2) Overall, rainstorms have a significant (p < 0.1) negative impact on vegetation activities in the northern parts of the study area, especially during the summer months. Furthermore, the negative impact of rainstorms on vegetation in autumn tends to be strengthened into the future. (3) Rainstorms overall have significant and negative impacts on shrub, coniferous forest, broad-leaved forest during the growing season. The strong-impact seasons and locations of rainstorm on vegetation activities identified by our study can dramatically benefit future ecosystem and water resource management in coastal China

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPMIMERG and Comprehensive Assessment (2000–2020)

In soil erosion estimation models, the variable with the greatest impact is rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000-2020. By using this method, a correlation of 0.7 was found between the PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 4831 M Jmmha−1h −1 was estimated with a general increase towards the lowland Amazon regions and high values are found on the north-coast Pacific area of Peru. The spatial identification of the most risk areas of erosion, was carried out through a relationship between the ED and rainfall. Both erosivity data sets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision

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

A Regional assessment on the influence of climate change on summer rainfall : an application to shallow landsliding in Wanzhou County, China

This study looks into the future of evolving triggering rainfall conditions in a changing climate, and aims to establish a link to tools in the present to assess shallow landslide susceptibility. Focus is given to the triggering rainfall conditions represented by extreme daily rainfall and mean seasonal rainfall. The study site selected in this research was Wanzhou County, China. This county lies in a region of China that receives 90% of its annual rainfall during the summer months. The effect of which is observed with 80% of shallow landslides occurring between June to August from 1995-2005. This research project delivered proof-of-concept for a methodological framework to derive shallow landslide triggering rainfall scenarios from climate model outputs. The presentation of the results and the identification of sources of uncertainties in this study demonstrated a viable link between for climate change projections to provide future rainfall scenarios as inputs for physically-based shallow landslide susceptibility models

Wadi Flash Floods

This open access book brings together research studies, developments, and application-related flash flood topics on wadi systems in arid regions. The major merit of this comprehensive book is its focus on research and technical papers as well as case study applications in different regions worldwide that cover many topics and answer several scientific questions. The book chapters comprehensively and significantly highlight different scientific research disciplines related to wadi flash floods, including climatology, hydrological models, new monitoring techniques, remote sensing techniques, field investigations, international collaboration projects, risk assessment and mitigation, sedimentation and sediment transport, and groundwater quality and quantity assessment and management. In this book, the contributing authors (engineers, researchers, and professionals) introduce their recent scientific findings to develop suitable, applicable, and innovative tools for forecasting, mitigation, and water management as well as society development under seven main research themes as follows: Part 1. Wadi Flash Flood Challenges and Strategies Part 2. Hydrometeorology and Climate Changes Part 3. Rainfall–Runoff Modeling and Approaches Part 4. Disaster Risk Reduction and Mitigation Part 5. Reservoir Sedimentation and Sediment Yield Part 6. Groundwater Management Part 7. Application and Case Studies The book includes selected high-quality papers from five series of the International Symposium on Flash Floods in Wadi Systems (ISFF) that were held in 2015, 2016, 2017, 2018, and 2020 in Japan, Egypt, Oman, Morocco, and Japan, respectively. These collections of chapters could provide valuable guidance and scientific content not only for academics, researchers, and students but also for decision-makers in the MENA region and worldwide

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

Application of Climatic Data in Hydrologic Models

Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models

Improving the quality of extreme precipitation estimates using satellite passive microwave rainfall retrievals

2017 Summer.Includes bibliographical references.Satellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval's performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate rainfall measurements. This is especially true over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., the ice-scattering signature) and rainfall rate. In the first part of this study, an extreme precipitation event that caused historical flooding over south-east Europe is analyzed using the GPM constellation. Performance of the rainfall retrieval is evaluated against ground radar and gage reference. It is concluded that satellite observations fully address the temporal evolution of the event but greatly underestimate total rainfall accumulation (by factor of 2.5). A primary limitation of the rainfall algorithm is found to be its inability to recognize variability in precipitating system structure. This variability is closely related to the structure of the precipitation regime and the large-scale environment. To address this influence of rainfall physics on the overall retrieval bias, the second part of this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that the Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Then, using the Amazon and Central African regions as testbeds, it demonstrates that the amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as Convective Available Potential Energy (CAPE), Cloud Condensation Nuclei (CCN), wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. Based on these findings, in the last part of the study, the GPM operational retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. When forming an estimate, the modified algorithm is allowed to use this ancillary information to filter out a priori states that do not match the general environmental condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. The results are compared to the ground Multi-Radar Multi-Sensor (MRMS) network over the US at various spatial and temporal scales demonstrating outstanding potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land