Applying probabilistic methods to infrequent but devastating natural events is intrinsically
challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated
because of the diﬀerent causes generating tsunamis (earthquakes, landslides, volcanic activity,
meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami
Hazard Analyses (PTHAs) are conducted in diﬀerent areas of the world at global, regional, and local scales
with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance
knowledge of the potential tsunamigenic threat by estimating the probability of exceeding speciﬁc
levels of tsunami intensity metrics (e.g., run-up or maximum inundation heights) within a certain period of
time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps
or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms
of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation
mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii)
statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric
uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the
rapid development of PTHA methods during the last decade and the globally distributed applications,
including the importance of considering multiple sources, their relative intensities, probabilities of
occurrence, and uncertainties in an integrated and consistent probabilistic framework.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017RG000579

Probabilistic Tsunami Hazard Analysis: Multiple Sources and Global Applications

Influence of climate

Urban water-related problems associated with rapid urbanization, including waterlogging, water pollution, the ecological degradation of water, and water shortages, have caused global concerns in recent years. In 2013, in order to mitigate increasingly severe urban water-related problems, China set forth a new strategy for integrated urban water management (IUWM) called the “Sponge City”. This is the first holistic IUWM strategy implemented in a developing country that is still undergoing rapid urbanization, and holds promise for application in other developing countries. This paper aims to comprehensively summarize the sponge city. First, this paper reviews prior studies and policies on urban water management in China as important background for the sponge city proposal. Then, the connotations, goals, and features of the sponge city are summarized and discussed. Finally, the challenges, research needs, and development directions pertinent to the sponge city are discussed based on investigations and studies conducted by the authors. The sponge city in China has a short history—given this, there are many issues that should be examined with regard to the stepwise implementation of the Sponge City Programme (SCP). Accordingly, the authors perceive this study as only the beginning of abundant studies on the sponge city.

A new strategy for integrated urban water management in China: Sponge city

This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Drought under Global Warming: A Review

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools. The application of BBNs to water resource management was rarely applied in developing countries and in tropical regions.Only 8% reviewed papers explored potential impacts of climate change on water resources.Only 11% and 6% of reviewed articles applied influence diagrams and Object-Oriented Bayesian Networks respectively.Most reviewed articles applied BBNs in strategic decision-making contexts (52%) for water resource management.Results from BBN models were rarely compared or tested against other modelling approaches to validate their performance.

Applications of Bayesian belief networks in water resource management

A reliable understanding of propagation from meteorological to hydrological drought is necessary for accurate forecasting of hydrological droughts. Our current understanding of drought propagation is limited because the characteristics of each drought and the lag time between droughts are uneven due to spatial variability in underlying conditions and climatic variables. The objective of this study is to identify the probabilistic relationship between lag time and the occurrence of different classes of hydrological drought in South Korea while considering propagation factors and using a Bayesian network model. The results show that the propagation rate varied from 27% to 60% and the maximum value of the lag time was projected to be 4 to 10 weeks. For moderate intensity of meteorological drought, the occurrence probability of lag time was high and decreased when the intensity changed to severe and extreme. In addition, the probability for each class of hydrological drought intensity varied with space and increased as the intensity of propagated meteorological drought class changed from moderate to extreme. The observed probabilistic characteristics of hydrological drought may be useful in decision-making strategies for mitigating water shortage.

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

Meteorological drought commonly originates from precipitation deficit and is then propagated to agricultural and hydrological droughts. Since drought impacts are more directly related to agricultural and hydrological systems of human society, it is important to understand the drought propagation process for reducing drought impacts. This study investigated the propagation of meteorological droughts through the hydrological system in South Korea. We performed comparative analyses between a meteorological drought indicator [Standardized Precipitation Index (SPI) at various timescales] and a hydrological drought indicator [Palmer Hydrological Drought Index (PHDI)], and pooled the mutually dependent drought events from the indicators. The results indicated that most hydrological droughts occurred following meteorological droughts, and drought events became fewer and longer when moving from 3and 6-month SPI to PHDI. In addition, when we investigated the meteorological conditions, about 70 80% of 3and 6-month SPI corresponding to the onset of hydrological drought (PHDI ≤ -2) were less than -1.0. We also calculated the propagation probability using the conditional probability theory with 33% (3-month SPI) and 48% (6-month SPI) of meteorological droughts propagated to hydrological drought. We concluded that even though the frequency of hydrological drought in South Korea was less than that of meteorological drought, the severity of hydrological drought was greater than that of meteorological drought. Article history: Received 25 March 2017

/pdf/investigation-of-drought-propagation-in-south-korea-using-mh0zovtgfc.pdf

Investigation of drought propagation in South Korea using drought index and conditional probability

Reliable drought forecasting is necessary to develop mitigation plans to cope with severe drought. This study developed a probabilistic scheme for drought forecasting and outlook combined with quantification of the prediction uncertainties. The Bayesian network was mainly employed as a statistical scheme for probabilistic forecasting that can represent the cause-effect relationships between the variables. The structure of the Bayesian network-based drought forecasting (BNDF) model was designed using the past, current, and forecasted drought condition. In this study, the drought conditions were represented by the standardized precipitation index (SPI). The accuracy of forecasted SPIs was assessed by comparing the observed SPIs and confidence intervals (CIs), exhibiting the associated uncertainty. Then, this study suggested the drought outlook framework based on probabilistic drought forecasting results. The overall results provided sufficient agreement between the observed and forecasted drought conditions in the outlook framework.

/pdf/a-bayesian-network-based-probabilistic-framework-for-drought-3qwuybrz0p.pdf

A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of <0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/met.1827

Ji Yae Shin

Papers

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

Investigation of drought propagation in South Korea using drought index and conditional probability

A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook

Probabilistic forecasting of drought: a hidden Markov model aggregated with the RCP 8.5 precipitation projection

Probabilistic long‐term hydrological drought forecast using Bayesian networks and drought propagation