A review of advances in flash flood forecasting

Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead‐time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation. Copyright © 2011 John Wiley & Sons, Ltd.     

Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain

In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.     

River temperature forecasting: case study for Little Southwest Miramichi River (New Brunswick,Canada)

River temperature models play an increasingly important role in the management of fisheries and aquatic resources. Among river temperature models, forecasting models remain relatively unused compared to water temperature simulation models. However, water temperature forecasting is extremely important for in-season management of fisheries, especially when short-term forecasts (a few days) are required. In this study, forecast and simulation models were applied to the Little Southwest Miramichi River (New Brunswick, Canada), where water temperatures can regularly exceed 25–29°C during summer, necessitating associated fisheries closures. Second- and third-order autoregressive models (AR2, AR3) were calibrated and validated using air temperature as the exogenous variable to predict minimum, mean and maximum daily water temperatures. These models were then used to predict river temperatures in forecast mode (1-, 2- and 3-day forecasts using real-time data) and in simulation mode (using only air temperature as input). The results showed that the models performed better when used to forecast rather than simulate water temperatures. The AR3 model slightly outperformed the AR2 in the forecasting mode, with root mean square errors (RMSE) generally between 0.87°C and 1.58°C. However, in the simulation mode, the AR2 slightly outperformed the AR3 model (1.25°C < RMSE < 1.90°C). One-day forecast models performed the best (RMSE ~ 1°C) and model performance decreased as time lag increased (RMSE close to 1.5°C after 3 days). The study showed that marked improvement in the modelling can be accomplished using forecasting models compared to water temperature simulations, especially for short-term forecasts.

EDITOR M.C. Acreman ASSOCIATE EDITOR S. Huang     

Rainfall and streamflow response to El Niño Southern Oscillation: a case study in a semiarid catchment,Australia

Abstract

This paper aims to compare the shift in frequency distribution and skill of seasonal climate forecasting of both streamflow and rainfall in eastern Australia based on the Southern Oscillation Index (SOI) Phase system. Recent advances in seasonal forecasting of climate variables have highlighted opportunities for improving decision making in natural resources management. Forecasting of rainfall probabilities for different regions in Australia is available, but the use of similar forecasts for water resource supply has not been developed. The use of streamflow forecasts may provide better information for decision-making in irrigation supply and flow management for improved ecological outcomes. To examine the relative efficacy of seasonal forecasting of streamflow and rainfall, the shift in probability distributions and the forecast skill were evaluated using the Wilcoxon rank-sum test and the linear error in probability space (LEPS) skill score, respectively, at three river gauging stations in the Border Rivers Catchment of the Murray-Darling Basin in eastern Australia. A comparison of rainfall and streamflow distributions confirms higher statistical significance in the shift of streamflow distribution than that in rainfall distribution. Moreover, streamflow distribution showed greater skill of forecasting with 0–3 month lead time, compared to rainfall distribution.     

Performance of operational systems with respect to water level forecasts in the Gulf of Finland

This paper is devoted to the validation of water level forecasts in the Gulf of Finland. Daily forecasts produced by four setups of operational, three-dimensional Baltic Sea oceanographic models are analyzed using statistical means and are compared with water level observations at three Finnish stations located on the northern coast of the Gulf of Finland. The overall conclusion is that the operational systems were skillful in forecasting water level variations during the study period from November 1, 2003, to January 31, 2005. The factors causing differences between the water level forecasts of different models are discussed as well. An important task of operational sea level forecasting services is to provide accurate and early information about extreme water levels, both positive and negative surges. During the study period, two major winter storms occurred which caused coastal flooding in the region. According to our analysis, the operational models forecast the rise of water levels during these events rather successfully. Nowadays, operational forecasts can provide early warnings of extreme water levels at least 1 day in advance, which may be regarded as a minimum requirement for an operational forecasting system. The paper concludes that the models generally performed very well, with over 93% of the hourly water level forecasts found to be within the range of ±15 cm of the observed water levels, and with the timing of the water level peaks accurately predicted. Further discussion and studies dealing with the assessment of the skills of both operational meteorological and oceanographic forecasts, especially in connection with rare surge events, will be necessary. Skill assessment of operational oceanographic models would be relatively easy if acceptable error limits or a quality system was developed for the Baltic Sea operational models.     

Spatial and temporal variability of annual and seasonal precipitation over the desert region of China during 1951–2005

The spatial and temporal variations of precipitation in the desert region of China (DRC) from 1951 to 2005 were investigated using a rotated empirical orthogonal function (REOF), the precipitation concentration index (PCI) and the Mann–Kendall trend test method (M‐K method). In addition, the association between variation patterns of precipitation and large‐scale circulation were also explored using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. The results indicated that the spatial pattern of precipitation was primarily the local climate effect significant type, with the first three EOFs explaining a total of 55·3% of the variance, and the large‐scale climate system effect type, which explained 9·8% of the variance. Prior to the 1970s, the East Asian summer monsoon was stronger, which resulted in abundant precipitation in the Inner Mongolia region. Conversely, the climate of the Xinjiang region was controlled by westerly circulation and had lower precipitation. However, this situation has been reversed since the 1980s. It is predicted that precipitation will decrease by 15–40 and 0–10 mm/year in the Inner Mongolia plateau and southern Xinjiang, respectively, whereas it will likely increase by 10–40 mm/year in northern Xinjiang. Additionally, 58–62% of the annual rainfall occurred during summer in the DRC, with precipitation increasing during spring and summer and decreasing in winter. The intra‐annual precipitation is becoming uniform, but the inter‐annual variability in precipitation has been increasing in the western portions of the DRC. The probability of precipitation during the study period increased by 30% and 22·2% in the extreme‐arid zones and arid zones, respectively. Conversely, the probability of precipitation during the study period decreased by 18·5% and 37·5% in the semi‐arid zones and semi‐wet zones, respectively. It is predicted that the northwest portion of the DRC will become warmer and wetter, while the central portion will become warmer and drier and the northeast portion will be subjected to drought. Copyright © 2010 John Wiley & Sons, Ltd.     

The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area,Oregon, USA

Climate changes brought on by increasing greenhouse gases in the atmosphere are expected to have a significant effect on the Pacific Northwest hydrology during the 21st century. Many climate model simulations project higher mean annual temperatures and temporal redistribution of precipitation. This is of particular concern for highly urbanized basins where runoff changes are more vulnerable to changes in climate. The Rock Creek basin, located in the Portland metropolitan area, has been experiencing rapid urban growth throughout the last 30 years, making it an ideal study area for assessing the effect of climate and land cover changes on runoff. A combination of climate change and land cover change scenarios for 2040 with the semi‐distributed AVSWAT (ArcView Soil and Water Assessment Tool) hydrological model was used to determine changes in mean runoff depths in the 2040s (2030–2059) from the baseline period (1973–2002) at the monthly, seasonal, and annual scales. Statistically downscaled climate change simulation results from the ECHAM5 general circulation model (GCM) found that the region would experience an increase of 1·2 °C in the average annual temperature and a 2% increase in average annual precipitation from the baseline period. AVSWAT simulation shows a 2·7% increase in mean annual runoff but a 1·6% decrease in summer runoff. Projected climate change plus low‐density, sprawled urban development for 2040 produced the greatest change to mean annual runoff depth (+5·5%), while climate change plus higher‐density urban development for 2040 resulted in the smallest change (+5·2%), when compared with the climate and land cover of the baseline period. This has significant implications for water resource managers attempting to implement adaptive water resource policies to future changes resulting from climate and urbanization. Copyright © 2008 John Wiley & Sons, Ltd.     

Precipitation and hydrological variations and related associations with large‐scale circulation in the Poyang Lake basin,China

Long streamflow series and precipitation data are analysed in this study with aim to investigate changing properties of precipitation and associated impacts on hydrological processes of the Poyang Lake basin. Underlying causes behind the precipitation variations are also explored based on the analysis of the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis data. Besides, water intrusion from the Yangtze River to the Poyang Lake basin is studied. The results indicate that (1) seasonal transitions of precipitation are observed, showing increasing precipitation in winter, slight increase and even decrease of precipitation in summer; (2) analysis of water vapour circulation indicates decreasing/increasing water vapour flux in summer/winter; in winter, water vapour flux tends to be from the Pacific. Altered water vapour flux is the major cause behind the altered precipitation changes across the Poyang Lake basin and (3) occurrence of water intrusion from the Yangtze River to the Poyang Lake basin is heavily influenced by hydrological processes of the Poyang Lake basin. Effects of the hydrological processes from the middle Yangtze River on the occurrence of water intrusion events are not significant. The results of this study indicate that floods and droughts should share the same concerns from the scholars and policy makers. Besides, the altered hydrological circulation and associated seasonal transition of precipitation drive us to face new challenges in terms of conservations of wetlands and ecological environment under the changing climate. Copyright © 2010 John Wiley & Sons, Ltd.     

Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins

In climate‐change studies, a macroscale hydrologic model (MHM) operating over large scales can be an important tool in developing consistent hydrological variability estimates over large basins. MHMs, which can operate at coarse grid resolutions of about 1° latitude by longitude, have been used previously to study climate change impacts on the hydrology of continental scale or global river basins. They can provide a connection between global atmospheric models and water resource systems on large spatial scales and long timescales. In this study, the variable infiltration capacity (VIC) MHM is used to study large scale hydrologic impacts of climate change for Indian river basins. Large‐scale changes in runoff, evapotranspiration and soil moisture for India, as well as station‐scale changes in discharges for three major river basins with distinct climatic and geographic characteristics are examined in this study. Climate model projections for meteorological variables (precipitation, temperature and wind speed) from three general circulation models (GCMs) and three emissions scenarios are used to drive the VIC MHM. GCM projections are first interpolated to a 1° by 1° hydrologic model grid and then bias‐corrected using a quantile–quantile mapping. The VIC model is able to reproduce observed statistics for discharges in the Ganga, Narmada and Krishna basins reasonably well, even at the coarse grid resolution employed using a calibration period for years 1965–1970 and testing period from 1971–1973/1974. An increasing trend is projected for summer monsoon surface runoff, evapotranspiration and soil moisture in most central Indian river basins, whereas a decrease in runoff and soil moisture is projected for some regions in southern India, with important differences arising from GCM and scenario variability. Discharge statistics show increases in mid‐flow and low flow at Farakka station on Ganga River, increased high flows at Jamtara station upstream of Narmada, and increased high, mid‐flow and low flow for Vijayawada station on Krishna River in the future. Copyright © 2013 John Wiley & Sons, Ltd.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Modelling and analysis of the impact of irrigation on local arid climate over northwest China

This study focuses on how irrigation processes affect local climate over arid areas. The chosen study area is northwest China, a typical arid region where three dominant land‐use types are irrigated cropland, grassland, and desert. Observational analysis indicates that the highest precipitation, the coolest surface temperatures, and the slowest warming trend are seen over irrigated cropland from 1979 to 2005. The single column atmospheric model (SCAM), developed by the National Center for Atmospheric Research (NCAR), was used to investigate and better understand the differences in long‐term climate conditions and change over the above three land‐use types. The results indicate that local climate conditions are predominantly controlled by large‐scale forcing in this arid region and that local land surface forcing related to vegetation cover change and irrigation processes also has a significant impact. This study strongly suggests that a realistic climate forecast for this region can be achieved only with both accurate large‐scale and local climate forcing. The irrigated cropland of the region generates stronger evaporation that cools the surface and slows the warming trend more than does the evaporation from the natural grassland and desert. Stronger evaporation also significantly increases precipitation, potentially alleviating the stress of irrigation demands in arid regions. A series of sensitivity SCAM simulations indicate that a drier and warmer climate occurs with decreasing vegetation cover in the irrigated cropland region. Copyright © 2011 John Wiley & Sons, Ltd.     

A numerical study of the rainstorm characteristics of the June 2005 flash flood with WRF/GSI data assimilation system over south‐east China

The evolution and structure of rainstorms associated with a flash‐flood event are simulated by the Advanced Weather Research and Forecasting (WRF‐ARW) model of the National Center for Atmospheric Research and the Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system of the National Oceanic and Atmospheric Administration (NOAA) of the United States. The event is based on a flash flood that occurred in the central Guangdong Province of south‐east China during 20–21 June 2005. Compared to an hourly mixed rain‐gauge and satellite‐retrieved precipitation data, the model shows the capability to reproduce the intensity and location of rainfall; however, the simulation depends on three conditions to a large extent: model resolution, physical processes schemes and initial condition. In this case, the Eta Ferrier microphysics scheme and the initialization with satellite radiance DA with a fine 4‐km grid spacing nested grid and coarse 12‐km grid spacing outer grid are the best options. The model‐predicted rain rates, however, are slightly overestimated, and the activities of the storms do not precisely correspond with those observed, although peak values are obtained. Abundant moisture brought by the south‐westerly winds with a mesoscale low‐level jet from the South China Sea or Bay of Bengal and trapped within the XingfengJiang region encompassed by northern Jiulian, southern Lianhua and eastern small mountains are apparently the primary elements responsible for the flood event. All simulated rainstorms were initiated over the southern slopes of the Jiulian Mountain and moved south or north‐eastward within the Xingfengjiang region. Meanwhile, the Skew‐T/Log‐P diagrams show that there is a fairly high convective available potential energy (CAPE) over the active areas of the rainstorms. The higher CAPE provides a beneficial thermodynamic condition for the development of rainstorms, but the higher convective inhibition near the northern, eastern and southern mountains prohibits the storms from moving out of the region and causes heavy rainfall that is trapped within the area. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Probabilistic radar rainfall nowcasts using empirical and theoretical uncertainty models

Weather radar has a potential to provide accurate short‐term (0–3 h) forecasts of rainfall (i.e. radar nowcasts), which are of great importance in warnings and risk management for hydro‐meteorological events. However, radar nowcasts are affected by large uncertainties, which are not only linked to limitations in the forecast method but also because of errors in the radar rainfall measurement. The probabilistic quantitative precipitation nowcasting approach attempts to quantify these uncertainties by delivering the forecasts in a probabilistic form. This study implements two forms of probabilistic quantitative precipitation nowcasting for a hilly area in the south of Manchester, namely, the theoretically based scheme [ensemble rainfall forecasts (ERF)‐TN] and the empirically based scheme (ERF‐EM), and explores which one exhibits higher predictive skill. The ERF‐TN scheme generates ensemble forecasts of rainfall in which each ensemble member is determined by the stochastic realisation of a theoretical noise component. The so‐called ERF‐EM scheme proposed and applied for the first time in this study, aims to use an empirically based error model to measure and quantify the combined effect of all the error sources in the radar rainfall forecasts. The essence of the error model is formulated into an empirical relation between the radar rainfall forecasts and the corresponding ‘ground truth’ represented by the rainfall field from rain gauges measurements. The ensemble members generated by the two schemes have been compared with the rain gauge rainfall. The hit rate and the false alarm rate statistics have been computed and combined into relative operating characteristic curves. The comparison of the performance scores for the two schemes shows that the ERF‐EM achieves better performance than the ERF‐TN at 1‐h lead time. The predictive skills of both schemes are almost identical when the lead time increases to 2 h. In addition, the relation between uncertainty in the radar rainfall forecasts and lead time is also investigated by computing the dispersion of the generated ensemble members. Copyright © 2013 John Wiley & Sons, Ltd.     

Development,implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.     

An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data

Reliable and prompt information on river ice condition and extent is needed to make accurate hydrological forecasts to predict ice jams breakups and issue timely flood warnings. This study presents a technique to detect and monitor river ice using observations from the MODIS instrument onboard the Terra satellite. The technique incorporates a threshold‐based decision tree image classification algorithm to process MODIS data and to determine the extent of ice. To differentiate between ice‐covered and ice‐free pixels within the riverbed, the algorithm combines observations in the visible and near‐infrared spectral bands. The developed technique presents the core of the MODIS‐based river ice mapping system, which has been developed to support National Oceanic and Atmospheric Administration NWS's operations. The system has been tested over the Susquehanna River in northeastern USA, where ice jam events leading to spring floods are a frequent occurrence. The automated algorithm generates three products: daily ice maps, weekly composite ice maps and running cloud‐free composite ice maps. The performance of the system was evaluated over nine winter seasons. The analysis of the derived products has revealed their good agreement with the aerial photography and with in situ observations‐based ice charts. The probability of ice detection determined from the comparison of the product with the high‐resolution Landsat imagery was equal to 91%. A consistent inverse relationship was found between the river discharge and the ice extent. The correlation between the discharge and the ice extent as determined from the weekly composite product reached 0.75. The developed CREST River Ice Observation System has been implemented at National Oceanic and Atmospheric Administration–Cooperative Remote Sensing Science and Technology Center as an operational Web tool allowing end users and forecasters to assess ice conditions on the river. Copyright © 2012 John Wiley & Sons, Ltd.     

Application of the Takagi–Sugeno fuzzy system for combination forecasting of river flow in semiarid mountain regions

Because of high spatial heterogeneity and the degree of uncertainty about hydrological processes in large‐scale catchments of semiarid mountain areas, satisfactory forecasting of daily discharge is seldom available using a single model in many practical cases. In this paper the Takagi–Sugeno fuzzy system (TS) and the simple average method (SAM) are applied to combine forecasts of three individual models, namely, the simple linear model (SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM) for modelling daily discharge, and the performance of modelling results is compared in five catchments of semiarid areas. It is found that the TS combination model gives good predictions. The results confirm that better prediction accuracy can be obtained by combining the forecasts of different models with the Takagi–Sugeno fuzzy system in semi‐arid mountain areas. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling of the 2011 Japan Tsunami: Lessons for Near-Field Forecast

During the devastating 11 March 2011 Japanese tsunami, data from two tsunami detectors were used to determine the tsunami source within 1.5 h of earthquake origin time. For the first time, multiple near-field tsunami measurements of the 2011 Japanese tsunami were used to demonstrate the accuracy of the National Oceanic and Atmospheric Administration (NOAA) real-time flooding forecast system in the far field. To test the accuracy of the same forecast system in the near field, a total of 11 numerical models with grids telescoped to 2 arcsec (~60 m) were developed to hindcast the propagation and coastal inundation of the 2011 Japanese tsunami along the entire east coastline of Japan. Using the NOAA tsunami source computed in near real-time, the model results of tsunami propagation are validated with tsunami time series measured at different water depths offshore and near shore along Japan’s coastline. The computed tsunami runup height and spatial distribution are highly consistent with post-tsunami survey data collected along the Japanese coastline. The computed inundation penetration also agrees well with survey data, giving a modeling accuracy of 85.5 % for the inundation areas along 800 km of coastline between Ibaraki Prefecture (north of Kashima) and Aomori Prefecture (south of Rokkasho). The inundation model results highlighted the variability of tsunami impact in response to different offshore bathymetry and flooded terrain. Comparison of tsunami sources inferred from different indirect methods shows the crucial importance of deep-ocean tsunami measurements for real-time tsunami forecasts. The agreement between model results and observations along Japan’s coastline demonstrate the ability and potential of NOAA’s methodology for real-time near-field tsunami flooding forecasts. An accurate tsunami flooding forecast within 30 min may now be possible using the NOAA forecast methodology with carefully placed tsunameters and large-scale high-resolution inundation models with powerful computing capabilities.     

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

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.