Predicting and forecasting flow discharge at sites receiving significant lateral inflow

Two models, one linear and one non‐linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non‐linear model is based on a multilayer feed‐forward back propagation (FFBP) artificial neural network (ANN) and uses flow‐stage data measured at the upstream and downstream stations. ANN predicted the real‐time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow‐stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4‐h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8‐h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. Copyright © 2007 John Wiley & Sons, Ltd.     

An intermediate coupled model for the tropical ocean-atmosphere system

An intermediate ocean-atmosphere coupled model is developed to simulate and predict the tropical interannual variability. Originating from the basic physical framework of the Zebiak-Cane(ZC) model, this tropical intermediate couple model(TICM) extends to the entire global tropics, with a surface heat flux parameterization and a surface wind bias correction added to improve model performance and inter-basin connections. The model well reproduces the variabilities in the tropical Pacific and Indian basins. The simulated El Ni?o-Southern Oscillation(ENSO) shows a period of 3–4 years and an amplitude of about 2°C, similar to those observed. The variabilities in the Indian Ocean, including the Indian Ocean basin mode(IOBM) and the Indian Ocean Dipole(IOD), are also reasonably captured with a realistic relationship to the Pacific. However, the tropical Atlantic variability in the TICM has a westward bias and is overly influenced by the tropical Pacific. A 47-year hindcast experiment using the TICM for the period of 1970–2016 indicates that ENSO is the most predictable mode in the tropics. Skillful predictions of ENSO can be made one year ahead, similar to the skill of the latest version of the ZC model, while a "spring predictability barrier" still exists as in other models. In the tropical Indian Ocean, the predictability seems much higher in the west than in the east. The correlation skill of IOD prediction reaches 0.5 at a 5-month lead, which is comparable to that of the state-of-the-art coupled general circulation models. The prediction of IOD shows a significant "winter-spring predictability barrier", implying combined influences from the tropical Pacific and the local sea-air interaction in the eastern Indian Ocean. The TICM has little predictive skill in the equatorial Atlantic for lead times longer than 3 months, which is a common problem of current climate models badly in need of further investigation.     

Verification of short-term runoff forecasts for a small Philippine basin (Marikina)

Storm runoff from the Marikina River Basin frequently causes flood events in the Philippine capital region Metro Manila. This paper presents and evaluates a system to predict short-term runoff from the upper part of that basin (380 km²). It was designed as a possible component of an operational warning system yet to be installed. For the purpose of forecast verification, hindcasts of streamflow were generated for a period of 15 months with a time-continuous, conceptual hydrological model. The latter was fed with real-time observations of rainfall. Both ground observations and weather radar data were tested as rainfall forcings. The radar-based precipitation estimates clearly outperformed the raingauge-based estimates in the hydrological verification. Nevertheless, the quality of the deterministic short-term runoff forecasts was found to be limited. For the radar-based predictions, the reduction of variance for lead times of 1, 2 and 3 hours was 0.61, 0.62 and 0.54, respectively, with reference to a “no-forecast” scenario, i.e. persistence. The probability of detection for major increases in streamflow was typically less than 0.5. Given the significance of flood events in the Marikina Basin, more effort needs to be put into the reduction of forecast errors and the quantification of remaining uncertainties.     

Hydro-climatology of the Lower Rhône Valley: historical flood reconstruction (AD 1300–2000) based on documentary and instrumental sources

From the HISTRHONE database we extracted 1483 hydro-meteorological events from AD 1300 to 2000 that occurred in the Lower Rhône Valley, France. Daily heights of the Rhône River at Beaucaire and Arles are also available, from 1816 and 1829, respectively. A total of 517 floods were divided into three categories and a synthetic frequency severity index (FSI) was computed. Running averages of 11 and 31 years show a succession of poor and rich flood fluctuations. Extreme floods tripled in the second half of the period (1650–2000). Singular spectrum analysis isolates a dominant irregular component (main positive anomalies in 1450–1580, around 1700, late 18th century, and most of the 20th century). We focus on the 17th century, with rare flooding events between two secular so-called “hyper phases”, i.e. frequent and/or severe floods. We also recorded 173 episodes of ice in the river, during the Little Ice Age.     

Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

ABSTRACT

Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.     

Forecast-skill-based simulation of streamflow forecasts

Streamflow forecasts are updated periodically in real time, thereby facilitating forecast evolution. This study proposes a forecast-skill-based model of forecast evolution that is able to simulate dynamically updated streamflow forecasts. The proposed model applies stochastic models that deal with streamflow variability to generate streamflow scenarios, which represent cases without forecast skill of future streamflow. The model then employs a coefficient of prediction to determine forecast skill and to quantify the streamflow variability ratio explained by the forecast. By updating the coefficients of prediction periodically, the model efficiently captures the evolution of streamflow forecast. Simulated forecast uncertainty increases with increasing lead time; and simulated uncertainty during a specific future period decreases over time. We combine the statistical model with an optimization model and design a hypothetical case study of reservoir operation. The results indicate the significance of forecast skill in forecast-based reservoir operation. Shortage index reduces as forecast skill increases and ensemble forecast outperforms deterministic forecast at a similar forecast skill level. Moreover, an effective forecast horizon exists beyond which more forecast information does not contribute to reservoir operation and higher forecast skill results in longer effective forecast horizon. The results illustrate that the statistical model is efficient in simulating forecast evolution and facilitates analysis of forecast-based decision making.     

Evaluation of a global ensemble flood prediction system in Peru

ABSTRACT

Flood early warning systems play a more substantial role in risk mitigation than ever before. Hydrological forecasts, which are an essential part of these systems, are used to trigger action against floods around the world. This research presents an evaluation framework, where the skills of the Global Flood Awareness System (GloFAS) are assessed in Peru for the years 2009–2015. Simulated GloFAS discharges are compared against observed ones for 10 river gauges. Forecasts skills are assessed from two perspectives: (i) by calculating verification scores at every river section against simulated discharges and (ii) by comparing the flood signals against reported events. On average, river sections with higher discharges and larger upstream areas perform better. Raw forecasts provide correct flood signals for 82% of the reported floods, but exhibit low verification scores. Post-processing of raw forecasts improves most verification scores, but reduces the percentage of the correctly forecasted reported events to 65%.     

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.     

Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model/Mise à Jour de Prévision de Crue en Temps Réel Grâce à un Modèle à Base de Règles Floues

Abstract

An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one- to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall—runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals that the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times.     

Flash flood forecasting model for the Ayalon stream,Israel

Abstract

A composite model for real time forecasting of flash floods in the Ayalon stream in central Israel has been constructed. The model is composed of four kinds of sub-models: an autoregressive model for discharges at upstream stations on the two major tributaries; a travel-time model for the flow from these stations to the downstream station located on the main stem of the stream; a time-area concentration curve for subwatershed drainage between the upstream and downstream stations; and a recession curve for the downstream station. The model incorporates an adaptive mechanism for continuous correction of forecast errors. This mechanism is calibrated during an initial period of operation, and is subsequently operated throughout a flow event. The model issues simultaneous forecasts for seven lead times ranging from 0.5 to 3.5 h. This provides a proper input for a flood warning system which is required for safe operation of a major highway running along the banks of a torrent stream in the metropolitan area of Tel-Aviv.     

The influence of storm characteristics and catchment conditions on extreme flood response : A case study based on the brue river basin,U.K.

Methods for estimating the magnitude of extreme floods are reviewed. A method which combines a probabilistic storm transposition technique with a physically-based distributed rainfallrunoff model is described. Synthetic storms with detailed spatial and temporal distributions are generated and applied to the calibrated model of the Brue river basin, U.K. (area 135 km²). The variability of catchment response due to storm characteristics (storm area, storm duration, storm movement, storm shape and within storm variation) and initial catchment wetness conditions is investigated. A probabilistic approach to estimating the return periods of extreme catchment responses is suggested.     

Spatial and temporal variability of flood seasonality in Wales

High‐magnitude floods across Europe within the last decade have resulted in the widespread reassessment of flood risk; this coupled with the introduction of the Water Framework Directive (2000) has increased the need for a detailed understanding of seasonal variability in flood magnitude and frequency. Mean day of flood (MDF) and flood seasonality were calculated for Wales using 30 years of gauged river‐flow records (1973–2002). Noticeable regional variations in timing and length of flood season are evident, with flooding occurring earlier in small catchments draining higher elevations in north and mid‐west Wales. Low‐altitude regions in West Wales exposed to westerly winds experience flooding during October–January, while large eastern draining catchments experience later flooding (January–February). In the northeast and mid‐east regions December–January months experience the greatest number of floods, while the southeast has a slightly longer flood season (December–February), with a noticeable increase in January floods. Patterns obtained from MDF data demonstrate their effectiveness and use in analysing regional patterns in flood seasonality, but catchment‐specific determinants, e.g. catchment wetness, size and precipitation regime are important factors in flood seasonality. Relatively strong correlations between precipitation and flood activity are evident in Wales, with a poorer relationship between flooding and weather types and the North Atlantic Oscillation (NAO). Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

The largest floods in the High Rhine basin since 1268 assessed from documentary and instrumental evidence

Abstract

The magnitudes of the largest known floods of the River Rhine in Basel since 1268 were assessed using a hydraulic model drawing on a set of pre-instrumental evidence and daily hydrological measurements from 1808. The pre-instrumental evidence, consisting of flood marks and documentary data describing extreme events with the customary reference to specific landmarks, was “calibrated” by comparing it with the instrumental series for the overlapping period between the two categories of evidence (1808–1900). Summer (JJA) floods were particularly frequent in the century between 1651–1750, when precipitation was also high. Severe winter (DJF) floods have not occurred since the late 19th century despite a significant increase in winter precipitation. Six catastrophic events involving a runoff greater than 6000 m ³ s^‐1 are documented prior to 1700. They were initiated by spells of torrential rainfall of up to 72 h (1480 event) and preceded by long periods of substantial precipitation that saturated the soils, and/or by abundant snowmelt. All except two (1999 and 2007) of the 43 identified severe events (SEs: defined as having runoff > 5000 and < 6000 m ³ s ^‐1) occurred prior to 1877. Not a single SE is documented from 1877 to 1998. The intermediate 121-year-long “flood disaster gap” is unique over the period since 1268. The effect of river regulations (1714 for the River Kander; 1877 for the River Aare) and the building of reservoirs in the 20th century upon peak runoff were investigated using a one-dimensional hydraulic flood-routing model. Results show that anthropogenic effects only partially account for the “flood disaster gap” suggesting that variations in climate should also be taken into account in explaining these features.

Citation Wetter, O., Pfister, C., Weingartner, R., Luterbacher, J., Reist, T., & Trösch, J. (2011) The largest floods in the High Rhine basin since 1268 assessed from documentary and instrumental evidence. Hydrol. Sci. J. 56(5), 733–758.     

大别山库区降水预报性能评估及应用对策

对降水预报进行性能评估及应用对策研究可以更好地发挥降水预报在水库调度中的决策支持作用.基于大别山库区近10 a汛期(2007—2016年5月1日—9月30日)24~168 h共7个预见期降水预报和地面降水观测资料,采用正确率、TS评分、概率统计、ROC曲线以及CTS等方法评估大别山库区降水预报性能,并以响洪甸水库为重点研究区域分析降水预报在水库调度中的应用对策.结果表明:1)大别山库区各量级的降水预报都有正预报技巧;24~72 h预见期降水预报的TS评分较高且空报率、漏报率也较低,具有较高的预报性能;但96 h及以上预见期降水预报性能明显下降,中雨以上量级空报率、漏报率较大,特别是对大暴雨及其以上量级的降水预报性能显著下降.2)大别山库区预报降水量级与实况降水量级基本符合,预报降水量级大于等于实况降水量级的概率超过75%;虽然降水预报量级上呈现出过度预报的现象,但降水过程预报对水库调度仍有较好的应用价值,应用时要考虑到降水预报量级可能存在偏差.3)转折性天气预报96 h及以上预见期CTS评分较低,但72 h以内预见期的性能明显改进,尤其是24 h预见期CTS评分也提高到了38.2%;水库调度可从长预见期的降水预报获取降水过程及其可能发生转折的信息,根据短预见期的降水预报进行调度方案调整.     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change

Floods play a critical role in geomorphic change, but whether peak magnitude, duration, volume, or frequency determines the resulting magnitude of erosion and deposition is a question often proposed in geomorphic effectiveness studies. This study investigated that question using digital elevation model differencing to compare and contrast three hydrologically distinct epochs of topographic change spanning 18 years in the 37-km gravel–cobble lower Yuba River in northern California, USA. Scour and fill were analysed by volume at segment and geomorphic reach scales. Each epoch's hydrology was characterized using 15-min and daily averaged flow to obtain distinct peak and recurrence, duration, and volume metrics. Epochs 1 (1999–2008) and 3 (2014–2017) were wetter than average with large floods reaching 3206 and 2466 m³/s, respectively, though of different flood durations. Epoch 2 (2008–2014) was a drought period with only four brief moderate floods (peak of 1245 m³/s). Total volumetric changes showed that major geomorphic response occurred primarily during large flood events; however, total scour and net export of sediment varied greatly, with 20 times more export in epoch 3 compared to epoch 1. The key finding was that greater peak discharge was not correlated with greater net and total erosion; differences were better explained by duration and volume above floodway-filling stage. This finding highlights the importance of considering flood duration and volume, along with peak, to assess flood magnitude in the context of flood management, frequency analysis, and resulting geomorphic changes.     

Natural flood management,land use and climate change trade-offs: the case of Tarland catchment,Scotland

A distributed hydrological model (WaSiM-ETH) was applied to a mesoscale catchment to investigate natural flood management as a nonstructural approach to tackle flood risks from climate change. Peak flows were modelled using climate projections (UKCP09) combined with afforestation-based land-use change options. A significant increase in peak flows was modelled from climate change. Afforestation could reduce some of the increased flow, with greatest benefit from coniferous afforestation, especially replacing lowland farmland. Nevertheless, large-scale woodland expansion was required to maintain peak flows similar to present and beneficial effects were significantly reduced for larger “winter-type” extreme floods. Afforestation was also modelled to increase low-flow risks. Land-use scenarios showed catchment-scale trade-offs across multiple objectives meant “optimal” flood risk solutions were unlikely, especially for afforestation replacing lowland farmland. Hence, combined structural/nonstructural measures may be required in such situations, with integrated catchment management to synergize multiple objectives.     

Flood changes during the past 50 years in Wujiang River,South China

Changing trends of peak flood and flood duration in the Wujiang River Basin are detected with the help of the Mann–Kendall test and the Pettitt analysis during the past 50 years. Results indicate that the peak flood and the flood duration at Lishi Station have different changing features. The peak flood showed only marginally increasing trend, whereas the flood duration exhibited decreasing trend at the significance level of 90%. The result shows a weak positive correlation between the peak flood and the flood duration. The changes of flood duration are influenced by the total rainfall duration, which is in downward trend at significance level 90%. In addition, the changing trends of peak flood are similar to the total rainfall amount. In the change‐point analysis, it was found that the change points for the peak flood and flood duration series were in the years 1993 and 1966, respectively. Human activities such as the construction of reservoirs were the main driving forces causing the change of flood duration. The periodicity of the peak flood during the period 1955–2007 at Lishi Station is detected by using the wavelet analysis. The result indicates that the peak flood at Lishi Station displayed alternation between big floods and small floods on the 25‐ to 26‐year period. At the same timescale, the peak flood of Wujiang River showed an evidence of change between big floods and small floods. It was essential to be prepared and aware of the consequences of climate changes and human influences affecting the water resources in the Wujiang River Basin. This result is expected to draw more attention from the local governments in its decision making and water resource management. Copyright © 2012 John Wiley & Sons, Ltd.