Incorporating daily rainfalls to derive rainfall DDF relationships at ungauged sites in Hong Kong and quantifying their uncertainty

Rainfall depth-duration-frequency (DDF) relationships are essential inputs for the design and management of various hydrosystem infrastructures (e.g., urban drainages, dams, dykes, etc.). In many cases, rainfall DDF relationships are required at a location where there is no gauge. However, due to the presence of intrinsic randomness of the precipitation process, limited rainfall record, and spatial interpolation, the derived DDF relationships at ungauged sites are subject to uncertainty. This is especially true in Hong Kong with regard to record length. To enhance the utilization of available rainfall data, a daily precipitation-based DDF generation framework for conventional rain gauges in Hong Kong has been developed by the authors utilizing a scaling model. In this article, the methodological framework is extended to derive rainfall DDF relationships at ungauged sites. Owing to the nonlinearity and complexity of the modeling process, exact statistical features of derived DDF relationships are difficult to obtain. In this study, Harr’s probabilistic point estimation method, known for its computational simplicity and accuracy, is applied to quantify the uncertainty features of rainfall DDF relationships derived for ungauged sites in Hong Kong. For illustration, four locations in different geographical locations in Hong Kong are considered. The results show that the uncertainty associated with the estimated statistical moments of annual maximum daily rainfall is significant in contributing to the overall uncertainty of derived rainfall DDF relationships.     

Identification and stochastic generation of representative rainfall temporal patterns in Hong Kong territory

In hydrosystem engineering design and analysis, temporal pattern for rainfall events of interest is often required. In this paper, statistical cluster analysis of dimensionless rainfall pattern is applied to identify representative temporal rainfall patterns typically occurred in Hong Kong Territory. For purpose of selecting an appropriate rainfall pattern in engineering applications, factors affecting the occurrence of different rainfall patterns are examined by statistical contingency tables analysis through which the inter-dependence of the occurrence frequency of rainfall patterns with respect to geographical location, rainfall duration and depth, and seasonality is investigated. Furthermore, due to inherent variability of rainfall mass curves or hyetographs within each classified rainfall pattern, a practical procedure to probabilistically generate plausible rainfall patterns is described. The procedure preserves the inherent stochastic features of random dimensionless rainfall hyetograph ordinates, which in general are correlated non-normal multivariate compositional variables.     

Addressing snow model uncertainty for hydrologic prediction

Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction.     

Oil spill in Hong Kong

Fish farming was seriously but only temporarily affected by a large spill of a very toxic product oil at Hong Kong. Field experiments were set up to follow tainting and depuration. Studies were made of hydrography, water quality, oil in sands, macro- and meio-fauna of shores, and some observations made of possible effects on plankton and open-water fisheries.     

Organochlorines in Hong Kong Fish

Muscle samples from 15 species of fish (n=1) purchased from markets in Hong Kong and 10 liver samples of tilapia (Tilapia mossambica) collected from the Shing Mun River were analysed for organochlorines (polychlorinated biphenyls, ΣDDTs, hexachlorobenzene, hexachlorocyclohexanes, chlordanes, mirex and dieldrin). Polychlorinated biphenyls (PCBs) were detected in 15 market fish samples but the levels were very low (around 1 ng g⁻¹). PCB levels in tilapia livers collected from Tai Wai (29.3–65.1 ng g⁻¹) were higher than those from Fo Tan (3.5–23.2 ng g⁻¹) suggesting that there may be some local point soucres. ΣDDTs were detected in all samples, ranging from 3.3 to 75.6 ng g⁻¹ in the market fish and from 7.1 to 88.8 ng g⁻¹ in tilapia. The DDE/DDT ratios in the market fish (0.12–0.75) showed higher variability than those of tilapia (0.30–0.46), suggesting that some of the market fish may have been collected from areas where DDT was recently used. Results of this preliminary study show that organochlorine levels in Hong Kong market fish are low and do not cause any concern for human consumption. An on-going monitoring program, however, is recommended.     

Marine pollution in Hong Kong

Hong Kong, with an exploding population rising from 600,000 in 1945 to over 4 million in 1970, has had to give first priority to housing. It is not surprising that there is heavy pollution of coastal waters. Tolo Harbour, already dangerously polluted, is an area which is scheduled for a great increase in population. This preliminary study of conditions in the bay will aid planning of future sewage treatment.     

Trace metals in Hong Kong waters

Hong Kong has a population of between four and five million and is becoming increasingly industrialized. Already there are some 19,000 factories concentrated in a few densely populated areas and most of the effluent and sewage is discharged untreated into the sea. Elevated heavy metal concentrations have been recorded in the receiving waters. Lead has 160 times and cadmium 180 times the concentration found in the open ocean.     

Reservoir performance under uncertainty in hydrologic impacts of climate change

Relatively few studies have addressed water management and adaptation measures in the face of changing water balances due to climate change. The current work studies climate change impact on a multipurpose reservoir performance and derives adaptive policies for possible future scenarios. The method developed in this work is illustrated with a case study of Hirakud reservoir on the Mahanadi river in Orissa, India, which is a multipurpose reservoir serving flood control, irrigation and power generation. Climate change effects on annual hydropower generation and four performance indices (reliability with respect to three reservoir functions, viz. hydropower, irrigation and flood control, resiliency, vulnerability and deficit ratio with respect to hydropower) are studied. Outputs from three general circulation models (GCMs) for three scenarios each are downscaled to monsoon streamflow in the Mahanadi river for two future time slices, 2045–65 and 2075–95. Increased irrigation demands, rule curves dictated by increased need for flood storage and downscaled projections of streamflow from the ensemble of GCMs and scenarios are used for projecting future hydrologic scenarios. It is seen that hydropower generation and reliability with respect to hydropower and irrigation are likely to show a decrease in future in most scenarios, whereas the deficit ratio and vulnerability are likely to increase as a result of climate change if the standard operating policy (SOP) using current rule curves for flood protection is employed. An optimal monthly operating policy is then derived using stochastic dynamic programming (SDP) as an adaptive policy for mitigating impacts of climate change on reservoir operation. The objective of this policy is to maximize reliabilities with respect to multiple reservoir functions of hydropower, irrigation and flood control. In variations to this adaptive policy, increasingly more weightage is given to the purpose of maximizing reliability with respect to hydropower for two extreme scenarios. It is seen that by marginally sacrificing reliability with respect to irrigation and flood control, hydropower reliability and generation can be increased for future scenarios. This suggests that reservoir rules for flood control may have to be revised in basins where climate change projects an increasing probability of droughts. However, it is also seen that power generation is unable to be restored to current levels, due in part to the large projected increases in irrigation demand. This suggests that future water balance deficits may limit the success of adaptive policy options.     

香港场地类别划分的初步研究

利用现场实测波速和钻孔资料,根据中国建筑抗震规范（ＧＢＪ８９－１１,１９８９）和美国ＦＥＭＡ编制的ＮＥＨＲＰ（１９９４）以及欧洲抗震规范（ＥＵＲＣＯＤＥ８,１９９８）关于场地分类划分的标准,对香港典型场地进行了场地类别划分,了划分结果的适用性。文章认为,对香港滨海地区带有海泥软夹层的回填场地,简单的划分方法可能对长周期地震动估计不足,应当考虑用其他方法估计地震动。文中同时根据实测数据给出了香港地区     

香港安山-英安质古火山颈群

香港中侏罗世屯门组火山岩主要由安山质熔岩、凝灰岩和凝灰角砾岩夹少量凝灰质砂岩组成。其中凝灰角砾岩初期曾误认为是沉积砾岩。1990年香港地质调查组重新研究后,确认是火山成因的凝灰角砾岩。按岩相分析,屯门组火山岩可分为火山通道相安山-英安质熔岩和爆发角砾岩,还有爆发空落相凝灰角砾岩。火山通道相又可分为火山颈相和岩墙相,分布在屯门组东西两侧。在青山东麓,出露一列NNW向呈断续、线状分布的火山颈群。近年来新出版的《香港地质考察指引》和《香港工程地质实践》等地质著作仍将该区爆发角砾岩误认为是沉积成因的砾岩,在地质勘探和工程设计上造成混乱,导致不应有的经济和时间上的损失。本文综合最新研究成果并与世界各地同类火山岩的特征进行对比,确证火山通道相爆发角砾岩的存在,并发现呈线状分布的古火山颈群。     

On selection of kernel parametes in relevance vector machines for hydrologic applications

Recent advances in statistical learning theory have yielded tools that are improving our capabilities for analyzing large and complex datasets. Among such tools, relevance vector machines (RVMs) are finding increasing applications in hydrology because of (1) their excellent generalization properties, and (2) the probabilistic interpretation associated with this technique that yields prediction uncertainty. RVMs combine the strengths of kernel-based methods and Bayesian theory to establish relationships between a set of input vectors and a desired output. However, a bias–variance analysis of RVM estimates revealed that a careful selection of kernel parameters is of paramount importance for achieving good performance from RVMs. In this study, several analytic methods are presented for selection of kernel parameters. These methods rely on structural properties of the data rather than expensive re-sampling approaches commonly used in RVM applications. An analytical expression for prediction risk in leave-one-out cross validation is derived. For brevity, the effectiveness of the proposed methods is assessed first by data generated from the benchmark sinc function, followed by an example involving estimation of hydraulic conductivity values over a field based on observations. It is shown that a straightforward maximization of likelihood function can lead to misleading results. The proposed methods are found to yield robust estimates of parameters for kernel functions.     

Sensitivity of hydrological models to uncertainty in rainfall input

Abstract

Different approaches used in hydrological modelling are compared in terms of the way each one takes the rainfall data into account. We examine the errors associated with accounting for rainfall variability, whether in hydrological modelling (distributed vs lumped models) or in computing catchment rainfall, as well as the impact of each approach on the representativeness of the parameters it uses. The database consists of 1859 rainfall events, distributed on 500 basins, located in the southeast of France with areas ranging from 6.2 to 2851 km². The study uses as reference the hydrographs computed by a distributed hydrological model from radar rainfall. This allows us to compare and to test the effects of various simplifications to the process when taking rainfall information (complete rain field vs sampled rainfall) and rainfall–runoff modelling (lumped vs distributed) into account. The results appear to show that, in general, the sampling effect can lead to errors in discharge at the outlet that are as great as, or even greater than, those one would get with a fully lumped approach. We found that small catchments are more sensitive to the uncertainties in catchment rainfall input generated by sampling rainfall data as seen through a raingauge network. Conversely, the larger catchments are more sensitive to uncertainties generated when the spatial variability of rainfall events is not taken into account. These uncertainties can be compensated for relatively easily by recalibrating the parameters of the hydrological model, although such recalibrations cause the parameter in question to completely lose physical meaning.

Citation Arnaud, P., Lavabre, J., Fouchier, C., Diss, S. & Javelle, P. (2011) Sensitivity of hydrological models to uncertainty of rainfall input. Hydrol. Sci. J. 56(3), 397–410.     

Lessons learned from public participation in hydrologic engineering projects

ABSTRACT

Public participation in engineering projects has been minimal to date, whereas it is growing in other fields. This paper assesses the lessons learned from public participation in two hydrologic engineering projects, as citizen scientists or through participatory mapping. The two projects were conducted in communities that faced a common problem of flooding due to sea-level rise. The lessons learned include the need to invest time at the beginning of a project to get an idea of what knowledge the public can contribute and have a plan in place to sustain participation at the level needed for the project to be successful. Our cost comparison shows that public participation should be encouraged when the project area is large enough to make travel for data collection cost prohibitive, or when extensive interaction with the public will already be required.     

Mixed estimation methods for Halphen distributions with applications in extreme hydrologic events

The Halphen family of distributions is a flexible and complete system to fit sets of observations independent and identically distributed. Recently, it is shown that this family of distributions represents a potential alternative to the generalized extreme value distributions to model extreme hydrological events. The existence of jointly sufficient statistics for parameter estimation leads to optimality of the method of maximum likelihood (ML). Nevertheless, the ML method requires numerical approximations leading to less accurate values. However, estimators by the method of moments (MM) are explicit and their computation is fast. Even though MM method leads to good results, it is not optimal. In order to combine the advantages of the ML (optimality) and MM (efficiency and fast computations), two new mixed methods were proposed in this paper. One of the two methods is direct and the other is iterative, denoted respectively direct mixed method (MMD) and iterative mixed method (MMI). An overall comparison of the four estimation methods (MM, ML, MMD and MMI) was performed using Monte Carlo simulations regarding the three Halphen distributions. Generally, the MMI method can be considered for the three Halphen distributions since it is recommended for a majority of cases encountered in hydrology. The principal idea of the mixed methods MMD and MMI could be generalized for other distributions with complicated density functions.     

香港地区晚更新世的构造活动性

河沥背西北走向的断层最近期的构造活动发生于晚更新世时期,至全新世已经完全停止.晚更新世后期香港地区(如河沥背、黄竹洋、南山和贝澳)滑坡、泥石流和巨型滚石等重力地质事件频繁发生,而且还有原地重复的情况.按照地面地质破坏现象的分布范围、规模和数量等指针综合分析,在晚更新世后期香港地区可能曾经遭受过强烈地震的袭击,其烈度估计可达Ⅸ-Ⅹ度,而且在数万年之内曾经多次发生.1万年以来,构造运动减弱,地震活动降低,地面运动的最大强度相应减小.     

Using data-derived perturbations to incorporate uncertainty in generating stochastic areal rainfall from point rainfall

Abstract

Raingauge measurements are commonly used to estimate daily areal rainfall for catchment modelling. The variation of rainfall between the gauges is usually inadequately captured and areal rainfall estimates are therefore very uncertain. A method of quantifying these uncertainties and incorporating them into ensembles of areal rainfall is demonstrated and tested. The uncertainties are imposed as perturbations based on the differences in areal rainfall that result when half of the raingauges are alternately omitted. Also included is a method of: (a) estimating the proportion rainfall that falls on areas where no gauges are located that are consequently computed as having zero rain, and (b) replacing them with plausible non-zero rainfalls. The model is tested using daily rainfall from two South African catchments and is found to exhibit the expected behaviour. One of the two parameters of the model is obtained from the rainfall data, while the other has direct physical interpretation.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Ndiritu, J., 2013. Using data-derived perturbations to incorporate uncertainty in generating stochastic areal rainfall from point rainfall. Hydrological Sciences Journal, 58 (8), 1704–1717.     

Estimates of changes in design rainfall values for Canada

Annual maximum rainfall data from 51 stations in Canada were analyzed for trends and changes by using the Mann–Kendall trend test and a bootstrap resampling approach, respectively. Rainfall data were analyzed for nine durations ranging from 5 min to 24 h. The data analyzed are typically used in the development of intensity‐duration‐frequency (IDF) curves, which are used for estimating design rainfall values that form an input for the design of critical water infrastructure. The results reveal more increasing than decreasing trends and changes in the data with more increasing changes and larger changes, noted for the longer rainfall durations. The results also indicate that a traditional trend test may not be sufficient when the interest is in identifying changes in design rainfall quantiles. Copyright © 2012 John Wiley & Sons, Ltd.     

Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement.