No abstract

The concept of return period and the associated risk of occurrence of extreme events are critical considerations in the management of water resources. A precondition for conducting hydrological frequency analysis to estimate return period and risk is the assumption of stationarity of the hydrological variable of interest, but this is problematic because climate change and human activities can act to make hydrological phenomena nonstationary. Two different interpretations of return period, i.e. the expected waiting time (EWT) and expected number of exceedances (ENE), have been proposed to consider nonstationarity in return period and risk analysis by introducing the time-varying moments method into nonstationary frequency analysis, under the assumption that the statistical parameters are functions only of time. This paper explored the use of meteorological variables to improve the characterization of nonstationary return period and risk under the ENE interpretation by employing meteorological covariates in the nonstationary frequency analysis. The advantage is that the downscaled future meteorological variables from the General Circulation Model (GCM) outputs can be used to calculate the statistical parameters and exceedance probabilities for future years. The EWT interpretation using time as covariate was also applied for comparison. Both interpretations of return period were applied to the low-flow (annual minimum monthly streamflow) series of the Wei River, China. Both interpretations gave an estimate of nonstationary return period and risk that was significantly different from the stationary case. The nonstationary return period and risk under ENE interpretation using temperature and precipitation as covariates were more reasonable and advisable than those of the EWT case using time as covariate. We concluded that nonstationary analysis can improve decision making in the management of water resources of the Wei River basin during dry seasons exacerbated by climate change.

Both unmanned aerial vehicle (UAV) technology and Mobile Mapping Systems (MMS) are important techniques for surveying and mapping. In recent years, the UAV technology has seen tremendous interest, both in the mapping community and in many other fields of application. Carrying off-the shelf digital cameras, the UAV can collect high quality aerial optical images for city modeling using photogrammetric techniques. In addition, a MMS can acquire high density point clouds of ground objects along the roads. The UAV, if operated in an aerial mode, has difficulties in acquiring information of ground objects under the trees and along façades of buildings. On the contrary, the MMS collects accurate point clouds of objects from the ground, together with stereo images, but it suffers from system errors due to loss of GPS signals, and also lacks the information of the roofs. Therefore, both technologies are complementary. This paper focuses on the integration of UAV images, MMS point cloud data and terrestrial images to build very high resolution 3D city models. The work we will show is a practical modeling project of the National University of Singapore (NUS) campus, which includes buildings, some of them very high, roads and other man-made objects, dense tropical vegetation and DTM. This is an intermediate report. We present work in progress.

Abstract. Many studies have shown that downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally performed by comparing the pre-dam and post-dam frequencies of certain streamflow indicators, such as floods and droughts. In this study, a rainfall–reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under a framework of a covariate-based nonstationary flood frequency analysis using the Bayesian inference method. The RRCI is derived from a combination of both a reservoir index (RI) for measuring the effects of reservoir storage capacity and a rainfall index. More precisely, the OR joint (the type of possible joint events based on the OR operator) exceedance probability (OR-JEP) of certain scheduling-related variables selected out of five variables that describe the multiday antecedent rainfall input (MARI) is used to measure the effects of antecedent rainfall on reservoir operation. Then, the RI-dependent or RRCI-dependent distribution parameters and five distributions, the gamma, Weibull, lognormal, Gumbel, and generalized extreme value, are used to analyze the annual maximum daily flow (AMDF) of the Ankang, Huangjiagang, and Huangzhuang gauging stations of the Han River, China. A phenomenon is observed in which although most of the floods that peak downstream of reservoirs have been reduced in magnitude by upstream reservoirs, some relatively large flood events have still occurred, such as at the Huangzhuang station in 1983. The results of nonstationary flood frequency analysis show that, in comparison to the RI, the RRCI that combines both the RI and the OR-JEP resulted in a much better explanation for such phenomena of flood occurrences downstream of reservoirs. A Bayesian inference of the 100-year return level of the AMDF shows that the optimal RRCI-dependent distribution, compared to the RI-dependent one, results in relatively smaller estimated values. However, exceptions exist due to some low OR-JEP values. In addition, it provides a smaller uncertainty range. This study highlights the necessity of including antecedent rainfall effects, in addition to the effects of reservoir storage capacity, on reservoir operation to assess the reservoir effects on downstream flood frequency. This analysis can provide a more comprehensive approach for downstream flood risk management under the impacts of reservoirs.

Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming Water Resour Manage (2015) 29:3615-3633 constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series.