A large sample investigation of temporal scale-invariance in rainfall over the tropical urban island of Singapore

Scaling behavior of rainfall time series is characterized using monofractal, spectral, and multifractal frameworks. The study analyzed temporal scale-invariance of rainfall in the tropical island of Singapore using a large dataset comprising 31 years of hourly and 3 years of 1-min rainfall measurements. First, the rainfall time series is transformed into an occurrence–non-occurrence binary series, and its scaling behavior is analyzed using box-counting analysis. The results indicated that the rainfall support displays fractal structure, but within a limited range of scales. The rainfall support has a fractal dimension (D f ) of 0.56 for scales ranging from 1 min to 1.5 h and a D f of 0.37 from 1.5 h to 1.5 days. The results further showed that the fractal dimension decreases with the increase in the threshold used to define binary series. Spectral analysis carried out on the rainfall time series and the corresponding binary series showed three distinct scaling regimes of 4 min–2 h, 2–24 h, and 24 h–1 month. In all the scaling regimes, the spectral exponents for the rainfall series were smaller than those for the binary series. The study then investigated the presence of multiscaling behavior in rainfall time series using moment scaling analysis. The results confirmed that the rainfall fluctuations display a multiscaling structure, which was modeled in the framework of universal multifractals. The results from this study would not only improve our understanding of the temporal rainfall structure in Singapore and the surrounding Maritime Continent but also help us build and parameterize parsimonious models and statistical downscaling techniques for rainfall in this region.MOE (Min. of Education, S’pore)Accepted versio

Assessing Shock Propagation and Cascading Uncertainties Using the Input–Output Framework: Analysis of an Oil Refinery Accident in Singapore

The impacts of shock events frequently cascade beyond the primarily affected sector(s), through the interdependent economic system, and result in higher-order indirect losses in other sectors. This study employed the inoperability input–output model (IIM) and the dynamic IIM (DIIM) to model recovery of sectors after a shock event and quantify associated total losses. Considering data limitations and uncertainties regarding sectoral recovery time, a key variable in DIIM, a probabilistic approach is used for modelling uncertainty in recovery times. The event analyzed is the 2011 oil refinery fire accident in Pulau Bukom (PB) island, Singapore, which caused the refinery to shut down for 11 days and be partially operational for several days thereafter. The impacts are assessed using the regrouped 15-sector Singapore IO data of year 2010, with manufacturing sector as the directly affected sector. The initial economic impact of the PB refinery fire is assessed in the top-down framework using the refinery’s contribution to the manufacturing sector and nation’s GDP. The higher-order losses are quantified considering different recovery paths for the directly affected sector and accounting for its inventory. Simulation experiments using synthetic IO tables are also carried out to understand relationship between recovery characteristics of directly and indirectly affected sectors. The results from IIM analysis show that the indirect losses are about 35–38% of direct losses. The DIIM analysis reveal that the utilities sectors (e.g., electricity, water supply and treatment) suffer the largest inoperability among indirectly affected sectors for a given direct damage to the manufacturing sector. The results also illustrate the dependence of overall losses on the recovery path of the directly affected sector, and associated uncertainties in sectoral recovery times

Evaluation of GPM IMERG rainfall estimates in Singapore and assessing spatial sampling errors in ground reference

We evaluated the Integrated Multi-satellite Retrievals for GPM (IMERG) V06B Early and Final Run products using data from a dense gauge network in Singapore as ground reference (GR). The evaluation is carried out at monthly, daily, and hourly scales, and conditioned on different seasons and rainfall intensities. Further, different spatial configurations and densities of the gauge networks (3-17 gauges per IMERG cell) used here allowed us to examine spatial sampling errors (SSE) in the GR. The results revealed a probability of detection of 0.95 (0.65), critical success index of 0.69 (0.35), and a correlation of 0.60 (0.41) for the daily (hourly) scale. Results also indicate an overestimation of rainy days (hours) by IMERG compared to GR, leading to a false alarm ratio of 0.29 (0.57) at daily (hourly) scales. Analysis of probability distributions and conditional error metrics showed overestimation of lighter (0.2-4 mm/d) and moderate (4-8 mm/d) rainfall by IMERG, but better performance for heavier rainfall (≥32 mm/d). The seasonal analysis showed improved performance of IMERG during November-February compared to June-September months. The hourly analysis further revealed large discrepancies in diurnal cycles during June-September. The SSE are studied in a Monte Carlo framework consisting of several synthetic networks with varying spatial configurations and densities. The effect of SSE on IMERG evaluation results is characterized following the error variance separation approach. For the gauge networks studied here, the contribution of SSE variance to IMERG daily error variance ranges from 4-24% depending on gauge spatial configuration, and is as large as 36% during inter-monsoon months when rainfall is highly convective in nature.National Research Foundation (NRF)Accepted versionThe authors appreciate the partial support from the Singapore ETH Centre Future Resilience Systems projec

Assessment of future changes in Southeast Asian precipitation using the NASA Earth Exchange Global Daily Downscaled Projections data set

Extreme precipitation and associated flooding cause severe damage to society and the environment. Future climate projections suggest an intensification of precipitation extremes in many regions. However, there is an increasing need for climate change impact assessment at higher spatial resolution, particularly for regions with complex geography such as Southeast Asia (SEA). In this study, we analysed the NASA Earth Exchange 0.25° resolution daily precipitation projections from an ensemble of 20 climate models under two emission scenarios RCP4.5 and RCP8.5. The variability in future precipitation projections is analysed and quantified for six geographical subregions, two climatological regions (wet and dry), and the low-elevation coastal zones in SEA. Various aspects of precipitation structure are studied using indices that characterize precipitation amount, number of heavy precipitation days, extreme precipitation amount, and maximum daily precipitation at annual and seasonal scales. The results show substantial increases in mean and extreme precipitation in many parts of SEA by the end of the 21st century under both emission scenarios, thus increasing the region's vulnerability to precipitation-driven hazards. The projected centennial increase in total annual precipitation relative to the baseline period of 1970–1999 when averaged over all land grid cells is about 15% under RCP8.5 scenario, with larger values (∼20%) over mainland SEA and Philippines and smaller values (∼6%) in Java island. The projected changes in extreme precipitation are stronger compared to the total annual precipitation under both emission scenarios. The New Guinea and Java regions show the largest and smallest increases in annual maximum daily precipitation, with ensemble mean values of 30 and 17%, respectively, under RCP8.5 scenario. The results also reveal large inter-model spread in projected changes, particularly during boreal winter and summer months.Accepted versio

Can Lagrangian extrapolation of radar fields be used for precipitation nowcasting over complex Alpine orography?

In this study, a Lagrangian radar echo extrapolation scheme (MAPLE) was tested for use in very short-term forecasting of precipitation over a complex orographic region. The high-resolution forecasts from MAPLE for lead times of 5 min-5 h are evaluated against the radar observations for 20 summer rainfall events by employing a series of categorical, continuous, and neighborhood evaluation techniques. The verification results are then compared with those from Eulerian persistence and high-resolution numerical weather prediction model [the Consortium for Small-scale Modeling model (COSMO2)] forecasts. The forecasts from the MAPLE model clearly outperformed Eulerian persistence forecasts for all the lead times, and had better skill compared to COSMO2 up to lead time of 3 h on average. The results also showed that the predictability achieved from the MAPLE model depends on the spatial structure of the precipitation patterns. This study is a first implementation of the MAPLE model over a complex Alpine region. In addition to comprehensive evaluation of precipitation forecast products, some open questions related to the nowcasting of rainfall over a complex terrain are discussed.Published versio

Seasonal and Interannual Variability of Wet and Dry Spells over Two Urban Regions in the Western Maritime Continent

Daily rainfall data from two urban regions in Southeast Asia are analyzed to study seasonal and interannual variability of wet and dry spells. The analysis is carried out using 35 years of data from Singapore and 23 years of data from Jakarta. The frequency distribution of wet (dry) spells and their relative contribution to the total number of wet (dry) days and to the total rainfall are studied using 15 statistical indicators. At the annual scale, Singapore has a greater number of wet spells and a larger mean wet spell length compared to Jakarta. However, both cities have equal probability of extreme wet spells. Seasonal-scale analysis shows that Singapore is drier (wetter) than Jakarta during boreal winter (summer). The probability of extreme wet spells is lower (higher) for Singapore than Jakarta during boreal winter (summer). The results show a stronger contrast between Singapore and Jakarta during boreal summer. The study also examined the time series of Singapore wet and dry spell indicators for the presence of interannual trends. The results indicate statistically significant upward trends for a majority of wet spell indicators. The wet day percentage and mean wet spell length are increasing at 2.0% decade−1 and 0.18 days decade−1, respectively. Analysis of dynamic and thermodynamic variables from ERA-Interim during the study period indicates a strengthening of low-level convergence and vertical motion and an increase in specific humidity and atmospheric instability (convective available potential energy), which explain the increasing trends observed in Singapore wet spell indicators.MOE (Min. of Education, S’pore)Published versio

Effect of climate change and urbanisation on flood protection decision‐making

The changing climate and the rapid urbanisation may alter flood severity and influence the decision‐making process for flood management. In this study, a Multi‐Criteria Decision Analysis (MCDA) framework for optimal decision‐making in flood protection is developed and applied to a central flood‐prone basin of Jakarta, Indonesia. Specifically, the decisions are on levees corresponding to protection under different rainfall return periods (RP), considering climate change and associated uncertainties, urbanisation, and evolving socio‐economic features of the flood plain. Three cases were studied to analyse future (year 2050) conditions (i) future rainfall/current urban, (ii) current rainfall/future urban and (iii) future rainfall/future urban. Future climate change projections from the NASA Earth Exchange are used to obtain information about changes in rainfall, whereas Landsat derived imperviousness maps along with the population projections are used for future urban conditions. Annual Expected Loss, Graduality, upgrade Construction cost and Net‐Socio‐Economic Vulnerability Index are the criteria used in the MCDA. It is found that climate change has a higher impact compared to urbanisation on the flood protection decisions. For the basin studied, the extreme future case of increased rainfall and urbanised conditions have the optimal decision in levee protection level corresponding to 250 years RP under current rainfall which corresponds to ~60 years RP under future rainfall.Ministry of Education (MOE)Nanyang Technological UniversitySingapore-ETH Centre for Global Environmental SustainabilityPublished versionFunding from the Singapore Ministry of Education Tier II program, NTU's Nanyang Environment and Water Research Institute, and the Singapore ETH Center Future Resilient Systems program is gratefully acknowledged

Effect of radar-rainfall uncertainties on the spatial characterization of rainfall events

Remotely sensed precipitation products, due to their large areal coverage and high resolution, have been widely used to provide information on the spatiotemporal structure of rainfall. However, it is well known that these precipitation products also suffer from large uncertainties that originate from various sources. In this study, we selected radar-rainfall (RR) data corresponding to 10 warm season events over a 256 × 256 km2 domain with a data resolution of 4 × 4 km2 in space and 1 h in time. We characterized their spatial structure using correlation function, power spectrum, and moment scaling function. We then employed a recently developed RR error model and rainfall generator to obtain an ensemble of probable rainfall fields that are consistent with the RR estimation error structure. We parameterized the spatial correlation functions with a two-parameter power exponential function, the Fourier spectra with a power law function, and the moment scaling functions with the universal multifractal model. The parameters estimated from the ensemble were compared with those obtained from the RR products to quantify the impact of radar-rainfall estimation errors on the spatial characterization of rainfall events. From the spatial correlation and power spectrum analyses, we observed that RR estimation uncertainties introduce spurious correlations with greater impact for the smaller scales. The RR errors also significantly bias the estimation of the moment scaling functions.Published versio

Dissecting the effect of rainfall variability on the statistical structure of peak flows

This study examines the role of rainfall variability on the spatial scaling structure of peak flows using the Whitewater River basin in Kansas as an illustration. Specifically, we investigate the effect of rainfall on the scatter, the scale break and the power law (peak flows vs. upstream areas) regression exponent. We illustrate why considering individual hydrographs at the outlet of a basin can lead to misleading interpretations of the effects of rainfall variability. We begin with the simple scenario of a basin receiving spatially uniform rainfall of varying intensities and durations and subsequently investigate the role of storm advection velocity, storm variability characterized by variance, spatial correlation and intermittency. Finally, we use a realistic space–time rainfall field obtained from a popular rainfall model that combines the aforementioned features. For each of these scenarios, we employ a recent formulation of flow velocity for a network of channels, assume idealized conditions of runoff generation and flow dynamics and calculate peak flow scaling exponents, which are then compared to the scaling exponent of the width function maxima. Our results show that the peak flow scaling exponent is always larger than the width function scaling exponent. The simulation scenarios are used to identify the smaller scale basins, whose response is dominated by the rainfall variability and the larger scale basins, which are driven by rainfall volume, river network aggregation and flow dynamics. The rainfall variability has a greater impact on peak flows at smaller scales. The effect of rainfall variability is reduced for larger scale basins as the river network aggregates and smoothes out the storm variability. The results obtained from simple scenarios are used to make rigorous interpretations of the peak flow scaling structure that is obtained from rainfall generated with the space–time rainfall model and realistic rainfall fields derived from NEXRAD radar data.Accepted versio

Risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables

Modern coupled power and water (CPW) systems exhibit increasing integration and interdependence, which challenges system performance to disasters and makes service restoration complex during post-disruption. Meanwhile, new technologies, such as small pumped-hydro storage (PHS) and rooftop renewables, are being widely installed and further deepen the interdependencies. To capture these features and improve overall performance, this paper proposes a coordinated restoration framework for a CPW system to respond to disruptions. The proposed CPW model comprises physical networks and mechanisms, considering available units, such as water desalination/treatment plants, pump stations and small PHS, in the water system, and rooftop renewables, distributed generators, in power system. The interdependencies are modeled through component-wise connections and consumer behavior, then grouped into three phases: production, distribution, and consumption. Aggregate service loss with respect to different consumer loads and time periods, is chosen as performance metric and to be minimized using network reconfiguration, energy/water dispatching, load curtailment, and operation management of components. A two-stage risk-averse stochastic programming is applied for reliable restoration and manage risks, to tackle the uncertainties in renewable power generations and water/power demands that affect method effectiveness. Finally, the method is implemented on a modified 33-bus/25-node CPW system, and the results demonstrate the effectiveness of the proposed restoration framework.National Research Foundation (NRF)This research is supported by the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme