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Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme condition implied from various RCM models would be more alarming. The study result would be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.

This study assessed the potential impact of climate change on spatiotemporal distribution of blue and green water resources in the Beijiang River basin, southern China, using the SWAT hydrological model and CORDEX-EAS regional climate models (RCMs). The outputs of three RCMs (namely RMOH, RMPI and RNCC) under two emission scenarios (RCP 2.6 and RCP 8.5) were bias-corrected by using the quantile delta mapping method for the control (1975–2004), near future (2021–2050) and far future (2061–2090) periods. Driven by the corrected climate variables, future blue and green water were assessed by using the SWAT model calibrated with streamflow data. The results indicated that the green water flow in the northwest basin predicted by RMOH under RCP 2.6 would increase up to about 2.7% in 2061–2090; RNCC suggests that most of the basin would experience the most significant reduction in green water storage (over 30%) under RCP 8.5 in the far future. For blue water, the ensemble mean of three RCMs indicates a slight decreasing trend in the northern part of the basin and an increasing one in the southern part under RCP 8.5 in the far future. Our findings could help watershed managers evaluate future hydrological risks and design appropriate adaptation strategies. Ministry of Education (MOE) Published version This work was supported by the National Natural Science Foundation of China (52009022) and AcRF Tier 1 project (2019- T1-001-160) from the Ministry of Education (MOE), Singapore.

This study investigated the temporal rainfall pattern in order to facilitate rainfall design, which normally requires a good understanding of the temporal patterns of rainstorm events. The analysis employed a storm-event-based approach using the concept of inter-event time definition (IETD) and rainfall depth/duration/intensity thresholds. The 5-min rainfall data at three raingauge stations were analysed to determine representative quartiles of a design storm in a tropical city. The temporal characteristics of the design storm could be determined from the rainfall depth ratios of consecutive peak rainfalls for each interval of storm duration, and time to the first peak rainfall depending on each quartile’s rainstorm events. The determination of the quantile distribution of tropical rainfall could help improve the representativeness of design rainfall and facilitate rainfall–runoff modelling for urban flood control in a tropical region. Ministry of Education (MOE) This work was partly supported by Academic Research Fund Tier 1 from the Ministry of Education (MOE), Singapore (2019-T1-001-160), and partly by the Chung-Ang University Research Grants in 2019.

Drought has become one of the most serious meteorological disasters for agricultural production in many areas around the world, and the situation could be worse under the impact of climate change. To facilitate better adaptation planning, this study proposed a drought assessment framework integrating downscaling method, drought index, copula technique, and bivariate frequency analysis, and applied it to investigate the change of the drought characteristics and drought risks from the past to the future in Huang-Huai-Hai River basin (HRB), North China. Drought was firstly defined by standardized precipitation evapotranspiration index (SPEI) based on 1497 observed grid data from 1979 to 2004. Then, we constructed the joint distribution of drought duration and severity based on copulas to detect and quantify the drought risks. To address the effect of climate change, similar calculation process was applied to the future climate data, which was downscaled using delta change method from representative concentration pathway (RCP 8.5) of 12 general circulation models (GCMs). The study results suggested that, under climate change condition, most irrigation districts over HRB would generally experience lower frequency of drought events but with extended duration; some districts would have more serious drought, but majority would experience similar or even lower level of severity. In light of the mean joint occurrence probability, the irrigation district at the south part of Huai River basin would likely experience the highest increase of drought risks in near future (by 0.86%) and distant future (by 0.76%), while most of other districts over HRB would face low risk of serious drought risks. The obtained results offer useful information to agricultural managers or water resources authorities who are interested in the development of effective long-term adaptation strategies for drought management. Nanyang Technological University This project was supported by Start-Up Grant (M4081327.030) from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore.

Water carrying capacity (WCC) is essential to support regional sustainable development. In this study, a nation-wide system dynamics model was developed to predict WCC of China for the near future period (2019–2030) based on system analysis of water resources and historical data from 2000 to 2018. The system dynamics model considers three water resources conditions (i.e., normal, wet, and dry) and four development modes (i.e., business-as-usual, priority-for-economy, environmental-constraint, and sustainable development) to holistically represent possible future conditions. From the prediction, the total population and GDP of China would continue growing under all scenarios, and the total wastewater discharge would generally rise first and then decline largely due to the growth of GDP per capita. The water supply-demand ratio would be greatly affected by development mode, depending on the combinative effects of population, GDP and water intake under each development mode. The impact of the wet condition on the modeling results is limited, but that of the dry condition is significant, especially on supply-demand ratio due to the limited water resources. From decision analysis, the four development modes have their own advantages. But from a long-term point of view, the sustainable development mode would outperform others due to its more balanced consideration. The study methodology and findings are valuable for assisting national or regional systems planning of water resources and water quality, especially in face of various challenges such as climate change, eco-environmental deterioration, and water resources shrinkage. This research work was supported by the Fundamental Research Funds for the Central Universities of Central South University (No. 2021zzts0239) and 2019 water conservancy science and technology project of Hunan Province (No. XSKJ2019081–57). The authors gratefully acknowledge financial support from China Scholarship Council (No. 202006370108).

Urban areas are becoming increasingly vulnerable to extreme storms and flash floods, which could be more damaging under climate change. This study presented an integrated framework for assessing climate change impact on extreme rainfall and urban drainage systems by incorporating a number of statistical and modelling techniques. Starting from synthetic future climate data generated by the stochastic weather generator, the simple scaling method and the Huff rainfall design were adopted for rainfall disaggregation and rainfall design. After having obtained 3-min level designed rainfall information, the urban hydrological model (i.e., Storm Water Management Model) was used to carry out the runoff analysis. A case study in a tropical city was used to demonstrate the proposed framework. Particularly, the impact of selecting different general circulation models and Huff distributions on future 1-h extreme rainfall and the performance of the urban drainage system were investigated. It was revealed that the proposed framework is flexible and easy to implement in generating temporally high-resolution rainfall data under climate model projections and offers a parsimonious way of assessing urban flood risks considering the uncertainty arising from climate change model projections, downscaling and rainfall design. Ministry of Education (MOE) Nanyang Technological University Published version This project was supported by Start-Up Grant (M4081327.030) from the School of Civil and Environmental Engineering, Nanyang Technological University, Singapore and in part by the AcRF Tier 1 project (M4012228.030) from the Ministry of Education (MOE), Singapore.

To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlati...

Seeking cost-effective design of urban hydrological facilities and drainage systems is an important task for many city planners. However, such a process has always been complicated with intrinsic uncertainties. This work presented an integrated fuzzy simulation-optimization model (FSOM) for supporting Low Impact Development (LID) design under model uncertainties. Various LID implementation schemes involving green roof, bio-retention cell, and permeable pavement were simulated through an urban hydrological model. Three model parameters were assumed as fuzzy sets. In a case study, fuzzy simulation (FS) and genetic algorithm (GA) were employed to search the optimal schemes of LIDs under various confidence levels of satisfying flood control constraints. Comparison of FSOM to traditional deterministic and stochastic models were also carried out. It was shown that FSOM could offer a flexible way of defining and assessing uncertainties associated with hydrological modeling and generate solutions that were comparable to those from either deterministic or stochastic models. However, FSOM also showed limitation of high computational requirement.

A probabilistic flood risk modelling chain is proposed for flood risk analysis with consideration of spatial spreading and temporal clustering of the flood events. The proposed method consists of (1) a continuous simulation of long-term climatic and hydrologic fields, (2) Monte-Carlo simulations of a 1D river and inundation model and (3) a probabilistic loss model. In the first stage, three multisite multivariate weather generators were tested and a K-nearest neighbor weather generator (KNN-CAD v4) was found most suitable to reproduce hydrological extremes. The methodology was then applied to identify variations in risk between spatially coherent and simplified event sets generated from 5000 years of synthetic data across four river basins in Uttarakhand, India. The results showed that preserving the spatial coherency of regional events lead to a spreading of the T-year local event distributions on both sides of a uniform regional T-year return period. The specificity of the exposure and the correlations between local events translated into minimal (4%) differences in losses at high return periods (> 50 year) but larger difference (20%) at low return periods (5 to 10-year). The redistribution of loss frequencies led to negligible differences in the Annual Average Losses between the different event sets. In this application, a simplified event set structure with uniform but appropriate T-year return periods proved to capture reasonably well the averaged risk metrics regionally. This study illustrates the complexity of estimating regional flood risk accumulation and exemplifies how a probabilistic risk model chain with continuous simulation can provide a more detailed picture of flood risks. Economic Development Board (EDB) Nanyang Technological University The study was funded by the Economic Development Board (EDB) of Singapore (IPP Scholarship) and the DHI-NTU Research Centre and Education Hub. The work is closely affiliated with the Disaster Risk Assessment of Uttarakhand, an initiative of the Uttarakhand Disaster Recovery Programme (UDRP) funded by the World Bank.

The potential global warming impact on the extreme and mean streamflow of the Upper Blue Nile (UBN) River Basin was projected for the 2050s and 2080s by three hydrological models [Nedbor-Af...

Water security is especially important for China who owns a huge population and has uneven temporal and spatial water resources distribution. Previous studies towards a comprehensive evaluation of water security issues at national or provincial level are still limited. In this study, a new supply-demand coupling model was proposed to evaluate water security situation more directly and comprehensively over China. The integrated supply-demand coupling model combines analytic hierarchy process, entropy weight method and fuzzy evaluation method and is particularly advantageous in handling complexity and uncertainty of water security measurement. The study result indicated that, from temporal dimension, China's overall water security situation has continuously improved, mainly because of the remarkable improvement of flood control capacity and water supply capacity. This is important to ensure China's own sustainable development goal. However, the ecological protection capacity was still highly insufficient, staying at an unsafe level for a long time; the security level of water resources and water ecology was still not optimistic and that of hydrological cycle was getting worse under the influence of human activities. From spatial distribution, the water security situation in the south part of China was generally better than that in the north. This study also highlighted the prominent water security problems in various regions, which provided a valuable reference for relevant authorities in their long-term water resources planning and management. This research work was supported by the Fundamental Research Funds for the Central Universities of Central South University (No. 2021zzts0239) and 2019 water conservancy science and technology project of Hunan Province (No. XSKJ2019081-57). The authors gratefully acknowledge financial support from China Scholarship Council (No. 202006370108).

This study investigated the frequency analysis of precipitation indices of maximum daily precipitation amount (Rx1day), maximum 5-day cumulative precipitation amount (Rx5day), and maximum length of consecutive dry days (CDD) using various bias-correction schemes over mainland Southeast Asia (MSEA). The bias-correction schemes were based on quantile delta mapping (QDM) with different strategies in selecting the data series, including monthly correction (MC), annual correction (AC), peak correction using annual raw data (PCRAW), and annual correction using extreme-index data series (ACEX). Two regional climate model (RCM) outputs from CORDEX-SEA and APHRODITE rainfall datasets were used as modelled and observed data for methodology verification. The study first compared four frequency analysis methods based on generalized extreme value (GEV), Gumbel (GB), Log-Pearson Type III (LP3), and Lognormal (LOGN) distributions, at 9 selected sites over MSEA, and found that LP3 was the best choice for frequency analysis for the study region. Then, the study compared the four bias-correction schemes at both individual sites and all grids over the entire MSEA and indicated that the PCRAW was the best performer in terms of bias-correction for frequency information. Finally, the study gave an ensembled projection of future extreme indices with 200-yr return based on seven CORDEX-SEA RCMs and suggested a general increasing trend of all indices over MSEA. The study explored the effect of uncertainty originated from adopting various bias-correction schemes in mapping future frequency of precipitation indices and is valuable in revealing the spatiotemporal distribution of precipitation extremes over large areas under climate change which is important for flood/drought risk assessment and adaptation planning. Ministry of Education (MOE) This study was supported by AcRF Tier 1 project (2019-T1-001-160) from Ministry of Education (MOE), Singapore. We acknowledge the data support from APHRODITE (http://aphrodite.st.hirosaki-u.ac.jp/) and the Southeast Asia Regional Climate Downscaling (SEACLID)/Coordinated Regional Climate Downscaling Experiment for Southeast Asia(CORDEX-SEA) project (which was funded by ARCP2014-07CMY-Tangang and ARCP2015-04CMY-Tangang).

This study explored an integrated framework to assess the effectiveness of adaptation measures on the water productivity (WP) of the agricultural water management (AWM) system in the Huai river basin of China considering climate change impact. The adaptation measures include optimization of cropping pattern (OCP) and upgradation of irrigation techniques (UIT). The delta change method was used to downscale the climate variables from RCP4.5 and RCP8.5 of general circulation models (GCMs) during 2021-2050, the water footprint theory was used to estimate the spatial distribution of blue water to calculate the WP, and the nonlinear optimization model was used to seek optimal cropping pattern aiming at maximizing the system's WP. The changes in WP due to climate change and adaptation measures (e.g. combinations of OCP and UIT) were compared. Results indicated that WP under RCP4.5 and RCP8.5 would be 4.56% and 6.51% lower than those under the benchmark scenario, respectively. The mitigation rates to the negative impact of climate change on WP under RCP4.5 and RCP8.5 would be (1) 3.05% and 3.37% for the combination of spay irrigation technique and OCP, and (2) 4.34% and 4.59% for the combination of drip irrigation technique and OCP, respectively. It was revealed that the combination of drip irrigation and cropping pattern optimization could largely offset the adverse effect from climate change on WP under RCP4.5. Under such a scenario, the total plant areas of wheat and maize would reduce over the basin and so would the net export of crops in the basin; this would lead to a decrease in the crop trade benefit of 7.07 × 109 $ and a relief of 7.50 × 109 m3 of blue water loss. This study results could offer strategic decision support for long-term sustainable AWM of Huai river basin in a changing environment. Ministry of Education (MOE) Nanyang Technological University This project was supported by Research Grant (M4082254.030) from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, and in part by Singapore Ministry of Education (MOE) AcRF Tier 1 Grant (M4012228.030).

In this research work, soil and water assessment tool (SWAT) and fuzzy credibility chance-constrained programming (FCCP) model were integrated into an optimization framework for supporting nutrient pollution control in a lake watershed. The framework, so-called SWAT-based FCCP (SFCCP) model, was advantageous in simulating non-point sources (NPS) pollution, optimizing best management practices (BMPs), and addressing system uncertainties. SFCCP was solved by genetic algorithm (GA) for searching optimal placement schemes of BMPs at a lower system cost, where the related uncertainties were addressed as fuzzy parameters. The developed SFCCP model was applied to seek optimal types, sizes and locations of BMPs for nutrient pollution control in a lake watershed system, i.e. Lake Dianchi watershed, China. The study results indicated that when the credibility level increased from 0.55 to 0.95, the total system cost would increase from $8.89 to $12.27 × 106; meanwhile, the total nitrogen (TN) load discharged into the lake would decrease from 1.22 to 1.19 × 106 kg/yr and the total phosphorus (TP) load would reduce from 51.37 to 50.11 × 103 kg/yr, respectively. It appeared that a higher credibility level would lead to a stricter control requirement, namely a higher reduction of nutrient load by BMPs and a higher system cost. The proposed model could be used to help generate a series of BMPs placement schemes under various credibility levels; this ensures that the nutrient load brought into the lake and tributaries could drop to an acceptable level, with a proper tradeoff between system cost and risk being considered.

A Gini-coefficient based stochastic optimization (GBSO) model was developed by integrating the hydrological model, water balance model, Gini coefficient and chance-constrained programming (CCP) into a general multi-objective optimization modeling framework for supporting water resources allocation at a watershed scale. The framework was advantageous in reflecting the conflicting equity and benefit objectives for water allocation, maintaining the water balance of watershed, and dealing with system uncertainties. GBSO was solved by the non-dominated sorting Genetic Algorithms-II (NSGA-II), after the parameter uncertainties of the hydrological model have been quantified into the probability distribution of runoff as the inputs of CCP model, and the chance constraints were converted to the corresponding deterministic versions. The proposed model was applied to identify the Pareto optimal water allocation schemes in the Lake Dianchi watershed, China. The optimal Pareto-front results reflected the tradeoff between system benefit ( α SB ) and Gini coefficient ( α G ) under different significance levels (i.e. q ) and different drought scenarios, which reveals the conflicting nature of equity and efficiency in water allocation problems. A lower q generally implies a lower risk of violating the system constraints and a worse drought intensity scenario corresponds to less available water resources, both of which would lead to a decreased system benefit and a less equitable water allocation scheme. Thus, the proposed modeling framework could help obtain the Pareto optimal schemes under complexity and ensure that the proposed water allocation solutions are effective for coping with drought conditions, with a proper tradeoff between system benefit and water allocation equity.

A comprehensive flood risk assessment should aim not only at quantifying uncertainties but also the variability of risk over time. In this study, an efficient modelling framework was proposed to perform probabilistic hazard and risk analysis in dike-protected river systems accounting for morphological variability and uncertainty. The modelling framework combined the use of: (1) continuous synthetic discharge forcing, (2) a stochastic dike breach model dynamically coupled to a stochastic unsteady one-dimensional hydraulic model (MIKE1D) describing river flows, (3) a catalogue of pre-run probabilistic inundation maps (MIKE SHE) and (4) a damage and loss model (CAPRA). The methodology was applied using continuous simulations to a 45-km reach of the Upper Koshi River, Nepal, to investigate the changes in breach and flood hazards and subsequent risks after 2 and 5 years of probable river bed aggradation. The study results indicated an increase in annual average loss of 4% per year driven by changes in loss distribution in the most frequent loss return periods (20–500 years). The use of continuous simulations and dike breach model also provided a more robust estimation of risk metrics as compared to traditional binary treatment of flood defence and/or the direct association of flow with loss return periods. The results were helpful to illustrate the potential impacts of dynamic river morphology, dike failure and continuous simulation and their significance when devising flood risk study methodologies.

In this study, a storm event-based frequency analysis method was proposed to mitigate the limitations of conventional rainfall depth–duration–frequency (DDF) analysis. The proposed method takes the number, rainfall depth, and duration of rainstorm events into consideration and is advantageous in estimation of more realistic rainfall quantiles for a given return period. For the purpose of hydraulics design, the rainfall depth thresholds are incorporated to retrieve the rainstorm events for estimating design rainfalls. The proposed method was tested against the observed rainfall data from 1961 to 2010 at Seoul, Korea and the computed rainfall quantiles were compared with those estimated using the conventional frequency analysis method. The study results indicated that the conventional method was likely to overestimate the rainfall quantiles for short rainfall durations. It represented that the conventional method could reflect rainfall characteristics of actual rainstorm events if longer durations (like 24 hours) were considered for estimation of design rainfalls.

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty. MOE (Min. of Education, S’pore)

Carbon nanotubes (CNTs), graphene (GRA), and their derivatives are promising materials for a wide range of applications such as pollutant removal, enzyme immobilization, bioimaging, biosensors, and drug delivery and are rapidly increasing in use and increasingly mass produced. The biodegradation of carbon nanomaterials by microbes and enzymes is now of great importance for both reducing their toxicity to living organisms and removing them from the environment. Here we review recent progress in the biodegradation field from the point of view of the primary microbes and enzymes that can degrade these nanomaterials, along with experimental and molecular simulation methods for the exploration of nanomaterial degradation. Further efforts should primarily aim toward expanding the repertoire of microbes and enzymes and exploring optimal conditions for the degradation of nanomaterials.

Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byrans Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to opaque RBF) which is more compact, up-to-date and more easily interpretable.

The question how microbial enzyme-catalyzed oxidation processes of organic pollutants and lignin model compounds (LMCs) are affected by the release of single-walled carbon nanotube (SWCNT) into the environment remains to be addressed at the molecular level. We have, therefore concentrated the effects of SWCNT on some important properties associated with enzyme activity and function during microbial oxidation of polycyclic aromatic hydrocarbons (benzo(a)pyrene, acenaphthene and anthracene), LMCs (2,6-dimethoxyphenol, guaiacol and veratryl alcohol) and β-hexachlorocyclohexane, including the behaviour of water molecules, hydrogen bonds (HBs) and hydrophobic interactions (HYs) between ligand and the enzyme, and conformational dynamics in N- and C-terminus. Our study revealed that SWCNT significantly affected the behaviour of water molecules within 5 A of both these substrates and their respective enzymes during oxidation (p

Optimization techniques have emerged as robust tools to aid the planning and design of urban drainage facilities in cost-effective ways. Such an effort was traditionally aided by heuristic methods (like genetic algorithm), which was generally time-consuming and also challenging in reaching convergence for large-scale problems with wide decision spaces. This study proposed a novel optimization method, denoted as two-level optimization (TO) scheme, for supporting rainwater storage pond design in an urban drainage system. Polynomial regression models were established as surrogate models to facilitate the solution of the optimization framework using traditional iteration algorithm. The TO scheme firstly sought the optimal layout of storage ponds on tributary sub-watersheds, and then proceeded to that of the mainstream one to yield the final solution. Through a case study, the TO scheme was compared with the traditional global optimization (GO) scheme where the physical simulation model was dynamically linked with genetic algorithm (GA) to seek the global optimal solution. The performance of two schemes under different constraint settings was analyzed. Effects of related issues such as start-point selection and mainstream design on tributary sub-watersheds were also discussed. The results showed that the proposed TO scheme is a prominent alternative to the traditional GO scheme to support urban water managers for a more science-based decision making towards storage pond implementation in large-scale practical problems. Ministry of Education (MOE) The research work was supported by Singapore’s Ministry of Education (MOE) AcRF Tier 1 Project (Ref No. RG170/16; WBS No.: 4011766.030).

Nanomaterials, like carbon nanotubes, graphene, metal and metal oxide nanoparticles, are increasingly applied in a wide range of areas with numerous benefits to economy and society. Large-scale production and applications of nanomaterials can increase the possibility of exposure to living organisms, pose risks to human health and ecosystems, and potentially lead to biodiversity losses. Previous environmental impact and safety studies that targeted nanomaterials typically focused on their toxicity, fate and behavior; little attention was paid on biodiversity consequences. Evidence for acute biodiversity change derived from nanomaterials is very limited. Several organizations and researchers have started to discern the relationship between biodiversity and nanotechnology. Nevertheless, more efforts are desired to explore the impacts of nanomaterials on biodiversity.

This study integrates water footprint theory, fuzzy chance-constrained programming (FCCP) and fractional programming (FP) into a general optimization framework to help seek the optimal crop planting patterns for the agricultural water management (AWM) system. The modeling framework can not only address the system objective as output-input ratios and tackles uncertainties by using fuzzy sets, but also help support the cleaner production of crops by controlling the portion of green, blue and grey water footprint. It also considers the efficiency of system's economic water productivity, water footprint components control, water-food nexus, balance of crop trade benefit and water footprint loss of trading crops. This framework is applied to agricultural water planning and management in Hai River Basin, China. The study results indicated that more rice, sorghum and millet are desired in the central districts, the south-central districts and almost all districts, while wheat and maize need to be reduced over most districts. This optimal crop planting pattern (under γ=0.75) would reduce blue and grey water in the central and southeastern, and almost all districts, respectively, improve the basin's economic water productivity by 139%, and increase the trade benefit by 0.35 × 109 $ and reduce the total water footprint loss by 1.38 × 109 m3. When the credibility level increased from 0.55 to 0.95, the optimal economic water productivity would decrease from 0.1280 to 0.1127 $/m3 associated with decreasing grey and blue water footprints, increasing trade benefit and decreasing footprint loss. It appeared that a higher credibility level would lead to a stricter control requirement for the fuzzy chance constrains and a greater drag on searching a better objective. Thus, the proposed modelling framework could help obtain a series of crop planting patterns under various credibility levels and ensure to optimize the water footprint of crop planting and trading under uncertainties. Nanyang Technological University This project was supported by Research Grant (M4082254.030) from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore.

Rainfall modelling is an essential component of different hydrological studies. However, rainfall modelling in tropical regions, especially urban areas, remains inadequate. To determine the applicability of different types of rainfall modelling approaches, simulations by two Markov models (Matlab-based weather generator (MulGETS) and multi-site rainfall simulator (MRS)) and a Neyman–Scott based Poisson cluster model (RainSim) were compared with a proposed modified k-nearest neighbour (KNN) model for 30 years rainfall in Singapore. The MRS model was determined to be suitable for single-site applications in tropical regions. However, for multi-site conditions, RainSim was adjudged the most suitable given its accuracy in preserving observed spatial information, despite its performance lagging in few statistical indicators. The KNN model was found to perform satisfactorily during the wet seasons, and was the only model that could repeat the extreme precipitation values closely. Although typical studies evaluate the performance of models based on a set of criteria, there exists a lacuna in understanding the quality of simulations by these models. Therefore, uncertainty analysis based on two different criteria was implemented to understand the performance of the stochastic processes within. Although the MulGETS model exhibited the lowest differences between Prediction Intervals, RainSim's Prediction Intervals were found to subsume observed data more often. The proposed study would be useful for users examining rainfall models for differing objectives.

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.

Flooding risk is increasing in many parts of the world and may worsen under climate change conditions. The accuracy of predicting flooding risk relies on reasonable projection of meteorological data (especially rainfall) at the local scale. The current statistical downscaling approaches face the difficulty of projecting multi-site climate information for future conditions while conserving spatial information. This study presents a combined Long Ashton Research Station Weather Generator (LARS-WG) stochastic weather generator and multi-site rainfall simulator RainSim (CLWRS) approach to investigate flow regimes under future conditions in the Kootenay Watershed, Canada. To understand the uncertainty effect stemming from different scenarios, the climate output is fed into a hydrologic model. The results showed different variation trends of annual peak flows (in 2080–2099) based on different climate change scenarios and demonstrated that the hydrological impact would be driven by the interaction between snowmelt and peak flows. The proposed CLWRS approach is useful where there is a need for projection of potential climate change scenarios.

This review article discusses the climate, water resources and historical droughts of Africa, drought indices, vulnerability, impact of global warming and land use for drought-prone regions in West, southern and the Greater Horn of Africa, which have suffered recurrent severe droughts in the past. Recent studies detected warming and drying trends in Africa since the mid 20th century. Based on the Fourth Assessment Report of the Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project Phase 5 (CMIP5), both northern and southern Africa are projected to experience drying, such as decreasing precipitation, runoff and soil moisture in the 21st century and could become more vulnerable to the impact of droughts. The daily maximum temperature is projected to increase by up to 8°C (RCP8.5 of CMIP5), precipitation indices such as total wet day precipitation (PRCPTOT) and heavy precipitation days (R10 mm) could decrease, while warm spell duration (WSDI) and consecutive dry days...

Summary An integrated statistical and data-driven (ISD) framework was proposed for analyzing river flows and flood frequencies in the Duhe River Basin, China, under climate change. The proposed framework involved four major components: (i) a hybrid model based on ASD (Automated regression-based Statistical Downscaling tool) and KNN (K-nearest neighbor) was used for downscaling rainfall and CDEN (Conditional Density Estimate Network) was applied for downscaling minimum temperature and relative humidity from global circulation models (GCMs) to local weather stations; (ii) Bayesian neural network (BNN) was used for simulating monthly river flows based on projected weather information; (iii) KNN was applied for converting monthly flow to daily time series; (iv) Generalized Extreme Value (GEV) distribution was adopted for flood frequency analysis. In this study, the variables from CGCM3 A2 and HadCM3 A2 scenarios were employed as the large-scale predictors. The results indicated that the maximum monthly and annual runoffs would both increase under CGCM3 and HadCM3 A2 emission scenarios at the middle and end of this century. The flood risk in the study area would generally increase with a widening uncertainty range. Compared with traditional approaches, the proposed framework takes the full advantages of a series of statistical and data-driven methods and offers a parsimonious way of projecting flood risks under climatic change conditions.

Increasing evidence has shown that carbon nanotubes (CNTs) present adverse effects on the environment and human health, which stresses the importance of exploring CNT biodegradation. In this study, we describe the molecular basis of single-walled carbon nanotube (SWCNT) biodegradation using a CNT-degrading enzyme (manganese peroxidase, MnP) and a CNT-nondegrading enzyme (lignin peroxidase, LiP, from Phanerochaete chrysosporium) with similar catalytic cycles. Our results showed that SWCNT impeded the native conformational changes in free LiP by anchoring its loop regions to avoid the degradation. In contrast, SWCNT did not limit conformational transitions in MnP and might induce larger conformational fluctuations than in free MnP by interacting with its helical and loop regions, providing the molecular basis of SWCNT degradation. SWCNT slightly affected the secondary structures and the mean smallest distances between residue pairs in LiP and MnP. These findings provide a better understanding of the biodegradation mechanism of CNTs and pre-estimating the biodegradation potential of CNTs and are useful when developing more promising CNT-degrading enzymes.

This study utilized a long-term (1951–2014) moderate-resolution observed gridded (0.5°×0.5°) rainfall dataset to analyze long-term and short-term rainfall variability and the seasonality in Southeast Asia (SEA). The previous studies for this region revealed significant enhancement in extreme events and seasonal variabilities in rainfall. In this study, the frequency and intensity-based rainfall extremity analysis was carried out by utilizing a number of rainfall extreme indices (e.g. Dry Days, Wet Days, 90P, 95P, and 99P). A seasonal non-parametric Mann-Kendall test was applied to detect significant increase and decrease of rainfall. The possible causes for the acceleration and variabilities of rainfall were investigated by analyzing the impact of large-scale ocean-atmospheric climate interactions through global climate indices such as El Nino (3.4 and 4.0), Southern Oscillation Index (SOI), Madden-Julian Oscillation (MJO), global mean land and ocean temperature index, QBO and NOI. Our results displayed a significant increase of rainfall amount in most of SEA regions, while a minimal decrease was also found over some regions. The rainfall indices displayed substantial intra-decadal variabilities over the SEA region. The intra-decadal percent of change analysis based on rainfall extreme indices revealed significant changes in rainfall extremes over SEA, which also evidenced the computed acceleration in rainfall over the region. The correlation analysis based findings exhibited that a strong correlation existed between the SEA rainfall and the large-scale global climate indices generated based on the ocean-atmosphere climate interactions. After regionalization of the SEA, the global climate indices based results described the spatiotemporal variabilities in rainfall, which were computed over SEA regions. The global climate indices such as El Nino (3.4 & 4.0), SOI, NOI and MJO suggested significant correlations with the seasonal rainfall and the rainfall extreme indices and highlighted that their collinearities vary within regions as per variations in the monsoon.

Synthetic rainfall distributions of the design storm have been commonly used for the hydrological design of urban stormwater infrastructures in many regions of the world. However, there are still limited studies in a tropical city dealing with design storm hyetographs for flood estimation even though rainfall intensity/depth-duration-frequency (IDF/DDF) relationships have been derived to estimate rainfall depths for a specific rainfall duration and return period. This study presents a storm-event based rainfall analysis method to determine a representative quartile of the design storm in which, both inter-event time definition (IETD) and depth/duration/intensity thresholds are considered. Similar to Huff’s method, 5-min rainfall data during three years at two rain gauge stations in Singapore were used to obtain the percentage frequency of the four types of quartile storms depending on the location where the heaviest rainfall occurred in each storm duration. It was found that the proposed approach could give the shape and magnitude of the design storm hyetograph from the location of peak rainfall corresponding to the largest portion of quartile storms. As a result, the second quartile design storm was suggested to be applied for flood estimation in order to better address the temporal characteristic of actual rainstorm events in the study area. It offers an alternative way of describing the temporal distribution of rainfall within a design storm period, which is helpful in improving the design of urban stormwater infrastructures in a tropical region.

Climate change on a global scale results in more intensive and frequent extreme rainfall events, which would likely increase the vulnerability of the urban drainage systems. Combing techniques of weather generator, Gumbel distribution, rainfall disaggregation and urban drainage modelling, this study examined impact of climate change on urban drainage flow based on the output from a number of global climate models (GCMs) for the timeline of the second half of this century. The proposed methods were employed on a small catchment in the tropical area for demonstration. The results showed that the selection of different GCMs (i.e., future climate conditions) exerted a remarkable effect on prediction of future maximum daily rainfall and the flooding condition of the drainage system. The impact of climate-induced extreme events is of significant concern and deserves more thorough investigation in the future.

A generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic i...

A novel combined statistical downscaling and disaggregation framework (CSDD) based on Long Ashton Research Station-Weather Generator (LARS-WG) and K-nearest neighbour (KNN) was proposed to examine the climate change impact on regional extreme rainfall in Singapore. The approach could generate high-resolution rainfall sequences both spatially (e.g. station level) and temporally (e.g. 5-min) based on 12 emission scenarios under 4 general circulation models. Three different routes were proposed to generate high-resolution synthetic rainfall time series: (1) the rainfall records at all stations were combined and treated as one station (Route 1); (2) the rainfall record at each station was treated individually (Route 2); and (3) the total areal rainfall at daily and hourly timescales was used to keep inter-site correlations (Route 3). The results indicated that all routes under CSDD framework could effectively reproduce the regional rainfall at various timescales. This study further examined the impacts of climate change on regional extremes. The results show that the maximum increase rates of 100-year extremes at the end of this century at durations of 24-h, 1-h and 5-min would be 14.8, 16.3 and 16.8%, respectively. The proposed framework takes the full advantage of both LARS-WG and KNN and could effectively help provide continuous high-resolution synthetic rainfall data (with consideration of spatial dependence and uncertainty) under climate-change conditions for hydrological impact study.

Fuzzy programming model has been widely used in water resources management, but its applicability has been significantly restricted in dealing with triangular or trapezoidal shaped fuzzy sets due to intrinsic complexity in converting fuzzy constraints into their deterministic forms. In this study, a novel superiority-inferiority-based sequential fuzzy programming (SISFP) model was proposed for supporting water supply–demand analysis under uncertainty. The SISFP method could transform fuzzy objective function and constraints with general-shaped fuzzy coefficients into their crisp equivalent by using fuzzy superiority and inferiority measures. The water supply–demand management system in Tianjin Binhai New Area, China, consisting of five sources of water, five water users at three districts (i.e. Tanggu, Hanggu, and Dagang), was used for methodology demonstration. The proposed model could effectively address the complex nature of fuzzy characterization of water-transfer safety factor, wastewater reclamation rate, the net benefits derived from water, and water-saving rate of the system; and also take demand management measures into consideration. The obtained solutions have sought a well balance among the water availability, water demand, adoption of water-saving measures, and benefit/cost of each water users. The advantage and necessity of SISFP in dealing with general-shaped fuzzy parameters were further verified by comparing to fuzzy models with both deterministic and specially-shaped fuzzy parameters.

A stochastic optimization model for urban drainage design (SODD) was proposed in this study to help analyze the tradeoff between investment and acceptable flood damage in urban drainage designs considering effects of both uncertainty and climate change. The simulation model (i.e. SWMM), driven by designed rainfall either from existing Intensity-Duration-Frequency (IDF) curves or future ones subjected to climate change conditions, was used to simulate flooding scenarios. The generalized uncertainty analysis estimation (GLUE) and Monte Carlo simulation methods were employed to quantify the system reliability which was adopted in the constraints of the optimization model. The results from a case study showed that the deterministic optimization was computationally efficient with no randomness encountered in hydrological simulation, although its solution was hardly reliable in achieving the target for flood mitigation. The stochastic version, on the other hand, could offer richer information on system reliability and help managers make a more robust decision. The results also revealed that the rainfall extremes under the impact of climate change could significantly affect system investment. The proposed method is advantageous in facilitating cost-effective planning towards a risk-based drainage design in light of various complexities.