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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.

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.

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...

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 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)

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.

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.

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 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.

In this study, a parsimonious framework for supporting Water Sensitive Urban Design (WSUD) was proposed to seek a tradeoff between investment of WSUD features and mitigation of urban flood damage. A two-dimensional (2D) hydrological-hydraulic simulation model, PCSWMM, was adopted to simulate the rainfall-runoff process and inundation scenarios, and the flood damages was evaluated based on inundated water depths and damage curves. The sensitivity of deploying various design features to flood control effects was also tested, which provided useful information for identifying potential design parameters (like conduit sizes and pond locations). The proposed framework was applied to a hypothetical case adapted from an urban district in the tropical region considering various WSUD features (i.e. rainwater storage pond, rain garden, and conduit upgrading). The results showed that when the gross investment of WSUD features increased from 0 to 1.19 million $, the damage cost would decrease from 4.61 to 3.41 million $; a linear relationship (with a R-square fit at 0.9) was found suitable to represent the relationship between the investment and the damage. The proposed framework is effective in helping assess the balance between mitigation of urban flood damage and adoption of WSUD features, and could be used to support urban water managers for a more science-based decision making towards flood risk management. MOE (Min. of Education, S’pore) Published version

An uncertainty assessment framework based on Karhunen–Loevè expansion (KLE) and probabilistic collocation method (PCM) was introduced to deal with flood inundation modelling under uncertainty. The Manning's roughness for channel and floodplain were treated as 1D and 2D, respectively, and decomposed by KLE. The maximum flow depths were decomposed by the 2nd-order PCM. Through a flood modelling case with steady inflow hydrographs based on five designed testing scenarios, the applicability of KLE-PCM was demonstrated. The study results showed that the Manning's roughness assumed as a 1D/2D random field could efficiently alleviate the burden of random dimensionality within the analysis framework, and the introduced method could significantly reduce repetitive runs of the physical model as required in the traditional Monte Carlo simulation (MCS). The study sheds some light on reducing the computational burden associated with flood modelling under uncertainty which is useful for the related damage quantification and risk management.

Climate change is expected to lead to more frequent and intensive flooding problems for watersheds in the south part of China. This study presented a coupled Long Ashton Research Station Weather Generator (LARS-WG) and Semidistributed Land Use–Based Runoff Processes (SLURP) approach for flood frequency analysis and applied it to the Heshui watershed, China. LARS-WG, as a weather generator, was used to offer 46 sets of climate data from seven general circulation models (GCMs) under various emission scenarios (i.e., A1B, B1, and A2) over near-term and future periods (i.e., T1, 2011–30; T2, 2046–65; and T3, 2080–99). SLURP is a continuous, spatially distributed hydrological model that uses parameters from physiographic data to simulate the hydrological cycle from precipitation to runoff. Flood frequency analysis based on Pearson type III distributions was followed to analyze statistics of annual peaks. The final results (from ensembles of multimodels and multiscenarios) indicated that the magnitudes of a 200-yr return flood for T1, T2, and T3 would increase by 5.23%, 4.08%, and 12.92%, respectively, in comparison to the baseline level; those under the most extreme condition (i.e., worst scenario) would be 25.18%, 31.00%, and 44.46%, respectively. Various GCMs and emission scenarios suggested different results. But the ECHAM5/Max Planck Institute Ocean Model was found to give a more worrying intensification of flood risks and the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0, and the Community Climate System Model, version 3, were relatively conservative. The study results were useful in helping gain insight into the flood risks and its uncertainty under future climate change conditions for the Heshui watershed, and the proposed methodology is also applicable to many other watersheds in Southeast Asia with similar climatic conditions.

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase. Accepted version

A coupled K-nearest neighbour (KNN) and Bayesian neural network (BNN) model was developed for downscaling daily rainfall at a single site. The KNN was used for classification of dry/wet day and rainfall typing based on rainfall magnitude. The BNN was applied for prediction of rainfall amount. The proposed method was applied to rainfall downscaling at Singapore Island. The Climate Forecast System Reanalysis (CFSR) data were used for providing large-scale predictors at a high spatial resolution; 31-years daily rainfall record at two typical weather stations on the island was used as predictand. The performance of KNN–BNN was compared with two classical downscaling tools including automated statistical downscaling tool (ASD) and generalized linear model (GLM). The study results indicated that, the proposed model performed equally good or better than both ASD and GLM, in terms of prediction of basic statistical indicators (i.e. mean, SD, probability of wet days, 90th percentile rainfall amount, and maximum rainfall); it notably outperformed others in generating narrower uncertainty intervals for all indicators, especially for monthly mean and maximum rainfall. It was also demonstrated that separation of yearly data into monthly or seasonal could considerably enhance the performance of KNN–BNN.

A green roof test bed, established at the Nanyang Technological University in Singapore, was used to investigate its benefit for storm water management and urban heat island effect mitigation. The system comprised 3 units, 2 in the form of vegetated roofs and the other a bare roof. The system was equipped with automatic monitoring devices for measuring the hydrological data. Continuous data monitoring on the roofs was conducted to evaluate the thermal and hydrological effects. The study shows that the green roof test bed can significantly reduce the roof surface temperature (by an average of 7.3ºC) and lower the ambient air temperature (by an average of 0.5ºC) when compared with a bare roof during day time hours (from 10:00 am to 4:00 pm). The ability to reduce and delay the peak runoff was demonstrated by using a typical rainfall event with 18 mm depth. The designed system is useful in evaluating both thermal and hydrological benefits of a green roof system in tropical areas and can offer guidance to local managers in mitigating the urban heat island effect and designing storm water management strategies.

A simulation-based fuzzy optimization method (SFOM) was proposed for regional groundwater pumping management in considering uncertainties. SFOM enhanced the traditional groundwater management models by incorporating a response matrix model (RMM) into a fuzzy chance-constrained programming (FCCP) framework. RMM was used to approximate the input–output relationship between pumping actions and subsurface hydrologic responses. Due to its explicit expression, RMM could be easily embedded into an optimization model to help seek cost-effective pumping solutions. A groundwater management case in Pinggu District of Beijing, China, was used to demonstrate the method's applicability. The study results showed that the obtained system cost and pumping rates would vary significantly under different confidence levels of constraints satisfaction. The decision-makers could identify the best groundwater pumping strategy through analyzing the tradeoff between the risk of violating the related water resources conservation target and the economic benefit. Compared with traditional approaches, SFOM was particularly advantageous in linking simulation and optimization models together, and tackling uncertainties using fuzzy chance constraints.

Groundwater quality deterioration has attracted world-wide concerns due to its importance for human water supply. Although more and more studies have shown that human activities and climate are changing the groundwater status, an investigation on how different groundwater heavy metals respond to human activity modes (e.g. mining, waste disposal, agriculture, sewage effluent and complex activity) in a varying climate has been lacking. Here, for each of six heavy metals (i.e. Fe, Zn, Mn, Pb, Cd and Cu) in groundwater, we use >330 data points together with mixed-effect models to indicate that (i) human activity modes significantly influence the Cu and Mn but not Zn, Fe, Pb and Cd levels, and (ii) annual mean temperature (AMT) only significantly influences Cu and Pb levels, while annual precipitation (AP) only significantly affects Fe, Cu and Mn levels. Given these differences, we suggest that the impacts of human activity modes and climate on heavy metal "spread" in groundwater are biased.

A factorial-design-based fuzzy-stochastic modeling system (FFSMS) was developed in this study to systematically investigate impacts of uncertainties associated with hydrocarbon contaminant transport in subsurface through integration of a compositional model, factorial design method, fuzzy modeling approach and Monte Carlo simulation technique. The goodness of fit of the numerical model was analyzed by means of a pilot-scale experimental system. Once the model was calibrated, it was used in order to predict the contaminant concentration depending on values of several parameters including intrinsic permeability, porosity, and longitudinal dispersivity. These parameters were imprecisely known, and such an imprecision was handled by means of both fuzzy sets and/or stochastic theory. The individual and joint effects of these uncertain parameters were analyzed by modeling the dependence between the prediction and the imprecise parameters (factors) through factorial design analysis. The study results indicated that the uncertainties associated with input parameters had significant impacts on modeling outputs; the degree of influence of each model input varied significantly with the level of its imprecision. The study results demonstrated that proposed FFSMS can efficiently analyze the impact of different uncertainty sources associated with different hydrogeological parameters on the prediction of the hydrocarbon concentrations in groundwater. Such studies would provide strong basis for performing successful risk assessment and efficient remediation design for the management of contaminated site.

An inexact chance-constrained linear programming ICCLP model for optimal water pollution management at the watershed scale was developed. We selected the net expenditures of the alternative strategies, including initial capital investment and operating costs, as the objectives of water pollution management. The total environmental capacity of the water bodies at different probability levels qi was considered a key constraint; other constraints included in the model were government minimum requirements on farmland area, land cover, treatment rate of domestic wastewater and rural wastes, and certain technical constraints. The ICCLP model was applied to Lake Qionghai watershed in China for water quality improvement with the goal of achieving a minimum total cost. Alternative strategies were incorporated following discussions with shareholders and experts. A three-period optimization was conducted based on the alternative strategies; the model parameters were based on field investigations. Five probability levels were considered in the model: qi=0.01, 0.25, 0.50. 0.90, and 0.99. The model results showed that the total optimized costs were between US$55,710.86,80,691.8110 4 and US$72,151.39,101,338.610 4 under different probability levels. The model results indicate that soil erosion treatment, nonpoint source control measures, and rural waste treatment have much higher costs than other strategies, and our findings indicate that the ICCLP model can effectively deal with optimal water pollution management under uncertainty at the watershed scale.

Geologic storage of CO2 in depleted oil reservoirs is considered to be an effective approach for both facilitating GHG sequestration and enhancing oil recovery. However, as a potential problem in the long run, risks associated with geological storage of CO2, such as leakage to the groundwater and atmosphere, might pose significant threats to local communities and surrounding environment. Identification and evaluation of such risks are essential for the long-term management of CO2 storage. Doing so requires a set of advanced technologies in order to well understand the long-range transportation of CO2 and its impact mechanisms. This study developed an integrated decision support framework for the Weyburn Field. This system included modules of data management, inexact hybrid numerical simulation, optimization for CO2 EOR processes, hybrid fuzzy-stochastic risk assessment, and post-modeling analysis. A user-friendly interface was designed through visual language programming. Such an effort would pro...

Lake areas in Chinese urban fringes are under increasing pressure of urbanization. Consequently, the conflict between rapid urban sprawl and the maintenance of water bodies in such areas urgently needs to be addressed. An integrated GIS-based analysis system (IGAS) for supporting land-use management of lake areas in urban fringes was developed in this paper. The IGAS consists of modules of land-use suitability assessment and change/demand analysis, and land evaluation and allocation. Multicriteria analysis and system dynamics techniques are used to assess land-use suitability and forecast potential land-use variation, respectively. Cost approximation and hypothetical development methods are used to evaluate land resource and market values, respectively. A case study implementing the system was performed on the Hanyang Lake area in the urban fringe of Wuhan City, central China, which is under significant urbanization pressure. Five categories of suitability were investigated by analyzing 11 criteria and related GIS data. Two scenarios for potential land-use changes from 2006 to 2020 were predicted, based on a systematic analysis and system dynamics modeling, and a hierarchical land-use structure was designed for the conservation of aquatic ecosystems. The IGAS may help local authorities better understand and address the complex land-use system, and develop improved land-use management strategies that better balance urban expansion and ecological conservation.

Lake areas in urban fringes are under increasing urbanization pressure. Consequently, the conflict between rapid urban development and the maintenance of water bodies in such areas urgently needs to be addressed. An inexact chance-constrained linear programming (ICCLP) model for optimal land-use management of lake areas in urban fringes was developed. The ICCLP model was based on land-use suitability assessment and land evaluation. The maximum net economic benefit (NEB) was selected as the objective of land-use allocation. The total environmental capacity (TEC) of water systems and the public financial investment (PFI) at different probability levels were considered key constraints. Other constraints included in the model were land-use suitability, governmental requirements on the ratios of various land-use types, and technical constraints. A case study implementing the system was performed for the lake area of Hanyang at the urban fringe of Wuhan, central China, based on our previous study on land-use suitability assessment. The Hanyang lake area is under significant urbanization pressure. A 15-year optimal model for land-use allocation is proposed during 2006 to 2020 to better protect the water system and to gain the maximum benefits of development. Sixteen constraints were set for the optimal model. The model results indicated that NEB was between $1.48 × 109 and $8.76 × 109 or between $3.98 × 109 and $16.7 × 109, depending on the different urban-expansion patterns and land demands. The changes in total developed area and the land-use structure were analyzed under different probabilities (q i ) of TEC. Changes in q i resulted in different urban expansion patterns and demands on land, which were the direct result of the constraints imposed by TEC and PFI. The ICCLP model might help local authorities better understand and address complex land-use systems and develop optimal land-use management strategies that better balance urban expansion and grassland conservation.

Inoculation is a human-induced measure that can significantly enhance the composting process by increasing the initial microbial population, formulating viable microbial communities, and generating desired enzymes. However, the inoculation efficiency was usually subjected to competitions of indigenous microorganisms. In this study, a temperature-guided three-stage inoculation (TGTSI) method was developed to control the indigenous cell concentrations, and in turn, enhance the composting efficiency. The experimental results indicated that the TGTSI method could effectively suppress the initial cell concentrations of indigenous microorganisms, which resulted in significantly enhancement of biodegradations as well as the reduction of odor emissions. Moreover, a two-stage kinetics method was employed to analyze the TGTSI mechanisms. The model results were consistent with the experimental data. In conclusion, this study implied that TGTSI could not only enhance the composting process but also save system cost.

Desertification is becoming a major ecological concern in arid and semiarid regions, especially under climate change. Globally, it is burning up lands for human habitats with a rapidly spreading tendency. Many scientists have been struggling to explore the related mechanisms. Challenges remain in revealing the fundamental principle in terms of desert-oasis interactions that are associated with nonstationary variations. Here we present a theory of desertification dynamics through examining nonstationary effects of climate change and human interference. We hypothesize that such dynamics can be described as the fate and transport of dry air mass continuously generated from desert. We simulate a region in northwestern China and reveal that dynamics of the nonstationary desertification process is subject to interactive impacts from a variety of factors. Our study moves forward the field of desertification studies through initiation of the dynamics and nonstationarity concepts which allow the fundamental mechanism to be disclosed. Published version

Hourly rainfall measurements from a network of 49 rain gauges on the tropical island of Singapore are analyzed to characterize variability of rainfall for temporal and spatial scales ranging from 1 to 24 h and from 1 to 45 km, respectively. First, the probability distributions of rain rates are characterized using the method of L moments. The analysis showed that the Pearson type-3 (PE3) distribution best fitted the rain rates for all time scales of concern. The parameters of the PE3 distribution are found to be related to the time scale through simple power laws. Second, the spatial structure of rainfall is characterized using spatial correlations. The decay of correlations with intergauge distance is parameterized using a powered-exponential function. In general, the e-folding correlation distance (distance at which the correlation drops to 1/e) varied from 10 km at hourly scales to 33 km at daily scales. The study also examined diurnal, seasonal, and anisotropic patterns in the spatial correlation structure of rainfall. The rainfall patterns are smoothest in December and January and are most variable in February, April, and October. Diurnal analysis of spatial correlations showed that the rainfall patterns are smoothest in the early hours between 0100 and 0600 local time and are most variable during the afternoon between 1500 and 1900 local time. The results also showed complex anisotropic patterns in spatial correlations, with considerable dependence of rainfall orientation on spatial scale and the time of the year.

In-situ bioremediation is one of the effective technologies for cleaning up petroleum-contaminated sites. To improve efficiencies of conventional bioremediation processes, biosurfactant treatment has received much attention due to the surface-activity, biodegradability and low toxicity of biosurfactants. In this study, an integrated mathematical modeling system was developed for simulating the biosurfactant-enhanced bioremediation (BEB) processes under typical Canadian site condition. The model included modules of multiphase multicomponent flow and transport, biological degradation, and biosurfactant-enhanced remediation. A pilot-scale physical modeling system, with Rhamnolipid being used as biosurfactant, is designed to simulate the BEB process. The study results demonstrated that the developed mathematical model is effective in examining the coupled effects of biodegradation and biosurfactant-enhancement within a multiphase multicomponent transport framework, and can be used for supporting management of petroleum-contaminated sites under cold climate conditions.

Risk assessment of contaminated sites is crucial for evaluating adverse health impacts on local communities, and provides effective decision support for site-remediation practices. In this study, a coupled simulation and fuzzy-rule-based method was proposed for health risk assessment of groundwater contamination, and applied to a petroleum contaminated site. Benzene concentrations under two remediation scenarios (i.e., natural attenuation and pump-and-treat remediation) at different time intervals (1, 10, 20, 30, and 60 years later) were examined. The obtained benzene concentrations were then used to quantify the health risks through the excess lifetime cancer risk (ELCR) model. The study results indicated that the proposed method was useful for assessing potential human health effects when the groundwater was used for drinking water purpose and could effectively identify variation of risk levels under different remediation scenarios. The study also found that the dissolved oxygen was the most thermodynamically favoured electron accepter used in the biodegradation of petroleum hydrocarbons. When implementing remediation actions, we can inject oxygen to subsurface to strengthen aerobic biodegradation process (i.e., air sparging).