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This study focused on the performance and limitations of the local inertial approximation form model (LIM) of the shallow water equations (SWEs) when applied in urban flood modeling. A numerical scheme of the LIM equations was created using finite volume method with a first-order spatiotemporal Roe Riemann solver. A simplified urban stormwater model (SUSM) considering surface and underground dual drainage system was constructed based on LIM and the US Environmental Protection Agency Storm Water Management Model. Moreover, a complete urban stormwater model (USM) based on the SWEs with the same solution algorithm was used as the evaluation benchmark. Numerical results of the SUSM and USM in a highly urbanized area under four rainfall return periods were analyzed and compared. The results reveal that the performance of the SUSM is highly consistent with that of the USM but with an improvement in computational efficiency of approximately 140%. In terms of the accuracy of the model, the SUSM slightly underestimates the water depth and velocity and is less accurate when dealing with supercritical flow in urban stormwater flood modeling. Overall, the SUSM can produce comparable results to USM with higher computational efficiency, which provides a simplified and alternative method for urban flood modeling.

Objective evaluation of the relationships among different types of droughts remains a challenging task due to the combined impacts of climate change and land surface modification caused by human activities. Based on the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) at the 3- and 6-month timescales, this study presents a systematic analysis of the relationships between the severity (S) and duration (D) of meteorological (MD) and hydrological droughts (HD) in the three catchments of the upper Huai River Basin in China. The relation between SPI and SRI is explored by the maximal information coefficient and the mutual entropy. The spatial propagation mechanism of MD is identified by the centroid trajectory, and the response of HD to MD is quantified by the model averaging method. The results indicate a drying (wetting) trend in the upstream (downstream) area, while the centroid trajectory of MD is found in the midstream area, but not associated with the large (or small) S and D simultaneously. There is a strong correlation (determination coefficient > 0.55) between SPI and SRI in all three subareas, particularly at the 6-month timescale. The increasing influences of human activities (e.g., regulation of water conservancy facilities) from upstream to downstream lead to a weaker correlation between SPI and SRI as well as a decreasing threshold of D for MD to trigger HD in downstream. By contrast, the drier climatic conditions are the main reason for the increasing threshold of S for MD to trigger HD from upstream to downstream.

Drought projections are accompanied with large uncertainties due to varying estimates of future warming scenarios from different modeling and forcing data. Using the standardized precipitation index (SPI), this study presents a global assessment of uncertainties in drought characteristics (severity S and frequency Df) projections based on the simulations of 28 general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). A hierarchical framework incorporating a variance-based global sensitivity analysis was developed to quantify the uncertainties in drought characteristics projections at various spatial (global and regional) and temporal (decadal and 30-yr) scales due to 28 GCMs, three representative concentration pathway scenarios (RCP2.6, RCP4.5, RCP8.5), and two bias-correction (BC) methods. The results indicated that the largest uncertainty contribution in the globally projected S and Df is from the GCM uncertainty (>60%), followed by BC (S than Df projections, especially in the SH. The GCM and BC uncertainties show overall decreasing trends with time, while the RCP uncertainty is expected to increase over time and even can be larger than BC uncertainty for some regions (e.g., northern Asia) by the end of this century.

Anthropogenic forcing is anticipated to increase the magnitude and frequency of precipitation-induced extremes such as the increase in drought risks. However, the model-projected future changes in global droughts remain largely uncertain, particularly in the context of the Paris Agreement targets. Here, by using the standardized precipitation index (SPI), we present a multiscale global assessment of the precipitation-driven meteorological drought characteristics at the 1.5° and 2°C warming levels based on 28 CMIP5 global climate models (GCMs) under three representative concentration pathways scenarios (RCP2.6, RCP4.5, and RCP8.5). The results show large uncertainties in the timing reaching 1.5° and 2°C warming and the changes in drought characteristics among GCMs, especially at longer time scales and under higher RCP scenarios. The multi-GCM ensemble mean projects a general increase in drought frequency (Df) and area (Da) over North America, Europe, and northern Asia at both 1.5° and 2°C of global warming. The additional 0.5°C warming from 1.5° to 2°C is expected to result in a trend toward wetter climatic conditions for most global regions (e.g., North America, Europe, northern Asia, and northern Africa) due to the continuing increase in precipitation under the more intensified 2°C warming. In contrast, the increase in Df is projected only in some parts of southwest Asia, South America, southern Africa, and Australia. Our results highlight the need to consider multiple GCMs in drought projection studies under the context of the Paris Agreement targets to account for large model-dependent uncertainties.

To select optimal LID in urban areas, an optimization assessment framework considering hydrological processes and cost-effectiveness is proposed. To provide the desired runoff reduction efficiencie...

Recent studies have extended the applicability of the Budyko framework from the long-term mean to annual or shorter time scales. However, the effects of water storage change ΔS on the overall water balance estimated from the Budyko models (BM) at annual-to-monthly time scales were less investigated, particularly at the continental or global scales, due to the lack of large-scale ΔS data. Here, based on a 25-yr (1984–2008) global gridded terrestrial water budget dataset and by using an analytical error-decomposition framework, we analyzed the effects of ΔS in evapotranspiration (ET) predicted from BM at both grid and basin scales under diverse climates for the annual, wet-seasonal, dry-seasonal, and monthly time scales. Results indicated that the BM underperforms in the short dry (wet) seasons of predominantly humid (dry) basins, with lower accuracy under more humid climates (at annual, dry-seasonal, and monthly scales) and under more arid climates (at wet-seasonal scale). When the effects of ΔS are incorporated into BM, improvements can be found mostly at annual and dry-seasonal scales, but not notable at wet-seasonal and monthly scales. The magnitudes of ΔS are positively correlated with the errors in BM-predicted ET for most global regions at annual and monthly scales, especially under arid climates. Under arid climates, the variability of ET prediction errors is controlled mainly by the ΔS variability at annual and monthly time scales. In contrast, under humid climates the effect of ΔS on ET prediction errors is generally limited, particularly at the wet-seasonal scale due to the more dominant influences of other climatic factors (precipitation and potential ET) and catchment responses (runoff).

Although polydopamine (PDA)-related modification is widely studied in the fabrication of superhydrophobic sponges, the high cost of dopamine limits its widespread application. To imitate PDA modification, a low-cost and facile one-step poly(phenol-amine) modification was performed on melamine sponges in this study. Low-cost catechol and diethylenetriamine (DETA) were used as the monomers, and n-dodecanethiol was used as an additive in the one-step modification. The results confirmed that the poly(phenol-amine) aggregations were successfully anchored on the sponge skeleton surface and that the aggregations were formed via the Schiff base reaction and the Michael addition reaction. Furthermore, the as-prepared sponges still showed excellent mechanical properties after modification. Additionally, the optimally modified sponge (MS-0.5) exhibited superhydrophobic properties with a contact angle value above 150° under various environments, high oil-absorption capacity for various oils and organic solvents, high continuous oil-water separation performance with efficiency greater than 98.8% in 30 cycles, outstanding demulsification performance with 99.52% toward oil-in-water emulsion, and excellent recoverability and long-term stability. Thus, this work provides a feasible facile one-step modification method that can be used in place of PDA-related modification.

Urban wetlands provide multiple functions including water treatment, recreation, and education, but they are also highly vulnerable, so it is important to monitor wetland water quality to ensure wetland health. In this study, water quality parameters of an urban wetland and rainfall were monitored at 6 sites for 1 year. The correlation analysis of water quality parameters and spatial-temporal variability analysis of water quality were carried out. Besides, the effects of season and rainfall on the wetland water quality were evaluated by the comprehensive water quality identification index (CWQII). These results have shown that there is a significant correlation between nutrient pollutants and Chl-a. Wetland water quality changed with the seasons, but it also varied due to changes in rainfall and location. The water quality of the shallow areas both had high susceptibility and response to seasonal changes and rainfall, but the water quality of the deepwater area was relatively stable. The CWQIIs in different seasons were ranked: Winter (5.98) spring (4.67) autumn (4.66) summer (4.26), and the CWQIIs of different rainfall intensities were ranked: torrential rain (5.09) heavy rain (4.88) light rain (4.50) no rain (4.39) moderate rain (3.95). The results of this study distinctly explained the effects of season and rainfall on water quality in an urban wetland in a subtropical monsoon climate zone and would be helpful to the policymakers and concerned authorities in developing better water quality management strategies for these wetlands.

The Pearl River Delta (PRD) is one of the most economically dynamic regions of China and has experienced rapid urbanization during the past decades. In this study, the effect of urbanization on summer rainfall events in the PRD region is investigated using the advanced Weather Research and Forecasting (WRF) model. Two comparative urban scenarios (2015 and 1991) were designed to test the sensitivity of summer rainfall to urbanization. The results indicate that the WRF model reasonably reproduces the general synoptic weather pattern, particularly for air temperature and relative humidity. Further comparative analysis reveals that extreme rainfall significantly increased in urban areas, while the total summer rainfall slight decreased. Urbanization causes changes in the surface energy balance, resulting in more sensible heat flux and less latent heat flux. The decreased latent heat flux dampens the moisture availability and results in the decline of the maximum convective available potential energy, which lead to the decrease of the total summer rainfall. In contrast, the increased sensible heat flux enhances the urban surface temperature and boundary layer circulations (e.g., elevated planetary boundary layer height and higher level of free convection and lifted condensation level) and carries more water mixing vapor into the atmosphere over urban areas, enhancing the frequency and intensity of extreme rainfall. The results suggest that urbanization plays an important role in the urban change of summer rainfall and causes more extreme rainfall events, thereby inducing flooding and property losses associated with large economic costs.

Theoretically, precipitation extremes will increase at a rate of 6–7% with temperature increasing, namely the Clausius-Clapeyron relationship. However, many gauge observations suggest a peak structure of the relationship between precipitation extremes and atmospheric temperature, deviating from the Clausius-Clapeyron relationship. In this study, a comprehensive investigation about the temperature dependence of precipitation extremes (hourly, daily, and event-based) across China is implemented. The results confirm the widespread existence of the peak structure for daily and hourly precipitation extremes and show that (1) there is a generally positive spatial correlation between the precipitation extremes at the peak and temperature at the peak, and this scaling rate is close to the C-C rate; (2) the scaling of event-based extremes for precipitation amount with temperature follows a similar pattern to the daily precipitation extremes while the event-based precipitation intensity does not show a peak structure; (3) the decrease of rain duration is the main cause for the peak structure of the rain amount scaling.

The Budyko framework (BF) has been used to predict evaporation (E) at annual or monthly time scales, but few studies have analyzed the errors in the predicted E in a systematic manner. This study develops an error-decomposition framework which expresses the errors in the BF-predicted annual and monthly E as a function of (1) the anomalies (i.e. deviations from the long-term mean) of precipitation (P), potential evapotranspiration (PET), runoff (R) and catchment water storage change (ΔS), (2) the (long-term) mean water storage change, and (3) the mean difference between the predicted and actual E. The error variance of BF-predicted E can be decomposed into the variance and covariance terms of P, PET, R and ΔS. The relative contribution of each of these controlling factors to the total error variance of E are evaluated at 14 major river basins in China with the mean annual aridity index ranging between 0.55 and 11.78. It is found that climatic factors (P and PET) and catchment responses (R and ΔS) play different roles in the errors of predicted E among diverse climates of 14 basins. Under the humid (energy-limited) condition, the variance and covariance terms of P, PET, R and ΔS are comparably important in the contribution to the prediction error variance of E. In contrast, under the arid (water-limited) condition the error variance of predicted E is dominated by the magnitude of ΔS anomalies. Results of this study suggest that the incorporation of ΔS into BF can improve the predictability of annual and monthly E more under the arid climates than humid climates.

Integrating powerful machine learning models with flood risk assessment and determining the potential mechanism between risk and the driving factors are crucial for improving flood management. In this study, six machine learning models were utilized for flood risk assessment of the Pearl River Delta, in which the Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) models were firstly applied in this field. Twelve indices were chosen and 2000 sample points were created for model training and testing. Hyperparameter optimization of the models was conducted to ensure fair comparisons. Due to uncertainty in the sample dataset, recorded inundation hot-spots were utilized to validate the rationality of the flood risk zoning maps. After determining the optimal model, the driving factors of different flood risk levels were investigated. Urban and rural areas and coastal and inland areas were also compared to determine the flood risk mechanism in different highest-risk areas. The results showed that the GBDT performed best and provided the most reasonable flood risk result among the six models. A comparison of the driving factors at different risk levels indicated that the disaster-inducing factor, disaster-breeding environment, and disaster-bearing body were not definitely becoming more serious as the flood risk increased. In the highest-risk areas, rural areas were featured by worse disaster-breeding environment than urban areas, and the disaster-inducing factors of coastal areas were more serious than those of inland areas. Moreover, the Digital Elevation Model (DEM), maximum 1-day precipitation (M1DP), and road density (RD) were the top three significant driving factors and contributed 52% to flood risk. This study not only expands the application of machine learning and deep learning methods for flood risk assessment, but also deepens our understanding of the potential mechanism of flood risk and provides insights into better flood risk management.

A new Bayesian framework for automatic calibration of SWMM, which simultaneously considers both parameter uncertainty and input uncertainty, was developed. The framework coupled an optimization algorithm DREAM and the Storm Water Management Model (SWMM) by newly developed API functions used to obtain and adjust parameter values of the model. Besides, a rainfall error model was integrated into the framework to consider systematic rainfall errors. A case study in Guangzhou, China was conducted to demonstrate the use of the framework. The calibration capability of the framework was tested and the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries were identified in the study area. the results show that calibration considering both parameter uncertainty and rainfall uncertainty captures peak flow much better and is more robust in terms of the Nash Sutcliffe index than that only considering parameter uncertainty.

Climate change and urbanization are converging to challenge the flood control in the Pearl River Delta (PRD) due to their adverse impacts on precipitation extremes and the urban areas environment. Previous studies have investigated temporal changes in flood risk with various single factor, few have considered the joint effects of climate change, urbanization and socio-economic development. Here, based on the representative concentration pathway (RCP) scenarios, we conducted a comprehensive assessment of future (2030-2050) flood risk over the PRD combined with a thorough investigation of climate change, urbanization and socio-economic development. Precipitation extremes were projected using the regional climate model RegCM4.6, and urbanization growth was projected based on the CA-Markov model. The economic and population development was estimated by the shared socio-economic pathways (SSPs). Flood risk mapping with different RCPs-urbanization-SSPs scenarios was developed for the PRD based on the set pair analyze theory. The results show that climate change and urbanization are expected to exacerbate flood risk in most parts of the PRD during the next few decades, concurrently with more intense extreme precipitation events. The high flood risk areas are projected mainly in the urban regions with unfavorable terrain and dense population. The highest flood risk areas are expected to increase by 8.72% and 19.80% under RCP4.5 and RCP8.5 scenarios, respectively. Reducing greenhouse gas emissions may effectively mitigate the flood risk over the PRD. This study highlight the links between flood risk and changing environment, suggesting that flood risk management and preventative actions should be included in regional adaptation strategies.

With rapid urbanization, rainstorm-induced inundation losses are becoming increasingly severe. Research on urban inundation response to rainstorm patterns can help engineers and governments make appropriate decisions and mitigate property losses. This paper introduces a new hydrodynamic model that couples the storm water management model (SWMM) and the shallow water model (SWM) and applies the validated model to the Haidian Island case to determine the inundation response to rainstorm patterns. The results show that the coupled model can predict rainstorm-induced inundation well, and that different rainstorm patterns would result in different inundation patterns. With increasing rainstorm peak position coefficients, urban inundation events become more severe and inundation peaks appear sooner. However, the times at which inundation occurs do not change. To determine the reasons for the differences in inundation among different rainstorm patterns, correlations between the inundation volume and the fully-filled pipes ratio were analyzed. Correlation analysis showed that inundation events are significantly related to the working state of the sewer system, indicating that a higher fully-filled pipes ratio prior to the rainstorm peak will result in more severe inundation. This conclusion suggests that the range of rainstorm peak position coefficients in China is quite reasonable. This result can provide insights to engineers and the government for urban design planning and maintenance.

Drought is one of the major natural hazards with a possibly devastating impact on the regional environment, agriculture, and water resources. Previous studies have assessed the historic changes in meteorological drought over various regional scales but have rarely considered hydrological drought due to limited hydrological observations. Here, we use long-term (1960–2012) hydro-meteorological data to analyze the meteorological and hydrological drought comparatively in the Pearl River basin (PRB) in southern China using the standardized precipitation index (SPI) and the standardized runoff index (SRI). The results indicate a strong positive correlation between the SPI and SRI, and the correlation tends to be stronger at the longer timescale. The SPI is reliable to substitute for the SRI to represent the hydrological drought at the long-term scale (e.g., 12 months or longer). Trend analysis reveals a noticeably wetting trend mainly in the eastern regions and a significant drying trend mainly in the western regions and the downstream area of the PRB. The drought frequency is spatially heterogeneous and varies slightly at the interannual scale. Overall, the drought is dominated by noticeable cycles of shorter periodicity (0.75–1.8 years), and periodic cycles in the meteorological drought are mainly responsible for those in the hydrological drought.

The growing worldwide threat of urban flooding has stimulated research on flood resilience so that more targeted measures can be implemented in vulnerable areas to mitigate flood losses. During flood events, people generally show different risk perceptions for different inundation areas according to their population density, economic degree, and service function. In this study, the risk perception was quantified into weights and integrated into the resilience calculation to identify areas vulnerable to floods and posing a danger to human life and property. An area in Zhuhai, China, was selected as the research region. With the proposed novel method, the grid-based flood resilience under different rainfall scenarios and different time periods of the day (human work and rest hours) was researched. Moreover, the resilience of different land uses and the impacts of two critical parameters in the resilience equations were also studied. The results indicated that the risk perception related to a given land-use generally changed according to different periods of the day. The overall resilience of the research region was lower when more severe rainstorm occurred in a given time period. The resilience in the work hours was higher than that in the rest hours during the same rainstorm event, indicating that the living/working state of people affected the flood resilience in urban areas. Analyses of the two parameters provided plausibility verification for the parameters and indicated that a slight increase in the height of building doorsills substantially decreased the areas with low and very low resilience. And this decrease effect was more pronounced in the rest hours than the work hours. Results of this study provide an in-depth understanding of urban flood resilience and targeted guidance for engineering construction and flood management.

Urban hydrological changes caused by uncertain climate affect the implementation of water management strategies. Recently, more attention has been paid to the hydrological performance of green infrastructure (GI) in future climate scenarios. In this study, the Storm Water Management Model (SWMM) was applied to undertake integrated assessments of the runoff and water quality control effect of GI. Eight GI scenarios were proposed in Guangzhou’s Tianhe Smart City, and simulated rainfall events under different climate scenarios were assessed. Considering the factors of runoff volume control, peak flow reduction, pollutant removal, and life cycle cost, the advantages of different GI combinations were compared using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results confirmed that GI affect runoff control under changing climatic conditions, but its application to future large rainfall events is limited. According to the results of the TOPSIS assessment, decision-makers can consider different cost weights to determine the optimal scenario of GI implementation. The conclusions can be applied to GI construction planning projects to enhance the adaptability of urban water resource management systems.

Climate warming is expected to have significant impacts on the global hydrologic cycle, including changes in precipitation-induced extremes such as droughts. Using 28 CMIP5 global climate models (GCMs), this study presents a global-scale analysis of the joint return periods (T) of meteorological drought characteristics (duration D, severity S, and peak P) at the 6- and 12-month scales under the Representative Concentration Pathways scenarios RCP2.6 and RCP4.5. The D, S, and P estimated based on the Runs theory are used to calculated from the Standardized Precipitation Index (SPI) at the global 1° × 1° grids. Six marginal probability distributions are used to fit S, P and D, whereas three Archimedean copula functions (Clayton, GH and Frank) are used to estimate T for the paired drought characteristics (S-D, P-D and S-P). Large spatial variability is found globally in the best-fitted copulas for the paired drought characteristics, with the Frank (Frank and GH) of the largest global percentage for SPI6 (SPI12). Relative to the baseline (1971–2000), the T of the paired drought characteristics above the moderate drought (S > 1, P > 1 and D > 3) is projected to decrease mostly in North America and Asia (

Quaternized poly phenylene oxide (QPPO) was synthesised and used as a modifier material to blend with polyvinyl chloride (PVC) to cast the substrate for thin-film composite (TFC) forward osmosis (FO) membranes. The addition of QPPO improved the porosity, hydrophilicity, and morphology of the substrate, thereby weakening the internal concentration polarisation of TFC membranes. Furthermore, the introduction of QPPO improved the structure of the active layer, which led to higher water permeability. Moreover, the mechanical properties of the TFC membranes were significantly improved after the introduction of QPPO. As a result, introducing QPPO significantly increased the FO performance. In addition, the TFC membrane based on a 40% QPPO blend ratio exhibited optimal FO performance: a high water flux of 71.21 L h−1 m−2 and an extremely low specific reverse solute flux of 0.079 g L−1 under the active layer facing the draw solution (DS) mode by utilising 1 M NaCl as the DS and deionised (DI) water as the feed solution; this FO performance remained fairly stable during long-term operation. This study suggests that introducing QPPO into the substrate is a sensible strategy for fabricating high-performance TFC FO membranes.

Karst waterlogging is a natural disaster that occurs frequently and it adversely affects the social and economic development of affected areas. An analysis of the causes of karst waterlogging with respect to climate and topography can serve as a foundation for disaster assessment and prevention. In this study, a karst waterlogging risk assessment indexing system was established. The system was based on a comprehensive analysis of risk factors, including the severity of the disaster and the vulnerability of the affected area. DeBao County in GuangXi was used as the study area. Nine risk indicators were chosen as evaluation indexes and combination weights were divided into subjective and objective weights based on the analytic hierarchy process (AHP) and entropy weight. A geographical information system (GIS) was applied to help with the calculations of the set pair analysis (SPA) and to pile up the layers of the evaluation indicators. Then, a risk rating map was drawn using GIS techniques. The results showed that the high risk locations were mainly distributed in the center of DeBao County; therefore, the map could be used as reference for the prevention and management of karst waterlogging risk.

Knowledge of the temporal variability in actual evapotranspiration ( E ) is essential for a better understanding of the interaction and feedback between atmospheric and land surface hydrologic processes under various natural and anthropogenic conditions. Recently, Zeng and Cai (2015) proposed a decomposition framework of the E variance, based on water balance and the Budyko hypothesis. On the basis of a long-term (1960–2008) land surface dataset, this study applies the theoretical framework to assess the effects of climate and terrestrial storage factors on the interannual and intra-annual variance in E across China. An error decomposition framework is developed to quantify the key factors in the error of the predicted E variance. The results show that the prediction of the E variance is more accurate in arid climates than in humid climates, and the corresponding error is primarily controlled by the variability of precipitation ( P ) and runoff ( R ). Climate is the primary source for the E variance, and the dominant sources shift from potential evaporation ( PET ) in humid climates to P in arid climates. The interactions between P and PET tend to dampen the E interannual variance and enhance the E intra-annual variance, and this effect is especially significant in humid climates. Terrestrial storage change is more capable of accommodating climatic fluctuations at the intra-annual scale than at the interannual scale, and for some arid regions it is the dominant factor influencing the E variance. The response of terrestrial storage to P is more significant than its response to PET , especially for regions with strong human impact. Neglecting the effects of terrestrial storage would possibly underestimate or overestimate the E variance in both humid and arid climates, due to the interactions between climate and the change in the terrestrial storage.

Climate warming is expected to occur with an increased magnitude of extreme precipitation and sea level rise, which leads to an increased probability of waterlogging in coastal cities. In this paper, a combined probability model is developed to evaluate the impact of climate change on waterlogging in Guangzhou by using eight climate models with four emissions scenarios [Special Report on Emissions Scenarios (SRES) scenario A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5]. The copula method was applied to derive the bivariate distributions of extreme rainfall and tidal level. The uncertainty in the projected future temperature, extreme rainfall, sea level, and the combined extreme rainfall and tidal level probability were discussed. The results show that although there is a large uncertainty driven by both climate models and emissions scenarios in the projection of climate change, most modeling results predict an increase in temperature and extreme precipitation in Guangzhou during the future period of 2020–50, relative to the historical period of 1970–2000. Moreover, greater increases are projected for higher emissions scenarios. The sea level is projected to increase in the range of 11.40–23.37 cm during the period 2020–50, consistent with climate warming. Both simultaneous probability and waterlogging probability are projected to show an upward trend in the future period 2020–50, with the largest and smallest increases in the RCP4.5 and RCP2.6 scenarios, respectively. The results of this paper provide a new scientific reference for waterlogging control in Guangzhou under climate change conditions.

Bioretention has been found to lower the effluent loads of various pollutants from rainfall runoff. However, it is still a challenge to effectively use bioretention for rainfall runoff control in lateritic red soil regions where have high rainfall intensity and low soil infiltration capacity. Hence, in this study, the hydrologic performance and rainfall runoff pollutant removal capacity of field-scale biochar-amended bioretention facilities were tested with four rainfall recurrence periods under different biochar distributions, internal water storage (IWS) zone heights, and exfiltration conditions. The results confirmed that incorporation of biochar into planting soil would improve its water content raising capacity (WCRC), especially when the biochar was uniformly mixed with the lateritic red soils. Besides, more infiltrating from the planting soil layer and higher IWS zone heights effectively enhanced WCRC of the stone chip packing layer. For runoff volume control, adding biochar and increasing the IWS zone height could effectively improve runoff volume control capacity. Besides, the unlined bioretention had a higher runoff volume control capacity than lined bioretention. Considering runoff pollutant removal performance, biochar could contribute to significantly improving the runoff pollutant event mean concentration removal rate (Rc) of nutrient pollutants (TN, NO3-N, NH3-N, and TP). The average runoff pollutant load removal rate (Rl) of different biochar distributions decreased as follows: biochar was uniformly mixed with the lateritic red soils > biochar was stratified with the lateritic red soils > biochar was excluded in the planting soil layer. The average Rc and average Rl of all pollutants except COD under different IWS zone heights decreased as follows: 40 cm > 20 cm > 0 cm. Meanwhile, the average Rl of the lined bioretention with an IWS zone height of 0 cm was lower than that of the unlined bioretention. Overall, higher rainfall recurrence periods would reduce the treatment capacity of bioretention facilities.

Urban inundation management should take various forms to deal with different flood situations, rapid population growth, and climate change. Research on the advantages and disadvantages of grey infrastructure, such as deep tunnels and pipe networks, is necessary to apply hybrid green-grey infrastructure scientifically and efficiently. A novel urban flood model was applied in a highly urbanized region to evaluate the grey infrastructure. Green-grey approaches were also compared. The results showed that deep tunnels decreased the peak flood volume and shortened the time of peak flow. Pipe network improvements decreased the peak flood volume but did not affect the time of peak flow. However, the deep tunnels could not mitigate local inundations caused by insufficient sewer pipe capacity, whereas pipe network improvements could not mitigate inundations caused by backwater flooding. Moreover, pipe network improvements tended to generate new downstream inundation hot-spots. Green-grey infrastructures performed better than grey infrastructures for urban flood reduction. A hybrid green-grey approach was recommended. Suitable combinations of green-grey infrastructures improve the stability and effectiveness of urban flood management.

Understanding the effects of climate and catchment characteristics on overall water balance at different temporal scales remains a challenging task due to the large spatial heterogeneity and temporal variability. Based on a long-term (1960–2008) land surface hydrologic dataset over China, this study presented a systematic examination of the applicability of the Budyko model (BM) under various climatic conditions at long-term mean annual, annual, seasonal and monthly temporal scales. The roles of water storage change (WSC, dS/dt) in water balance modeling and the dominant climate control factors on modeling errors of BM are investigated. The results indicate that BM performs well at mean annual scale and the performance in arid climates is better than humid climates. At other smaller timescales, BM is generally accurate in arid climates, but fails to capture dominant controls on water balance in humid climates due to the effects of WSC not included in BM. The accuracy of BM can be ranked from high to low as: dry seasonal, annual, monthly, and wet seasonal timescales. When WSC is incorporated into BM by replacing precipitation (P) with effective precipitation (i.e., P minus WSC), significant improvements are found in arid climates, but to a lesser extent in humid climates. The ratio of the standard deviation of WSC to that of evapotranspiration (E), which increases from arid to humid climates, is found to be the key indicator of the BM simulation errors due to the omission of the effect of WSC. The modeling errors of BM are positively correlated with the temporal variability of WSC and hence larger in humid climates, and also found to be proportional to the ratio of potential evapotranspiration (PET) to E. More sophisticated models than the BM which explicitly incorporate the effect of WSC are required to improve water balance modeling in humid climates particularly at all the annual, seasonal, and monthly timescales.

With rapid urbanization, inundation-induced property losses have become more and more severe. Urban inundation modeling is an effective way to reduce these losses. This paper introduces a simplified urban stormwater inundation simulation model based on the United States Environmental Protection Agency Storm Water Management Model (SWMM) and a geographic information system (GIS)-based diffusive overland-flow model. SWMM is applied for computation of flows in storm sewer systems and flooding flows at junctions, while the GIS-based diffusive overland-flow model simulates surface runoff and inundation. One observed rainfall scenario on Haidian Island, Hainan Province, China was chosen to calibrate the model and the other two were used for validation. Comparisons of the model results with field-surveyed data and InfoWorks ICM (Integrated Catchment Modeling) modeled results indicated the inundation model in this paper can provide inundation extents and reasonable inundation depths even in a large study area.

It is expected that climate warming will be experienced through increases in the magnitude and frequency of extreme events, including droughts. This paper presents an analysis of observed changes and future projections for meteorological drought for four different time scales (1 month, and 3, 6 and 12 months) in the Beijiang River basin, South China, on the basis of the standardized precipitation evapotranspiration index (SPEI). Observed changes in meteorological drought were analysed at 24 meteorological stations from 1969 to 2011. Future meteorological drought was projected based on the representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, as projected by the regional climate model RegCM4.0. The statistical significance of the meteorological drought trends was checked with the Mann–Kendall method. The results show that drought has become more intense and more frequent in most parts of the study region during the past 43 years, mainly owing to a decrease in precipitation. Furthermore, long-term dryness is expected to be more pronounced than short-term dryness. Validation of the model simulation indicates that RegCM4.0 provides a good simulation of the characteristic values of SPEIs. During the twenty first century, significant drying trends are projected for most parts of the study region, especially in the southern part of the basin. Furthermore, the drying trends for RCP8.5 (or for long time scales) are more pronounced than for RCP4.5 (or for short time scales). Compared to the baseline period 1971–2000, the frequency of drought for RCP4.5 (RCP8.5) tends to increase (decrease) in 2021–2050 and decrease (increase) in 2051–2080. The results of this paper will be helpful for efficient water resources management in the Beijiang River basin under climate warming.

Based on the representative concentration pathway (RCP) scenarios, projected changes in climate extremes events in the Zhujiang River basin were investigated using the regional climate model RegCM4.0. Four extreme temperature indices (TXx, TNn, SU30, and TMINmean) and five extreme precipitation indices (Rx1day, Rx5day, SDII, R50mm, and SDII) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) were used in the investigation. The performance of the model was validated through comparison between observations and simulations for the period 1971–2000. The results show that although RegCM4.0 has certain abilities to reproduce both the spatial distributions of the values of extreme climate indices and their trends in the Zhujiang River basin, there are some large biases in the model results. The model simulates extreme temperatures more accurately than extreme precipitation. Compared to the baseline period 1971–2000, the changes in future (2021–2050 and 2051–2080) climate extremes under the RCP4.5 and RCP8.5 scenarios show very similar patterns, with an increase in extreme temperatures across the entire region and an increase in extreme precipitation in most parts of the region. For the period 2006–2099, a significant warming trend can be expected under both emissions scenarios, especially under the high emissions scenario (RCP8.5). The risk of drought shows statistically significant increasing trends under the RCP8.5 scenario in most parts of the region, especially in the central part of the basin. Meanwhile, more intense rainfall events are expected to occur in most parts of the region, especially in the southern parts of the basin. Furthermore, the increases predicted under the RCP8.5 scenario are more pronounced than those predicted under the RCP4.5 scenario. Overall, our results suggest that the Zhujiang River basin will suffer more natural disasters, such as catastrophic floods, droughts, and heat weaves, in the future.

To analyze extreme precipitation patterns in Hainan Island, hourly precipitation datasets from 18 stations, for the period from 1967 to 2012, were investigated. Two precipitation concentration indices (PCI) and 11 extreme precipitation indices (EPI) were chosen. PCI1 indicated a moderate seasonality in yearly precipitation and PCI2 showed that at least 80% of the total precipitation fell in 20% of the rainiest hours. Furthermore, the spatial variations of PCI1 and PCI2 differed. Linear regression indicated increasing trends in 11 of the calculated EPI. Principal component analysis found that the first recalculated principal component represented the 11 EPI. The recalculated principal component revealed an increasing trend in precipitation extremes for the whole island (except the interior section). Trend stability analysis of several of EPI suggested that the southern parts of Hainan Island, and especially the city of Sanya, should receive more attention to establish the drainage facilities necessary to prevent waterlogging.