Search

Compound climate events can significantly impact vegetation productivity, yet the direct and lagged vegetation productivity responses to seasonal compound warm-dry and cold-dry events remain unclear. Using observationally-constrained and process-based model data, we analyze vegetation productivity responses to the compound conditions of precipitation and temperature in spring and summer. Our results show the regional asymmetries in direct and lagged effects of compound warm-dry events. In high-latitudes (>50°N), compound warm-dry events raise productivity. In contrast, in mid-latitudes (23.5-50°N/S), compound warm-dry events reduce productivity and compound warm-dry springs can cause and amplify summer droughts, thereby reducing summer productivity. Moreover, compound cold-dry events impose directly and indirectly adverse synergistic effects on productivity in mid-to-high latitudes and their effects exceed individual cold and dry impacts. Our results highlight that a multivariate perspective is necessary to appropriately investigate the impacts of climate extremes on vegetation productivity as precipitation and temperature often covary.

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings.

Due to the increasingly frequent occurrence of urban waterlogging, the spatial optimization of low impact development (LID) practices has been commonly used to detain and reduce storm water runoff in the most cost-effective way. In this study, the flow transmission chain (FTC) was proposed to replace the routing portion of the Storm Water Management Model (SWMM) and was combined with the runoff component of the SWMM to simulate LID practices (SWMM-FTC). In the SWMM-FTC, the third Evolution Step of Generalized Differential Evolution (GDE3) was employed to optimize the LID layout design. The results showed that the relative error between the modified SWMM-FTC and the calibrated SWMM was less than 0.25% under various LID scenarios, and the computational efficiency of the SWMM-FTC was improved by 19.3 times. Moreover, the GDE3 outperformed the commonly used non-dominated sorting genetic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), and the multi-objective shuffled frog leaping algorithm (MOSFLA) due to its ability to find the most cost-effective solution. The LID layout obtained from the SWMM-FTC with the GDE3 saved $210-1067 to achieve a 1% reduction in storm water runoff. This result demonstrates that the SWMM-FTC with the GDE3 can achieve higher environmental benefits than comparable models, providing better guidance for managers and stakeholders.

With the release of Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM products, hydrologists can obtain precipitation data with higher resolution and wider coverage. However, great uncertainties still exist in the accuracy and hydrological utility of these data in alpine and gorge regions with sparse gauge stations. In this study, the Lancang River Basin in China was used as an example, and near real-time products (IMERG-E and IMERG-L) and post-processed products (IMERG-F and TMPA 3B42-V7) were evaluated. Different indexes and methods were applied to evaluate the accuracy of these products. The variable infiltration capacity hydrological model was adopted to evaluate their hydrological utility. The following findings were obtained. (1) Compared with observed precipitation data, the near real-time products tend to underestimate, while the post-processed products tend to overestimate precipitation. The performance of the four products in winter is poor. (2) IMERG products offer improvements in two aspects: first, the near real-time products achieve good accuracy and second, the detectability and the accuracy in gorge areas have been greatly improved. (3) The near real-time products have the potential for hydrological applications. The best simulation result was obtained based on IMERG-F, followed by 3B42-V7, IMERG-E, and IMERG-L. (4) The four products can provide reliable precipitation data for the hydrological application over the Lancang River Basin.

Quantifying forecast uncertainty is of great importance for reservoir operation and flood control. However, deterministic hydrological forecasts do not consider forecast uncertainty. This study develops a conditional probability model based on copulas to quantify forecast uncertainty. Three updating models, namely auto-regressive (AR) model, AR exogenous input model, and adaptive neuro fuzzy inference system model, are applied to update raw deterministic inflow forecasts of the Three Gorges Reservoir on the Yangtze River, China with lead times of 1d, 2d, and 3d. Results show that the conditional probability model provides a reasonable and reliable forecast interval. The updating models both enhance the forecast accuracy and improve the reliability of probabilistic forecasts. The conditional probability model based on copula functions is a useful tool to describe and quantify forecast uncertainty, and using an updating model is an effective measure to improve the accuracy and reliability of probabilistic forecast.

Summer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982 to 2010 are used to make the forecast. The results show that the SST‐based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations.

Design flood quantiles are crucial for hydraulic structures design, water resources planning and management, whereas previous multivariate hydrological quantile estimation methods usually do not consider historical flood information. To overcome such limitations, a meta-heuristic inference function for margins (MHIFM) approach, coupling meta-heuristic algorithm with a modified inference function for margins (IFM) method, is developed for modeling the joint distributions of flood peak and volumes with incorporation of historical flood information. Then, the most likely realization (MLR) and equivalent frequency combination (EFC) methods are employed for selecting multivariate design floods on a quantile iso-surface. The Danjiangkou reservoir located in Hanjiang River basin, the first pilot basin of most regulated water resources management policy in China, is selected as a case study. Application results indicate that the MHIFM approach shows good performance for estimating the parameters of marginal and joint distributions; moreover, the MLR method yields safer design flood quantiles than the EFC method in terms of highest routed reservoir water levels. The proposed MHIFM approach associated with the MLR method is safer and more rational for reservoir design, which would provide rich information as the reference for flood risk assessment, reservoir operation and management.

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.

While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro-climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP-SVR, ASFP-ELM, and ASFP-RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1- and 3-month lead times. Results show that the ASFP-ELM model can effectively predict space-time evolutions of drought events with satisfactory skills, outperforming the ASFP-SVR and ASFP-RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.

The excessive application of agricultural irrigation water and chemical fertilizer has increased crop yields to help meet the demand for food, but it has also led to major water environment problem, i.e. non-point source (NPS) pollution, which needs to be addressed to achieve sustainable development targets. Although numerous studies have focused on the control and reduction of agricultural NPS pollution from the perspective of irrigation and fertilizer, the effects of different cropping systems on NPS pollution (ammonia nitrogen (NH

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960&ndash, 2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960&ndash, 1984, wider range of the three indices over SDR and ADR were detected for 1985&ndash, 2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

The preceding and succeeding precipitation (P-S-P) of a precipitation extreme often contribute to flooding initiated by the extreme event itself. In this study, the concept of event-based extreme precipitation (EEP), defined as a precipitation event (daily precipitation ≥ 1 mm for successive days) having at least one daily precipitation extreme (daily precipitation ≥ the 99th percentile), is proposed to consider P-S-P of daily extreme precipitation. Further, the time distribution patterns, trends, and return levels of EEPs across China are analyzed based on a 0.5 × 0.5° gridded precipitation dataset covering 1961–2016. The data demonstrate the EEP concept, that multi-day EEP with daily extreme precipitation occurring late in the event is predominant in China except for the Northwest where single-day EEP prevails. Over west China, EEP is increasing and becomes more temporally concentrated. In the lower Lantsang River, along the southeast coastline and on the Hainan Island, the 20- and 50-yr return levels of EEP would exceed 300 and 400 mm, respectively. Moreover, global warming possibly induces more single-day EEPs in the Northwest. The EEP concept may help guide attempts to manage extreme precipitation on event basis, which is particularly useful for regions characterized by long-lasting extreme precipitation.

A rapid increase in the risk of urban flooding in recent years has urged the research community to enrich approaches to deal with urban flooding problems. The state-of-the-art approach consists of coupling one-dimensional (1D) and two-dimensional (2D) hydrodynamic models. However, at present such coupled 1D/2D models are mostly commercial and complex to build and run. The present study has proposed a new simple approach for modeling urban flooding by coupling Storm Water Management Model (SWMM) and LISFLOOD-FP, two widely used freewares with relatively simple components. The coupled model was firstly applied to the Shiqiao Creek District in Dongguan City, South China, and verified against four major historical floods. The testing results demonstrate the capability of the coupled model in predicting urban flooding. The successful coupling of SWMM and LISFLOOD-FP offers another simple, practical approach for urban flooding estimation, which can be readily used by non-expert users or those who do not have access to commercial modules.

Quantification of the inherent uncertainty in hydrologic forecasting is essential for flood control and water resources management. The existing approaches, such as Bayesian model averaging (BMA), hydrologic uncertainty processor (HUP), copula-BMA (CBMA), aim at developing reliable probabilistic forecasts to characterize the uncertainty induced by model structures. In the probability forecast framework, these approaches either assume the probability density function (PDF) to follow a certain distribution, or are unable to reduce bias effectively for complex hydrological forecasts. To overcome these limitations, a copula Bayesian processor associated with BMA (CBP-BMA) method is proposed with ensemble lumped hydrological models. Comparing with the BMA and CBMA methods, the CBP-BMA method relaxes any assumption on the distribution of conditional PDFs. Several evaluation criteria, such as containing ratio, average bandwidth and average deviation amplitude of probabilistic application, are utilized to evaluate the model performance. The case study results demonstrate that the CBP-BMA method can improve hydrological forecasting precision with higher cover ratios more than 90%, which are increased by 4.4% and 3.2%, 2.2% and 1.7% over those of BMA and CBMA during the calibration and validation periods, respectively. The proposed CBP-BMA method provides an alternative approach for uncertainty estimation of hydrological multi-model forecasts.

One major threat to cities at present is the increased inundation hazards owing to changes in climate and accelerated human activity. Future evolution of urban inundation is still an unsolved issue, given large uncertainties in future environmental conditions within urbanized areas. Developing model techniques and urban inundation projections are essential for inundation management. In this paper, we proposed a 2D hydrodynamic inundation model by coupling SWMM and LISFLOOD-FP models, and revealed how future urban inundation would evolve for different storms, sea level rise and subsidence scenarios based on the developed model. The Shiqiao Creek District (SCD) in Dongguan City was used as the case study. The model ability was validated against the June 13th, 2008 inundation event, which occurred in SCD, and proved capable of simulating dynamic urban inundation. Scenario analyses revealed a high degree of consistency in the inundation patterns among different storms, with larger magnitudes corresponding to greater return periods. Inundations across SCD generally vary as a function of storm intensity, but for lowlands or regions without drainage facilities inundations tend to aggravate over time. In riverfronts, inundations would exacerbate with sea level rise or subsidence; however, the inland inundations are seemingly insensitive to both factors. For the combined scenario of 100-yr storm, 0.5 m subsidence and 0.7 m sea level rise, the riverside inundations would occur much in advance, whilst catastrophic inundations sweep across SCD. Furthermore, the optimal low-impact development found for this case study includes 0.2 km2 of permeable pavements, 0.1 km2 of rain barrels and 0.7 km2 of green roofs.

Owing to their essential influence on human society and natural environment exerted by inducing disasters, such as floods and droughts, further studies on precipitation extremes in China are needed. This study presents the regional frequency and spatial-temporal patterns of precipitation extremes in China based on a high-resolution (0.5° × 0.5°) daily precipitation dataset from 1961 to 2013. With fuzzy c-means, L-moments methods and other scientific statistical tests, a regional frequency analysis (RFA) is conducted, aiming to further understand the regional and spatial distribution of precipitation extremes across China. The results show that: (1) the whole Mainland China can be divided into 50 homogeneous regions on the basis of the characteristics of mean annual precipitation and location indices. (2) For most of the regions, Generalized Extreme Value (GEV), Generalized Normal (GNO) and Pearson type III (PE3) distributions of precipitation extremes fit well, according to the results of goodness-of-fit (GOF) test. (3) For RX1DAY, GEV has the best-fit distribution in the east, northeast and southwest of China, whereas GNO distribution mostly fits the northern and parts of southwest and southeast; in addition, regions which fit PE3 and Generalized Logistic (GLO) distribute dispersedly across the country. (4) For RX5DAY, GEV mainly fits in the middle, southwestern and southern; GNO and PE3 apply best to the northeastern and northern, respectively. (5) Return periods of 20, 50 and 100 years for their best-fit distributions decrease gradually from southeastern China to northwestern China. Compared with the results of GEV distribution fitted to each grid, RFA may provide more accurate estimates of rainfall quantiles. Definitely, the study results will not only benefit further understanding of the unique and complex features of extreme precipitation in the whole Mainland China but also contribute to the nation-scale flood prevention, control and management in the backdrop of the changing climate.

Reference evapotranspiration (ETo) is an important parameter for characterization of the hydrological cycle, and it is also important for agricultural, environmental and other studies. The ETo for 4189 grid points in China from 1961 to 2013 was calculated in this study utilizing the FAO Penman-Monteith method (P-M) based on an updated high-resolution (0.5° × 0.5°) gridded dataset. Five climatic variables including wind speed (WS), sunlight duration (SD), relative humidity (RH), maximum daily temperature (Tm) and minimum daily temperature (Tn), were selected to identify the contribution to variability of ETo. The temporal evolution and spatial distribution of each climatic variable was also investigated. Results indicate that (1) ETo distribution in China differed significantly both in seasonal and spatial scale in general, and annual ETo significantly decreased 6.84 mm/decade (P

Climate change has emerged as a key issue for hydropower management and development in future. This study systematically evaluates the impact of climate change on reservoir inflow, hydropower output, sustainability, and efficiency under the representative concentration pathway (RCP) scenarios (RCP2.6, 4.5 and 8.5) by taking the Pearl River basin in China as the case. The variable infiltration capacity model is coupled with global climate models to project future hydropower changes. It is shown that future reservoir inflow and hydropower output significantly differ from the historical ones. Dry years in future are projected to become drier leading to decrease in hydropower output that in turn reduces hydropower reliability and resiliency and increases the vulnerability. Wet years would get wetter, but the hydropower output does not necessarily increase possibly due to more surplus water released from reservoirs during flood seasons. For normal years, neither of reservoir inflow and hydropower output displays obvious changes. Moreover, water use efficiency of the West River in wet years and that of the North River in wet, normal and dry years would be lower in future, while the East River during non-flood seasons in dry years is expected to have higher water use efficiency. Our study can potentially provide an insight into the response of hydropower to climate change and help policy-makers and stakeholders manage future hydropower generation.

By characterizing the patterns of temperature extremes over nine integrated agricultural regions (IARs) in China from 1961 to 2011, this study performed trend analyses on 16 extreme temperature indices using a high-resolution (0.5° × 0.5°) daily gridded dataset and the Mann-Kendall method. The results show that annually, at both daytime and nighttime, cold extremes significantly decreased but warm extremes significantly increased across all IARs. Overall, nighttimes tended to warm faster than daytimes. Diurnal temperature ranges (DTR) diminished, apart from the mid-northern Southwest China Region and the mid-Loess Plateau Region. Seasonally, DTR widely diminished across all IARs during the four seasons except for spring. Higher minimum daily minimum temperature (TNn) and maximum daily maximum temperature (TXx), in both summer and winter, were recorded for most IARs except for the Huang-Huai-Hai Region; in autumn, all IARs generally encountered higher TNn and TXx. In all seasons, warming was observed at daytime and nighttime but, again, nighttimes warmed faster than daytimes. The results also indicate a more rapid warming trend in Northern and Western China than in Southern and Eastern China, with accelerated warming at high elevations. The increases in TNn and TXx might cause a reduction in agriculture yield in spring over Northern China, while such negative impact might occur in Southern China during summer. In autumn and winter, however, the negative impact possibly occurred in most of the IARs. Moreover, increased TXx in the Pearl River Delta and Yangtze River Delta is possibly related to rapid local urbanization. Climatically, the general increase in temperature extremes across Chinese IARs may be induced by strengthened Northern Hemisphere Subtropical High or weakened Northern Hemisphere Polar Vortex.

Peak flows are the most important flood parameter which relatively reflects the highest level and potential destructive power of a flood. Understanding peak flow changes can effectively capture a flood characteristic and is essential for developing flood control strategies. This study aims to reveal how regional peak flows evolved in recent decades, mainly from a non-linear perspective. The Beijiang River Basin (BRB) was chosen for the analysis, and hydrological data from four hydrologic stations were used. Methods including ensemble empirical mode decomposition and rescaled range analysis were applied to advance the research. Results indicate a non-significant uptrend and a multiple periodicity of peak flows in BRB. However, short periods were more distinct than long ones. In the future, peak flows may continue to increase over time. Such changes in peak flows are possibly due to local reservoir operations and the changing South Asian Summer Monsoon (SASM). The research suggests an increasing flood risk and recommends more regional flood adaptations to avoid flood losses for BRB. Synchronously, it provides a reference for studies regarding periodicity and the future trend of peak flows in other regions.

Accelerated soil erosion is an undesirable process that adversely affects the conservation of water and soil. Rainfall erosivity is used to measure the potential ability of rain to cause erosion. This study explores spatio-temporal variations in rainfall erosivity in the Pearl River Basin of China during 1960–2012, at annual and seasonal scales based on a daily model proposed by Yu. Analysis methods including linear regression, Mann-Kendall and the Kriging interpolation technology were applied to determine fluctuating spatial and temporal patterns. Yu's model was proven suitable for the Pearl River Basin by comparison with another model proposed by Zhang. Computation results show an average annual rainfall erosivity of 9918.98 MJ·mm/(hm2·h) and that the Pearl River Delta region, most regions of the Dongjiang River Basin, and the Beijiang River Basin were all under considerable threat from rainfall erosion. The annual rainfall erosivity of the basin does not show significant or abrupt changes, and has fluctuated moderately over the past few decades. The rainfall erosivity of the basin shows an upward trend, and the probability of water and soil erosion caused by rainfall was increasing overall. The problem of water and soil loss in the Pear River Basin remains severe due to aggravating vegetation deterioration, rocky desertification, and climate change. Relevant response strategies and appropriate measures must be considered to guard against further deterioration of the basin.

Anthropogenic activities have a tremendous impact on water ecosystems worldwide, especially in China. To quantitatively evaluate the hydrological alteration connected with aquatic lives and river ecological risks, we took the Beijiang River located in South China as the case study and used ecosurplus (defined as ecological carrying capacity exceeding ecological consumption)/ecodeficit (defined as ecological consumption exceeding carrying capacity) and Indicators of Hydrological Alterations to evaluate hydrological changes. The Ecologically Relevant Hydrologic Indicators were employed to select the key indices of Indicators of Hydrological Alterations, and the eco-environmental water demand calculation provide an effective way for the reservoir operation. Results showed that: (1) High flows contributed more to the ecodeficit, while low flows contributed more to the ecosurplus, (2) the ecodeficit in some parts of the river basin might exceed the ecosurplus after reservoir construction, especially along the main stream, and (3) the determination of eco-environmental water demand is a feasible way for improving the environment by controlling reservoirs. The current study can help guide the optimization of hydrological operation in the basin toward making the ecosystem healthier and has potential to further provide a reference for other basins in terms of hydrological alterations driven by anthropogenic activities.

Heat wave flash drought or precipitation deficit flash drought has devastating impacts on society and the environment. This study explored the historical changes (1960–2015) of the two categories of flash drought over the Pearl River Basin (PRB) in China, and revealed how they would change in the future (2016–2100), by coupling the variable infiltration capacity mode with the global climate model under representative concentration pathway (RCP) 2.6, 4.5, and 8.5 scenarios. Our results indicate that during 1960–2015, the mid-northern PRB has experienced heat wave flash drought frequently while the western PRB suffered from precipitation deficit flash drought. In future, heat wave flash drought under RCP2.6 and 4.5 would occur mostly in the western and eastern PRB. Specifically, heat wave flash drought would become severe under RCP8.5, especially for the eastern PRB. However, precipitation deficit flash drought would be concentrated in the western PRB. Except for the central regions, PRB generally exhibits a significant upward trend in heat wave flash drought under RCP4.5. Under RCP8.5, distinct increases in both categories of flash drought across almost the whole PRB are expected. For precipitation deficit flash drought, only a few regions show significant upward trends under RCP2.6 and 4.5.

While rapid urbanization promotes social and economic development, it poses a serious threat to the health of rivers, especially the small and medium-scale rivers. Flood control for small and medium-scale rivers in highly urbanized areas is particularly important. The purpose of this study is to explore the most effective flood control strategy for small and medium-scale rivers in highly urbanized areas. MIKE 11 and MIKE 21 were coupled with MIKE FLOOD model to simulate flooding with the flood control standard, after which the best flooding control scheme was determined from a whole region perspective (both the mainstream and tributary conditions were considered). The SheGong River basin located near the Guangzhou Baiyun international airport in Guangzhou city over south China was selected for the case study. The results showed that the flooding area in the basin of interest accounts for 42% of the total, with maximum inundation depth up to 0.93 m under the 20-year return period of the designed flood. The flood-prone areas are the midstream and downstream where urbanization is high, however the downstream of the adjacent TieShan River is still able to bear more flooding. Therefore, the probable cost-effective flood control scheme is to construct two new tributaries transferring floodwater in the mid- and downstream of the SheGong River into the downstream of the TieShan River. This infers that flood control for small and medium-scale rivers in highly urbanized areas should not simply consider tributary flood regimes but, rather, involve both tributary and mainstream flood characters from a whole region perspective.

Extremes of weather and climate can cause disasters such as floods and droughts. Further studies of precipitation extremes are crucial for enabling reliable projections of future changes. In this study, we used a high-resolution (0.5° × 0.5°) gridded precipitation data set to analyse these events across the 11 basins in China between 1961 and 2013 mainly by the Mann–Kendall and generalized extreme value methods. Our results indicated a wetter trend in the Tarim River Basin (TRB) but drier conditions in the central Yangtze River Basin (YRB), western Pearl River Basin and eastern Yarlung Tsangpo River Basin. There was an increasing risk of flooding in the mid-lower YRB and Minjiang River Basin (MRB), and an increasing risk of drought in the central Lantsang River Basin. The results revealed strengthened maximum 1-day precipitation (RX1DAY) in the Liaohe River Basin (LB), YRB, MRB and TRB, together with strengthened maximum 5-day precipitation (RX5DAY) in the Songhua River Basin, MRB and TRB during 1987–2013. RX1DAY and RX5DAY for return periods of 20 and 50 years gradually decreased from southeast to northwest. Along the south-eastern coastline of the Mainland and on Hainan Island, the RX1DAY for the 20- and 50-year return periods would exceed 165 and 195 mm, respectively, whereas the RX5DAY would exceed 270 and 320 mm for the same return periods. Furthermore, summer Southern Oscillation Index (SOI) positively impacted precipitation extremes in the Yellow River Basin and YRB, and autumn SOI negatively impacted the extremes of the next year in LB and YRB, while winter SOI negatively impacted the extremes of the next year in YRB. Additionally, the East Asia summer monsoon (EASM) and the South Asia summer monsoon (SASM) were both associated with precipitation extremes across China; however, EASM contributed more to these events than did the SASM.

As a result of global warming, the occurrences of floods have increased in frequency and severity. Flooding often occurs near rivers and low-lying areas, which makes such areas higher-risk locations. Flood-risk evaluation represents an essential analytic step in preventing floods and reducing losses. However, the uncertainty and nonlinear relation between evaluation indices and risk levels are always difficult points in the evaluation process. Fuzzy comprehensive evaluation (FCE), an effective method for solving random, fuzzy and multi-index problems, has led to progress in understanding this relation. Thus, in this study, an assessment model based on FCE is adopted to evaluate flood risk in the Dongjiang River Basin. To correct the one-sidedness of the single weighting method, a combination weight integrating subjective weight and objective weight is adopted based on game theory. The evaluation results show that high-risk areas are mainly located in regions that include unfavorable terrain, developed industries and dense population. These high-risk areas appropriately coincide with the integrated risk zoning map and inundation areas of historical floods, proving that the evaluation model is feasible and rational. The results also can be used as references for the prevention and reduction of floods and other applications in the Dongjiang River Basin.

Anthropogenic activities have had a great impact on the characteristics of runoff and sediment load along the Pearl River in China in recent decades. We investigated the spatiotemporal variations, including the trends, abrupt changes, and periodicities of annual runoff and sediment load in the Pearl River by using the datasets from nine hydrological stations for the period of 1953&ndash, 2017. We found that annual runoff was stable during the study period, with only two stations in the upper reach showing decreasing trends. Annual sediment load has generally experienced a significant decreasing trend, while one of the stations in the middle reach showed an opposite trend due to severe rocky desertification and soil erosion in the local karst terrain. Abrupt changes in sediment load were mainly between the 1990s and 2000s, when many hydraulic projects were conducted, implying the significant impact of anthropogenic activities on river sediment load. Results also indicate 2&ndash, 4 year and 4&ndash, 8 year periodicities in both annual runoff and sediment load, with long periodicities less significant than the short ones. Our study is conducive to water and soil resource management in the Pearl River basin, whilst provides a guide for other basins, particularly those characterized by karst terrains where local desertification and soil erosion might likewise cause increase in river sediment load.