Search

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China's Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall's rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions. [ABSTRACT FROM AUTHOR]

• The extent of flooding in China is greater than in any other country. • Nearly 66% of China's landmass is submerged by flooding. • Financial toll of flooding accounts for approximately 1.42% of the annual GDP. • Various forms of climatic change contribute to flooding. The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China's landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China's flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding. [ABSTRACT FROM AUTHOR]

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020-50 would increase under all the scenarios (relative to the baseline period 1970-2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020-50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020-50. The reservoir would be likely to undergo more flooding in 2020-50. [ABSTRACT FROM AUTHOR]

In the summer of 2022, a long-lasting La Niña entered its third year. In Asia, southern China was in the grip of a historic drought while heavy rainfall ravaged Pakistan. Using a climate model forced by observed sea surface temperatures (SST) over the equatorial Pacific, we show that the back-to-back La Niña events from 2020 to 2022 are a key contributor to the global SST pattern in 2022, including the negative-phase Pacific decadal oscillation and exceptionally strong negative Indian Ocean dipole. The model reproduces the observed precipitation pattern over South and East Asia, including enhanced rainfall over Pakistan-northwest India and reduced rainfall over southern China. Additional model simulations indicate that the negative Indian Ocean dipole combined with La Niña reduces southern China rainfall by causing anomalous subsidence and anticyclonic flows. These results highlight the dominant role of long-lasting La Niña in modulating rainfall over heavily populated monsoon Asia. [ABSTRACT FROM AUTHOR]

Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM+k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteorology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE > 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance. [ABSTRACT FROM AUTHOR]

Abstract: Following construction of the Three Gorges Dam, many farmers resettled in surrounding mountain areas and cultivated marginal lands, which are mostly on steep slopes with soil of poor structure. In general, high soil loss rates occur during intense storms. Therefore, soil erosion is a major environmental problem in the Three Gorges area (TGA) of China. Understanding and quantifying sediment load is important for sustainable agriculture and the environment in this region. This paper analyzes the relationships between rainfall, runoff, and sediment transport in the Wangjiaqiao watershed. Strong seasonal and monthly variability in sediment load was found. Sediment was strongly transported during summer months, a period when frequent flood events of high magnitude and intensity occurred. Analysis of the relationships between precipitation, discharge and sediment transport at an individual event scale showed significant correlations between total precipitation, peak discharge, total water yield, maximum 30min rainfall intensity, and sediment-related variables. Stepwise multiple regression analysis revealed that rainfall amount is the major cause of runoff, while events producing a large discharge in a short time play an important role in inducing severe soil erosion. During 40 flood events, three different types of hysteretic loops were observed: clockwise (28 events, 70%), figure-eight (5 events, 12.5%), and complex (7 events, 17.5%). The results of this study confirm the complex and heterogeneous nature of sediment response in the Wangjiaqiao watershed. [Copyright &y& Elsevier]

Drought and flood are investigated in the Pearl River basin (PRB) using long-term terrestrial water storage anomaly (TWSA) data from the mascon (mass concentration) solutions based on Gravity Recovery and Climate Experiment (GRACE) satellite measurements (2002–19) and reanalysis data (1980–2019). The GRACE mascon solutions capture two major drought periods (2003–06 and 2009–12) with similar onsets and endings over the last two decades, but show considerable differences in quantifying total drought severity. The reanalysis data significantly overestimate drought duration and severity during 1980–2000 owing to overestimated negative TWSA forced by underestimated precipitation. The GRACE mascon solutions identify four major flood events in August 2002, June 2008, and July in 2006 and 2019. The flood potential is influenced by the precipitation in both the current and antecedent months. The flood potential index of the most recent flood in 2008 showed a similar spatial pattern compared to precipitation at monthly and subbasin scales. The precipitation and TWSA in the PRB are mainly influenced by El Niño–Southern Oscillation (ENSO). TWSA exhibits a lag of 1–3 months responding to ENSO during 1980–2019. This study emphasizes the significance of removing water storage changes in new large reservoirs before long-term drought and flood characterization. The inclusion of reservoir water storage would expand (shrink) the drought duration and overestimate (underestimate) drought severity for the period before (after) reservoir impoundment and overestimate flood potential for the period after reservoir impoundment. This study highlights the intensifying drought conditions in the PRB over the last four decades under the circumstances of more frequent human activities (reservoir construction and regulation) and the complex changing climate system. [ABSTRACT FROM AUTHOR]

Understanding the temporal variations of extreme floods that occur in response to climate change is essential to anticipate the trends in flood magnitude and frequency in the context of global warming. However, long-term records of paleofloods in arid regions are scarce, thus preventing a thorough understanding of such events. In this study, a reconstruction of paleofloods over the past 300 years was conducted through an analysis of grain sizes from the sediments of Kanas Lake in the Altay Mountains of northwestern China. Results showed that grain parameters and frequency distributions can be used to infer possible abrupt environmental events within the lake sedimentary sequence, and two extreme flood events corresponding to ca. 1736-1765 AD and ca. 1890 AD were further identified based on canonical discriminant analysis (CDA) and coarse percentile versus median grain size (C-M) pattern analysis, both of which occurred during warmer and wetter climate conditions by referring to tree-ring records. These two flood events are also evidenced by lake sedimentary records in the Altay and Tianshan mountains. Furthermore, through a comparison with other records, the flood event from ca. 1736-1765 AD in the study region seems to have occurred in both the arid central Asia and the Alps in Europe, and thus may have been associated with changes in the North Atlantic Oscillation (NAO) index. [ABSTRACT FROM AUTHOR]

Annual precipitation anomalies over eastern China are characterized by a north-south dipole pattern, referred to as the 'southern flooding and northern drought' pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Niño-Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric response to the Pacific SST pattern is a large-scale cyclonic or anticyclonic circulation anomaly in the lower troposphere occupying the entire northern North Pacific. During the warm phase of the Pacific SST pattern, it is cyclonic with northwesterly wind anomalies over northern China pushing the monsoon front to the south and consequently SF/ND. During the cold phase of the Pacific SST pattern, the circulation anomaly reverses with southeasterly winds over northern China allowing the monsoon front and the associated rainband to migrate northward, resulting in southern drought and northern flooding. The Atlantic SST pattern plays a supplementary role, enhancing the dipole pattern when the Pacific SST and Atlantic SST patterns are in opposite phases and weakening it when the phases are the same. [ABSTRACT FROM AUTHOR]

Floods are one of the most hazardous types of natural disaster and cause huge losses and casualties every year. A good understanding of extreme stream flows is important for identifying potential flood events and thereby achieving the goals of disaster monitoring and risk management. Remote sensing precipitation data with high spatial-temporal resolution have been shown to be a potential alternative to ground-gauged data, which is sparse or unavailable in many locations. The objective of this study is to evaluate the applicability of satellite-based precipitation data (TRMM 3B42V7) in driving the HEC-HMS hydrological model for flood monitoring in humid Xiangjiang River Basin in China. The results indicate that the TRMM precipitation data can be applied to identify flood events with hydrological model despite biases in the time and magnitude of flood peaks compared to those derived from historical records. In addition, the hydrological model is shown to have smoothing effects on the propagation of biases or errors in the TRMM precipitation data in the hydrological simulations. However, for a few extreme storm events, the data produced relatively large overestimations in precipitation volume and biases in the precipitation time, which caused overestimations in the streamflow simulations and deviation in the peak time, and should be regarded with caution. [ABSTRACT FROM AUTHOR]

Future changes in floods and water availability across China under representative concentration pathway 2.6 (RCP2.6) and RCP8.5 are studied by analyzing discharge simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) with the consideration of uncertainties among global climate models (GCMs) and hydrologic models. Floods and water availability derived from ISI-MIP simulations are compared against observations. The uncertainties among models are quantified by model agreement. Only model agreement >50% is considered to generate reliable projections of floods and water availability and their relationships with climate change. The results show five major points. First, ISI-MIP simulations have acceptable ability in modeling floods and water availability. The spatial patterns of changes in floods and water availability highly depend on the outputs of GCMs. Uncertainties from GCMs/hydrologic models predominate the uncertainties in the wet/dry areas in eastern/northwestern China. Second, the magnitudes of floods throughout China increase during 2070-99 under RCP8.5 relative to those with the same return periods during 1971-2000. The increase rates of larger floods are higher than those of the smaller ones. Third, water availability decreases/increases in southern/northern China under RCP8.5, but changes negligibly under RCP2.6. Fourth, more severe floods in the future are driven by more intense precipitation extremes over China. The negligible change in mean precipitation and the increase in actual evapotranspiration reduce the water availability in southern China. Fifth, model agreements are higher in simulated floods than water availability because increasing precipitation extremes are more consistent among different GCM outputs compared to mean precipitation. [ABSTRACT FROM AUTHOR]

Climate change is expected to have large impacts on peak flows. However, there may be bias in the simulation of peak flows by hydrological models. This study aims to improve the simulation of peak flows under climate change in Lanjiang catchment, east China, by comparing two approaches: postprocessing of peak flows and composite objective calibration. Two hydrological models [Soil and Water Assessment Tool (SWAT) and modèle du Génie Rural à 4 paramètres Journalier (GR4J)] are employed to simulate the daily flows, and the peaks-over-threshold method is used to extract peak flows from the simulated daily flows. Three postprocessing methods, namely, the quantile mapping method and two generalized linear models, are set up to correct the biases in the simulated raw peak flows. A composite objective calibration of the GR4J model by taking the peak flows into account in the calibration process is also carried out. The regional climate model Providing Regional Climates for Impacts Studies (PRECIS) with boundary forcing from two GCMs (HadCM3 and ECHAM5) under greenhouse gas emission scenario A1B is applied to produce the climate data for the baseline period and the future period 2011-40. The results show that the postprocessing methods, particularly quantile mapping method, can correct the biases in the raw peak flows effectively. The composite objective calibration also resulted in a good simulation performance of peak flows. The final estimated peak flows in the future period show an obvious increase compared with those in the baseline period, indicating there will probably be more frequent floods in Lanjiang catchment in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT The spatial scale effect on sediment concentration in runoff has received little attention despite numerous studies on sediment yield or sediment delivery ratio in the context of multiple spatial scales. We have addressed this issue for hilly areas of the Loess Plateau, north China where fluvial processes are mainly dominated by hyperconcentrated flows. The data on 717 flow events observed at 17 gauging stations and two runoff experimental plots, all located in the 3906 km2 Dalihe watershed, are presented. The combination of the downstream scour of hyperconcentrated flows and the downstream dilution, which is mainly caused by the base flow and is strengthened as a result of the strong patchy storms, determines the spatial change of sediment concentration in runoff during flood events. At the watershed scale, the scouring effect takes predominance first but is subordinate to the downstream dilution with a further increase in spatial scale. As a result, the event mean sediment concentration first increases following a power function with drainage basin area and then declines at the drainage basin area of about 700 km2. The power function in combination with the proportional model of the runoff-sediment yield relationship we proposed before was used to establish the sediment-yield model, which is neither the physical-based model nor the regression model. This model, with only two variables (runoff depth and drainage basin area) and two parameters, can provide fairly accurate prediction of event sediment yield with model efficiency over 0·95 if small events with runoff depth lower than 1 mm are excluded. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Using hourly station rain gauge data during 1966–2005, the authors studied changes in the characteristics of the late-summer (July–August) rainfall, which has exhibited a so-called southern flooding and northern drought (SFND) pattern over eastern China in recent decades. Although the rainfall amount and frequency have significantly increased (decreased) in the mid–lower reaches of the Yangtze River valley (North China) during this period, the rainfall intensity has decreased (increased). This finding differs from previous results based on daily data, which showed that the rainfall intensity has increased in the mid–lower reaches of the Yangtze River valley. In this region, the mean rainfall hours on rainy days have increased because of the prolonged rainfall duration, which has led to an increased daily rainfall amount and to a decreased hourly rainfall intensity. Results also show that the SFND pattern is mostly attributed to changes in precipitation with moderate and low intensity (≤10 mm h−1), which contributes 65% (96%) of rainfall amount to the “flooding” (“drought”) in the mid–lower reaches of the Yangtze River valley. Neither frequency nor amount of strong intensity (>20 mm h−1) rainfall exhibits the SFND pattern. [ABSTRACT FROM AUTHOR]

The Poyang Lake, the largest freshwater lake in China, is famous for its ecological and economic importance as well as frequent flood characteristics. In this study, multiple satellite remote sensing observations (e.g., GRACE, MODIS, Altimetry, and TRMM), hydrological models, and in situ data are used to characterize the flood phenomena over the Poyang Lake basin between 2003 and 2016. To improve the accuracy of the terrestrial water storage (TWS) estimates over the Poyang Lake basin, a modified forward-modeling method is introduced in the GRACE processing. The method is evaluated using the contaminated noise onboard observations for the first time. The results in both spectral and spatial domains infer a good performance of the method on the suppression of high-frequency noise while reducing the signal loss. After applying forward-modeling method, the TWS derived from the GRACE spherical harmonic coefficients presents a comparable performance with the solution derived from the newly released CSR Release05 mascon product over the Poyang Lake basin. The flood events in 2010 and 2016 are identified from the positive anomalies in non-seasonal TWSs derived by GRACE and hydrological models. The flood signatures also coincide with the largest inundated areas estimated from MODIS data, and the observed areas in 2010 and 2016 are 3370.3 km2 (30% higher than the long-term mean) and 3445.0 km2 (33% higher), respectively. The water levels in the Hukou station exceed the warning water level for 25 days in 2010 and 28 days in 2016. These continuous warning-exceeded water levels also imply the severe flood events, which are primarily driven by the local plenteous precipitation in the rainy season (1528 mm in 2010, 1522 mm in 2016). [ABSTRACT FROM AUTHOR]