Drought is one of the most detrimental natural hazards in Yellow River Basin (YRB). In this research, spatio-temporal variation and statistical characteristic of drought in YRB is studied by using ...dry spell. Two extreme series, including annual maximum series (AMS) and partial duration series (PDS), are used and simulated with generalized extreme value (GEV), generalized Pareto (GP), and Pearson type III (PE3) distributions. The results show that the northern part is drier than the southern part of YRB. Besides, the maximum dry spell usually starts in October, November, and December. According to the trend analysis, mean maximum length of dry spell (MxDS) shows a negative trend in most stations. From the L-moments and Kolmogorov–Smirnov test method, it can be found that GEV model can better fit AMS while GP and PE3 can better fit PDS. Moreover, the quantiles from optimal model of AMS and PDS depict a similar distribution with values increases from south to north. The spatial distribution of scale and location parameters of GEV model for AMS shows a south-to-north gradient, while the distribution of shape parameter is a little irregularity. Furthermore, based on the linear correlation analysis, there is an evident linear relation between location and scale parameters with mean and standard variation of MxDS, respectively.
Droughts represent the most complex and damaging type of natural disaster, and they have taken place with increased frequency in China in recent years. Values of the standardized precipitation ...evapotranspiration index (SPEI) calculated using station-based meteorological data collected from 1961 to 2013 in the middle and lower reaches of the Yangtze River Basin (MLRYRB) are used to monitor droughts. In addition, the SPEI is determined for different timescales (1, 3, 6, and 12 months) to characterize dry or wet conditions in this study area. Moreover, remote sensing methods can cover large areas, and multispectral and temporal observations are provided by satellite sensors. The temperature vegetation dryness index (TVDI) is selected to permit assessment of drought conditions. In addition, the correlation between the SPEI and TVDI values is calculated. The results show that the SPEI values over different timescales reflect complex variations in drought conditions and have been well applied in the MLRYRB. Droughts occurred on an annual basis in 1963, 1966, 1971, 1978, 1979, 1986, 2001, 2011, and 2013, particularly 2011. In addition, the regional average drought frequency in the study area during 1961–2013 is 30%, as determined using the SPEI. An analysis of the correlation between the monthly values of the TVDI and the SPEI-3 shows that a negative relationship exists between the SPEI-3 and the TVDI. That is, smaller TVDI values are associated with greater SPEI-3 values and reduced drought conditions, whereas larger TVDI values are associated with smaller SPEI-3 values and enhanced drought conditions. Therefore, this study of the relationship between the SPEI and the TVDI can provide a basis for government to mitigate the effects of drought.
Under the current condition of climate change, droughts and floods occur more frequently, and events in which flooding occurs after a prolonged drought or a drought occurs after an extreme flood may ...have a more severe impact on natural systems and human lives. This challenges the traditional approach wherein droughts and floods are considered separately, which may largely underestimate the risk of the disasters. In our study, the sudden alternation of droughts and flood events (ADFEs) between adjacent seasons is studied using the multivariate L-moments theory and the bivariate copula functions in the Huai River Basin (HRB) of China with monthly streamflow data at 32 hydrological stations from 1956 to 2012. The dry and wet conditions are characterized by the standardized streamflow index (SSI) at a 3-month time scale. The results show that: (1) The summer streamflow makes the largest contribution to the annual streamflow, followed by the autumn streamflow and spring streamflow. (2) The entire study area can be divided into five homogeneous sub-regions using the multivariate regional homogeneity test. The generalized logistic distribution (GLO) and log-normal distribution (LN3) are acceptable to be the optimal marginal distributions under most conditions, and the Frank copula is more appropriate for spring-summer and summer-autumn SSI series. Continuous flood events dominate at most sites both in spring-summer and summer-autumn (with an average frequency of 13.78% and 17.06%, respectively), while continuous drought events come second (with an average frequency of 11.27% and 13.79%, respectively). Moreover, seasonal ADFEs most probably occurred near the mainstream of HRB, and drought and flood events are more likely to occur in summer-autumn than in spring-summer.
Observations show that metropolitan areas throughout China, which are experiencing rapid urbanization, may have enhanced extreme precipitation. However, the underlying urban‐induced mechanism is ...poorly understood, particularly for entire characteristics of extreme precipitation. Focusing on the Beijing metropolitan area, we investigate regional patterns of extreme precipitation characterized by magnitude, frequency, duration, and timing metrics according to daily observations from 1975 to 2015 at 20 weather stations. Urbanization effects are explored by physical metrics of urbanization, including area, complexity, fragmentation, and dominance deduced from five periods of land use maps. Results show that the magnitudes and frequencies of extreme precipitation have decreased over time, the consecutive precipitation days have been extended, and the Julian date of maximum precipitation has been delayed. Temporal trends at ~40% of weather stations are significant. According to precipitation metrics and geographical features, three representative regions are identified: the central urban region, the windward slope of topographic area, and the mountainous region. Compared with the metrics in mountainous region, the magnitudes of windward slope and central urban region are 24.3–60.6% and 5.9–47.3% greater, respectively; the frequencies are increased by 1.17 and 1.10 days, respectively; and the average date of maximum precipitation values are delayed by 7.0 and 4.0 days, respectively. The magnitudes and frequencies are enhanced by expansion of urban area, complexity, fragmentation, dominance, and heat islands. The durations are positive for urban area and dominance but negative for urban complexity, fragmentation, and heat islands. Furthermore, the effects in central urban region are more significant due to high urbanization rates.
Key Points
Extreme precipitation metrics in central urban region and windward slope of topographic area are greater than those in mountainous region
Magnitude and frequency of extreme precipitation are enhanced by expansions of urban area, irregularity, patch, dominance, and heat islands
Duration of extreme precipitation is positive for the urban area and dominance but negative for irregularity, patch, and heat islands
The integration of green‐gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. ...However, a major challenge lies in balancing the substantial investments of these infrastructure projects with their environmental benefits, such as reduced flooding volume and lower peak flow. Model predictive control (MPC), a dynamic and intelligent control approach, optimizes these environmental benefits but is underutilized in the system design phase for cost‐effectiveness analysis. This study introduces a multi‐scenario model framework that incorporates MPC and other control approaches into stormwater system designs, including the implementation of controlled storage tanks and green infrastructures. This framework provides comprehensive modeling tools for practitioners to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, ultimately identifying solutions that are both environmentally and economically viable. A case study conducted in a small urban catchment area in Shenzhen City, China, demonstrates the effectiveness of this framework. The results indicate that MPC outperforms other control scenarios, particularly under heavy or extreme rainfall conditions. Notably, MPC not only provides superior environmental benefits but also yields considerable cost savings, ranging from 1,787 to 9,371 USD per hectare compared to static control, equating to a 5% reduction in cost relative to rule‐based control. Such findings suggest that integrating MPC is a cost‐effective alternative to extensive infrastructure expansion for flood management, which significantly enhances the benefit contribution of controlled infrastructures without substantial additional expenses.
Plain Language Summary
Implementing advanced control methods for green‐gray infrastructures is a new method to reduce urban flooding. However, constructing and updating these infrastructures can be very expensive, which is a significant challenge for many urban areas. Our research explores how to use a smart control approach, specifically the model predictive control (MPC), to enhance environmental benefits and save money in the system design phase. We present a multi‐scenario model framework that combines MPC and other methods into the design of stormwater systems, which include controlled storage tanks and green infrastructures. This framework can be used to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, and to identify the solutions that are both environmentally and economically viable. We conducted a case study in Shenzhen City, China, to test our framework. The results show that MPC is effective particularly during heavy or extreme rainfalls, offering higher environmental benefits and cost savings compared to the scenarios without MPC. Integrating MPC is more cost‐effective than expanding infrastructures for flood management as it notably increases the benefit contribution of controlled infrastructures at a modest cost.
Key Points
A framework is proposed to assess the environmental and economic impacts of integrating model predictive control (MPC) with stormwater infrastructure designs
Assessments are conducted in a small urban catchment involving heavy rainfall events
The MPC yields higher environmental benefits and saves economic costs compared to other control approaches
Long records (1960–2013) of monthly streamflow observations from 8 hydrological stations in the East Asian monsoon region are modeled using a nonstationarity framework by means of the Generalized ...Additive Models in Location, Scale and Shape (GAMLSS). Modeling analyses are used to characterize nonstationarity of monthly streamflow series in different geographic regions and to select optimal distribution among five two-parameter distributions (Gamma, Lognormal, Gumbel, Weibull and Logistic). Based on the optimal nonstationarity distribution, a time-dependent Standardized Streamflow Index (denoted SSIvar) that takes account of the possible nonstationarity in streamflow series is constructed and then employed to identify drought characteristics at different time scales (at a 3-month scale and a 12-month scale) in the eight selected catchments during 1960–2013 for comparison. Results of GAMLSS models indicate that they are able to represent the magnitude and spread in the monthly streamflow series with distribution parameters that are a linear function of time. For 8 hydrological stations in different geographic regions, a noticeable difference is observed between the historical drought assessment of Standardized Streamflow Index (SSI) and SSIvar, indicating that the nonstationarity could not be ignored in the hydrological drought analyses, especially for stations with change point and significant change trends. The constructed SSIvar is, to some extent, found to be more reliable and suitable for regional drought monitoring than traditional SSI in a changing environment, thereby providing a feasible alternative for drought forecasting and water resource management at different time scales.
Our research analyzes the regional changes of extreme dry spell, represented by the annual maximum dry spell length (noted as AMDSL) during the rainy season in the Wei River Basin (WRB) of China for ...1960–2014 using the L-moments method. The mean AMDSL values increase from the west to the east of the WRB, suggesting a high dry risk in the east compared to the west in the WRB. To investigate the regional frequency more reasonably, the WRB is clustered into four homogenous subregions via the K-means method and some subjective adjustments. The goodness-of-fit test shows that the GEV, PE3, and GLO distribution can be accepted as the “best-fit” model for subregions 1 and 4, subregion 2, and subregion 3, respectively. The quantiles of AMDSL under various return levels figure out a similar spatial distribution with mean AMDSL. We also find that the dry risk in subregion 2 and subregion 4 might be higher than that in subregion 1. The relationship between ENSO events and extreme dry spell events in the rainy season with cross wavelet analysis method proves that ENSO events play a critical role in triggering extreme dry events during rainy season in the WRB.
Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study ...selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRI
var
) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRI
var
is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions.
•The joint distribution of extreme precipitation and extreme flood is established.•The extreme values of future AM30D are larger and show the “bimodal” distribution.•The changes of future extreme ...precipitation will cause higher flood risk.
Climate change intensifies hydrological cycle, bringing critical challenges to the global and regional socio-economic development. Here we used four global climate models to project future precipitation that drives hydrological modeling of future 30-year daily runoff in a large international river basin, the Lancang-Mekong River Basin (LMRB). We applied four probability distribution functions to fit the flood peak and maximum 3-day flood volume series at four major hydrologic stations. The copula function was used to establish the joint distribution between extreme precipitation and flood peak to estimate the impact of precipitation change on floods. Results show that annual maximum 30-day precipitation (AM30D) and flood peak at Pakse station are highly correlated. Future basin-averaged AM30D is projected to increase under most climate scenarios, although AM30D varies widely in spatial distribution. The extreme values of AM30D (e.g., AM1D and AM3D) in the future (2021–2050) are larger than those in historical period (1981–2004). The temporal distribution of future AM30D is more uneven, showing the “bimodal” distribution. For the Yun Jinghong station, large uncertainty is estimated in change of direction of annual maximum flood peak and maximum 3-day flood volume. As for the other three stations, they are all projected to have larger flood risks in spite of different magnitudes. The larger the return period, the larger the increase, the greater the impact of climate change. The change of basin-averaged AM30D precipitation in the future will lead to the increase of flood peak at Pakse station via the bivariate frequency analysis. From the perspective of water conservancy project safety, considering the “adverse principle”, flood control design according to the design results derived from the joint distribution of two variables between AM30D and flood peak can reduce the risk of flood disaster. This study can provide scientific reference and basis for flood control and disaster reduction and water resources cooperation policy development in LMRB.
This study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized ...precipitation–evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998–2015 compared with those during 1980–97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the three-dimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity–area–duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEI-detected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km² and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km² and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.
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