Reference evapotranspiration (ET0) is important for agricultural, environmental and other studies, and understanding the attribution of its change is helpful to provide information for irrigation ...scheduling and water resources management. The present study investigates the attribution of the change of ET0 at 49 meteorological stations in the middle reaches of Yellow River basin (MRYRB) of China from 1960 to 2012. Results show that annual ET0 increases from the northwest to the southeast of MRYRB in space. We find that annual ET0 clearly presents a zigzag change pattern rather than a monotonically change during the whole period. The detected three breakpoints at 1972, 1988 and 1997 divide the whole period into four subperiods. The sensitivity analysis indicates that the ET0 is the most sensitive to surface solar radiation (Rs), followed by relative humidity (RH) and mean air temperature (T), and the least sensitive to wind speed (u) in our study area. Furthermore, we find that ET0 is becoming less sensitive to RH and more sensitive to T during 1960–2012. The attributions of the change in ET0 vary largely at different regions and subperiods. The declined wind speed is the dominant factor, followed by Rs to the ET0 reduction during 1960–2012. Further analysis shows that Rs and u are the two major contributing factors that control the change of ET0 at most stations and during most subperiods. Our study confirms that the change of ET0 is influenced by the complex interactions of climatic factors, and the dominant factor to the change of ET0 is different in various regions and time periods. The results presented here can provide a reference for agricultural production and water resources management in MRYRB as well as other semi-arid and semi-humid regions.
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•The annual ET0 series exhibits a zigzag change pattern rather than a monotonically change.•The ET0 is the most sensitive to Rs, and least sensitive to wind speed during 1960 to 2012.•The decline in Rs and u is the two major driver to the decreased ET0 during the whole period.
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
•The values of αc in the generalized nonlinear complementary relationship have shown both temporal and spatial variability.•The change characteristics of αc is closely correlated to aridity index ...(AI) and average NDVI during growing season in the Loess Plateau.•Climate change and revegetation result in evapotranspiration increase in the Loess Plateau.
Evapotranspiration (ET) is a key component in terrestrial climate and vegetation interactions. A generalized complementary relationship proposed by Brutsaert (2015); Brutsaert et al. (2020) has been shown to be a powerful tool for ET estimation. As the single parameter in the generalized complementary relationship, it is generally accepted that αc has high spatial variability and is closely correlated to the land-surface conditions. However, understanding of the temporal variability in αc is also important in a changing environment. In this study, we investigated the impacts of climate change and revegetation on ET by establishing the relationship between αc and the climatic-vegetation factors in 14 catchments of the Loess Plateau. The results showed that αc presented both spatial and temporal variability, where the aridity index (AI) and the average Normalized Difference Vegetation Index (NDVI) during the growing season are two dominant factors that control the variability in αc. Furthermore, combing the generalized complementary relationship and the empirical model of αc proposed in this study, the impacts of climate change and revegetation on the ET increase were quantified in the Loess Plateau. The results show that climate change (mainly expressed by an increase in precipitation) contributed the most to the ET increase (approximately 68% on average), whereas revegetation (quantified by the NDVI increase) also played a dominant role (approximately 32% on average) in the ET increase, which suggests that revegetation planning management should pay more attention to the increased water consumption by evapotranspiration in the sustainable economic and ecological development of the Loess Plateau.
Rivers play an important role in water supply, irrigation, navigation, and ecological maintenance. Forecasting the river hydrodynamic changes is critical for flood management under climate change and ...intensified human activities. However, efficient and accurate river modeling is challenging, especially with complex lake boundary conditions and uncontrolled downstream boundary conditions. Here, we proposed a coupled framework by taking the advantages of interpretability of physical hydrodynamic modeling and the adaptability of machine learning. Specifically, we coupled the Gated Recurrent Unit (GRU) with a 1‐D HydroDynamic model (GRU‐HD) and applied it to the middle and lower reaches of the Yangtze River, the longest river in China. We show that the GRU‐HD model could quickly and accurately simulate the water levels, streamflow, and water exchange rates between the Yangtze River and two important lakes (Poyang and Dongting), with most of the Kling‐Gupta efficiency coefficient (KGE $\mathrm{K}\mathrm{G}\mathrm{E}$) above 0.90. Using machine learning‐based predicted water levels, instead of the rating curve approach, as the downstream boundary conditions could improve the accuracy of modeling the downstream water levels of the lake‐connected river system. The GRU‐HD model is dedicated to the synergy of physical modeling and machine learning, providing a powerful avenue for modeling rivers with complex boundary conditions.
Key Points
Coupling machine learning into physical hydrodynamics for river modeling with complex boundary conditions
Streamflow and water levels were modeled with Kling‐Gupta efficiency coefficient above 0.90 at most hydrologic stations and 38 times faster than traditional 1‐D/2‐D coupled models
The proposed machine learning‐based downstream boundary condition showed better performance than the rating curve method
Global and regional flood risk is increasing, especially in Eurasia, threatening public security and socioeconomic development. However, the duration, intensity and spatial extent of floods in the ...past few decades have not been robustly explored, largely because previous identification methods ignored the dual nature of floods in space and time. Here we presented a first comprehensive diagnosis of flood events in Eurasia by developing a novel three‐dimensional Image‐CONnectivity based FLOOD identification (ICON‐FLOOD) approach. By the ICON‐FLOOD approach, we revealed a four‐phase quasi‐interdecadal pattern and significant intensification of flood events in the last 12 years of the study period (1979–2012), contrary to the declining trend derived from two well‐known global data sets. Further analysis indicated that the flood characteristics had switched from “large impacted area but short duration, low intensity” to “high intensity, long duration but small impacted area.” Exploring the spatiotemporal evolution of flood events by ICON‐FLOOD will provide a basis for flood risk management and flood adaptation strategy development.
Plain Language Summary
The occurrence, development and end of flood events have obvious three‐dimensional characteristics in time (first dimension) and space (longitude and latitude as the second and third dimensions). However, previous dimensionality reduction methods destroyed the overall spatiotemporal structure of flood events and failed to effectively reveal the process and underlying mechanisms of the development and evolution of flood events. The ICON‐FLOOD method proposed in this study can effectively and completely present the three‐dimensional structure of torrential rain and flood events. Also, recent intensification of torrential rain and flood events suggests that climate‐driven changes are already happening and supports calls for the consideration of climate change in flood risk management. Scientifically identifying regional torrential rain and flood events will improve our understanding of where floods are changing and how best to adapt.
Key Points
Based on the three‐dimensional perspective, the flood events in the past few decades in Eurasia were comprehensively diagnosed
The study showed that the recent floods had increased significantly, contrary to the declining trend derived from two global data sets
The flood characteristics had switched to greater concentration of flood impacted areas and severer flood extremes in recent past
•Traditional magnitude-oriented calibration is hard to capture entire flow regimes.•Multi-metric calibration of hydrological model is performed by improved SCE-UA.•Flow metrics of magnitude, ...frequency, duration and rating are better predicted.
Flow regimes (e.g., magnitude, frequency, variation, duration, timing and rating of change) play a critical role in water supply and flood control, environmental processes, as well as biodiversity and life history patterns in the aquatic ecosystem. The traditional flow magnitude-oriented calibration of hydrological model was usually inadequate to well capture all the characteristics of observed flow regimes. In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm. Two headwater watersheds in the arid Hexi Corridor were selected for the case study. Sixteen metrics were selected as optimization objectives, which could represent the major characteristics of flow regimes. Model performance was compared with that of the single objective calibration. Results showed that most metrics were better simulated by the multi-objective approach than those of the single objective calibration, especially the low and high flow magnitudes, frequency and variation, duration, maximum flow timing and rating. However, the model performance of middle flow magnitude was not significantly improved because this metric was usually well captured by single objective calibration. The timing of minimum flow was poorly predicted by both the multi-metric and single calibrations due to the uncertainties in model structure and input data. The sensitive parameter values of the hydrological model changed remarkably and the simulated hydrological processes by the multi-metric calibration became more reliable, because more flow characteristics were considered. The study is expected to provide more detailed flow information by hydrological simulation for the integrated water resources management, and to improve the simulation performances of overall flow regimes.
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•Best management practices (BMPs) reduction effects have stochastic characteristics.•The effects of different pollutants and BMPs are correlated.•Coupling SWAT with Vine Copula model ...can assess multivariate effect of BMPs.•Joint probabilities of designed BMPs scenarios are below separate probabilities.
Best management practices (BMPs) have wide application in non-point source (NPS) pollution abatement in agricultural watersheds. Multivariate analysis of BMPs reduction effects taking their randomness and correlations into account is significant to spatial optimization of BMPs configuration. However, quantifying the correlations among high-dimensional random variables of BMPs effects is challenging and remains unexplored thoroughly. This study coupled the SWAT with the Vine Copula model to conduct multivariate analysis of BMPs reduction effects considering their randomness caused by hydro-meteorological variability along with correlations among different indicators (ammonium nitrogen, NH3-N; and total phosphorus, TP) and BMPs. The coupled model was applied to evaluate the multi-indicator effect of individual BMP and combined effect of various BMPs in the upper Boyang River basin, China. Results showed that bivariate copulas and three-dimensional vine copulas can efficaciously describe the dependence of BMPs effects. Simulation results indicate 43–100% probabilities of 45% NH3-N loads reduction, while 0–79% probabilities of 45% TP loads reduction for combined BMPs scenarios. Besides, the joint probabilities of different indicators in combined BMPs scenarios are generally lower than separate probabilities with 0–21% decrease, which is similar to individual BMP. Generally, joint probabilities using copulas can provide more accurate and factual knowledge of the risk and dependability of implementation of BMPs than univariate variables. The proposed model can conduct multivariate analysis of BMPs reduction effects and has great prospect in the future risk-based decision-making of NPS pollution management.
Compound drought and heatwave (CDHW) events have received considerable attention in recent years due to their devastating effects on human society and ecosystem. In this study, we systematically ...investigated the changes of CDHW events in multi‐spatiotemporal scales for historical period (1951–2014) and four future scenarios (2020–2100) (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) over global land by using Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The responses of the CDHW events to the changes of maximum air temperature and the climatic water balance variable are also examined. The results show that the multi‐model ensembles project a significant increasing trend in CDHW characteristics over almost all global lands under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, especially across northern North‐America, Caribbean, Mediterranean and Russian‐Arctic, there is a stronger increasing trend. A significantly increasing CDHW risk will occur across most global land for the medium to long term future without aggressive adaptation and mitigation strategies. The results of path analysis suggest that temperature is the dominant factor influencing CDHW events. Additionally, higher sensitivity of CDHW events to global warming will occur in the future. Particularly, each 1°C global warming increases the duration of the CDHW events by 3 days in the historical period, but by about 10 days in the future period. Overall, this study improves our understanding in the projection of CDHW events and the impacts of climate drivers to the CDHW events under various future scenarios, which could provide supports about the risk assessment, adaptation and mitigation strategies under climate change.
Plain Language Summary
Compound drought and heatwave (CDHW) events (co‐occurring hot and dry extremes) always cause severe damages to human society and natural system, often beyond separate impacts from heatwaves and droughts. Understanding the changes of CDHW events under global warming can help to manage the risks of associated disasters and advance climate change adaptation. Therefore, we systematically investigated the future changes of CDHW events (characterized by duration, severity, and magnitude) and the relationship between CDHW characteristics and the relevant climate factors in multi‐spatiotemporal scales using the state‐of‐the‐art climate simulations. Here we show that future will witness a strong increase in CDHW events. A significantly increasing CDHW risk will occur across most global land for the medium to long term future without aggressive adaptation and mitigation strategies. We further find global temperature rise is the main reason for the future increase in CDHW events. In addition, compared with the historical period, higher sensitivity of CDHW events to global warming over most global land will occur in the future. These tell us that measures to limit the temperature increase are urgently needed to survive and thrive.
Key Points
There is a significantly increasing trend for compound drought and heatwave characteristics over almost global land in the future
The increasing temperature dominates the increase of compound drought and heatwave events
Future will witness higher sensitivity of compound drought and heatwave events to global warming over almost all global land
Lake water level is an essential indicator of environmental changes caused by natural and human factors. The water level of Poyang Lake, the largest freshwater lake in China, has exhibited a dramatic ...variation for the past few years, especially after the completion of the Three Gorges Dam (TGD). However, there is a lack of more accurate assessment of the effect of the TGD on the Poyang Lake water level (PLWL) at finer temporal scales (e.g., the daily scale). Here, we used three machine learning models, namely, an Artificial Neural Network (ANN), a Nonlinear Autoregressive model with eXogenous input (NARX), and a Gated Recurrent Unit (GRU), to simulate the daily lake level during 2003–2016. We found that machine learning models with historical memory (i.e., the GRU model) are more suitable for simulating the PLWL under the influence of the TGD. The GRU-based results show that the lake level is significantly affected by the TGD regulation in the different operation stages and in different periods. Although the TGD has had a slight but not very significant impact on the yearly decline of the PLWL, the blocking or releasing of water at the TGD at certain moments has caused large changes in the lake level. This machine-learning-based study sheds light on the interactions between Poyang Lake and the Yangtze River regulated by the TGD.
Abstract
Drought as a hazardous natural disaster has been widely studied based on various drought indices. However, the characteristics of droughts have not been robustly explored considering its ...dual nature in space and time across China in the past few decades. Here, we characterized meteorological drought events from a three-dimensional perspective for the 1961–2018 period in the mainland of China, and attributed the variation of drought intensity to its influencing factors. We further assessed associated drought risk with socioeconomic data for the 2002–2018 period. We found that drought events with high intensity, large area, and long duration are mainly distributed in western and northern China, especially in Inner Mongolia, Xinjiang, Tibet, and Qinghai. The drought intensity and affected area anomalies present a six-phase pattern of ‘negative-positive-negative-positive-negative-positive’ during 1961–2018. The intensity of drought events showed a decreasing trend but the affected area and duration showed an increasing trend in 2009–2018. Over the decades, the centers of high drought intensity and long duration tend to move eastward and northeastward, respectively. The PET variations contributes larger than precipitation variations to drought intensity variations in the arid regions while being opposite in the humid southern regions. Drought risk assessment further indicates that high drought risk areas are concentrated in northern China, including Inner Mongolia, Xinjiang, Gansu, Sichuan, Hebei, and Heilongjiang. Increasing trends in drought risk for the 2002–2018 period are detected in Inner Mongolia, Xinjiang, Sichuan, Henan, Gansu, Hunan, Shanxi, Qinghai. Our findings provide scientific guidance for policymakers to develop adaptive disaster prevention measures.