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 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
•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.
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
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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.
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.
The Yangtze River, the largest river in China, has been facing major challenges in massive flooding and eco-environmental health over the past decades. Sustainable socioeconomic development in the ...Yangtze River Basin depends on water and ecosystem security. This overview addresses eco-water security under the changing environment of the Yangtze River Basin. Looking forward to a healthy Yangtze River in the future, there are still uncertainties regarding how to assess and wisely manage the Yangtze River through a systematic, integrated approach applied to multiple dimensions, water, biodiversity, ecological services, and resilience, for the sustainable development of ecosystems and human beings. The Yangtze Simulator, an integrated river basin model powered by artificial intelligence and interdisciplinary science, is introduced and discussed, and it will serve as a robust tool for good governance of the Yangtze River Basin.
The modeling of changes in surface water and groundwater in the areas of inter-basin water diversion projects is quite difficult because surface water and groundwater models are run separately most ...of the time and the lack of sufficient data limits the application of complex surface-water/groundwater coupling models based on physical laws, especially for developing countries. In this study, a distributed surface-water and groundwater coupling model, named the distributed time variant gain model–groundwater model (DTVGM-GWM), was used to assess the influence of climate change and inter-basin water diversion on a watershed hydrological cycle. The DTVGM-GWM model can reflect the interaction processes of surface water and groundwater at basin scale. The model was applied to the Haihe River Basin (HRB) in eastern China. The possible influences of climate change and the South-to-North Water Diversion Project (SNWDP) on surface water and groundwater in the HRB were analyzed under various scenarios. The results showed that the newly constructed model DTVGM-GWM can reasonably simulate the surface and river runoff, and describe the spatiotemporal distribution characteristics of groundwater level, groundwater storage and phreatic recharge. The prediction results under different scenarios showed a decline in annual groundwater exploitation and also runoff in the HRB, while an increase of groundwater storage and groundwater level after the SNWDP’s operation. Additionally, as the project also addresses future scenarios, a slight increase is predicted in the actual evapotranspiration, soil water content and phreatic recharge. This study provides valuable insights for developing sustainable groundwater management options for the HRB.
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