Changes in precipitation extremes in the Yangtze River Basin during 1960-2019 and the association with global warming, ENSO, and local effects

Associated impact mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze river basin based on ocean-atmosphere anomaly patterns

Heavy rainfall and flooding disasters are increasing due to global warming. A clear understanding of the mechanism of heavy rain and floods is the basic premise of disaster risk management. However, most previous studies emphasized more on the single anomalous signal from the average state in the whole season, which may neglect the combined influence of multiple signals in the ocean-atmosphere and differential characteristics of anomalous signals at different periods. Here, our study aimed to reveal the possible influence mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze River Basin (MLRYRB) by systematically analyzing the monthly-scale and daily-scale ocean-atmosphere anomaly patterns in the preceding periods of heavy rainfall and flooding events. The results showed that heavy rainfall and flooding events were highly likely to occur in the region one month after El Niño decayed, with the flooding intensity in June having the negative correlation with the sea ice concentration anomaly in the Arctic with a lag of about 5 months (150 days). Besides, North Atlantic Oscillation, Western Pacific subtropical high, blocking, East Asian subtropical westerly jet, and the water vapor fluxes from the Arabian Sea and western Pacific Ocean could be used as the anomalous signals inducing heavy rain and floods. The daily-scale conceptual model inducing heavy rainfall and flooding events was built based on the patterns of all anomalous signals, which detailed the possible impact mechanism of heavy rain and floods in the region. By making targeted forecasts of anomalous signals and using this information in water resources planning and management based on climate mechanisms, it will have a significant impact on water management in the country.

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

How nutrient retention and TN:TP ratios depend on ecosystem state in thousands of Chinese lakes

Worldwide, anthropogenic activities threaten surface water quality by aggravating eutrophication and increasing total nitrogen to total phosphorus (TN:TP) ratios. In hydrologically connected systems, water quality management may benefit from in-ecosystem nutrient retention by preventing nutrient transport to downstream systems. However, nutrient retention may also alter TN:TP ratios with unforeseen consequences for downstream water quality. Here, we aim to increase understanding of how nutrient retention may influence nutrient transport to downstream systems to improve long-term water quality management. We analyzed lake ecosystem state, in-lake nutrient retention, and nutrient transport (ratios) for 3482 Chinese lakes using the lake process-based ecosystem model PCLake+. We compared a low climate change and sustainability-, and a high climate change and economy-focused scenario for 2050 against 2012. In both scenarios, the effect of nutrient input reduction outweighs that of temperature rise, resulting in more lakes with good ecological water quality (i.e., macrophyte-dominated) than in 2012. Generally, the sustainability-focused scenario shows a more promising future for water quality than the economy-focused scenario. Nevertheless, most lakes remain phytoplankton-dominated. The shift to more macrophyte-dominated lakes in 2050 is accompanied by higher nutrient retention fractions and less nutrient transport to downstream waterbodies. In-lake nutrient retention also alters the water's TN:TP ratio, depending on the inflow TN:TP ratio and the ecosystem state. In 2050 higher TN:TP ratios are expected in the outflows of lakes than in 2012, especially for the sustainability-focused scenario with strong TP loading reduction. However, the downstream impact of increased TN:TP ratios depends on actual nutrient loadings and the limiting nutrient in the receiving system. We conclude that nutrient input reductions, improved water quality, higher in-lake nutrient retention fractions, and lower nutrient transport to downstream waterbodies go hand in hand. Therefore, water quality management could benefit even more from nutrient pollution reduction than one would expect at first sight.

Assisting decision-makers select multi-dimensionally efficient infrastructure designs - Application to urban drainage systems

Multi-objective design approaches can help identify future infrastructure system designs that appropriately balance different engineering, environmental, and other societal goals. Planners benefit from assessing the trade-offs implied by the best-performing infrastructure system solutions. However, a large number of possible efficient system designs, obtained when using multi-objective optimization, can be overwhelming to interpret. This study attempts to aid decision-making in multi-criteria infrastructure system design by reducing the complexity of the identified set of efficient infrastructure designs, i.e., the Pareto-front. A soft clustering algorithm is applied, which identifies similarities between solutions, partitions the front accordingly, and selects a set of representative solutions while preserving the multi-dimensional structure of the solutions on the efficiency frontier. Three post-optimization decision-making metrics are introduced to help quantify the overall performance of the Pareto-optimal designs to further summarize design process outputs for decision-makers. We apply the method to an illustrious urban drainage network case study. Results show how the approach can simplify Pareto-fronts with thousands of solutions into sets of highlighted designs that aid interpreting the trade-offs implied by the best-performing simulated systems.

Changing patterns of the East Asian monsoon drive shifts in migration and abundance of a globally important rice pest

Numerous insects including pests and beneficial species undertake windborne migrations over hundreds of kilometers. In East Asia, climate-induced changes in large-scale atmospheric circulation systems are affecting wind-fields and precipitation zones and these, in turn, are changing migration patterns. We examined the consequences in a serious rice pest, the brown planthopper (BPH, Nilaparvata lugens) in East China. BPH cannot overwinter in temperate East Asia, and infestations there are initiated by several waves of windborne spring or summer migrants originating from tropical areas in Indochina. The East Asian summer monsoon, characterized by abundant rainfall and southerly winds, is of critical importance for these northward movements. We analyzed a 42-year dataset of meteorological parameters and catches of BPH from a standardized network of 341 light-traps in South and East China. We show that south of the Yangtze River during summer, southwesterly winds have weakened and rainfall increased, while the summer precipitation has decreased further north on the Jianghuai Plain. Together, these changes have resulted in shorter migratory journeys for BPH leaving South China. As a result, pest outbreaks of BPH in the key rice-growing area of the Lower Yangtze River Valley (LYRV) have declined since 2001. We show that these changes to the East Asian summer monsoon weather parameters are driven by shifts in the position and intensity of the Western Pacific subtropical high (WPSH) system that have occurred during the last 20 years. As a result, the relationship between WPSH intensity and BPH immigration that was previously used to predict the size of the immigration to the LYRV has now broken down. Our results demonstrate that migration patterns of a serious rice pest have shifted in response to the climate-induced changes in precipitation and wind pattern, with significant consequences for the population management of migratory pests.

Improved feed forward with bald eagle search for conjunctive water management in deficit region

Due to increasing requirements on water resources and a lower recharge rate, the farming seasons are a vital season for the management of groundwater and surface water resource management. This condition necessitates the use of combined water distribution to meet the full water requirements. Analysis of existing surface water resources and related restrictions, this research suggested an algorithm for aquifer stabilization and fulfilling optimum water requirements. To manage the optimum withdrawals and the subsequent drop, this technique first employed the MODFLOW model for simulating the water levels. Next, an improved feed-forward neural network (IFFNN) was combined with an optimization method to create a machine learning (ML) framework. During the last phase, the findings of the optimized connectives approach as well as the relevant fields technologies to determine using improved bald eagle search with least square SVM(IBES-LSSVM) method that predicted the level of water deficit for every period, especially during farming seasons. This approach is based on an improved bald eagle search (IBES) optimization technique for finding the best settings for a least-squares support vector machine (LSSVM). The findings revealed that between 2005 and 2020, the year with the biggest water deficit was 2018 when only roughly 64 percent of water need was satisfied by groundwater (69 percent) and surface water (64 percent) (33 percent). The water depth may have risen by around 0.7 m during the study period if the optimum model had been used. The outcome of this research will help the management forecast future water shortages and make smarter water strategic choices.

Association between sequential extreme precipitation-heatwaves events and hospitalizations for schizophrenia: The damage amplification effects of sequential extremes

OBJECTIVES

In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss.

METHODS

A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events.

RESULTS

EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)).

CONCLUSIONS

Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.

Climatology and changes in hourly precipitation extremes over China during 1970-2018

Sub-daily precipitation extremes could intensify with temperature at a higher rate than the scaling for daily precipitation extremes, posing increasing risks to natural ecosystem and human society in the era of global warming. A systematic investigation of the climatology and spatiotemporal changes in sub-daily precipitation extremes is of paramount importance to inform future precipitation projection as well as to guide climate adaptation. Here, leveraging a newly proposed set of sub-daily extreme precipitation indices, we examine the climatology and changes in hourly precipitation extremes in mainland China across the major river basins during the warm period of 1970-2018. Our results show that the southern and eastern parts of China tend to experience more frequent hourly precipitation extremes with larger intensity, and the Pearl river basin has the most frequent and intense extreme precipitation at hourly timescale. The Southeast and Yangtze river basins and the mainland China as a whole have field significantly increasing trends in average and extreme precipitation intensities as well as in extreme precipitation frequencies. The intensification signals in hourly precipitation extremes of mainland China seem to emerge from internal climate variability around 2010, whereas average precipitation intensity since 1970 could become field significant earlier than 1999. Besides, we note a marked shift in the probability distributions of the extreme indices, with a wetting tendency toward more frequent and more intense precipitation extremes from the 1970-1999 period to the recent two decades in the 21st century. Our findings provide an alternative line of evidence for changes in precipitation extremes at hourly timescale over China and could contribute to societal decision-making for climate adaptation.

Spatiotemporal Variations of Extreme Precipitation in Wuling Mountain Area (China) and Their Connection to Potential Driving Factors

Changes in extreme precipitation have become a significant issue of regional disaster risk assessment and water resources management. Extreme precipitation variability is affected by multiple factors and shows disparities across different regions. Especially in mountain areas, geographic feature and local characteristics put more complexity and uncertainty on the changes of precipitation extremes. In this study, ten extreme precipitation indices of Wuling Mountain Area (WMA) during 1960–2019 have been used to analyzed the spatiotemporal variations of precipitation extremes. The relationships between extreme precipitation and potential driving factors, including geographic factors, global warming, local temperature, and climate indices, were investigated via correlation analysis. The results indicated that extreme precipitation tends to have a shorter duration and stronger intensity in WMA. Decreasing trends in R10mm, R20mm, R25mm, and the consecutive wet days (CWD) series account for 92%, 68%, 52%, and 96% of stations, while most stations in WMA have rising trends in Rx1day (68%), SDII (64%), R95p (72%), and R99p (72%). Significant abrupt changes in extreme precipitation indices mainly occurred in the 1980s–1990s. Geographic factors, local temperature, and climate indices exert different impacts on extreme precipitation. Longitude and elevation instead of latitude significantly affect extreme precipitation indices except for the maximum duration of wet spells. Global warming is likely to increase the intensity and decrease the duration of extreme precipitation, while the influence of local temperature is not exactly the same as that of global warming. The study reveals that summer monsoon indices are the dominant climate factor for variations of precipitation extremes in WMA. The correlation coefficient between extreme precipitation indices (such as Rx1day, R95p, R99p) and the East Asian summer monsoon index is around 0.5 and passed the significant test at the 0.01 level. The weakening of the summer monsoon indices tends to bring extreme precipitation with stronger intensity. The findings provide more understanding of the drivers and reasons of extreme precipitation changes in the mountain area.

Influence of Terrestrial Water Storage on Flood Potential Index in the Yangtze River Basin, China

In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger’s causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk.

Dam construction attenuates trace metal contamination in water through increased sedimentation in the Three Gorges Reservoir

Dam construction has a far-reaching impact on trace metal accumulation and the metal-induced quality of the aquatic environment. However, the long-term impacts of dam construction and impoundments on the spatial distribution of trace metals and water quality remain poorly understood. Here, we found that the concentrations of trace metals in the mainstream water of the world's largest reservoir, Three Gorges Reservoir (TGR), decreased after impoundment, while their concentrations and contamination in the sediments of the water-level fluctuation zone increased significantly, especially for anthropogenic sources of metals such as cadmium, lead, and zinc. The spatial and temporal variations of anthropogenic metals in the sediments revealed increased anthropogenic dominance in their distribution under current hydrological management, especially for the urban area of the upper TGR. Sediment fluxes, particle composition, and extreme climate modulated the distribution of trace metals in the sediments. The results demonstrate that human activities have increasingly determined the distribution and contamination state of trace metals in the mainstream TGR. However, in contrast to our previously thought, the anthropogenic discharge of trace metals did not adversely affect water quality. Our results indicate that dam construction in riverine systems attenuates trace metal contamination in water through sediment sorting and deposition.

Grid-Point Rainfall Trends, Teleconnection Patterns, and Regionalised Droughts in Portugal (1919–2019)

This paper describes the long-term grid-point rainfall trends in the context of climate change, recent regionalised rainfall decline and drought events for mainland Portugal, which is teleconnected, in most cases, to the trends of mathematical descriptions of the North Atlantic Oscillation (NAO) during the century from October 1919 to September 2019. Grid-point rainfall dataset (1919–2019, from 126 centroids in a regular mesh over the country) have been constructed from high-quality ground-based data and as such, it provides a reliable source for the analysis of rainfall trends at different timescales: October–December, January–March, December–March, and the hydrological year. The Mann–Kendall (MK) coupled with Sen’s slope estimator test are applied to quantify the trends. The Sequential Mann–Kendall (SQMK) analysis is implemented to obtain the fluctuation of the progressive trends along the studied 100-year period. Because of their pivotal role in linking and synchronising climate variability, teleconnections to the North Atlantic Ocean are also explored to explain the rainfall trends over the Portuguese continuum. The results provide a solid basis to explain the climate change effects on the Portuguese rainfall based on significant associations with strong negative correlations between changes in rainfall and in NAO indices. These strong opposing correlations are displayed in most of the winter seasons and in the year. After the late 1960s, a generalised rainfall decrease emerges against a background of significant upward trends of the NAO; such coupled behaviour has persisted for decades. Regionalised droughts at three identified climatic regions, based on factor analysis and Standardised Precipitation Index (SPI), are also discussed, concluding that the frequency of severe droughts may increase again, accompanied by a stronger influence of the recently more positive and unusual winter season and annual NAO indices.

Spatiotemporal Variability in Precipitation Extremes in the Jianghuai Region of China and the Analysis of Its Circulation Features

In the context of global warming, changes in extreme-precipitation events are becoming increasingly complex, and investigating the spatial and temporal variation characteristics of extreme precipitation is extremely important for scientific water-resource planning, preventing new climate risks and maintaining ecosystem balances. Based on the daily precipitation from 1960–2017 at 15 meteorological stations in the Jianghuai region, the extreme-precipitation indices were calculated. The variations in 12 extreme-precipitation indices were detected by using the Mann–Kendall test in the Jianghuai region. The periodicity of indices was examined by wavelet analysis detecting significant time sections. Through the cross wavelet transform and wavelet coherence analyses, the nonlinear connections between extreme precipitation and atmospheric circulation were explored. The results indicate significant increasing trends in the max one-day precipitation amount (Rx1day), extreme wet days (R99p), and simple precipitation intensity index (SDII). The intensity of extreme precipitation increased significantly. The variation in extreme precipitation showed different trends in different regions, with a greater likelihood of increasing extreme-precipitation intensity and frequency in the southern region compared to the central and northern regions. The period of most oscillations of the indices tend toward be on a time scale of 2–4 years and are in the 1990s. The number of heavy precipitation days (R10 mm) and number of very heavy precipitation days (R20 mm) had, mainly, periods of 5.84 years. Additionally, there were significant resonance periods between the extreme-precipitation indices and the atmospheric circulation index; however, there were obvious differences in time domains. The North Atlantic Oscillation (NAO) and East Asian summer monsoon (EASM) had the most significant effect on the duration of extreme precipitation; Atlantic Oscillation (AO) and EASM had the most significant influence on the extreme-precipitation intensity. The results of the study can provide a scientific basis for water-resource management and disaster prevention and control in the Jianghuai region.

Projected changes in terrestrial water storage and associated flood potential across the Yangtze River basin

Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031-2060) and far (2071-2100) future with reference to the historical period (1985-2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

Machine Learning for the Estimation of Diameter Increment in Mixed and Uneven-Aged Forests

Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.

A new perspective into the impact of renewable and nonrenewable energy consumption on environmental degradation in Argentina: a time-frequency analysis

In most nations across the world, the fundamental goal of economic policy is to achieve sustainable economic growth. Economic development, on the other hand, may have an influence on climate change and global warming, which are major worldwide concerns and problems. Thus, this research offers a new perceptive on the influence of renewable and nonrenewable energy consumption on CO₂ emissions in Argentina utilizing data from the period between 1965 and 2019. The current research applied the wavelet tools to assess these interconnections. The outcomes of these analyses reveal that the association between the series evolves over both frequency and time. The current analysis uncovers notable wavelet coherence and significant lead and lag connections in the frequency domain, while in the time domain, contradictory correlations are indicated among the variables of interest. From an economic perspective, the outcomes of the wavelet analysis affirm that in the medium and long term, renewable energy consumption contributes to environmental sustainability. Furthermore, in the medium term, trade openness mitigates CO₂, although in the long term, no significant connection was found. Moreover, both nonrenewable energy and economic growth contribute to environmental degradation in the short and long term. Finally, the frequency domain causality outcomes reveal that in the long term, economic growth, trade openness, and nonrenewable energy can predict CO₂ emissions. The present analysis offers an innovative insight into the interconnection and comovement between CO₂ and trade openness, renewable energy utilization, and GDP in the Argentinean economy. The findings from this research should be of interest to economists, researchers, and policymakers.

A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran

Agricultural months are the critical period for the allocation of surface water and groundwater resources due to the increased demands on water supplies and decreased recharge rate. This situation urges the necessity of using conjunctive water management to fulfill the entire water demand. Here, we proposed an approach for aquifer stabilization and meeting the maximum water demand based on the available surface and groundwater resources and their limitations. In this approach, we first used the MODFLOW model to simulate the groundwater level to control the optimal withdrawal and the resulting drop. We next used a whale optimization algorithm (WOA) to develop an optimized model for the planning of conjunctive use to minimize the monthly water shortage. In the final step, we incorporated the results of the optimized conjunctive model and the available field data into the least squares-support vector machine (LS-SVM) model to predict the amounts of water shortage for each month, particularly for the agricultural months. The results showed that during the period from 2005 to 2020, the most water shortage belonged to 2018, in which only about 52% of water demand was met with the contribution of groundwater (67%) and surface water (33%). However, the groundwater level could have increased by about 0.7 m during the study period by implementing the optimized model. The results of the third part revealed that LS-SVM could predict the water shortage with better performance with a root-mean-square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe Index of 5.70 m, 3.43%, and 0.89 m, respectively. The findings of this study will enable managers to predict the water shortage in future periods to make more informed decisions for water resource allocation.

Assessment and Forecast of Green Total Factor Energy Efficiency in the Yellow River Basin—A Perspective Distinguishing the Upper, Middle and Lower Stream

As the fifth-longest river globally, the Yellow River is of great importance to the world’s ecological protection. Due to its location as an essential ecological barrier and economic zone, it is imperative to balance energy support and ecological management in the basin. In this process, improving energy efficiency is crucial solution. Distinguished into upstream, midstream, and downstream, we measured the trajectory of green total factor energy efficiency over the past fifteen years using the Super-Epsilon-based model. Further, we identified the heterogeneity of energy efficiency within different river basins with the help of kernel density estimation. We used it to analyze the geographical and policy reasons affecting energy efficiency fluctuations. Finally, we constructed high, medium, and low GDP growth scenarios, and used a long short-term memory neural network model to predict energy efficiency forecasts in each scenario. The study results clarified that the overall energy efficiency showed an upward trend since 2013. Among them, the most significant improvement in energy efficiency was observed upstream, while the energy efficiency in the middle and lower stream showed a decreasing trend. Regarding future development trends, an economic growth rate of 6.5% was most favorable for energy efficiency compared to 6% and 7%. This finding reminded us to be alert to the ecological condition of the lower Yellow River basin. In addition, maintaining an appropriate economic growth rate is helpful for the balance between development and ecology.

Moisture Transport versus Precipitation Change in Sub-Basins of the Yangtze River Basin

The Yangtze River Basin (YRB) exhibits great climate heterogeneity, from high-elevated source areas dominated by westerlies to downstream wetlands sensitive to monsoon flows. However, the atmospheric hydrological cycle and associated precipitation changes are rarely being synthetically studied in different sub-basins of the YRB, which are particularly important since floods in the main stream largely result from the superposition of precipitation-runoff peaks from different sub-basins. By dividing the entire YRB into 12 sub-basins, this study presents a preliminary analysis of precipitation features and the associated moisture transport characteristics at the sub-basin scale during 1961–2015. Results suggest that the peak month of precipitation in the northwest sub-basins (July) is one month later than that in the southeast sub-basins (June). The highest total column water vapor (TCWV) contributes to the peak precipitation in July in the northwest sub-basins, while the peak precipitation in June in the southeast sub-basins is more relative to the interaction among multi-circulations (featured by relatively high westerly moisture transport and relatively low south monsoon contribution in the progression process of monsoon precipitation belt). The south monsoon moisture during summer seldom reaches the source region basin (SRB), the Jinshajiang River Basin (JRB), and the Mintuojiang River Basin (MTB). During 1961–2015, the precipitation mainly exhibits an “increase–decrease–increase” pattern from the source region to downstream; however, it is unlikely that this pattern is forced by the TCWV and zonal/meridional moisture transport. In addition, the moisture transport anomalies between wet and dry years are also defined in the 12 sub-basins, and these anomalies are characterized by significantly different moisture transport patterns.

Characteristics and Cause Analysis of the 1954 Yangtze Precipitation Anomalies

In 1954, the Yangtze River valley was hit by heavy precipitation anomalies, which caused large casualties and economic losses; however, systematic analyses of the causes are lacking. Adopting the latest national historical precipitation data collected by the China Meteorological Administration (CMA) and global sea surface temperature (SST) records, this retrospective study determined the spatial–temporal distribution characteristics of the precipitation in 1954 in Wuhan, a city situated in the Yangtze River valley. The results confirmed that the 1954 precipitation anomalies were characterized by a high volume and a long period of rainfall, plus numerous cloudbursts, with most of the precipitation concentrated during June and July at the mid- and low-Yangtze areas along the Yangtze. An El Niño event caused the West Pacific subtropical highs to continually move southward during the summer, creating a long-term rainband in the drainage basin. Moreover, the continued low SSTs in the Sea of Okhotsk generated an active blocking high that continuously brought high-latitude cold air into the south, boosting precipitation over the drainage basin. This study proposed a new causal model of summertime precipitation across the Yangtze River valley in 1954, whereby the unusual SST changes initially triggered atmospheric circulation anomalies, which caused the precipitation anomalies of 1954.

Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China

Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 °C, and 1.57 °C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies.

Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

Nonstationary Analyses of the Maximum and Minimum Streamflow in Tamsui River Basin, Taiwan

This study aims to detect non-stationarity of the maximum and minimum streamflow regime in Tamsui River basin, northern Taiwan. Seven streamflow gauge stations, with at least 27-year daily records, are used to characterize annual maximum 1- and 2-day flows and annual minimum 1-, 7-, and 30-day flows. The generalized additive models for location, scale, and shape (GAMLSS) are used to dynamically detect evolution of probability distributions of the maximum and minimum flow indices with time. Results of time-covariate models indicate that stationarity is only noted in the 4 maximum flow indices out of 35 indices. This phenomenon indicates that the minimum flow indices are vulnerable to changing environments. A 16-category distributional-change scheme is employed to classify distributional changes of flow indices. A probabilistic distribution with complex variations of mean and variance is prevalent in the Tamsui River basin since approximate one third of flow indices (34.3%) belong to this category. To evaluate impacts of dams on streamflow regime, a dimensionless index called the reservoir index (RI) serves as an alternative covariate to model nonstationary probability distribution. Results of RI-covariate models indicate that 7 out of 15 flow indices are independent of RI and 80% of the best-fitted RI-covariate models are generally worse than the time-covariate models. This fact reveals that the dam is not the only factor in altering the streamflow regime in the Tamsui River, which is a significant alteration, especially the minimum flow indices. The obtained distributional changes of flow indices clearly indicate changes in probability distributions with time. Non-stationarity in the Tamsui River is induced by climate change and complex anthropogenic interferences.