High-quality reconstruction of China's natural streamflow

Urbanization significantly increases greenhouse gas emissions from a subtropical headwater stream in Southeast China

Streams are disproportionately significant contributors to increases in greenhouse gas (GHG) effluxes in river networks. In the context of global urbanization, a growing number of streams are affected by urbanization, which has been suggested to stimulate the water-air GHG emissions from fluvial systems. This study investigated the seasonal and longitudinal profiles of GHG (N2O, CH4, and CO2) concentrations of Jiuxianghe Stream, a headwater stream undergoing urbanization, and estimated its GHG diffusive fluxes and global warming potentials (GWPs) using the boundary layer method. The results showed that N2O, CH4, and CO2 concentrations in Jiuxianghe Stream were 0.45-7.19 μg L-1, 0.31-586.85 μg L-1, and 0.16-11.60 mg L-1, respectively. N2O, CH4, and CO2 concentrations in the stream showed 4.55-, 23.70-, and 7.68-fold increases from headwaters to downstream, respectively, corresponding to the forest-urban transition within the watershed. Multiple linear regression indicated that NO3--N, NH4+-N, and DOC:NO3--N accurately predicted N2O and CO2 concentrations, indicating that N nutrients were the driving factors. The Jiuxianghe Stream was a source of atmospheric GHGs with a daily GWP of 7.31 g CO2-eq m-2 d-1 on average and was significantly positively correlated with the ratio of construction land and forest in the sub-watershed. This study highlights the critical role of urbanization in amplifying GHG emissions from streams, thereby augmenting our understanding of GHG emissions from river networks. With global urbanization on the rise, streams experiencing urbanization are expected to make an unprecedentedly significant contribution to riverine GHG budgets in the future.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

Impacts of climate change and anthropogenic stressors on runoff variations in major river basins in China since 1950

Runoff is one of the main components of hydrological cycle and an important index for water resources evaluation, understanding the runoff change and their causes is vital to water resource management. In the study, we analyzed the runoff change and the impacts of climate change and land use alteration on runoff variation based on natural runoff and previous studies in China. The results showed that there was a significant increasing trend in the annual runoff during 1961-2018 (p < 0.05), with change rate of 0.4 mm/a and abrupt point at 1999 across China, climate change dominated the runoff variation with a contribution of 54 %. In previous studies, the runoff of the major basins in China had a downward trend on the whole (-0.99 mm/a) except Continental River Basin (CRB) showed an increasing trend (0.25 mm/a), the abrupt points were mainly concentrated in 1991-2000, and human activity was the leading factor of runoff change with the contribution of 54 % across China. Human activity was the dominant factor of runoff change in Songhua and Liao River Basin (SLRB), Yellow River Basin (YRB), Hai River Basin (HRB) and Pearl River Basin (PRB), the contribution was >56 %, while climate change was the dominant factor of runoff change in Huai River Basin (HuRB), CRB, and Yangtze River Basin (YZRB). Overall, there was a significant correlation between runoff and precipitation, unused land, urban and grassland in China. We concluded that runoff change and the contribution of climate change and human activities varies greatly among different basins. The findings in this work can shed light on the quantitative understanding of runoff changes in national scale and offer a scientific basis for sustainable water management.

Monthly-scale hydro-climatic forecasting and climate change impact evaluation based on a novel DCNN-Transformer network

Climate change has emerged as one of the foremost global challenges confronting humanity today, leading to a heightened frequency and intensity of extreme weather phenomena, including droughts, floods, and erratic rainfall patterns. Accurately predicting changes in runoff patterns under future climate conditions holds significant importance for effective regional water resource planning and management. Recent research on runoff forecast has centered on optimizing hyperparameters of ELM, RNN, LSTM models using PSO, GWO, SSA, and other algorithms. Additionally, key features are extracted through input variable decomposition and preprocessing methods like EMD, EEMD, and VMD. However, these approaches have difficulties in extracting the long-term dependencies information of sequence units, parallel computing, and hyperparameter sensitivity. To address these shortcomings, this study proposes a novel end-to-end deep runoff prediction model based on deep convolutional neural network and Transformer (DCTN). The deep convolutional modules of DCTN employs the deep convolutional operation to extract local features of climate data while the Transformer of DCTN makes full use of self-attention to capture the long-term dependencies, which can achieve more accurate runoff predictions. Experiments on historical runoff forecasting at the Shanjiaodi hydrology station in the Dagu River Basin show that the proposed DCTN obtains a notable improvement of approximately 30.9% compared to traditional models. Based on the prediction results of three shared socioeconomic pathways, the potential impacts of climate change on runoff in Dagu River Basin were evaluated using the DCTN model. The results reveal that the likelihood of spring floods is substantially amplified in the mid-century and late-century, while the probability of extreme summer runoff diminishes. This study advances the understanding of runoff prediction and its implications under changing climate scenarios, paving the way for more informed decision-making and effective water resource management strategies.

Sediment reduction in the middle Yellow River basin over the past six decades: Attribution, sustainability, and implications

Understanding the underlying driving forces causing changes in sediment yield is crucial for decision-making and major strategy development for the management of the middle Yellow River basin (MYRB). In this work, we quantified the causes of sediment yield reduction in the MYRB and investigated the sustainability of sediment reduction strategies. The sediment yield in the middle Yellow River during 1957-2017 exhibits a significant downward trend. The average sediment yield in 1970-2017 decreased by 798.84 × 10⁶ t compared with that during the 1950s to 1960s, with 27.40 % ascribed to decreased precipitation and 72.60 % attributed to human activities. The sediment yield modulus of all sub-basins within the MYRB has been reduced to <5000 t/km², demonstrating the dominant influence of water and soil conservation measures. Check dams have limited on-site effectiveness in reducing sediment yield but exhibit a dominant effect in trapping the already yielded sediment and preventing it from being delivered into the lower Yellow River. The strong dependence on the storage capacity of check dams makes the system unsustainable in the long run, since it necessitates ongoing investment in check dam construction to maintain the sediment trapping effect. Promoting biological measures such as planting trees and grass to increase vegetation coverage is a more sustainable way to fix the sediment on-site and keep it from being eroded. These efforts should be intensified, with appropriate consideration for local conditions.

Global assessment of nature's contributions to people

Synergistically maintain or enhance the numerous beneficial contributions of nature to the quality of human life is an important but challenging question for achieving Sustainable Development Goals. However, the spatiotemporal distributions of global nature's contributions to people (NCPs) and their interactions remain unclear. We built a rapid assessment indicator framework and produced the first spatially explicit assessment of all 18 NCPs at a global scale. The 18 global NCPs in 1992 and 2018 were globally assessed in 15,204 subbasins based on two spatial indicator dimensions, including nature's potential contribution and the actual contribution to people. The results show that most of the high NCP values are highly localized. From 1992 to 2018, 6 regulating NCPs, 3 material NCPs, and 2 nonmaterial NCPs declined; 29 regulating-material NCP combinations (54 in total) dominated 76% of the terrestrial area, and the area with few NCPs accounted for 22%; and synergistic relationships were more common than tradeoff relationships, while the relationships among regulating and material NCPs generally traded-off with each other. Transitional climate areas contained few NCPs and have strong tradeoff relationships. However, the high synergistic relationship among NCPs in low latitudes could be threatened by future climate change. These findings provide a general spatiotemporal understanding of global NCP distributions and can be used to interpret the biogeographic information in a functional way to support regional coordination and achieve landscape multifunctionality for the enhancement of human well-being.

Constrained CMIP6 projections indicate less warming and a slower increase in water availability across Asia

Climate projections are essential for decision-making but contain non-negligible uncertainty. To reduce projection uncertainty over Asia, where half the world's population resides, we develop emergent constraint relationships between simulated temperature (1970-2014) and precipitation (2015-2100) growth rates using 27 CMIP6 models under four Shared Socioeconomic Pathways. Here we show that, with uncertainty successfully narrowed by 12.1-31.0%, constrained future precipitation growth rates are 0.39 ± 0.18 mm year^-1 (29.36 mm °C^-1, SSP126), 0.70 ± 0.22 mm year^-1 (20.03 mm °C^-1, SSP245), 1.10 ± 0.33 mm year^-1 (17.96 mm °C^-1, SSP370) and 1.42 ± 0.35 mm year^-1 (17.28 mm °C^-1, SSP585), indicating overestimates of 6.0-14.0% by the raw CMIP6 models. Accordingly, future temperature and total evaporation growth rates are also overestimated by 3.4-11.6% and -2.1-13.0%, respectively. The slower warming implies a lower snow cover loss rate by 10.5-40.2%. Overall, we find the projected increase in future water availability is overestimated by CMIP6 over Asia.

Evaluation of historical CMIP6 model simulations and future projections of temperature over the Pan-Third Pole region

The Pan-Third Pole (PTP) region, which encompasses the Eurasian highlands and their surroundings, has experienced unprecedented, accelerated warming during the past decades. This study evaluates the performance of historical simulation runs of the Coupled Model Intercomparison Project (CMIP6) in capturing spatial patterns and temporal variations observed over the PTP region for mean and extreme temperatures. In addition, projected changes in temperatures under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are also reported. Four indices were used to characterize changes in temperature extremes: the annual maximum value of daily maximum temperature (TXx), the annual minimum value of daily minimum temperature (TNn), and indices for the percentage of warm days (TX90p) and warm nights (TN90p). Results indicate that most CMIP6 models generally capture the characteristics of the observed mean and extreme temperatures over the PTP region, but there still are slight cold biases in the Tibetan Plateau. Future changes of mean and extreme temperatures demonstrate that a strong increase will occur for the entire PTP region during the twenty-first century under all four SSP scenarios. Between 2015 and 2099, ensemble area-averaged annual mean temperatures are projected to increase by 1.24 °C/100 year, 3.28 °C/100 year, 5.57 °C/100 year, and 7.40 °C/100 year for the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. For TXx and TNn, the most intense warming is projected in Central Asia. The greatest number of projected TX90p and TN90p will occur in the Southeast Asia and Tibetan Plateau, respectively.