Global river water quality under climate change and hydroclimatic extremes

Droughts are one of the most damaging weather-related hazards and can have detrimental impacts on ecosystem carbon uptake. However, little is known about the physical mechanisms underlying drought evolution as well as their potential effects on the carbon budget under historical and future climates. Here, we first quantify the impacts of water stress on carbon uptake under climate change in China. While heat and water stress play a crucial role in carbon uptake, the effect of ecosystem complexity is also significant. Then, we employ a machine learning model to explore the driving mechanisms of droughts, which are identified through the depletion of terrestrial water storage (TWS). Our results indicate that TWS droughts tend to be governed by atmospheric dryness, with precipitation, relative humidity (RH) and temperature playing dominant roles in drought evolution across most land areas. Precipitation and RH control moisture supply and demand, while rising temperature signifies increasing evaporative demand and enhanced evapotranspiration, leading to soil moisture depletion and reduced surface runoff, thereby intensifying drought. Further, by combining satellite data, field measurements, six global hydrological models, a global land surface model and a dynamic vegetation model, we find that water and heat stress have negative impacts on gross primary productivity (GPP), total ecosystem respiration (TER) and net ecosystem productivity (NEP), under both current and future climates. By the end of the 21st century (2071–2100), drought occurrence is projected to increase by sixfold over more than 60 % of land areas, leading to disproportionate negative impacts on carbon assimilation. Negative anomalies of NEP under drought stress are projected to decline from -0.09 g·m−2·day−1 (historical period) to −0.16 g·m−2·day−1(future period) under SSP370, with even more severe effects on future carbon assimilation under higher emission pathways. Our results suggest that more severe drought conditions might challenge ecosystem sustainability, and highlight the necessity of improving ecosystem resilience to climate warming. 

Quantifying the drivers of terrestrial drought and water stress impacts on carbon uptake in China

The seasonal operation of drought-limited water level (DLWL) can efficiently boost the synergies of hydropower production and drought defense. This study proposes a seasonal DLWL operation framework for mimicking reservoir drawdown operation to improve hydropower output and mitigate drought risk. Firstly, three statistical analysis methods were used to divide the drought season into three segments. And then, the simulation model of seasonal DLWL operation was configured to evaluate the effect of reservoir drawdown operation on risk and benefits. Lastly, the combined distance-based assessment method was employed to identify the best scheme of seasonal DLWL operation. The Three Gorges Reservoir sited in the Yangtze River of China constitutes the case study. The results suggested that the proposed framework could facilitate reservoir drawdown operation to achieve 2346.95 million kW·h (7.0% improvement) in annual hydroelectricity production and 3.3% improvement in annual utilization efficiency of water resources, while could reduce 22.2% in drought risk during the dry year, compared with the annual DLWL operation scheme. This study not only can provide scientific and technical support for reservoir drawdown operation benefiting synergies of hydropower generation and drought defense but also can suggest policymakers with various favorable strategies to experience potential risks and benefits of seasonal DLWL operation in the interest of sustainable hydropower development. 

Exploring a novel reservoir drawdown operation framework for boosting synergies of hydropower generation and drought defense

Reliable projection of future drought conditions is critical for informing adaptation in response to future climate change. However, the uncertainty associated with drought projection impedes the precise estimation of drought risk and consequently efficient adaptation strategy. To better understand the role of uncertainty in drought projection, this study distinguished the total uncertainty of drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty by variance analysis using realizations from the Single Model Initial-condition Large Ensembles (SMILEs) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that SMILEs differ from CMIP6 mainly in the magnitude of internal variability and model uncertainty. The contribution of internal variability to total drought projection uncertainty in SMILEs (31–56 %) is greater than that in CMIP6 (9–21 %), while model uncertainty of SMILEs (36–41 %) is nearly half that of CMIP6 (67–76 %). The estimation of total uncertainty in drought projection is comparable between SMILEs and CMIP6, indicating that total uncertainty attains a minimum in the mid-21st century (the 2060 s for SMILEs and 2050 s for CMIP6). In addition, SMILEs and CMIP6 consistently show that model uncertainty is dominant in tropical regions, and scenario uncertainty is the main uncertainty contributor in western North America, eastern South America, the Mediterranean, and southern Australia. 

Uncertainty separation of drought projection in the 21st century using SMILEs and CMIP6

The Upper Mekong River Basin (UMRB), Southwest China. With climate change unfolding and climate change knowledge evolving over time, it is imperative to investigate whether the latest CMIP6 models offer enhanced utility in climate change impact studies compared to their predecessors. This study strengthens the comparison between CMIP5 and CMIP6 models in assessing hydrological responses to future climate change. This was achieved utilizing the Soil and Water Assessment Tool, driven by downscaled CMIP5/CMIP6 model outputs under RCP8.5/SSP5–8.5. Both streamflow and sediment responses, encompassing the spatial and temporal changes, and the relationships between streamflow and sediment loads, were carefully evaluated and compared between CMIP5 and CMIP6. CMIP6 indicates a stronger warming in 2071–2100 over the UMRB compared to CMIP5. Mean annual precipitation/streamflow is projected to increase by 22.7%/26.3% using CMIP5 and 28.4%/34.4% using CMIP6. Mean annual sediment load changes, however, show a discrepancy between CMIP5 (−3.7%) and CMIP6 (+13.8%). CMIP6 exhibits larger inter-model variability in both climate and hydrological projections. Regarding future spatial distributions of mean annual water and sediment yields, a considerable agreement is demonstrated between CMIP5 and CMIP6, despite CMIP6 showing larger projections over most subbasins. Additionally, both ensembles exhibit approximate relationships between streamflow and sediment loads, indicating a comparable decline in watershed sediment generation and transport capacity under future climate change. Overall, CMIP6 suggests more severe climate change impacts on streamflow and sediment loads in the UMRB than CMIP5, highlighting the need to update climate change adaptation and mitigation policies based on the latest insights derived from CMIP6. 

Assessing streamflow and sediment responses to future climate change over the Upper Mekong River Basin: A comparison between CMIP5 and CMIP6 models

Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting. 

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Short-term wind speed forecasting based on a novel KANInformer model and improved dual decomposition

Anthropogenic climate warming is expected to accelerate the hydrological cycle with significant consequences for hydrological droughts. However, a systematic understanding of climate warming impacts on the global hydrological droughts and their driving mechanisms is still lacking. Here, we integrate bias‐corrected climate experiments, multiple hydrological models (HYs), and a multivariate analysis of variance (ANOVA) with a machine learning modeling framework, to examine the evolving frequency and multivariate characteristics of hydrological droughts and their mechanisms under climate warming for 6,688 catchments in the five principal Köppen‐Geiger climate zones. Results show that the total frequency of hydrological droughts is likely to stay unchanged while extreme hydrological droughts (e.g., events with a 30 yr joint return period, JRP) are projected to occur more frequently across the 21st century. The historical 30 yr JRP events assessed during the historical baseline period of 1985–2014 could become twice as frequent over ∼60% of global catchments by 2071–2100 under the middle and high emission scenarios (ESs). Climate uncertainty (i.e., from global climate models and ESs) is the major source of uncertainty over temperate and tropical catchments, versus HY uncertainty in arid catchments with locally complex runoff regimes. Our machine learning framework indicates that precipitation stress controls the development of historical droughts over ∼87% of global catchments. However, with climate warming, air temperature variations are expected to become the new primary driver of droughts in high‐latitude cold catchments. This study highlights an increasing risk of global extreme hydrological droughts with warming and suggests that rising temperatures in high latitudes may lead to more extreme hydrological droughts.

Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming

A Dynamic Time-varying Distributed Unit Hydrograph Considering the Heterogeneity of Runoff Generation

Joint Probabilistic Forecasting of Wind and Solar Power Exploiting Spatiotemporal Complementarity

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