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Hydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time. Key Points We explore the literature review capability of a large language model combined with geocoding over the whole field of hydrological research We quantify the evolution of hydrological research, hotspot topics, collaboration networks, and basins in focus, worldwide over time Hydrological research publication exhibits major and accelerating growth with WRR emerging as the top influential journal over time

Reliable, spatiotemporally continuous runoff records are necessary for identifying climate change impacts and planning effective water management strategies. Existing Chinese runoff data to date have been produced from sparse, poor-quality gauge measurements at different time scales. We have developed a new, quality-controlled gridded runoff dataset, the China Natural Runoff Dataset version 1.0 (CNRD v1.0), which provides daily, monthly, and annual 0.25° runoff estimates for the period 1961–2018 over China. CNRD v1.0 was generated using the Variable Infiltration Capacity (VIC) model. A comprehensive parameter uncertainty analysis framework incorporating parameter sensitivity analysis, optimization, and regionalization with 200 natural or near-natural gauge catchments was used to train the VIC model. Overall, the results show well-calibrated parameters for most gauged catchments except arid and semiarid areas, and the skill scores present high values for all catchments. For the pseudo-/test-ungauged catchments, the model parameters estimated by the multiscale parameter regionalization technique offer the best regionalization solution. CNRD v1.0 is the first free public dataset of gridded natural runoff estimated using a comprehensive model parameter uncertainty analysis framework for China. These results indicate that CNRD v1.0 has high potential for application to long-term hydrological and climate studies in China and to improve international runoff databases for global-scale studies.

Planting has been widely adopted to battle the loss of salt marshes and to establish living shorelines. However, the drivers of success in salt marsh planting and their ecological effects are poorly understood at the global scale. Here, we assemble a global database, encompassing 22,074 observations reported in 210 studies, to examine the drivers and impacts of salt marsh planting. We show that, on average, 53% of plantings survived globally, and plant survival and growth can be enhanced by careful design of sites, species selection, and novel planted technologies. Planting enhances shoreline protection, primary productivity, soil carbon storage, biodiversity conservation and fishery production (effect sizes = 0.61, 1.55, 0.21, 0.10 and 1.01, respectively), compared with degraded wetlands. However, the ecosystem services of planted marshes, except for shoreline protection, have not yet fully recovered compared with natural wetlands (effect size = −0.25, 95% CI −0.29, −0.22). Fortunately, the levels of most ecological functions related to climate change mitigation and biodiversity increase with plantation age when compared with natural wetlands, and achieve equivalence to natural wetlands after 5–25 years. Overall, our results suggest that salt marsh planting could be used as a strategy to enhance shoreline protection, biodiversity conservation and carbon sequestration. Salt marsh planting strategies aim to reduce coastal degradation. Here, the authors conduct a global meta-analysis showing that planting enhances coastal wetland ecosystem services although not to the level of natural wetlands.

Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures. Plain Language Summary Global climate models (GCMs) have a powerful ability to reproduce past climate characteristics and project future climate evolution, and they are currently one of the most effective tools for climate change research. However, quantitative climate projections from GCMs are subject to high uncertainty due to our incomplete knowledge of climate, insufficient representation of climate system, and limited computer resources. Clarifying uncertainty sources can provide important scientific support for enhancing the credibility of future projection results and resolve relevant scientific questions for subsequent modeling applications. In this study, we tried to quantify the uncertainty in temperature and precipitation projections from three sources‐model uncertainty, scenario uncertainty, and internal variability‐arising from model outputs of 21 phase 5 of the Coupled Model Intercomparison Project and 26 phase 6 of the Coupled Model Intercomparison Project GCMs. We also investigated the potential of four bias correction methods for narrowing uncertainty in temperature and precipitation projections over the globe and continents. Key Points For uncertainties in phase 6 of the Coupled Model Intercomparison Project temperatures, the contribution of model uncertainty decreases with time, while the contribution of scenario uncertainty increases with time For precipitation projections, the model uncertainty predominates (98%) the total uncertainty Four bias correction (BC) methods have exhibited excellent ability to reduce uncertainty, and BC and spatial disaggregation has the best performance

The El Niño–Southern Oscillation (ENSO) and local temperature are important drivers of extreme precipitation. Understanding the impact of ENSO and temperature on the risk of extreme precipitation over global land will provide a foundation for risk assessment and climate-adaptive design of infrastructure in a changing climate. In this study, nonstationary generalized extreme value distributions were used to model extreme precipitation over global land for the period 1979–2015, with ENSO indicator and temperature as covariates. Risk factors were estimated to quantify the contrast between the influence of different ENSO phases and temperature. The results show that extreme precipitation is dominated by ENSO over 22% of global land and by temperature over 26% of global land. With a warming climate, the risk of high-intensity daily extreme precipitation increases at high latitudes but decreases in tropical regions. For ENSO, large parts of North America, southern South America, and southeastern and northeastern China are shown to suffer greater risk in El Niño years, with more than double the chance of intense extreme precipitation in El Niño years compared with La Niña years. Moreover, regions with more intense precipitation are more sensitive to ENSO. Global climate models were used to investigate the changing relationship between extreme precipitation and the covariates. The risk of extreme, high-intensity precipitation increases across high latitudes of the Northern Hemisphere but decreases in middle and lower latitudes under a warming climate scenario, and will likely trigger increases in severe flooding and droughts across the globe. However, there is some uncertainties associated with the influence of ENSO on predictions of future extreme precipitation, with the spatial extent and risk varying among the different models.

Identifying the optimal threshold of a peaks over threshold (POT) series is crucial for effective flood distribution analysis and decision‐making for risk reduction. Here we propose a threshold detection method based on the Shuffled Complex Evolution (SCE‐UA) optimization algorithm that can automatically identify the global optimal threshold without any objective specification. Results show that the proposed method efficiently located the optimal threshold with fewer than approximately 4–13 times the number of goodness of fit tests and Anderson‐Darling tests compared to traditional methods at 10 river gauge stations across China. The automatically identified threshold matched well with the threshold identified by graphical diagnostics, and it reduced fitting biases of the generalized Pareto model over commonly used fixed thresholds. This detection method was subsequently applied to a large‐scale flood distribution analysis across 380 stations of the Eastern Monsoon Region of China. The range of optimal thresholds for the POT series was between 0.14 m3/s and 49,062.53 m3/s, with a median value of 293.55 m3/s for the 380 stations. The high‐flow threshold was particularly high in wet regions and low in arid/semiarid regions. It is also shown that small dry catchments with lower elevation, lower field capacity, and larger saturated hydraulic conductivity tend to display heavier flood tails (i.e., a higher probability of extreme flood occurrence). Our study demonstrates the potential of an SCE‐UA‐based threshold detection framework for large‐scale flood distribution analysis and also provides a general framework for automatic extraction of excess extremes.

The increasing incidence of flash droughts globally presents a great challenge to the agriculture sector, ecosystem resilience and water resource systems. Here we introduce a methodology that improves the accuracy of quantifying drought-induced global vegetation loss (using Normalized Difference Vegetation Index (NDVI)-derived metric). Our results reveal that NDVI loss during flash droughts (9.0%) is approximately 1.5 times higher than that during conventional droughts (5.3%), highlighting the increasing role of flash droughts as the key driver of drought-induced NDVI loss worldwide. Furthermore, we identify a significant upward trend (1.8% per decade) in global NDVI loss due to flash droughts, primarily driven by the increasing frequency of such events, which account for 81.2% of the overall trend. Although NDVI typically recovers within 36 pentads across more than 9256.3 × 10 4  km 2 of the global land surface after flash droughts, there is a notable increase (0.4 pentads per year) in NDVI recovery time from 1982 to 2020, particularly in tropical rainforests and temperate forests. These findings highlight the alarming ecological consequences of increasingly frequent and intense flash droughts, with impacts expected to intensify in the future. Climate change is increasing the frequency of flash droughts worldwide, posing threats to global ecosystems. This study suggests that flash droughts cause 1.5 times greater vegetation loss than conventional droughts and delay ecosystem recovery, with impacts intensifying over recent decades.

This study focuses on changing trends in precipitation across mainland China during the period 1957-2014. We explore the influence of the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and related large-scale atmospheric circulation variables on the changes in precipitation. The number of wet days showed statistically significant downward trends in North China, Jianghuai, South China, and Southwest of China, but upward trends on the Tibetan Plateau and in Northwest China. However, the number of very wet days increased in Jianghuai, South China and regions in Southwest China, and there was an increase in the spatial variability of a number of rainfall extremes over China. Because the changes in the frequency of wet days and very wet days were non-uniform, an increasing percentage of the total annual precipitation was derived from extreme events over large regions of mainland China. The ENSO and the PDO had a zonal influence on precipitation variability through the modulation of large-scale atmospheric circulation. Both the number of wet days and the frequency of extreme precipitation increased in southern Jianghuai and South China in El Niño years compared with La Niña years. A decrease (increase) in the number of wet days was observed in northern China (southeastern China) during positive PDO-phase years, which was likely a response to the large decrease in Southerly winds.

This study examined the overall performance of the climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the key energy and water fluxes over land. For this purpose, this study selected multiple land flux products as reference data sets and assessed the global spatial means, patterns, trends, seasonal cycles, and regional mean estimates of the sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) simulated by 32 CMIP6 models in recent decades. The global (Antarctica, Greenland, and hot deserts are not included) mean SH, LH, RN, RF, and PR simulated by the CMIP6 models are 37.55 ± 4.81 W m−2, 49.88 ± 5.31 W m−2, 89.10 ± 4.45 W m−2, 351.31 ± 95.28 mm yr−1, and 948.35 ± 88.77 mm yr−1, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) can provide robust estimates of global and regional land fluxes, which are within the ranges given by the reference data sets, and highly consistent spatiotemporal patterns of these fluxes. The comparison of CMIP6‐MED with the first preferred reference data sets shows that CMIP6‐MED generally overestimates the water and energy fluxes over land, except for the simulated RF and PR in the Amazon region. The most disagreements between CMIP6‐MED and the reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau. Finally, the sources of model biases are discussed. It is suggested that current land flux products should be widely used to optimize the structures and parameters of climate models in future work. Plain Language Summary Land surface models are an indispensable part of weather and climate models. Sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) are the key components of the energy and water cycles over global land. This study evaluated these five fluxes from 1979 to 2014 simulated by 32 climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), with multiple global land products. The RN and RF estimated by the CMIP6 models exhibit the lowest and highest uncertainties, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) provides robust estimates of global and regional land fluxes, which are within reference ranges. We select the first preferred reference data sets from multiple global land products according to the evaluations against site observations and previous studies, and find that CMIP6‐MED generally overestimates the water and energy fluxes over land. The most disagreements between CMIP6 simulations and references occur in South America and the Tibetan Plateau. This study is helpful for determining the overall performance of the current climate models in simulating land surface processes. Key Points Performance of CMIP6 models on land surface energy and water fluxes during 1979–2014 is comprehensively evaluated using multiple land flux products Ensemble median of CMIP6 simulations can provide robust estimates of global and regional land fluxes, which are within the ranges given by reference data sets The most disagreements between CMIP6 simulations and reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates are typically associated with large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution (<30min) and high fidelity rainfall recordings over long periods of time (>10 years). Here, we present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds (~1 km) based on a Gaussian Process Regression (GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha-1 h-1 yr-1, with the highest values (>5,200 MJ mm ha-1 h-1 yr-1) in major parts of South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values (< 200 MJ mm ha-1 h-1 yr-1) are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has by far the highest mean rainfall erosivity (7,104 MJ mm ha-1 h-1 yr-1) followed by the temperate (3,729 MJ mm ha-1 h-1 yr-1), whereas the lowest mean (493 MJ mm ha-1 h-1 yr-1) was estimated in the cold climate zone.