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Abstract The Mekong River basin (MRB) is a transboundary basin that supports livelihoods of over 70 million inhabitants and diverse terrestrial-aquatic ecosystems. This critical lifeline for people and ecosystems is under transformation due to climatic stressors and human activities (e.g., land use change and dam construction). Thus, there is an urgent need to better understand the changing hydrological and ecological systems in the MRB and develop improved adaptation strategies. This, however, is hampered partly by lack of sufficient, reliable, and accessible observational data across the basin. Here, we fill this long-standing gap for MRB by synthesizing climate, hydrological, ecological, and socioeconomic data from various disparate sources. The data— including groundwater records digitized from the literature—provide crucial insights into surface water systems, groundwater dynamics, land use patterns, and socioeconomic changes. The analyses presented also shed light on uncertainties associated with various datasets and the most appropriate choices. These datasets are expected to advance socio-hydrological research and inform science-based management decisions and policymaking for sustainable food-energy-water, livelihood, and ecological systems in the MRB. 

Various climate, hydro-meteorological, ecological, and socio-economic datasets are synthesized and made available for the Mekong River Basin. The sources of each dataset are also mentioned in the associated readme file. Dam attribute data, inundation data, and Cambodia census data can be made available upon request to the authors. 

Abstract. Excessive anthropogenic nitrogen (N) inputs to the biosphere have disruptedthe global nitrogen cycle. To better quantify the spatial and temporalpatterns of anthropogenic N inputs, assess their impacts on thebiogeochemical cycles of the planet and the living organisms, and improvenitrogen use efficiency (NUE) for sustainable development, we have developeda comprehensive and synthetic dataset for reconstructing the History ofanthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere. The HaNi datasettakes advantage of different data sources in a spatiotemporally consistentway to generate a set of high-resolution gridded N input products from thepreindustrial period to the present (1860–2019). The HaNi dataset includes annual ratesof synthetic N fertilizer, manure application/deposition, and atmospheric Ndeposition on cropland, pasture, and rangeland at a spatial resolution of5 arcmin × 5 arcmin. Specifically, the N inputs are categorized, according to the Nforms and land uses, into 10 types: (1) NH4+-N fertilizer applied to cropland,(2) NO3--N fertilizer applied to cropland, (3) NH4+-N fertilizer applied to pasture,(4) NO3--N fertilizer applied to pasture, (5) manure N application oncropland, (6) manure N application on pasture, (7) manure N deposition onpasture, (8) manure N deposition on rangeland, (9) NHx-N deposition, and(10) NOy-N deposition. The total anthropogenic N (TN) inputs to globalterrestrial ecosystems increased from 29.05 Tg N yr−1 in the 1860s to267.23 Tg N yr−1 in the 2010s, with the dominant N source changing fromatmospheric N deposition (before the 1900s) to manure N (in the 1910s–2000s)and then to synthetic fertilizer in the 2010s. The proportion of syntheticNH4+-N in fertilizer input increased from 64 %in the 1960s to 90 % in the 2010s, while synthetic NO3--N fertilizerdecreased from 36 % in the 1960s to 10 % in the 2010s. Hotspots of TNinputs shifted from Europe and North America to East and South Asia duringthe 1960s–2010s. Such spatial and temporal dynamics captured by the HaNidataset are expected to facilitate a comprehensive assessment of the coupledhuman–Earth system and address a variety of social welfare issues, such as theclimate–biosphere feedback, air pollution, water quality, and biodiversity. Thedata are available at https://doi.org/10.1594/PANGAEA.942069(Tian et al., 2022). 

Abstract. Global water models (GWMs) simulate the terrestrial watercycle on the global scale and are used to assess the impacts of climatechange on freshwater systems. GWMs are developed within different modellingframeworks and consider different underlying hydrological processes, leadingto varied model structures. Furthermore, the equations used to describevarious processes take different forms and are generally accessible onlyfrom within the individual model codes. These factors have hindered aholistic and detailed understanding of how different models operate, yetsuch an understanding is crucial for explaining the results of modelevaluation studies, understanding inter-model differences in theirsimulations, and identifying areas for future model development. This studyprovides a comprehensive overview of how 16 state-of-the-art GWMs aredesigned. We analyse water storage compartments, water flows, and humanwater use sectors included in models that provide simulations for theInter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). Wedevelop a standard writing style for the model equations to enhance modelintercomparison, improvement, and communication. In this study, WaterGAP2used the highest number of water storage compartments, 11, and CWatM used 10compartments. Six models used six compartments, while four models (DBH,JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments.WaterGAP2 simulates five human water use sectors, while four models (CLM4.5,CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. Weconclude that, even though hydrological processes are often based on similarequations for various processes, in the end these equations have beenadjusted or models have used different values for specific parameters orspecific variables. The similarities and differences found among the modelsanalysed in this study are expected to enable us to reduce the uncertaintyin multi-model ensembles, improve existing hydrological processes, andintegrate new processes. 

Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low, mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale.