We present an overview of the contributions collected to celebrate the fiftieth anniversary of Water Resources Research along with a critical discussion of the legacy and perspectives for the science of hydrology in the 21st century. This collection of papers highlights exciting pathways to the future of water sciences. New monitoring and modeling techniques and increasing opportunities for data and knowledge sharing from hydrological research will provide innovative means to improve water management and to ensure a sustainable development to society. We believe that this set of papers will provide valuable inspiration for future hydrologists, and will support the intensification of international cooperation among scientists.

On behalf of the journal, AGU, and the scientific community, the editors would like to sincerely thank those who reviewed manuscripts for Water Resources Research in 2015. The hours reading and commenting on manuscripts not only improves the manuscripts themselves but it also increases the scientific rigor of future research in the field. Many of those listed below went beyond and reviewed three or more manuscripts for our journal, and those are indicated in italics. The refereeing contributions they made contributed to 3622 individual reviews of 1434 manuscripts. Thank you again. We look forward to a 2016 of exciting advances in the field and communicating those advances to our community and to the broader public.

The year 2015 marks the 50th anniversary of Water Resources Research (WRR), which was founded in 1965. More than 15,000 papers have been published in WRR since its inception, and these papers have been cited more than 430,000 times. The history of hydrology and the water sciences are also reflected in WRR, which has served as a premier publication outlet and instigator of scientific growth over the last 50 years. The legacy of WRR provides a strong scientific foundation for the hydrology community to rise to the challenges of sustainable water resources management in a future where dramatic environmental change and increasing human population are expected to stress the world's water resources from local to global scales.

Existing estimates of methane (CH4) fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these differences, this study uses atmospheric CH4 observations from the US and Canada to analyze seven different bottom-up, wetland CH4 estimates reported in a recent model comparison project. We first use synthetic data to explore whether wetland CH4 fluxes are detectable at atmospheric observation sites. We find that the observation network can detect aggregate wetland fluxes from both eastern and western Canada but generally not from the US. Based upon these results, we then use real data and inverse modeling results to analyze the magnitude, seasonality, and spatial distribution of each model estimate. The magnitude of Canadian fluxes in many models is larger than indicated by atmospheric observations. Many models predict a seasonality that is narrower than implied by inverse modeling results, possibly indicating an oversensitivity to air or soil temperatures. The LPJ-Bern and SDGVM models have a geographic distribution that is most consistent with atmospheric observations, depending upon the region and season. These models utilize land cover maps or dynamic modeling to estimate wetland coverage while most other models rely primarily on remote sensing inundation data.

The terrestrial biosphere can release or absorb the greenhouse gases, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), and therefore has an important role in regulating atmospheric composition and climate1. Anthropogenic activities such as land-use change, agriculture and waste management have altered terrestrial biogenic greenhouse gas fluxes, and the resulting increases in methane and nitrous oxide emissions in particular can contribute to climate change(2,3). The terrestrial biogenic fluxes of individual greenhouse gases have been studied extensively(4-6), but the net biogenic greenhouse gas balance resulting from anthropogenic activities and its effect on the climate system remains uncertain. Here we use bottom-up (inventory, statistical extrapolation of local flux measurements, and process-based modelling) and top-down (atmospheric inversions) approaches to quantify the global net biogenic greenhouse gas balance between 1981 and 2010 resulting from anthropogenic activities and its effect on the climate system. We find that the cumulative warming capacity of concurrent biogenic methane and nitrous oxide emissions is a factor of about two larger than the cooling effect resulting from the global land carbon dioxide uptake from 2001 to 2010. This results in a net positive cumulative impact of the three greenhouse gases on the planetary energy budget, with a best estimate (in petagrams of CO2 equivalent per year) of 3.9 +/- 3.8 (top down) and 5.4 +/- 4.8 (bottom up) based on the GWP100 metric (global warming potential on a 100-year time horizon). Our findings suggest that a reduction in agricultural methane and nitrous oxide emissions, particularly in Southern Asia, may help mitigate climate change.

Validation of ground-based and satellite remote sensing CO2 observations involves comparisons among platforms and with in situ airborne measurements. Several factors unrelated to observational errors can lead to mismatches between measurements, and must be assessed to avoid misinterpreting actual differences in observed values as errors. Here we explore the impact of CO2 horizontal variability and differences in the spatial support of measurements. Case studies based on flights over Walnut Grove and Petaluma, California, are used to compare hypothetical airborne, TCCON, GOSAT, and OCO-2 measurements. We find that high CO2 variability can lead to differences in inferred X-CO2 (1) of over 0.5 ppm between airborne and remote sensing observations, due to the spatial mismatch between spiral flight trajectories and atmospheric columns, and (2) of up to 0.3 ppm among remote sensing platforms, due to differences in the spatial support of observations. Horizontal CO2 variability must therefore be considered in intercomparisons aimed at validation of remote sensing observations. (C) 2016 Elsevier Ltd. All rights reserved.

The seasonal-cycle amplitude (SCA) of the atmosphere-ecosystem carbon dioxide (CO2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere-ecosystem CO2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901-2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO2, climate, land-use, and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr(-1)). In the case of land- use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.

Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.351.25 and 0.140.094PgCyr(-1), respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012PgCyr(-1) during 1981-2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate-carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO2 with the atmosphere ( NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub-Basin Amazon NBE estimates have relied heavily on process-based biosphere models, despite lack of model agreement with plot-scale observations. We present a new analysis of airborne measurements that reveals monthly, regional-scale (similar to 1-8 x 10(6) km(2)) NBE variations. We develop a regional atmospheric CO2 inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere-atmosphere carbon exchange and that is minimally influenced by biosphere model-based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long-term mean climate. In particular, we observe increased NBE ( more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation ( negative correlation) and temperature ( positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long-term water availability. We examine satellite proxies and find evidence for higher gross primary productivity ( GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short-term temperature and moisture extremes in the Amazon, where monthly and sub-Basin estimates have not been previously available.