NAC<sup>2</sup>H: The North American Climate Change and Hydroclimatology Data Set

Arsenault, R.J.; Brissette, François; Chen, Jie; Guo, Qinglian; Dallaire, Gabrielle

doi:10.1029/2020wr027097

Cited by 17 publications

(12 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

Anthropogenic warming due to increasing greenhouse gas emission has altered the terrestrial energy budget and water cycle (Arsenault et al, 2020;Erler et al, 2019). These changes have aggravated the uneven redistribution of water resources and resulted in more severe weather-related disasters (Broderick et al, 2019;Do et al, 2020).

…”

mentioning

confidence: 99%

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Yin

Guo

Gentine

et al. 2021

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tpp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates.

show abstract

“…

…”

mentioning

confidence: 99%

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Yin

Guo

Gentine

et al. 2021

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…In particular, GHGESs, downscaling and bias correction were not included in the analysis. In comparison, the North American Climate Change and Hydroclimatology Dataset (NAC 2 H) database (Arsenault et al, 2020) offers 16 000 combinations, allowing the examination of future streamflow uncertainty. In this regard, the relative variance contribution of the climate dataset is best examined in comparison to that of GCMs, the most studied source of climate change impact uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…A total of 10 CMIP5 GCMs from 10 different modeling centers were selected for this study, as shown in Table 1. They were selected as a subset of the GCMs used to set up the North American Climate Change and Hydroclimatology (NAC 2 H) database (Arsenault et al, 2020). The number of GCMs (10) was selected as a compromise between having an accurate representation of GCM climate sensitivity variability and keeping the large computational burden of this project reasonable.…”

Section: General Circulation Models (Gcms)mentioning

confidence: 99%

“…The ANOVA was performed for six streamflow metrics out of the 51 metrics defined in Arsenault et al (2020) for each of the 1145 catchments. These six metrics cover a wide range of streamflow conditions, i.e., mean annual (mean Q), seasonal (winter Q and summer Q) values, the 5th and 95th distribution quantiles (QQ5 and QQ95, respectively), as well as annual daily extreme (QX1) metrics.…”

Section: Variance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

Mostafa

Brissette²,

Arsenault³

2021

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

show abstract

“…The observed daily streamflow data used in this study were obtained from three sources, i.e. the Global Runoff Data Centre (GRDC; http://www.bafg.de/GRDC/, Lehner 2012), the Canadian model parameter experiment (CANOPEX) database (Arsenault et al 2016(Arsenault et al , 2020, and some catchments of China (Gu et al 2018). The GRDC dataset comprises river discharge data for more than 9,500 stations from 161 countries.…”

Section: Observed Streamflow Datamentioning

confidence: 99%

Regionalization of catchment hydrological model parameters for global water resources simulations

Chen

et al. 2022

Hydrology Research

Self Cite

View full text Add to dashboard Cite

Parameter regionalization of hydrological models is one of the most commonly used methods for hydrological prediction over ungauged catchments. Although there were many regional studies, there is no clear conclusion on the best-performed regionalization method for global hydrological modelling. The objective of this study is to determine an appropriate global-scale regionalization scheme (GSRS) for global hydrological modelling. To this end, the performance of five regionalization methods with two different average options, two weighting approaches, and seven efficiency thresholds (i.e. Kling-Gupta efficiency (KGE) values to measure hydrological model performances) was compared over thousands of catchments based on four conceptual hydrological models. Results of nine global models from the Global Earth Observation for Integrated Water Resource Assessment (EartH2Observe) project were selected to validate the accuracy of GSRS in estimating global runoff. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting method and the output average option offers the best regionalization result when using the KGE ≥ 0.5 as an efficiency threshold for all four hydrological models, (2) the regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, consistently performs better than the nine global models from EartH2Observe project in the estimation of runoff for most catchments, with varying degrees of improvement in the median, upper and lower quartiles, and whiskers of each performance metric, and (3) the global long-term annual water resources estimated by RGHSs range between 42,592 and 46,810 km3/yr.

show abstract

NAC²H: The North American Climate Change and Hydroclimatology Data Set

Cited by 17 publications

References 46 publications

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

Regionalization of catchment hydrological model parameters for global water resources simulations

Contact Info

Product

Resources

About

NAC2H: The North American Climate Change and Hydroclimatology Data Set

Cited by 17 publications

References 46 publications

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Does the Hook Structure Constrain Future Flood Intensification Under Anthropogenic Climate Warming?

Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

Regionalization of catchment hydrological model parameters for global water resources simulations

Contact Info

Product

Resources

About

NAC²H: The North American Climate Change and Hydroclimatology Data Set