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Hui Guo
,
Ying Hou
,
Yuting Yang
, and
Tim R. McVicar

Abstract

Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs.

Significance Statement

Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models.

Restricted access
Hylke E. Beck
,
Albert I. J. M. van Dijk
,
Pablo R. Larraondo
,
Tim R. McVicar
,
Ming Pan
,
Emanuel Dutra
, and
Diego G. Miralles

Abstract

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.

Full access
Cesar Azorin-Molina
,
Tim R. McVicar
,
Jose A. Guijarro
,
Blair Trewin
,
Andrew J. Frost
,
Gangfeng Zhang
,
Lorenzo Minola
,
Seok-Woo Son
,
Kaiqiang Deng
, and
Deliang Chen

Abstract

Wind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, for example, human safety, maritime–terrestrial–aviation activities, engineering and insurance applications, and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed “stilling,” but a lack of knowledge on the multidecadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for 1941–2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer–spring–autumn and weak negative or nontrending (even positive) trends in winter. We demonstrate that ocean–atmosphere oscillations such as the Indian Ocean dipole and the southern annular mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the “stilling” phenomenon, suggesting that global warming may have reduced Australian wind gusts.

Full access
Hylke E. Beck
,
Eric F. Wood
,
Tim R. McVicar
,
Mauricio Zambrano-Bigiarini
,
Camila Alvarez-Garreton
,
Oscar M. Baez-Villanueva
,
Justin Sheffield
, and
Dirk N. Karger

Abstract

We introduce a set of global high-resolution (0.05°) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile—regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1 (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online (http://www.gloh2o.org/pbcor/).

Open access
Gangfeng Zhang
,
Cesar Azorin-Molina
,
Deliang Chen
,
Jose A. Guijarro
,
Feng Kong
,
Lorenzo Minola
,
Tim R. McVicar
,
Seok-Woo Son
, and
Peijun Shi

Abstract

Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975–2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p < 0.05) declining trend of −0.038 m s−1 decade−1 for all China annually, with decreases in winter (−0.355 m s−1 decade−1, p < 0.05) and autumn (−0.108 m s−1 decade−1; p < 0.05) and increases in summer (+0.272 m s−1 decade−1, p < 0.05) along with a weak recovery in spring (+0.032 m s−1 decade−1; p > 0.10); that is, DMWS declined during the cold semester (October–March) and increased during the warm semester (April–September). Correlation analysis of the Arctic Oscillation, the Southern Oscillation, and the west Pacific modes exhibited significant correlation with DMWS variability, unveiling their complementarity in modulating DMWS. Further, we explored potential physical processes relating to the atmospheric circulation changes and their impacts on DMWS and found that 1) overall weakened horizontal airflow [large-scale mean horizontal pressure gradient (from −0.24 to +0.02 hPa decade−1) and geostrophic wind speed (from −0.6 to +0.6 m s−1 decade−1)], 2) widely decreased atmospheric vertical momentum transport [atmospheric stratification thermal instability (from −3 to +1.5 decade−1) and vertical wind shear (from −0.4 to +0.2 m s−1 decade−1)], and 3) decreased extratropical cyclones frequency (from −0.3 to 0 month decade−1) are likely causes of DMWS change.

Free access
Cuan Petheram
,
Paul Rustomji
,
Tim R. McVicar
,
WenJu Cai
,
Francis H. S. Chiew
,
Jamie Vleeshouwer
,
Thomas G. Van Niel
,
LingTao Li
,
Richard G. Cresswell
,
Randall J. Donohue
,
Jin Teng
, and
Jean-Michel Perraud

Abstract

The majority of the world’s population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall–runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%) and half resulted in a decrease (by up to 26%). However, ~70% of the GCMES resulted in a difference of within ±5% of the historical rainfall (1930–2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet–dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall–runoff modeling.

Full access
Sergio M. Vicente-Serrano
,
Diego G. Miralles
,
Fernando Domínguez-Castro
,
Cesar Azorin-Molina
,
Ahmed El Kenawy
,
Tim R. McVicar
,
Miquel Tomás-Burguera
,
Santiago Beguería
,
Marco Maneta
, and
Marina Peña-Gallardo

Abstract

This article developed and implemented a new methodology for calculating the standardized evapotranspiration deficit index (SEDI) globally based on the log-logistic distribution to fit the evaporation deficit (ED), the difference between actual evapotranspiration (ETa) and atmospheric evaporative demand (AED). Our findings demonstrate that, regardless of the AED dataset used, a log-logistic distribution most optimally fitted the ED time series. As such, in many regions across the terrestrial globe, the SEDI is insensitive to the AED method used for calculation, with the exception of winter months and boreal regions. The SEDI showed significant correlations (p < 0.05) with the standardized precipitation evapotranspiration index (SPEI) across a wide range of regions, particularly for short (<3 month) SPEI time scales. This work provides a robust approach for calculating spatially and temporally comparable SEDI estimates, regardless of the climate region and land surface conditions, and it assesses the performance and the applicability of the SEDI to quantify drought severity across varying crop and natural vegetation areas.

Full access
Cesar Azorin-Molina
,
Sergio M. Vicente-Serrano
,
Tim R. McVicar
,
Sonia Jerez
,
Arturo Sanchez-Lorenzo
,
Juan-I. López-Moreno
,
Jesus Revuelto
,
Ricardo M. Trigo
,
Joan A. Lopez-Bustins
, and
Fátima Espírito-Santo

Abstract

Near-surface wind speed trends recorded at 67 land-based stations across Spain and Portugal for 1961–2011, also focusing on the 1979–2008 subperiod, were analyzed. Wind speed series were subjected to quality control, reconstruction, and homogenization using a novel procedure that incorporated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)-simulated series as reference. The resultant series show a slight downward trend for both 1961–2011 (−0.016 m s−1 decade−1) and 1979–2008 (−0.010 m s−1 decade−1). However, differences between seasons with declining values in winter and spring, and increasing trends in summer and autumn, were observed. Even though wind stilling affected 77.8% of the stations in winter and 66.7% in spring, only roughly 40% of the declining trends were statistically significant at the p < 0.10 level. On the contrary, increasing trends appeared in 51.9% of the stations in summer and 57.4% in autumn, with also around 40% of the positive trends statistically significant at the p < 0.10 level. In this article, the authors also investigated (i) the possible impact of three atmospheric indices on the observed trends and (ii) the role played by the urbanization growth in the observed decline. An accurate homogenization and assessment of the long-term trends of wind speed is crucial for many fields such as wind energy (e.g., power generation) and agriculture–hydrology (e.g., evaporative demand).

Full access
Hylke E. Beck
,
Eric F. Wood
,
Ming Pan
,
Colby K. Fisher
,
Diego G. Miralles
,
Albert I. J. M. van Dijk
,
Tim R. McVicar
, and
Robert F. Adler

Abstract

We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

Full access
M. Ades
,
R. Adler
,
Rob Allan
,
R. P. Allan
,
J. Anderson
,
Anthony Argüez
,
C. Arosio
,
J. A. Augustine
,
C. Azorin-Molina
,
J. Barichivich
,
J. Barnes
,
H. E. Beck
,
Andreas Becker
,
Nicolas Bellouin
,
Angela Benedetti
,
David I. Berry
,
Stephen Blenkinsop
,
Olivier. Bock
,
Michael G. Bosilovich
,
Olivier. Boucher
,
S. A. Buehler
,
Laura. Carrea
,
Hanne H. Christiansen
,
F. Chouza
,
John R. Christy
,
E.-S. Chung
,
Melanie Coldewey-Egbers
,
Gil P. Compo
,
Owen R. Cooper
,
Curt Covey
,
A. Crotwell
,
Sean M. Davis
,
Elvira de Eyto
,
Richard A. M de Jeu
,
B.V. VanderSat
,
Curtis L. DeGasperi
,
Doug Degenstein
,
Larry Di Girolamo
,
Martin T. Dokulil
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
G. Duveiller
,
James W. Elkins
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Richard A. Frey
,
Stacey M. Frith
,
Lucien Froidevaux
,
J. Garforth
,
S. K. Gupta
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
Andrew K Heidinger
,
D. L. Hemming
,
Shu-peng (Ben) Ho
,
Daan Hubert
,
Dale F. Hurst
,
I. Hüser
,
Antje Inness
,
K. Isaksen
,
Viju John
,
Philip D. Jones
,
J. W. Kaiser
,
S. Kelly
,
S. Khaykin
,
R. Kidd
,
Hyungiun Kim
,
Z. Kipling
,
B. M. Kraemer
,
D. P. Kratz
,
R. S. La Fuente
,
Xin Lan
,
Kathleen O. Lantz
,
T. Leblanc
,
Bailing Li
,
Norman G Loeb
,
Craig S. Long
,
Diego Loyola
,
Wlodzimierz Marszelewski
,
B. Martens
,
Linda May
,
Michael Mayer
,
M. F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
W. Paul Menzel
,
Christopher J. Merchant
,
Ben R. Miller
,
Diego G. Miralles
,
Stephen A. Montzka
,
Colin Morice
,
Jens Mühle
,
R. Myneni
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Tim J. Osborn
,
T. Park
,
A. Pasik
,
Andrew M. Paterson
,
Mauri S. Pelto
,
S. Perkins-Kirkpatrick
,
G. Pétron
,
C. Phillips
,
Bernard Pinty
,
S. Po-Chedley
,
L. Polvani
,
W. Preimesberger
,
M. Pulkkanen
,
W. J. Randel
,
Samuel Rémy
,
L. Ricciardulli
,
A. D. Richardson
,
L. Rieger
,
David A. Robinson
,
Matthew Rodell
,
Karen H. Rosenlof
,
Chris Roth
,
A. Rozanov
,
James A. Rusak
,
O. Rusanovskaya
,
T. Rutishäuser
,
Ahira Sánchez-Lugo
,
P. Sawaengphokhai
,
T. Scanlon
,
Verena Schenzinger
,
S. Geoffey Schladow
,
R. W Schlegel
,
Eawag Schmid, Martin
,
H. B. Selkirk
,
S. Sharma
,
Lei Shi
,
S. V. Shimaraeva
,
E. A. Silow
,
Adrian J. Simmons
,
C. A. Smith
,
Sharon L Smith
,
B. J. Soden
,
Viktoria Sofieva
,
T. H. Sparks
,
Paul W. Stackhouse Jr.
,
Wolfgang Steinbrecht
,
Dimitri A. Streletskiy
,
G. Taha
,
Hagen Telg
,
S. J. Thackeray
,
M. A. Timofeyev
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald J. van der A
,
Robin, VanderSat B.V. van der Schalie
,
Gerard van der SchrierW. Paul
,
Guido R. van der Werf
,
Piet Verburg
,
Jean-Paul Vernier
,
Holger Vömel
,
Russell S. Vose
,
Ray Wang
,
Shohei G. Watanabe
,
Mark Weber
,
Gesa A. Weyhenmeyer
,
David Wiese
,
Anne C. Wilber
,
Jeanette D. Wild
,
Takmeng Wong
,
R. Iestyn Woolway
,
Xungang Yin
,
Lin Zhao
,
Guanguo Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
, and
Markus Ziese
Free access