This page has only limited features, please log in for full access.
Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide an insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the heaviness of the tail obtained with MEV was relatively unaffected by the precipitation duration. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.
Gaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. 2021, 1 .
AMA StyleGaby J Gründemannid, Enrico Zorzetto, Hylke E Beck, Marc Schleiss, Nick Van de GieseniD, Marco Marani, Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. . 2021; ():1.
Chicago/Turabian StyleGaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. 2021. "Extreme Precipitation Return Levels for Multiple Durations on a Global Scale." , no. : 1.
A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
R. Baatz; H. J. Hendricks Franssen; E. Euskirchen; D. Sihi; M. Dietze; S. Ciavatta; K. Fennel; H. Beck; G. De Lannoy; V. R. N. Pauwels; A. Raiho; C. Montzka; M. Williams; U. Mishra; C. Poppe; S. Zacharias; A. Lausch; L. Samaniego; K. Van Looy; H. Bogena; M. Adamescu; M. Mirtl; A. Fox; K. Goergen; B. S. Naz; Y. Zeng; H. Vereecken. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis. Reviews of Geophysics 2021, 59, 1 .
AMA StyleR. Baatz, H. J. Hendricks Franssen, E. Euskirchen, D. Sihi, M. Dietze, S. Ciavatta, K. Fennel, H. Beck, G. De Lannoy, V. R. N. Pauwels, A. Raiho, C. Montzka, M. Williams, U. Mishra, C. Poppe, S. Zacharias, A. Lausch, L. Samaniego, K. Van Looy, H. Bogena, M. Adamescu, M. Mirtl, A. Fox, K. Goergen, B. S. Naz, Y. Zeng, H. Vereecken. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis. Reviews of Geophysics. 2021; 59 (3):1.
Chicago/Turabian StyleR. Baatz; H. J. Hendricks Franssen; E. Euskirchen; D. Sihi; M. Dietze; S. Ciavatta; K. Fennel; H. Beck; G. De Lannoy; V. R. N. Pauwels; A. Raiho; C. Montzka; M. Williams; U. Mishra; C. Poppe; S. Zacharias; A. Lausch; L. Samaniego; K. Van Looy; H. Bogena; M. Adamescu; M. Mirtl; A. Fox; K. Goergen; B. S. Naz; Y. Zeng; H. Vereecken. 2021. "Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis." Reviews of Geophysics 59, no. 3: 1.
Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.
Peter Uhe; Daniel Mitchell; Paul D. Bates; Nans Addor; Jeff Neal; Hylke E. Beck. Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0. Geoscientific Model Development 2021, 14, 4865 -4890.
AMA StylePeter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, Hylke E. Beck. Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0. Geoscientific Model Development. 2021; 14 (8):4865-4890.
Chicago/Turabian StylePeter Uhe; Daniel Mitchell; Paul D. Bates; Nans Addor; Jeff Neal; Hylke E. Beck. 2021. "Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0." Geoscientific Model Development 14, no. 8: 4865-4890.
The eastern North Pacific (ENP) has the highest density of tropical cyclones (TCs) on earth, and yet the controls on TCs, from individual events to seasonal totals, remain poorly understood. One effect that has not been fully considered is the unique geography of the Central American mountains. Although observational studies suggest these mountains can readily fuel individual TCs through dynamical processes, here we show that these mountains indeed play the opposite role on the seasonal timescale, hindering seasonal ENP TC activity by up to 35%. We found that these mountains significantly interrupt the abundant moisture transport from the Caribbean Sea to the ENP, limiting deep convection over the open ocean area where TCs preferentially occur. This study advances our fundamental understanding of ENP TC genesis mechanisms across the weather-to-climate timescales, and also highlights the importance of topography representation in improving the ENP regional climate simulations, as well as TC seasonal predictions and future projections.
Dan Fu; Ping Chang; Christina M. Patricola; R. Saravanan; Xue Liu; Hylke E. Beck. Central American mountains inhibit eastern North Pacific seasonal tropical cyclone activity. Nature Communications 2021, 12, 1 -11.
AMA StyleDan Fu, Ping Chang, Christina M. Patricola, R. Saravanan, Xue Liu, Hylke E. Beck. Central American mountains inhibit eastern North Pacific seasonal tropical cyclone activity. Nature Communications. 2021; 12 (1):1-11.
Chicago/Turabian StyleDan Fu; Ping Chang; Christina M. Patricola; R. Saravanan; Xue Liu; Hylke E. Beck. 2021. "Central American mountains inhibit eastern North Pacific seasonal tropical cyclone activity." Nature Communications 12, no. 1: 1-11.
Elevation in atmospheric carbon dioxide concentration (eCO2) affects vegetation water use, with consequent impacts on terrestrial runoff (Q). However, the sign and magnitude of the eCO2 effect on Q are still contentious. This is partly due to eCO2-induced changes in vegetation water use having opposing responses at the leaf scale (i.e., water-saving effect caused by partially stomatal closure) and the canopy scale (i.e., water-consuming induced by foliage cover increase), leading to highly debated conclusions among existing studies. In addition, none of the existing studies explicitly account for eCO2-induced changes to plant rooting depth that is overwhelmingly found in experimental observations. Here we develop an analytical ecohydrological framework that includes the effects of eCO2 on plant leaf, canopy density, and rooting characteristics to attribute changes in Q and to detect the eCO2 signal on Q via vegetation feedbacks over 1982–2010. Globally, we detect a very small decrease of Q induced by eCO2 during 1982–2010 (−1.7 %). Locally, we find a small positive trend (p < 0.01) in the Q–eCO2 response along a resource availability (β) gradient. Specifically, the Q–eCO2 response is found to be negative (i.e., eCO2 reduces Q) in low-β regions (typically dry and/or cold) and gradually changes to a small positive response (i.e., eCO2 increases Q) in high-β areas (typically warm and humid). Our findings suggest a minor role of eCO2 on changes in global Q over 1982–2010, yet we highlight that a negative Q–eCO2 response in semiarid and arid regions may further reduce the limited water resource there.
Yuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: accounting for aboveground and belowground vegetation–CO2 effects. Hydrology and Earth System Sciences 2021, 25, 3411 -3427.
AMA StyleYuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, Hylke E. Beck. Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: accounting for aboveground and belowground vegetation–CO2 effects. Hydrology and Earth System Sciences. 2021; 25 (6):3411-3427.
Chicago/Turabian StyleYuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. 2021. "Low and contrasting impacts of vegetation CO2 fertilization on global terrestrial runoff over 1982–2010: accounting for aboveground and belowground vegetation–CO2 effects." Hydrology and Earth System Sciences 25, no. 6: 3411-3427.
A vector‐river network explicitly uses realistic geometries of river reaches and catchments for spatial discretization in a river model. This enables improving the accuracy of the physical properties of the modelled river system, compared to a gridded river network that has been used in Earth System Models. With a finer‐scale river network, resolving smaller‐scale river reaches, there is a need for efficient methods to route streamflow and its constituents throughout the river network. The purpose of this paper is two‐fold: 1) develop a new method to decompose river networks into hydrologically independent tributary domains, where routing computations can be performed in parallel; and 2) perform global river routing simulations with two global river networks, with different scales, to examine the computational efficiency and the differences in discharge simulations at various temporal scales. The new parallelization method uses a hierarchical decomposition strategy, where each decomposed tributary is further decomposed into many sub‐tributary domains, enabling hybrid parallel computing. This parallelization scheme has excellent computational scaling for the global domain where it is straightforward to distribute computations across many independent river basins. However, parallel computing for a single large basin remains challenging. The global routing experiments show that the scale of the vector‐river network has less impact on the discharge simulations than the runoff input that is generated by the combination of land surface model and meteorological forcing. The scale of vector‐river networks needs to consider the scale of local hydrologic features such as lakes that are to be resolved in the network.
Naoki Mizukami; Martyn P. Clark; Shervan Gharari; Erik Kluzek; Ming Pan; Peirong Lin; Hylke E. Beck; Dai Yamazaki. A Vector‐Based River Routing Model for Earth System Models: Parallelization and Global Applications. Journal of Advances in Modeling Earth Systems 2021, 13, 1 .
AMA StyleNaoki Mizukami, Martyn P. Clark, Shervan Gharari, Erik Kluzek, Ming Pan, Peirong Lin, Hylke E. Beck, Dai Yamazaki. A Vector‐Based River Routing Model for Earth System Models: Parallelization and Global Applications. Journal of Advances in Modeling Earth Systems. 2021; 13 (6):1.
Chicago/Turabian StyleNaoki Mizukami; Martyn P. Clark; Shervan Gharari; Erik Kluzek; Ming Pan; Peirong Lin; Hylke E. Beck; Dai Yamazaki. 2021. "A Vector‐Based River Routing Model for Earth System Models: Parallelization and Global Applications." Journal of Advances in Modeling Earth Systems 13, no. 6: 1.
We used environmental metrics developed from multi‐source satellite observations to quantify the global influence of El Niño‐Southern Oscillation (ENSO) events on surface wetting and drying anomalies, and their impact on vegetation health. The environmental metrics included a microwave surface wetness index (ASWI) incorporating near‐surface atmospheric vapor pressure deficit (VPD), volumetric soil moisture (VSM), and land surface fractional water cover (FW) derived from Advanced Microwave Scanning Radiometer (AMSR) observations, and the vegetation health index (VHI) derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) observations. The combined ASWI and VHI analysis reveals complex ENSO related impacts on the distribution of water availability to plant communities, and variable vegetation sensitivity to associated drought and pluvial events. A delayed VHI response to changes in surface wetness (up to 3.4 months) was observed, whereby the ASWI may provide an effective forecast predictor of climate impacts on vegetation health. The intense 2015/16 El Niño event coincided with strong ASWI and VHI latitudinal correspondence (R≥0.73). The cascading impacts of climate anomalies on water cycle components and vegetation were further investigated over ENSO‐sensitive sub‐regions including Amazonia, Australia, southern Africa, and the South American Paraná delta region. The ASWI component information linked the effect of drought and pluvial events on vegetation health to underlying changes in surface water inundation, soil moisture and atmospheric moisture deficits. The new satellite‐based assessments reveal the global complexity of ENSO‐related impacts on surface water storages, and the influence of these climate and hydrologic perturbations on ecosystem productivity.
J. Du; J. S. Kimball; J. Sheffield; I. Velicogna; M. Zhao; M. Pan; C. K. Fisher; H. E. Beck; J. D. Watts; G. A; E. F. Wood. Synergistic Satellite Assessment of Global Vegetation Health in Relation to ENSO‐Induced Droughts and Pluvials. Journal of Geophysical Research: Biogeosciences 2021, 126, 1 .
AMA StyleJ. Du, J. S. Kimball, J. Sheffield, I. Velicogna, M. Zhao, M. Pan, C. K. Fisher, H. E. Beck, J. D. Watts, G. A, E. F. Wood. Synergistic Satellite Assessment of Global Vegetation Health in Relation to ENSO‐Induced Droughts and Pluvials. Journal of Geophysical Research: Biogeosciences. 2021; 126 (5):1.
Chicago/Turabian StyleJ. Du; J. S. Kimball; J. Sheffield; I. Velicogna; M. Zhao; M. Pan; C. K. Fisher; H. E. Beck; J. D. Watts; G. A; E. F. Wood. 2021. "Synergistic Satellite Assessment of Global Vegetation Health in Relation to ENSO‐Induced Droughts and Pluvials." Journal of Geophysical Research: Biogeosciences 126, no. 5: 1.
Oscar M. Baez-Villanueva; Mauricio Zambrano-Bigiarini; Pablo A. Mendoza; Ian McNamara; Hylke E. Beck; Joschka Thurner; Alexandra Nauditt; Lars Ribbe; Nguyen Xuan Thinh. Supplementary material to "On the selection of precipitation products for the regionalisation of hydrological model parameters". 2021, 1 .
AMA StyleOscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, Nguyen Xuan Thinh. Supplementary material to "On the selection of precipitation products for the regionalisation of hydrological model parameters". . 2021; ():1.
Chicago/Turabian StyleOscar M. Baez-Villanueva; Mauricio Zambrano-Bigiarini; Pablo A. Mendoza; Ian McNamara; Hylke E. Beck; Joschka Thurner; Alexandra Nauditt; Lars Ribbe; Nguyen Xuan Thinh. 2021. "Supplementary material to "On the selection of precipitation products for the regionalisation of hydrological model parameters"." , no. : 1.
Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide an insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the heaviness of the tail obtained with MEV was relatively unaffected by the precipitation duration. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.
Gaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. 2021, 1 .
AMA StyleGaby J Gründemannid, Enrico Zorzetto, Hylke E Beck, Marc Schleiss, Nick Van de GieseniD, Marco Marani, Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. . 2021; ():1.
Chicago/Turabian StyleGaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. 2021. "Extreme Precipitation Return Levels for Multiple Durations on a Global Scale." , no. : 1.
Over the past years, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects individual catchment calibration and verification, as well as the relative performance of three well-known regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We configured and calibrated a conceptual semi-distributed HBV-like hydrological model for each catchment, using four P products (ERA5, MSWEPv2.8, RF-MEPv2, and CR2MET), and two objective functions. The three regionalisation techniques were applied and evaluated for each combination of P product and objective function, using a leave-one-out cross-validation procedure. Despite differences in the spatio-temporal distribution of P quantities, all P products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation results (with median KGE's for the best method ranging from 0.56 to 0.63). Our results suggest that model calibration is able to compensate, to some extent, differences between forcing datasets, and that the spatial resolution of P products does not substantially affect the regionalisation performance. Overall, feature similarity provided the best results, followed closely by spatial proximity, while parameter regression performed the worst, thus reinforcing the importance of transferring complete parameter sets to ungauged catchments. Our results suggest that: i) merging P products and ground-based measurements does not necessarily translate into an improved hydrological modelling performance; ii) a P product that provides the best individual model performance during calibration and verification does not necessarily provide the best performance in terms of parameter regionalisation; and iii) the hydrological regime affects the performance of regionalisation methods, with rain-dominated catchments with a snow component performing the best over Chile for spatial proximity and feature similarity.
Oscar M. Baez-Villanueva; Mauricio Zambrano-Bigiarini; Pablo A. Mendoza; Ian McNamara; Hylke E. Beck; Joschka Thurner; Alexandra Nauditt; Lars Ribbe; Nguyen Xuan Thinh. On the selection of precipitation products for the regionalisation of hydrological model parameters. 2021, 2021, 1 -43.
AMA StyleOscar M. Baez-Villanueva, Mauricio Zambrano-Bigiarini, Pablo A. Mendoza, Ian McNamara, Hylke E. Beck, Joschka Thurner, Alexandra Nauditt, Lars Ribbe, Nguyen Xuan Thinh. On the selection of precipitation products for the regionalisation of hydrological model parameters. . 2021; 2021 ():1-43.
Chicago/Turabian StyleOscar M. Baez-Villanueva; Mauricio Zambrano-Bigiarini; Pablo A. Mendoza; Ian McNamara; Hylke E. Beck; Joschka Thurner; Alexandra Nauditt; Lars Ribbe; Nguyen Xuan Thinh. 2021. "On the selection of precipitation products for the regionalisation of hydrological model parameters." 2021, no. : 1-43.
Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R2) of 0.57 and mean absolute error (MAE) of 310 kg ha−1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.
Noemi Vergopolan; Sitian Xiong; Lyndon Estes; Niko Wanders; Nathaniel W. Chaney; Eric F. Wood; Megan Konar; Kelly Caylor; Hylke E. Beck; Nicolas Gatti; Tom Evans; Justin Sheffield. Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrology and Earth System Sciences 2021, 25, 1827 -1847.
AMA StyleNoemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, Justin Sheffield. Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrology and Earth System Sciences. 2021; 25 (4):1827-1847.
Chicago/Turabian StyleNoemi Vergopolan; Sitian Xiong; Lyndon Estes; Niko Wanders; Nathaniel W. Chaney; Eric F. Wood; Megan Konar; Kelly Caylor; Hylke E. Beck; Nicolas Gatti; Tom Evans; Justin Sheffield. 2021. "Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields." Hydrology and Earth System Sciences 25, no. 4: 1827-1847.
Impacts of climate change on floods have been recently suggested to be more consistently seen in flood timing (or flood seasonality) as opposed to flood magnitude and frequency. Changes in flood timing can threaten the finely tuned water resource management systems and, if poorly understood, can alter flood risks in unpredictable ways. Nevertheless, patterns of global flood timing trend remain elusive. Whether climate change has played a significant role in shifting flood timing worldwide also remains unknown.
Here we obtained an unprecedented set of discharge records from tens of thousands of global gauges and model-reconstructed naturalized discharge at ~3 million river reaches to delineate flood timing trend across the global river networks from 1980 to 2019. Hydroclimate drivers possibly causing these trends, including maximum precipitation, antecedent soil moisture, and snowmelt timing, are also investigated to disentangle climate change signals on floods. We found that the flood timing has been significantly earlier over the lower Mississippi, the Amur and the Amazon river basins, as well as large parts of the high-latitude Northern Hemisphere. Significant later floods are observed over the Yangtze and the lower Congo river basins, and the southeast Asia. However, ascribing these flood timing shifts to changing climate is not as obvious as previously suggested, implying the need for further research on this topic.
Peirong Lin; Eric Wood; Ming Pan; Yuan Yang; Hylke Beck; Zhenzhong Zeng. Patterns of flood timing trend across the global river networks. 2021, 1 .
AMA StylePeirong Lin, Eric Wood, Ming Pan, Yuan Yang, Hylke Beck, Zhenzhong Zeng. Patterns of flood timing trend across the global river networks. . 2021; ():1.
Chicago/Turabian StylePeirong Lin; Eric Wood; Ming Pan; Yuan Yang; Hylke Beck; Zhenzhong Zeng. 2021. "Patterns of flood timing trend across the global river networks." , no. : 1.
Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
Hylke E. Beck; Ming Pan; Diego G. Miralles; Rolf H. Reichle; Wouter A. Dorigo; Sebastian Hahn; Justin Sheffield; Lanka Karthikeyan; Gianpaolo Balsamo; Robert M. Parinussa; Albert I. J. M. van Dijk; Jinyang Du; John S. Kimball; Noemi Vergopolan; Eric F. Wood. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences 2021, 25, 17 -40.
AMA StyleHylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, Eric F. Wood. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences. 2021; 25 (1):17-40.
Chicago/Turabian StyleHylke E. Beck; Ming Pan; Diego G. Miralles; Rolf H. Reichle; Wouter A. Dorigo; Sebastian Hahn; Justin Sheffield; Lanka Karthikeyan; Gianpaolo Balsamo; Robert M. Parinussa; Albert I. J. M. van Dijk; Jinyang Du; John S. Kimball; Noemi Vergopolan; Eric F. Wood. 2021. "Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors." Hydrology and Earth System Sciences 25, no. 1: 17-40.
Yuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects". 2020, 1 .
AMA StyleYuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, Hylke E. Beck. Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects". . 2020; ():1.
Chicago/Turabian StyleYuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. 2020. "Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects"." , no. : 1.
Elevation in atmospheric carbon dioxide concentration (eCO2) affects vegetation water use, with consequent impacts on terrestrial runoff (Q). However, the sign and magnitude of the eCO2 effect on Q is still contentious. This is partly due to the poor understanding of the opposing eCO2-induced water effects at different scales, being water-saving caused by partial stomatal closure at the leaf-level contrasting with increased water-consumption due to increase foliage cover at the canopy level, leading to highly debated findings among existing studies. None of the existing studies implicitly account for eCO2-induced changes to below-ground vegetation functioning. Here we develop an analytical eco-hydrological framework that includes the effects of eCO2 on plant leaf, canopy density, and rooting characteristics to attribute changes in Q and detect the eCO2 signal on Q over the past three decades. Globally, we detect a very small decrease of Q induced by eCO2 during 1982–2010 (−1.69 %). When assessed locally, along the resource availability (α) gradient, a positive trend (p
Yuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects. 2020, 2020, 1 -29.
AMA StyleYuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, Hylke E. Beck. Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects. . 2020; 2020 ():1-29.
Chicago/Turabian StyleYuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. 2020. "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects." 2020, no. : 1-29.
There is an urgent need for the climate community to translate their meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities as we seek to understand how anthropogenic climate change has, and will, impact our society. This can be particularly challenging because there are often multiple specialized steps to model the hazard. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (~ 90 m) river flooding (fluvial) hazards. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be directly related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data-sets and thus can be applied anywhere in the world, but we use the Brahmaputra river in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework.
Peter Uhe; Daniel Mitchell; Paul D. Bates; Nans Addor; Jeff Neal; Hylke E. Beck. Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0. 2020, 2020, 1 -34.
AMA StylePeter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, Hylke E. Beck. Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0. . 2020; 2020 ():1-34.
Chicago/Turabian StylePeter Uhe; Daniel Mitchell; Paul D. Bates; Nans Addor; Jeff Neal; Hylke E. Beck. 2020. "Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0." 2020, no. : 1-34.
All hydrological models need to be calibrated to obtain satisfactory streamflow simulations. Here we present a novel parameter regionalization approach that involves the optimization of transfer equations linking model parameters to climate and landscape characteristics. The optimization was performed in a fully spatially distributed fashion at high resolution (0.05°), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4229 headwater catchments (< 5000 km2) worldwide. The optimized equations were subsequently applied globally to produce parameter maps for the entire land surface including ungauged regions. The approach was evaluated using the Kling‐Gupta Efficiency (KGE) and a gridded version of the hydrological model HBV. Ten‐fold cross‐validation was used to evaluate the generalizability of the approach and to obtain an ensemble of parameter maps. For the 4229 independent validation catchments, the regionalized parameters yielded a median KGE of 0.46. The median KGE improvement (relative to uncalibrated parameters) was 0.29 and improvements were obtained for 88 % of the independent validation catchments. These scores compare favourably to those from previous large catchment sample studies. The degree of performance improvement due to the regionalized parameters did not depend on climate or topography. Substantial improvements were obtained even for independent validation catchments located far from the catchments used for optimization, underscoring the value of the derived parameters for poorly gauged regions. The regionalized parameters ⸺ available via www.gloh2o.org/hbv ⸺ should be useful for hydrological applications requiring accurate streamflow simulations.
Hylke E. Beck; Ming Pan; Peirong Lin; Jan Seibert; Albert Van Dijk; Eric F. Wood. Global fully‐distributed parameter regionalization based on observed streamflow from 4229 headwater catchments. Journal of Geophysical Research: Atmospheres 2020, 125, 1 .
AMA StyleHylke E. Beck, Ming Pan, Peirong Lin, Jan Seibert, Albert Van Dijk, Eric F. Wood. Global fully‐distributed parameter regionalization based on observed streamflow from 4229 headwater catchments. Journal of Geophysical Research: Atmospheres. 2020; 125 (17):1.
Chicago/Turabian StyleHylke E. Beck; Ming Pan; Peirong Lin; Jan Seibert; Albert Van Dijk; Eric F. Wood. 2020. "Global fully‐distributed parameter regionalization based on observed streamflow from 4229 headwater catchments." Journal of Geophysical Research: Atmospheres 125, no. 17: 1.
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h−1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist.
Hylke E. Beck; Seth Westra; Jackson Tan; Florian Pappenberger; George J. Huffman; Tim R. McVicar; Gaby J. Gründemann; Noemi Vergopolan; Hayley J. Fowler; Elizabeth Lewis; Koen Verbist; Eric F. Wood. PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018. Scientific Data 2020, 7, 1 -12.
AMA StyleHylke E. Beck, Seth Westra, Jackson Tan, Florian Pappenberger, George J. Huffman, Tim R. McVicar, Gaby J. Gründemann, Noemi Vergopolan, Hayley J. Fowler, Elizabeth Lewis, Koen Verbist, Eric F. Wood. PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018. Scientific Data. 2020; 7 (1):1-12.
Chicago/Turabian StyleHylke E. Beck; Seth Westra; Jackson Tan; Florian Pappenberger; George J. Huffman; Tim R. McVicar; Gaby J. Gründemann; Noemi Vergopolan; Hayley J. Fowler; Elizabeth Lewis; Koen Verbist; Eric F. Wood. 2020. "PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018." Scientific Data 7, no. 1: 1-12.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Hylke E. Beck; Niklaus E. Zimmermann; Tim R. McVicar; Noemi Vergopolan; Alexis Berg; Eric F. Wood. Publisher Correction: Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 2020, 7, 1 -2.
AMA StyleHylke E. Beck, Niklaus E. Zimmermann, Tim R. McVicar, Noemi Vergopolan, Alexis Berg, Eric F. Wood. Publisher Correction: Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data. 2020; 7 (1):1-2.
Chicago/Turabian StyleHylke E. Beck; Niklaus E. Zimmermann; Tim R. McVicar; Noemi Vergopolan; Alexis Berg; Eric F. Wood. 2020. "Publisher Correction: Present and future Köppen-Geiger climate classification maps at 1-km resolution." Scientific Data 7, no. 1: 1-2.
Gaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. 2020, 1 .
AMA StyleGaby J Gründemannid, Enrico Zorzetto, Hylke E Beck, Marc Schleiss, Nick Van de GieseniD, Marco Marani, Ruud J. van der EntiD. Extreme Precipitation Return Levels for Multiple Durations on a Global Scale. . 2020; ():1.
Chicago/Turabian StyleGaby J Gründemannid; Enrico Zorzetto; Hylke E Beck; Marc Schleiss; Nick Van de GieseniD; Marco Marani; Ruud J. van der EntiD. 2020. "Extreme Precipitation Return Levels for Multiple Durations on a Global Scale." , no. : 1.