Joshua K. Roundy

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

Monitoring Earths terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earths terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

CFSv2-Based Seasonal Hydroclimatic Forecasts over the Conterminous United States

AbstractThere is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEPs operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of proba...

Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins

AbstractSeasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ense...

High‐resolution modeling of the spatial heterogeneity of soil moisture: Applications in network design

The spatial heterogeneity of soil moisture remains a persistent challenge in the design of in situ measurement networks, spatial downscaling of coarse estimates (e.g., satellite retrievals), and hydrologic modeling. To address this challenge, we analyze high-resolution (∼9 m) simulated soil moisture fields over the Little River Experimental Watershed (LREW) in Georgia, USA, to assess the role and interaction of the spatial heterogeneity controls of soil moisture. We calibrate and validate the TOPLATS distributed hydrologic model with high to moderate resolution land and meteorological data sets to provide daily soil moisture fields between 2004 and 2008. The results suggest that topography and soils are the main drivers of spatial heterogeneity over the LREW. We use this analysis to introduce a novel network design method that uses land data sets as proxies of the main drivers of local heterogeneity (topography, land cover, and soil properties) to define unique and representative hydrologic similar units (subsurface, surface, and vegetation) for probe placement. The calibration of the hydrologic model and network design method illustrates how the use of hydrologic similar units in hydrologic modeling could minimize computation and guide efforts toward improved macroscale land surface modeling.

Reply to comment by Keith J. Beven and Hannah L. Cloke on ''Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water''

WATER RESOURCES RESEARCH, VOL. 48, W01802, doi:10.1029/2011WR011202, 2012 Reply to comment by Keith J. Beven and Hannah L. Cloke on ‘‘Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water’’ Eric F. Wood, 1 Joshua K. Roundy, 1 Tara J. Troy, 1 Rens van Beek, 2 Marc Bierkens, 2 Eleanor Blyth, 3 Ad de Roo, 4 Petra Doll, 5 Mike Ek, 6 James Famiglietti, 7 David Gochis, 8 Nick van de Giesen, 9 Paul Houser, 10 Peter Jaffe, 1 Stefan Kollet, 11 Bernhard Lehner, 12 Dennis P. Lettenmaier, 13 Christa D. Peters-Lidard, 14 Murugesu Sivapalan, 15 Justin Sheffield, 1 Andrew J. Wade, 16 and Paul Whitehead 17 Received 25 July 2011; revised 9 November 2011; accepted 3 December 2011; published 21 January 2012. Citation: Wood, E. F., et al. (2012), Reply to comment by Keith J. Beven and Hannah L. Cloke on ‘‘Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water’’ Water Resour. Res., 48, W01802, doi:10.1029/ 2011WR011202. Introduction [ 1 ] The authors of Wood et al. [2011, hereafter W2011] would like to thank Beven and Cloke [2012, hereafter BC2012] for furthering the discussion about the pathway to- ward a global-scale hyper-resolution water-energy-biogeo- chemistry land surface modeling capability: its need, feasibility and development. Their comment brings focus to the discussion and shows that the proposed challenge to our community is one element in a long history of hydrology Department of Civil and Environmental Engineering, Princeton Uni- versity, Princeton, New Jersey, USA. Department of Physical Geography, University of Utrecht, Utrecht, Netherlands. Centre for Ecology and Hydrology, Wallingford, UK. Institute for Environment and Sustainability, European Commission Joint Research Center, Ispra, Italy. Institute of Physical Geography, J. W. Goethe University, Frankfurt am Main, Germany. Environmental Modeling Center, National Centers for Environmental Protection, Suitland, Maryland, USA. UC Center for Hydrologic Modeling, University of California-Irvine, Irvine, California, USA. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA. Department of Water Management, Delft University of Technology, Delft, Netherlands. Department of Geography and GeoInformation Science, George Mason University, Fairfax, Virginia, USA. Meteorological Institute, University of Bonn, Bonn, Germany. Department of Geography, McGill University, Montreal, Quebec, Canada. Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA. Hydrological Sciences Laboratory, NASA Goddard Space Flight Cen- ter, Greenbelt, Maryland, USA. Department of Civil and Environmental Engineering and Department of Geography, University of Illinois Urbana-Champaign, Urbana, Illinois, USA. School of Human and Environmental Science, University of Reading, Reading, UK. School of Geography and the Environment, Oxford University, Oxford, UK. Copyright 2012 by the American Geophysical Union 0043-1397/12/2011WR011202 model developments with the goal to improving hydrologic predictions and understanding. [ 2 ] What is laid out in W2011 is, first and foremost, a grand challenge because (1) there is a grand need, (2) there are great new opportunities, and (3) if the hydrologic com- munity does not do it someone else will do it, albeit poorly. The reader is directed to W2011 for a discussion of the growing need for continental-scale land surface models that consider improved, scale-appropriate parameteriza- tions of the water, energy and biogeochemical cycles at resolutions on the order of 10 2 to 10 3 m grid resolutions. Some examples are presented, which were not meant be to comprehensive in their scope of detail, that include surface-subsurface interactions, land-atmospheric interac- tions and coupling, water quality that includes nonpoint pollution, and human impacts that include water manage- ment, land cover change and the effects of climate change. [ 3 ] The commentary by BC2012 focuses on just one chal- lenge or building block described in W2011: the issue of parameterization of subgrid heterogeneity and the resulting uncertainty—what they refer to as ‘‘epistemic uncertainty.’’ BC2012 interprets the Grand Challenge in W2011 as ‘‘simply moving to finer resolutions.’’ This is not what W2011 says or proposes. There are many new building blocks available for the research into hyper-resolution modeling: (1) new data sources and measurement techniques for precipitation, topog- raphy, vegetation cover, soils, but also soil moisture, evapo- transpiration, water storages (rivers, lakes, groundwater storages, soil moisture); (2) new physics—new sets of gov- erning equations, including new approaches to developing closure relations; (3) new approaches to handling known and unknown uncertainties in model structure, variables and numerics, including characterizing subgrid heterogeneity (including new ways to capture their effects) based on new insights into ecohydrology and hydropedology and approaches that utilize the coevolution of climate, soils, vege- tation and topography; (4) new approaches that can better include nonlinear feedbacks between various subsystems, and local, regional and global cycles and teleconnections; (5) new regionalization efforts aimed at learning from compara- tive analysis across climatic, geologic and human-impact W01802 1 of 3

Temporal Variability of Land-Atmosphere Coupling and Its Implications for Drought over the Southeast United States

AbstractDroughts represent a significant source of social and economic damage in the southeast United States. Having sufficient warning of these extreme events enables managers to prepare for and potentially mitigate the severity of their impacts. A seasonal hydrologic forecast system can provide such warning, but current forecast skill is low during the convective season when precipitation is affected by regionally varying land surface heat flux contributions. Previous studies have classified regions into coupling regimes based on the tendency of surface soil moisture anomalies to trigger convective rainfall. Until now, these classifications have been aimed at assessing the long-term dominant feedback signal. Sufficient focus has not been placed on the temporal variability that underlies this signal. To better understand this aspect of coupling, a new classification methodology suitable at daily time scales is developed. The methodology is based on the joint probability space of surface soil moisture, co...

Quantifying the Land-Atmosphere Coupling Behavior in Modern Reanalysis Products over the U.S. Southern Great Plains

AbstractThe coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and ...

Impact of land-atmospheric coupling in CFSv2 on drought prediction

Recent summers in the United States have been plagued by intense droughts that have caused significant damage to crops and have had a large impact on society. The ability to forecasts such events would allow for preparations that could help reduce the impact on society. Coupled land–atmosphere–ocean models were created to provide such forecasts but there are large uncertainties associated with their predictions. The predictive skill of these models is particularly low during the convective season due to the weaker connections with the oceans and an increase in the land–atmosphere interactions. To better understand the degradation of forecasts skill during the summer months and its connection to the land–atmosphere interactions we analyze National Centers for Environmental Prediction’s Climate Forecast System Version 2 (CFSv2) in terms of its climatological land–atmosphere interactions. To do this we use a recently developed classification of land–atmosphere interactions and other diagnostic variables to compare the reanalysis from the Climate Forecast System (CFSR) with CFSv2 re-forecasts (CFSRR) over the period 1982–2009. Coupling in the CFSRR tends toward the wet coupling regime for most areas east of the Rocky Mountains. Although the specific mechanism driving CFSRR to wet coupling state varies by region, the overall cause is enhanced vegetation rooting depth, originally implemented to address a near-surface warm bias in CFSR. The long-term tendency to wet coupling precludes the forecast model from consistently predicting and maintaining drought over the continental US.

A Framework for Diagnosing Seasonal Prediction through Canonical Event Analysis

AbstractHydrologic extremes in the form of flood and drought have large impacts on society that can be reduced through preparations made possible by seasonal prediction. However, the skill of seasonal predictions from global climate models is uncertain, which severely limits their practical use. In the past, the skill assessment has been limited to a single temporal or spatial resolution for a short hindcast period, which is prone to sampling errors, and noise that leads to uncertainty. In this work a framework that uses “canonical” forecast events, or averages in space–time, to provide a more certain assessment of when and where models are skillful is developed. This framework is demonstrated by using NCEP’s Climate Forecast System, version 2, hindcast dataset for precipitation and temperature over the contiguous United States (CONUS). As part of the canonical event analyses, the probabilistic predictability metric (PPM) is used to define spatial and seasonal variability of forecast skill and its attribu...

Land–Atmosphere Coupling at the Southern Great Plains Atmospheric Radiation Measurement (ARM) Field Site and Its Role in Anomalous Afternoon Peak Precipitation

AbstractThe multimodel Global Land–Atmosphere Coupling Experiment (GLACE) identified the semiarid Southern Great Plains (SGP) as a hotspot for land–atmosphere (LA) coupling and, consequently, land-derived temperature and precipitation predictability. The area including and surrounding the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) SGP Climate Research Facility has in particular been well studied in the context of LA coupling. Observation-based studies suggest a coupling signal that is much weaker than modeled, if not elusive. Using North American Regional Reanalysis and North American Land Data Assimilation System data, this study provides a 36-yr (1979–2014) climatology of coupling for ARM-SGP that 1) unifies prior interdisciplinary efforts and 2) isolates the origin of the (weak) coupling signal. Specifically, the climatology of a prominent convective triggering potential–low-level humidity index (CTP–HIlow) coupling classification is linked to corresponding synoptic–mesoscale wea...