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  • Author or Editor: Bart Nijssen x
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William Ryan Currier
,
Andrew W. Wood
,
Naoki Mizukami
,
Bart Nijssen
,
Joseph J. Hamman
, and
Ethan D. Gutmann

Abstract

Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario, differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, Arizona, relative to the historical period (1960–2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River basin, projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the upper basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models.

Significance Statement

Understanding systemic changes in annual Colorado River basin flows is critical for managing long-term reservoir levels. Single-digit percentage decreases have the potential to degrade the regions’ water supply, hydropower generation, and environmental concerns. Hydrology projections under climate change have largely been based on simulations from the Variable Infiltration Capacity model. Updating the model’s vegetation representation based on updated satellite information highlighted the sensitivity of the hydrologic projections to the models’ vegetation representation primarily within forested areas. This updated model did not increase in evapotranspiration by the end of the century as much as previous simulations. This increased the mean and ensemble spread of the projected streamflow changes, emphasizing the need to properly characterize the hydrologic model’s vegetation parameters and better constrain model climate sensitivity.

Open access
Bart Nijssen
,
Shraddhanand Shukla
,
Chiyu Lin
,
Huilin Gao
,
Tian Zhou
,
Ishottama
,
Justin Sheffield
,
Eric F. Wood
, and
Dennis P. Lettenmaier

Abstract

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near–real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources.

Full access
Andrew J. Newman
,
Martyn P. Clark
,
Jason Craig
,
Bart Nijssen
,
Andrew Wood
,
Ethan Gutmann
,
Naoki Mizukami
,
Levi Brekke
, and
Jeff R. Arnold

Abstract

Gridded precipitation and temperature products are inherently uncertain because of myriad factors, including interpolation from a sparse observation network, measurement representativeness, and measurement errors. Generally uncertainty is not explicitly accounted for in gridded products of precipitation or temperature; if it is represented, it is often included in an ad hoc manner. A lack of quantitative uncertainty estimates for hydrometeorological forcing fields limits the application of advanced data assimilation systems and other tools in land surface and hydrologic modeling. This study develops a gridded, observation-based ensemble of precipitation and temperature at a daily increment for the period 1980–2012 for the conterminous United States, northern Mexico, and southern Canada. This allows for the estimation of precipitation and temperature uncertainty in hydrologic modeling and data assimilation through the use of the ensemble variance. Statistical verification of the ensemble indicates that it has generally good reliability and discrimination of events of various magnitudes but has a slight wet bias for high threshold events (>50 mm). The ensemble mean is similar to other widely used hydrometeorological datasets but with some important differences. The ensemble product produces a more realistic occurrence of precipitation statistics (wet day fraction), which impacts the empirical derivation of other fields used in land surface and hydrologic modeling. In terms of applications, skill in simulations of streamflow in 671 headwater basins is similar to other coarse-resolution datasets. This is the first version, and future work will address temporal correlation of precipitation anomalies, inclusion of other data streams, and examination of topographic lapse rate choices.

Full access
Martyn P. Clark
,
Reza Zolfaghari
,
Kevin R. Green
,
Sean Trim
,
Wouter J. M. Knoben
,
Andrew Bennett
,
Bart Nijssen
,
Andrew Ireson
, and
Raymond J. Spiteri

Abstract

The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are twofold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral subsurface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as “laugh tests” for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model implemented using the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against 1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature, 2) analytical solutions, and 3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models.

Open access
Ashley E. Van Beusekom
,
Lauren E. Hay
,
Andrew R. Bennett
,
Young-Don Choi
,
Martyn P. Clark
,
Jon L. Goodall
,
Zhiyu Li
,
Iman Maghami
,
Bart Nijssen
, and
Andrew W. Wood

Abstract

Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

Full access
Naoki Mizukami
,
Martyn P. Clark
,
Ethan D. Gutmann
,
Pablo A. Mendoza
,
Andrew J. Newman
,
Bart Nijssen
,
Ben Livneh
,
Lauren E. Hay
,
Jeffrey R. Arnold
, and
Levi D. Brekke

Abstract

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

Full access