scholarly journals Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

2021 ◽  
Vol 14 (12) ◽  
pp. 7223-7254
Author(s):  
Mary M. F. O'Neill ◽  
Danielle T. Tijerina ◽  
Laura E. Condon ◽  
Reed M. Maxwell
Keyword(s):  
United States ◽  
High Resolution ◽  
Water Balance ◽  
Water Table ◽  
Performance Metrics ◽  
System Modeling ◽  
Hydrologic Model ◽  
Surface Model ◽  
Water Balance Components ◽  
Continental Scale

Abstract. Recent advancements in computational efficiency and Earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific, and operational benefits. The coupled hydrology–land surface model ParFlow–CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date by comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude, especially for the Ohio, Missouri, and Arkansas River basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance.

scholarly journals Assessment of the ParFlow-CLM CONUS 1.0 integrated hydrologic model: Evaluation of hyper-resolution water balance components across the contiguous United States

2020 ◽  
Author(s):  
Mary M. F. O'Neill ◽  
Danielle T. Tijerina ◽  
Laura E. Condon ◽  
Reed M. Maxwell
Keyword(s):  
United States ◽  
High Resolution ◽  
Water Balance ◽  
Water Table ◽  
Performance Metrics ◽  
System Modeling ◽  
Hydrologic Model ◽  
Surface Model ◽  
Water Balance Components ◽  
Continental Scale

Abstract. Recent advancements in computational efficiency and earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific and operational benefits. The coupled hydrology-land surface model ParFlow-CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented, process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date, comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products, and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to appropriately reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude especially for the Ohio, Missouri and Arkansas river basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance.

Evaluating the added-value of state-of-the-art soil property maps in land surface modeling over the Contiguous United States

2021 ◽  
Author(s):  
Chengcheng Xu ◽  
Laura Torres Rojas ◽  
Nathaniel W Chaney
Keyword(s):  
United States ◽  
Soil Properties ◽  
Eddy Covariance ◽  
Land Surface ◽  
Performance Metrics ◽  
State Of The Art ◽  
Surface Fluxes ◽  
Added Value ◽  
Surface Model ◽  
Continental Scale

<p>The accurate representation of soil properties in the land component of Earth system models (land surface models; LSMs) remains a persistent challenge. The emergence of state-of-the-art continental-scale digital soil mapping (DSM) provides a unique opportunity to address this weakness (e.g., SoilGrids and POLARIS). However, it remains unclear whether these data are able to improve the modeling of land surface fluxes and states (e.g., latent heat flux). This presentation addresses this question by running and evaluating a field-scale resolving land surface model (HydroBlocks) at each of the eddy covariance sites in the NEON and Ameriflux networks over the Contiguous United States (~250 sites). More explicitly, the HydroBlocks LSM is run at a 30-meter spatial resolution in 5 km boxes centered around each of the NEON eddy covariance sites using both the POLARIS and Soilgrids soil properties databases. The model is also run using the CONUS-Soil (i.e., STATSGO) soil properties database as a baseline for comparison. Each simulation is run between 2002 and 2018 at a 1-hour resolution. The remaining datasets used to parameterize and force HydroBlocks includes the Princeton Climate Forcing meteorological dataset (PCF), USGS elevation data, and the National Land Cover dataset (NLCD) with a 5-year spin-up period. The simulated soil moisture and land surface fluxes are then evaluated using available in-situ and eddy covariance measurements in the NEON and Ameriflux networks using a suite of performance metrics over multiple temporal scales. </p>

scholarly journals Verification and Intercomparison of Multimodel Simulated Land Surface Hydrological Datasets over the United States

2011 ◽  
Vol 12 (4) ◽  
pp. 531-555 ◽  
Author(s):  
Yun Fan ◽  
Huug M. van den Dool ◽  
Wanru Wu
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Surface Water ◽  
Water Balance ◽  
Time Scales ◽  
Land Surface ◽  
The United States ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Water Balance Components

Abstract Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

scholarly journals Continental Runoff into the Oceans (1950–2008)

2015 ◽  
Vol 16 (4) ◽  
pp. 1502-1520 ◽  
Author(s):  
Elizabeth A. Clark ◽  
Justin Sheffield ◽  
Michelle T. H. van Vliet ◽  
Bart Nijssen ◽  
Dennis P. Lettenmaier
Keyword(s):  
South America ◽  
Land Surface ◽  
Water Cycle ◽  
Land Surface Model ◽  
Hydrologic Model ◽  
Surface Model ◽  
Infiltration Capacity ◽  
Continental Scale ◽  
Wide Range ◽  
Continental Runoff

Abstract A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world’s oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world’s oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia–Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia–Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa.

scholarly journals A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates

2020 ◽  
Vol 22 (2) ◽  
pp. 440-451
Author(s):  
George Falalakis ◽  
Alexandra Gemitzi
Keyword(s):  
Water Balance ◽  
Performance Metrics ◽  
Empirical Relationship ◽  
Remotely Sensed ◽  
Empirical Method ◽  
Data Availability ◽  
Water Balance Components ◽  
Time Step ◽  
Simple Method ◽  
Satisfactory Outcome

Abstract Developing a methodology for water balance estimation is a significant challenge, especially in areas with little or no gauging. This is because direct measurements of all the water balance components are not feasible. To overcome this issue, we propose a simple methodology based on the predefined empirical relationship between remotely sensed evapotranspiration (ET), i.e. Moderate Resolution Imaging Spectroradiometer (MODIS) ET and groundwater recharge (GR), and readily available precipitation data at the monthly time step. The developed methodology was applied in seven catchments in NE Greece using time series of precipitation and remotely sensed ET from 2009 to 2019. The potential of the proposed method to accurately estimate the water balance was assessed by the comparison of the individual water balance components against modeled values. Three performance metrics were examined and indicated that the methodology produces a satisfactory outcome. Our results indicated mean ET accounting for approximately 54% of precipitation, mean GR of 24% and mean surface runoff approximately 22% of precipitation in the study area. The proposed approach was implemented using freely available remotely sensed products and the free R software for statistical computing and graphics, offering thus a convenient and inexpensive alternative for water balance estimation, even for basins with limited data availability.

Improvement of the simulation of the water and energy cycle using Multiscale Parameter Regionalization (MPR)

2020 ◽  
Author(s):  
Stephan Thober ◽  
Matthias Kelbling ◽  
Florian Pappenberger ◽  
Christel Prudhomme ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
High Resolution ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Surface Model ◽  
Model Parameters ◽  
Environmental Models ◽  
Energy Cycle

<p>The representation of the water and energy cycle in environmental models is closely linked to the parameter values used in the process parametrizations. The dimension of the parameter space in spatially distributed environmental models corresponds to the number of grid cells multiplied by the number of parameters per grid cell. For large-scale simulations on national and continental scales, the dimensionality of the parameter space is too high for efficient parameter estimation using inverse estimation methods. A regularization of the parameter space is necessary to reduce its dimensionality. The Multiscale Parameter Regionalization (MPR) is one approach to achieve this.</p><p>MPR translates local geophysical properties into model parameters. It consists of two steps: 1) local high-resolution geophysical data sets (e.g. soil maps) are translated into model parameters using a transfer function. 2) the high-resolution model parameters are scaled to the model resolution using suitable upscaling operators (e.g., harmonic mean). The MPR technique was introduced into the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013) and it is key factor for its success on transferring parameters across scales and locations.  </p><p>In this study, we apply MPR to vegetation and soil parameters in the land surface model HTESSEL. This model is the land-surface component of the European Centre for Medium-Range Weather Forecasting seasonal forecasting system. About 100 hard-coded parameters have been extracted to allow for a comprehensive sensitivity analysis and parameter estimation.</p><p>We analyze simulated evaporation and runoff fluxes by HTESSEL using parameters estimated by MPR in comparison to a default HTESSEL setup over Europe. The magnitude of simulated long-term fluxes deviates the most (up to 10% and 20% for evapotranspiration and runoff, respectively) in regions with a large subgrid variability in geophysical attributes (e.g., soil texture). The choice of transfer functions and upscaling operators influences the magnitude of these differences and governs model performance assessed after calibration against observations (e.g. streamflow).</p><p><strong>References:</strong></p><p>Samaniego L., et al.  <strong>https://doi.org/10.1029/2008WR007327</strong></p><p>Kumar, R., et al.  <strong>https://doi.org/10.1029/2012WR012195</strong></p>

scholarly journals mizuRoute version 1: a river network routing tool for a continental domain water resources applications

2016 ◽  
Vol 9 (6) ◽  
pp. 2223-2238 ◽  
Author(s):  
Naoki Mizukami ◽  
Martyn P. Clark ◽  
Kevin Sampson ◽  
Bart Nijssen ◽  
Yixin Mao ◽  
...  
Keyword(s):  
United States ◽  
Water Resources ◽  
The United States ◽  
Hydrologic Model ◽  
River Network ◽  
Surface Model ◽  
Network Data ◽  
Unit Hydrograph ◽  
Spatially Distributed ◽  
Grid Based

Abstract. This paper describes the first version of a stand-alone runoff routing tool, mizuRoute. The mizuRoute tool post-processes runoff outputs from any distributed hydrologic model or land surface model to produce spatially distributed streamflow at various spatial scales from headwater basins to continental-wide river systems. The tool can utilize both traditional grid-based river network and vector-based river network data. Both types of river network include river segment lines and the associated drainage basin polygons, but the vector-based river network can represent finer-scale river lines than the grid-based network. Streamflow estimates at any desired location in the river network can be easily extracted from the output of mizuRoute. The routing process is simulated as two separate steps. First, hillslope routing is performed with a gamma-distribution-based unit-hydrograph to transport runoff from a hillslope to a catchment outlet. The second step is river channel routing, which is performed with one of two routing scheme options: (1) a kinematic wave tracking (KWT) routing procedure; and (2) an impulse response function – unit-hydrograph (IRF-UH) routing procedure. The mizuRoute tool also includes scripts (python, NetCDF operators) to pre-process spatial river network data. This paper demonstrates mizuRoute's capabilities to produce spatially distributed streamflow simulations based on river networks from the United States Geological Survey (USGS) Geospatial Fabric (GF) data set in which over 54 000 river segments and their contributing areas are mapped across the contiguous United States (CONUS). A brief analysis of model parameter sensitivity is also provided. The mizuRoute tool can assist model-based water resources assessments including studies of the impacts of climate change on streamflow.

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