Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Saxe, Samuel; Farmer, William; Driscoll, Jessica M.; Hogue, T. S.

doi:10.5194/hess-25-1529-2021

Cited by 14 publications

(11 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although model adoption can be more of a function of legacy than adequacy, models' reliability rests on performance evaluation (Adorr and Melsen, 2019). Performance evaluation, which includes model benchmarking and diagnostic efforts, benefits from standardized methods and ready-to-use toolkits that implement those methods (Kollet et al, 2017;Nearing et al, 2018;Lane et al, 2019;Saxe et al, 2021;Tijerina et al, 2021). Standardized methods and toolkits also help modeling communities and model users build trust in a model's operational reliability.…”

Section: Introductionmentioning

confidence: 99%

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

et al. 2022

View full text Add to dashboard Cite

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

show abstract

Section: Introductionmentioning

confidence: 99%

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…As the applications have expanded, the availability of TWB estimates has increased rapidly (Peters-Lidard et al, 2018;Saxe et al, 2021;Zhang et al, 2018). Commonly used estimation methods include remote sensing, in-situ observations, and model simulations (Saxe et al, 2021;McCabe et al, 2017;Pan et al, 2012;Gao et al, 2010;Trenberth et al, 2007). Among these methods, land surface models (LSMs) are apt for continuously producing physically consistent TWBs over a large domain and long period, and their characteristics are particularly favorable for certain circumstances.…”

Section: Introductionmentioning

confidence: 99%

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

Zheng

Fei

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS‑2) four-LSM ensemble, this study presents a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP). The 48 Noah‑MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The ensemble simulated the 1980–2015 monthly TWB over the conterminous United States (CONUS) at a 1/8° spatial resolution. Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). This dataset is available at https://doi.org/10.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilities the identification of model deficiencies. The parameterizations of shallow snow, lakes, and near-surface atmospheric turbulence should be improved in future model versions.

show abstract

“…In this equation, R represents runoff, A is the area of the lake, and E and P are evaporation and precipitation, respectively. However, some studies have referred to the snow water equivalent (SWE) and soil moisture (SM) parameters for hydrological estimates (Saxe et al, 2021). This equation can estimate the share of the annual, monthly, or daily balance of a catchment.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data

sharif¹,

Abdollahi

‎³

2022

Preprint

View full text Add to dashboard Cite

Caspian Sea level (CSL) fluctuations are driven by reciprocal hydro-meteorological processes that extend not only over the entire catchment area but also far beyond. According to the research literature, CSL has dropped approximately 3 m in the short-term (1929–1977). In this study, change in precipitation, runoff, and evaporation in the catchment area, river discharge, sea surface temperature (SST), and also CSL change were investigated over 40-year decades. Study results reveal two major change in the increasing trend of SST anomalies. With an increasing trend of 0.03 at the CS level, the results indicate an increase in temperature of more than 1.5°C during the study period, which can be a response to the increase in air and land surface temperature on a regional and global scale. The decline in river discharge levels has also been significant in recent decades. Reconstruction of long-term change in CSL with daily water level data between 1981–2020 through fluxes also showed that the average sea level rise was about 20 cm/yr in the 1981–1995 period and a decrease of 6 cm/yr during the 1996–2020 period was well visible in the results of the water balance equation. Moreover, according to the observed peak values of the four parameters precipitation, evaporation, runoff, and SST, the trend of increasing sea surface temperature and decreasing precipitation (with a slope of -1.8 mm/40 year) is more consistent with CSL change.

show abstract

Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Cited by 14 publications

References 116 publications

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

Monitoring of Caspian Sea level change affected by atmospheric parameters using remote sensing data

Contact Info

Product

Resources

About