The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility

Hamman, Joseph; Nijssen, Bart; Bohn, Theodore J.; Gergel, Diana R.; Mao, Yixin

doi:10.5194/gmd-11-3481-2018

Cited by 170 publications

(108 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We selected seven frequently used conceptual hydrologic models: the Hydrologiska Byråns Vattenbalansavdelning model (HBV; Bergström, 1976;Seibert & Vis, 2012), the Variable Infiltration Capacity model (VIC; Hamman et al, 2018;Liang et al, 1994), the Sacramento soil moisture accounting model (SACRAMENTO; Burnash et al, 1973), the Génie Rural 4 model (GR4J; Perrin et al, 2003), the mesoscale Hydrological Model (mHM; Samaniego et al, 2010), the TOPography based hydrological model (TOPMODEL; Beven & Freer, 2001;Beven & Kirkby, 1979;Metcalfe et al, 2015), and the Precipitation Runoff Modeling System (PRMS; Leavesley et al, 1983;Markstrom et al, 2015). The selection of these seven models is based on their popularity and similarity.…”

Section: Methodsmentioning

confidence: 99%

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

Addor

Melsen

2019

Water Resources Research

151

103

View full text Add to dashboard Cite

The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to stick to the model they have experience with, and rarely switch to competing models, although these models might be more adequate given the study objectives. To gain quantitative insights into model selection, we explored the use of seven rainfall‐runoff models based on the abstract of 1,529 peer‐reviewed papers published between 1991 and 2018. The models selected were the Hydrologiska Byråns Vattenbalansavdelning model (HBV), the Variable Infiltration Capacity model (VIC), the mesoscale Hydrological model (mHM), the TOPography‐based hydrologic model (TOPMODEL), the Precipitation Runoff Modelling System (PRMS), the Génie Rural model à 4 paramètres Journaliers (GR4J), and the Sacramento soil moisture accounting model. We provide quantitative evidence of regional preferences in model use across the world and demonstrate that specific models are consistently preferred by certain institutes. Model attachment is particularly strong. In ~74% of the studies, the model selected can be predicted solely based on the affiliation of the first author. The influence of adequacy on the model selection process is less clear. Our data reveal that each model is used across a wide range of purposes, landscapes, and temporal and spatial scales (i.e., as a model of everything and everywhere). Model intercomparisons can provide guidance for model selection and improve model adequacy, but they are still rare (because each model must usually be setup individually) and the insights they provide are currently limited (because they are rarely controlled experiments). We suggest that moving from fixed‐structure models to modular modeling frameworks (master templates for model generation) can overcome these issues, enable a more collaborative and responsive model development environment, and result in improved model adequacy.

show abstract

Section: Methodsmentioning

confidence: 99%

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

Addor

Melsen

2019

Water Resources Research

151

103

View full text Add to dashboard Cite

show abstract

“…Many hydrological models include a surface module, snow module, meteorological driving module, frozen soil module for calculating the effect of the freezing-thawing process on heat flux and humidity, lake and wetland module for calculating the water-heat balance of lakes and wetlands, carbon cycle module, and confluence algorithm for connecting the grids with river channels [35]. Due to the difficulty of data acquisition and computational complexity, the modules generally considered in estimating runoff yield and confluence using the hydrological model are the surface module, meteorological driving module, and confluence module; that is, the digital elevation model (DEM) grid data, meteorological data, soil data, and vegetation parameters data of the study area.…”

Section: Selection Of Input Parametersmentioning

confidence: 99%

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

et al. 2020

View full text Add to dashboard Cite

Modeling the relationship between precipitation and water level is of great significance in the prevention of flood disaster. In recent years, the use of machine learning algorithms for precipitation–water level prediction has attracted wide attention in flood forecasting and other fields; however, a clear method to model the relationship of precipitation and water level using grid precipitation products with a neural network model is lacking. The issues of the method include how to select a neural network model, as well as how to influence the modeling results with different types and resolutions of remote sensing data. The purpose of this paper is to provide some findings for the issues. We used the back-propagation (BP) neural network and a nonlinear autoregressive exogenous model (NARX) time series network to model the relationship between precipitation and water level, respectively. The water level of Pingshan hydrographic station at a catchment area in the Jinsha River Basin was simulated by the two network models using three different grid precipitation products. The results showed that when the ground station data are missing, the grid precipitation product is a good alternative to construct the precipitation–water level relationship. In addition, using the NARX network as a model fitting network using extra inputs was better than using the BP neural network; the Nash efficiency coefficients of the former were all higher than 97%, while the latter were all lower than 94%. Furthermore, the input of grid products with different spatial resolutions has little significant effect on the modeling results of the model.

show abstract

“…At present, soil moisture data are obtained through three main methods: (1) Simulating soil moisture using land surface process models and hydrological models [10][11][12]. (2) Measuring soil moisture through ground meteorological observatories [13].…”

Section: Introductionmentioning

confidence: 99%

Novel Soil Moisture Estimates Combining the Ensemble Kalman Filter Data Assimilation and the Method of Breeding Growing Modes

Hong

Mousa

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Soil moisture plays an important role in climate prediction and drought monitoring. Data assimilation, as a method of integrating multi-geographic spatial data, plays an increasingly important role in estimating soil moisture. Model prediction error, an important part of the background field information, occupies a position that could not be ignored in data assimilation. The model prediction error in data assimilation consists of three parts: forcing data error, initial field error, and model error. However, the influence of model error in current data assimilation methods has not been completely considered in many studies. Therefore, we proposed a theoretical framework of the ensemble Kalman filter (EnKF) data assimilation based on the breeding of growing modes (BGM) method. This framework used the BGM method to perturb the initial field error term w of EnKF, and the EnKF data assimilation to assimilate the data to obtain the soil moisture analysis value. The feasibility and superiority of the proposed framework were verified, taking into consideration breeding length and ensemble size through experiments. We conducted experiments and evaluated the accuracy of the BGM and the Monte Carlo (MC) methods. The experiment showed that the BGM method could improve the estimation accuracy of the assimilated soil moisture and solve the problem of model error which is not fully expressed in data assimilation. This study can be widely used in data assimilation and has a significant role in weather forecast and drought monitoring.

show abstract

The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility

Cited by 170 publications

References 84 publications

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models

Modeling the Relationship of Precipitation and Water Level Using Grid Precipitation Products with a Neural Network Model

Novel Soil Moisture Estimates Combining the Ensemble Kalman Filter Data Assimilation and the Method of Breeding Growing Modes

Contact Info

Product

Resources

About