Establishing a Framework for Community Modeling in Hydrologic Science

Murdoch, Lawrence C.; Lakshmi, V.; Hooper, Richard; Arrigo, J. S.

doi:10.4211/techrpts.20110317.tr10

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The precipitation from TRMM, GPCP, and the four GLDAS models (NOAH, VIC, CLM, and MOS), and ET from MODIS and the four GLDAS models are compared with in situ data. To verify LWSC, the time derivative (dS /dt) is determined (Famiglietti et al 2011;Long et al 2014) by the following equations:…”

Section: Verification Of P Et and Lwscmentioning

confidence: 99%

“…GRACE and other remote sensing tasks cannot replace ground-based hydrological observations now, but they provide a unique approach to global water resource management. With the development of satellite technology, regional and global hydrological models obtained by means of satellite observation will better serve the management of water resources (Famiglietti et al 2011). Combined hydrological remote sensing observations, hydrological models, and existing high-precision hydrological station observations can provide a wide range of high-precision available freshwater maps for a particular region or the entire world.…”

Section: Uncertainty Of Gwsc Assessment In Jrbmentioning

confidence: 99%

See 1 more Smart Citation

Groundwater Storage Change in the Jinsha River Basin from GRACE, Hydrologic Models, and In Situ Data

Chao¹,

Chen

et al. 2019

Groundwater

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Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76 ± 0.10 cm/year during the period 2003 to 2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change, GWSC, and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.

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Section: Verification Of P Et and Lwscmentioning

confidence: 99%

Section: Uncertainty Of Gwsc Assessment In Jrbmentioning

confidence: 99%

Groundwater Storage Change in the Jinsha River Basin from GRACE, Hydrologic Models, and In Situ Data

Chao¹,

Chen

et al. 2019

Groundwater

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“…We approach the workflow problem from a catchment modeling perspective within the wider Earth System modeling community (see the definitions of different communities in Archfield et al, 2015). Calls for more efficient, transparent, and shareable model configuration approaches are not new in the catchment modeling community (see e.g., Blair et al, 2019;Famiglietti et al, 2011;Hutton et al, 2016;Tarboton et al, 2009;Weiler & Beven, 2015) and considerable progress along these lines has been made. For example, Sen Gupta et al (2015) standardize model inputs and outputs to efficiently couple a snow accumulation and melt routine with an existing open-source modeling framework; Ecohydrolib (Miles, 2014;Miles & Band, 2015;Miles et al, 2022) is a Python API that automatically preprocesses ecohydrologic parameter fields and forms the basis of a model configuration workflow for the RHESSys model; Bandaragoda et al (2019) develop a general interface for building and coupling multiple models, using the Landlab toolkit (Barnhart et al, 2020;Hobley et al, 2017); Gan et al (2020) integrate a web-based hydrologic model service with a data sharing system to promote reproducible workflows; HydroDS (Dash & Tarboton, 2022;Gichamo et al, 2020) is a web-based service that can be used to prepare input data for modeling; Bennett et al (2018); Bennett et al (2020) create a tool to estimate hourly forcing input for physics-based models from commonly available daily data; Bavay et al (2022) describe a tool that can be used to effectively create a Graphical User Interface for a given model; Essawy et al (2016) provide an example of how containerization (storing a full computational environment into a software container) enhances reproducibility; and Kurtzer et al (2017Kurtzer et al ( , 2021 develop a means of saving and transferring software and computing environments on and between High Performance Computing clusters.…”

Section: Where Do Workflows Stand In the Existing Reproducibility Lan...mentioning

confidence: 99%

Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models

Knoben

Clark

Bales

et al. 2022

Water Resources Research

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Despite the proliferation of computer‐based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process‐based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model‐agnostic preprocessing of specific data sets from the model‐specific requirements that models impose on their input files. We use this workflow to create large‐domain (global and continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling” (CWARHM; pronounced “swarm”).

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“…There was also a series of workshops under the CUAHSI initiative led to a position paper defining the components of a Community Water Model for North America (see https://www.cuahsi.org/PageFiles/docs/ CHyMP-position-paper.pdf) based on gridded topography, parameters, meteorological inputs, and continuum process representations, structured so as to make use of large-scale computing resources [Famiglietti et al, 2011]. This is now not being implemented, but effort has gone into improving the hydrological component of the Community Land Model (CLM) that is aimed at providing a land surface parameterization for gridded atmospheric circulation models.…”

Section: So What Should a Community Hydrological Model Look Like?mentioning

confidence: 99%

“…But perhaps the most important issue is whether the community might be satisfied to work within the type of continuum differential equation approach that has been the basis for the Community Hydrological Models proposed to date [e.g., Famiglietti et al, 2011]. This essentially follows the Freeze and Harlan [1969] blueprint that has been criticized within the hydrological modeling community at least since Beven [1989].…”

Section: Can We Agree On the Modeling Concepts?mentioning

confidence: 99%

Do we need a Community Hydrological Model?

Weiler

Beven

2015

Water Resour. Res.

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We believe that there are too many models in hydrology and we should ask ourselves the question, if we are currently wasting time and effort in developing another model again instead of focusing on the development of a Community Hydrological Model. In other fields, this kind of models has been quite successful, but due to several reasons, no single community model has been developed in the field of hydrology yet. The concept, strength, and weakness of a community model were discussed at the Chapman Conference on Catchment Spatial Behaviour and Complex Organisation held in Luxembourg in September 2014. This discussion as well as our own opinions about the potential of a community models or at least the necessary discussion to establish one are debated in this commentary. There are Too Many Models in HydrologyThis is partly because every generation of PhD students thinks they could do better, partly because there is a wide range of different types of applications with different needs in hydrology, and partly because there is no agreement on a common set of concepts for the process representations. However, many of those models are similar in structure (for both the ''bucket'' model and the ''physically based'' model cases) so that the same, or very similar, concepts have been programmed and tested over and over again. There are advantages to this when models are simple, of course, but as model and data implementations get more and more complex this could be considered a waste of community effort and resources.In the atmospheric modeling domain, there have been initiatives to have community models, both for the atmosphere itself (e.g., CESM [Hurrell et al., 2013] and WRF [Skamarock and Klemp, 2008]) and for the land surface boundary condition, including a representation of the hydrology (e.g., NOAH-MP within WRF [Niu et al., 2011] and CLM [Lawrence et al., 2011]). These models have structured version release and development test bed programs. The Community Land Model (CLM) is widely used as a land surface parameterization in the hydrometeorological community but has not made much impact on the hydrological modeling community. It is not even mentioned in Beven [2012], because it is aimed at partitioning the surface energy balance for different surfaces within a grid, rather than getting better predictions of river discharges. Indeed, it would not pass scrutiny as a satisfactory hydrological model for many hydrologists. Some models developed within the hydrological modeling community have also been released in open source form with a view to building up a large user community (e.g., SWAT, HYPE, or different model codes in R and MATLAB for HBV, TOPMODEL, or Dynamic TOPMODEL (see R codes for both models at http://cran. r-project.org) [e.g., Str€ omqvist et al., 2012;Buytaert, 2013;Fuka et al., 2014;Metcalfe et al., 2015]), and a number of model repositories also exist (e.g., at CSDMS, http://csdms.colorado.edu).The trend in hydrology has rather been to create even more new models, or to provide metamodeling fr...

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Establishing a Framework for Community Modeling in Hydrologic Science

Cited by 4 publications

References 9 publications

Groundwater Storage Change in the Jinsha River Basin from GRACE, Hydrologic Models, and In Situ Data

Groundwater Storage Change in the Jinsha River Basin from GRACE, Hydrologic Models, and In Situ Data

Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models

Do we need a Community Hydrological Model?

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