A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow

Shrestha, Prabhakar; Sulis, Mauro; Masbou, M.; Kollet, Stefan; Forschungszentrum, J; Abc, Geoverbund

doi:10.1175/mwr-d-14-00029.1

Cited by 176 publications

(220 citation statements)

References 56 publications

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“…In a two-way coupling, the feedback is allowed, which leads to production of subgrid scale land surface fluxes and generally an improvement of model simulations (Zabel and Mauser 2013). It is argued that the coupled modeling approach has the advantage of including the soil moisture redistribution feedback in the lower boundary conditions of atmospheric models, which may lead to an improved representation of water and energy fluxes between land and atmosphere (Maxwell et al 2011;Shrestha et al 2014;Senatore et al 2015;Arnault et al 2016;Wagner et al 2016). Maxwell et al (2007) showed that the fully coupled modeling system yields a topographically driven soil moisture distribution and depicts a spatial and temporal correlations between surface and lower atmospheric variables and water depth.…”

Section: Introductionmentioning

confidence: 99%

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Kerandi

Arnault

Laux

et al. 2017

Theor Appl Climatol

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For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water balances are investigated. This is achieved through the application of the Weather Research and Forecasting (WRF) and the fully coupled WRF-Hydro modeling system over the MathioyaSagana subcatchment (3279 km 2 ) and its surroundings in the upper Tana River basin for 4 years (2011)(2012)(2013)(2014). The model setup consists of an outer domain at 25 km (East Africa) and an inner one at 5-km (Mathioya-Sagana subcatchment) horizontal resolution. The WRF-Hydro inner domain is enhanced with hydrological routing at 500-m horizontal resolution. The results from the fully coupled modeling system are compared to those of the WRF-only model. The coupled WRF-Hydro slightly reduces precipitation, evapotranspiration, and the soil water storage but increases runoff. The total precipitation from March to May and October to December for WRF-only (974 mm/year) and coupled WRF-Hydro (940 mm/year) is closer to that derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (989 mm/year) than from the TRMM (795 mm/year) precipitation product. The coupled WRF-Hydro-accumulated discharge (323 mm/year) is close to that observed (333 mm/ year). However, the coupled WRF-Hydro underestimates the observed peak flows registering low but acceptable NSE (0.02) and RSR (0.99) at daily time step. The precipitation recycling and efficiency measures between WRF-only and coupled WRF-Hydro are very close and small. This suggests that most of precipitation in the region comes from moisture advection from the outside of the analysis domain, indicating a minor impact of potential land-precipitation feedback mechanisms in this case. The coupled WRF-Hydro nonetheless serves as a tool in quantifying the atmospheric-terrestrial water balance in this region.

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Section: Introductionmentioning

confidence: 99%

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Kerandi

Arnault

Laux

et al. 2017

Theor Appl Climatol

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“…Kurtz et al (2016) recently provided a framework to couple PDAF with the land surface-subsurface part of the Terrestrial Systems Modelling Platform (TerrSysMP; Gasper et al, 2014;Shrestha et al, 2014). They showed the efficient use of parallel computational resources by TerrSysMP-PDAF, which is needed to simulate predicted states and fluxes over large 30 spatial domains and long simulations.…”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

“…Discuss., https://doi.org /10.5194/hess-2018- Additionally, by selecting the ESA CCI soil moisture product for assimilation, the potential impact of a long term soil moisture observations on hydrologic simulations can be assessed for climate change studies. Moreover, CLM3.5 is also part of the fully coupled Terrestrial Systems Model Platform (TerrSysMP; Shrestha et al, 2014, Gasper et al, 2014, Keune et al, 2016) that simulates the full terrestrial hydrologic cycle including feedbacks between atmosphere, land-surface and subsurface compartments of the water cycle. The impact of satellite soil moisture assimilation on other water cycle variables 5 across the soil-vegetation-atmosphere system using TerrSysMP and its effects on the accuracy of model simulations at the continental scale remains to be explored.…”

mentioning

confidence: 99%

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Naz

Kurtz

Montzka

et al. 2018

Preprint

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Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated 15 ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm 3 /mm 3 in open-loop simulations to 0.004 mm 3 /mm 3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring. 25

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“…ParFlow parallel I/O is via task-local and shared files in a binary format for each time step. ParFlow is also part of fully coupled model systems such as the Terrestrial Systems Modeling Platform (TerrSysMP) (Shrestha et al, 2014) or PF.WRF (Maxwell et al, 2011), which can reproduce the water cycle from deep aquifers into the atmosphere.…”

Section: Test Case Experimental Designmentioning

confidence: 99%

Best practice regarding the three P's: profiling, portability and provenance when running HPC geoscientific applications

Sharples

Zhukov

Geimer

et al. 2017

Preprint

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Abstract. Geoscientific modeling is constantly evolving, with next generation geoscientific models and applications placing high demands on high performance computing (HPC) resources. These demands are being met by new developments in HPC architectures, software libraries, and infrastructures. New HPC developments require new programming paradigms leading to substantial investment in model porting, tuning, and refactoring of complicated legacy code in order to use these resources effectively. In addition to the challenge of new massively parallel HPC systems, reproducibility of simulation and analysis 5 results is of great concern, as the next generation geoscientific models are based on complex model implementations and profiling, modeling and data processing workflows.Thus, in order to reduce both the duration and the cost of code migration, aid in the development of new models or model components, while ensuring reproducibility and sustainability over the complete data life cycle, a streamlined approach to profiling, porting, and provenance tracking is necessary. We propose a run control framework (RCF) integrated with a workflow 10 engine which encompasses all stages of the modeling chain: 1. preprocess input, 2. compilation of code (including code instrumentation with performance analysis tools), 3. simulation run, 4. postprocess and analysis, to address these issues. Within this RCF, the workflow engine is used to create and manage benchmark or simulation parameter combinations and performs the documentation and data organization for reproducibility. This approach automates the process of porting and tuning, profiling, testing, and running a geoscientific model. We show that in using our run control framework, testing, benchmarking, profiling, 15 and running models is less time consuming and more robust, resulting in more efficient use of HPC resources, more strategic code development, and enhanced data integrity and reproducibility.

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A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow

Cited by 176 publications

References 56 publications

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Best practice regarding the three P's: profiling, portability and provenance when running HPC geoscientific applications

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