Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model: Evidence from MOPEX Basins

Huang, Maoyi; Hou, Zhangshuan; Leung, L. Ruby; Ke, Yinghai; Liu, Ying; Fang, Zhufeng; Sun, Yu

doi:10.1175/jhm-d-12-0138.1

Cited by 63 publications

(102 citation statements)

References 57 publications

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“…Other modeling approaches often need additional data and longer time series to calibrate the model [Melsen et al, 2014]. Predictions of low flow conditions are often based on multimodel assessments [e.g., Prudhomme et al, 2014] that have large uncertainty in parameters related to subsurface runoff generation [Hou et al, 2012;Huang et al, 2013] and do not provide efficient guidance in understanding regional landscape differences.…”

Section: Discussionmentioning

confidence: 99%

Streamflow sensitivity to water storage changes across Europe

Berghuijs

Hartmann

Woods

2016

Geophysical Research Letters

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Terrestrial water storage is the primary source of river flow. We introduce storage sensitivity of streamflow (ϵS), which for a given flow rate indicates the relative change in streamflow per change in catchment water storage. ϵS can be directly derived from streamflow observations. Analysis of 725 catchments in Europe reveals that ϵS is high in, e.g., parts of Spain, England, Germany, and Denmark, whereas flow regimes in parts of the Alps are more resilient (that is, less sensitive) to storage changes. A regional comparison of ϵS with observations indicates that ϵS is significantly correlated with variability of low (R2 = 0.41), median (R2 = 0.27), and high flow conditions (R2 = 0.35). Streamflow sensitivity provides new guidance for a changing hydrosphere where groundwater abstraction and climatic changes are altering water storage and flow regimes.

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Section: Discussionmentioning

confidence: 99%

Streamflow sensitivity to water storage changes across Europe

Berghuijs

Hartmann

Woods

2016

Geophysical Research Letters

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“…To be consistent with the grid-based modeling approach, the subbasins are organized into a two-dimensional matrix of subbasins, each being treated as a single node for the resolution of interest. For detailed description of SCLM and its applications, interested readers are referred to Tesfa et al [2014], Li et al [2011], and Huang et al [2013]. In this effort, we set up SCLM at four spatial resolutions (Table 1 and Figure 1) with average subbasin size equivalent to the grid-based modeling approach at 0.125°, 0.25°, 0.5°, and 1°spatial resolutions, where the study domain is delineated into 5999, 1139, 299, and 75 subbasins that are organized as 77 × 78, 38 × 30, 20 × 15, and 10 × 8 matrices, respectively.…”

Section: Subbasin-based Clm (Sclm)mentioning

confidence: 99%

“…In addition to the land surface parameters described above, CLM requires a topographic parameter, F max , which is used to calculate the saturated fraction (F sat ) of each modeling unit (grid cells/subbasins), which in turn is used in the calculation of the saturated component of surface runoff (SRF) [Oleson et al, 2010;Li et al, 2011;Huang et al, 2013] as:…”

Section: Study Domainmentioning

confidence: 99%

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

Tesfa

Leung

Huang

et al. 2014

J. Geophys. Res. Atmos.

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This paper investigates the relative merits of grid-and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., ability to perform consistently across spatial resolutions) in simulating runoff generation. Simulations are produced by the grid-and subbasin-based Community Land Model at 0.125°, 0.25°, 0.5°, and 1°spatial resolutions over the U.S. Pacific Northwest. Using the 0.125°simulation as the "reference" solution, statistical metrics are calculated by comparing simulations at 0.25°, 0.5°, and 1°resolutions with the 0.125°simulation for each approach. Statistical significance test results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach. Basin level annual average relative errors of surface runoff at 0.25°, 0.5°, and 1°r esolutions compared to the 0.125°simulation are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/ rainfall partitioning because of scalability related to air temperature and surface elevation. Scalability of a topographic parameter used in runoff parameterization also contributes to improved scalability of the rain-driven saturated surface runoff component, particularly during winter. Hence, this study demonstrates the importance of spatial structure for multiscale modeling of hydrological processes.

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“…Currently, the CLM-PDAF setup only performs state updates. In future, joint update of states and hydraulic parameters related to soil texture and runoff generation may further improve runoff estimates (Huang et al, 2013).…”

Section: Regional Evaluation Of Seasonal Runoffmentioning

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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Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model: Evidence from MOPEX Basins

Cited by 63 publications

References 57 publications

Streamflow sensitivity to water storage changes across Europe

Streamflow sensitivity to water storage changes across Europe

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

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

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