On the non-uniqueness of the hydro-geomorphic responses in a zero-order catchment with respect to soil moisture

Kim, Jong-Ho; Dwelle, M. C.; Kampf, Stephanie K.; Fatichi, Simone; Ivanov, V. Y.

doi:10.1016/j.advwatres.2016.03.019

Cited by 22 publications

(15 citation statements)

References 84 publications

(94 reference statements)

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“…A first-order priority for land surface models (LSMs) is accurately capturing the degree to which prestorm soil moisture levels constrain event runoff coefficients (Koster & Milly, 1997; i.e., the fraction of rainfall accumulation volume converted into stormflow during a storm event). The relationship between prestorm soil moisture and hydrologic basin response has received considerable attention in small-scale field studies (e.g., Western & Grayson, 1998) and the development of hydro-geomorphologic models capable of capturing the coupled relationship between stormflow, erosion, and sediment transport (e.g., Kim et al, 2016). Such work has contributed to an improved understanding of the complex role soil moisture plays in various runoff generation processes (e.g., Mirus & Loague, 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Crow¹,

Chen²,

Reichle

et al. 2018

Geophysical Research Letters

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Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between pre-storm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation depth converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration-excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent sub-surface stormflow or saturation-excess runoff generation processes.

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Section: Introductionmentioning

confidence: 99%

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Crow¹,

Chen²,

Reichle

et al. 2018

Geophysical Research Letters

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“…They also found that this nonuniqueness in the erosive response was due to the first storm resulting in different compositions of the deposited layer (or initial conditions) prior to the start of the second storm. Kim et al [2016b] also demonstrated the role of the subsurface initial moisture content in causing nonunique sediment transport under the same rainfall history. Numerical simulations reported by Kim et al [2016a] on total sediment loss at the plot scale provide evidence (their supporting information Figure S2) of the dependence of clockwise and counter-clockwise hysteresis loops on the initial state of the deposited layer.…”

Section: Publicationsmentioning

confidence: 86%

Hysteretic sediment fluxes in rainfall‐driven soil erosion: Particle size effects

Cheraghi

Jomaa

Sander

et al. 2016

Water Resources Research

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A detailed laboratory study was conducted to examine the effects of particle size on hysteretic sediment transport under time‐varying rainfall. A rainfall pattern composed of seven sequential stepwise varying rainfall intensities (30, 37.5, 45, 60, 45, 37.5, and 30 mm h−1), each of 20 min duration, was applied to a 5 m × 2 m soil erosion flume. The soil in the flume was initially dried, ploughed to a depth of 20 cm and had a mechanically smoothed surface. Flow rates and sediment concentration data for seven particle size classes (<2, 2–20, 20–50, 50–100, 100–315, 315–1000, and >1000 µm) were measured in the flume effluent. Clockwise hysteresis loops in the sediment concentration versus discharge curves were measured for the total eroded soil and the finer particle sizes (<2, 2–20, and 20–50 µm). However, for particle sizes greater than 50 µm, hysteresis effects decreased and suspended concentrations tended to vary linearly with discharge. The Hairsine and Rose (HR) soil erosion model agreed well with the experimental data for the total eroded soil and for the finer particle size classes (up to 50 µm). For the larger particle size classes, the model provided reasonable qualitative agreement with the measurements although the fit was poor for the largest size class (>1000 µm). Overall, it is found that hysteresis varies amongst particle sizes and that the predictions of the HR model are consistent with hysteretic behavior of different sediment size classes.

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“…Real-time forecasting is an important component of flood risk management and mitigation but is subject to multiple uncertainties caused by meteorological inputs, initial states, model structures, and model parameters (Ajami et al, 2007;Beven, 1989;Mockler et al, 2016;Moradkhani & Sorooshian, 2008). Due to the complexities of natural phenomena represented by equifinality (Beven, 2006;Beven & Freer, 2001), hysteresis (Fatichi et al, 2015;Ivanov et al, 2010;Wei & Dewoolkar, 2006), nonuniqueness (Beven, 2000;McKenna et al, 2003;Kim & Ivanov, 2014;Kim, Dwelle, et al, 2016), nonlinearity (Kim & Ivanov, 2015;Kitanidis & Bras, 1980;Xie & Zhang, 2010), and internal variability (Kim et al, 2016a(Kim et al, , 2016bKim et al, 2018;Lafaysse et al, 2014;Mondal & Mujumdar, 2012;Nikiema & Laprise, 2011), perfect predictions using numerical models are infeasible. The problem exacerbates, if one attempts to simulate constitutive models derived from empirical or phenomenological observations rather than basic conservation laws of physics that would also require embracing a large number of parameters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification

Tran

Dwelle

Sargsyan

et al. 2020

Water Resources Research

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A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so‐called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high‐fidelity hydrologic and hydraulic models should be increasingly adopted for real‐time and ensemble flood forecasting.

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On the non-uniqueness of the hydro-geomorphic responses in a zero-order catchment with respect to soil moisture

Cited by 22 publications

References 84 publications

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Hysteretic sediment fluxes in rainfall‐driven soil erosion: Particle size effects

A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification

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