Improvement and evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach

Quintero, Felipe; Krajewski, Witold F.; Seo, Bong-Chul; Mantilla, Ricardo

doi:10.1016/j.jhydrol.2020.124686

Cited by 49 publications

(42 citation statements)

References 45 publications

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“…The model decomposes the landscape into hillslopes and links that describe water movement at the hillslope and river channels, respectively [55]. Previous studies (e.g., [56], [57]) have demonstrated successful applications of HLM. The state-wide hydrologic domain consists of more than 400,000 hillslopes with median area of 0.3 km 2 .…”

Section: A Hillslope-link Model (Hlm)mentioning

confidence: 99%

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Jadidoleslam¹,

Hornbuckle²,

Krajewski³

et al. 2021

Preprint

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L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

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Section: A Hillslope-link Model (Hlm)mentioning

confidence: 99%

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Jadidoleslam¹,

Hornbuckle²,

Krajewski³

et al. 2021

Preprint

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“…The performance of the HLM has been extensively evaluated using seven years of radar rainfall data. Model simulations of streamflow have been compared to observed values at approximately 140 USGS streamflow gauges in Iowa, showing skillful performance, especially for eastern Iowa basins [4].…”

Section: Regional Modelmentioning

confidence: 99%

“…Hydrological and hydraulic models are commonly used as tools to understand past, present, and future flood risk profiles at various scales. [1,[3][4][5]. Integrating regional-and local-scale hydrological models is difficult because of the varying heterogenous spatial and temporal scales and dimensionality within the watershed [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Streamflow Predictions in a Small Urban–Rural Watershed: The Effects of Radar Rainfall Resolution and Urban Rainfall–Runoff Dynamics

2020

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The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed. The nested model coupled the hillslope-link model (HLM), used to simulate the upstream rural basins, and XPSWMM, which was used to simulate the more complex rainfall–runoff dynamics and surface and subsurface drainage in the urban areas, making it capable of producing flood maps at the street level. By integrating these models built for different purposes, we enabled fast and accurate simulation of hydrological processes in the rural basins while also modeling the flows in an urban environment. Using the model, we investigated how the spatial and temporal resolution of radar rainfall inputs can affect the modeled streamflow. We used a combination of three radar rainfall products to capture the uncertainty of rainfall estimation in the model results. Our nested model was able to simulate the hydrographs and timing and duration above the threshold known to result in nuisance flooding in Manchester. The spatiotemporal resolution the radar rainfall input to the model impacted the streamflow outputs of the regional, local, and nested models differently depending on the storm event.

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“…In this study, our goal is to explore the potential of SMAP and SMOS satellite-based soil moisture data assimilation to improve streamflow predictions using a calibration-free approach following [23]. We conduct our study using a state-wide distributed operational hydrologic model developed at the Iowa Flood Center [24].…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

2021

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The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.

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Improvement and evaluation of the Iowa Flood Center Hillslope Link Model (HLM) by calibration-free approach

Cited by 49 publications

References 45 publications

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Exploring the Potential of SMAP Soil Moisture for Improving Real-time Streamflow Prediction in the U.S. Corn Belt

Streamflow Predictions in a Small Urban–Rural Watershed: The Effects of Radar Rainfall Resolution and Urban Rainfall–Runoff Dynamics

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

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