Review: Mutli-source data assimilation for physically-based hydrological modeling of an experimantal hill by A. Botto, E. Belluco and M. Camporese

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
Data Assimilation ◽  
Hydrological Modeling ◽  
Source Data ◽  
Physically Based

scholarly journals Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”

Hydrology ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Huidae Cho ◽  
Lorena Liuzzo
Keyword(s):  
Data Assimilation ◽  
Hydrological Modeling ◽  
Critical Role ◽  
Special Issue ◽  
Hydrologic Models ◽  
Source Data ◽  
Physically Based

Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...]

scholarly journals Multi-source data assimilation for physically-based hydrological modeling of an experimental hillslope

2018 ◽  
Author(s):  
Anna Botto ◽  
Enrica Belluco ◽  
Matteo Camporese
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Hydrological Modeling ◽  
Nonlinear Models ◽  
Pressure Head ◽  
Richards Equation ◽  
Hydrological Models ◽  
Filter Performance ◽  
Trade Offs ◽  
Source Data

Abstract. Data assimilation has been recently the focus of much attention for integrated surface-subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) have been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the model CATHY (CATchment HYdrology) is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant trade-offs: the assimilation of additional variables can lead to degradation of model predictions for other variables that were otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

scholarly journals Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

2018 ◽  
Vol 22 (8) ◽  
pp. 4251-4266 ◽  
Author(s):  
Anna Botto ◽  
Enrica Belluco ◽  
Matteo Camporese
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Hydrological Modeling ◽  
Nonlinear Models ◽  
Pressure Head ◽  
Richards Equation ◽  
Test Case ◽  
Hydrological Models ◽  
Filter Performance ◽  
Source Data

Abstract. Data assimilation has recently been the focus of much attention for integrated surface–subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) has been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant tradeoffs: the assimilation of additional variables can lead to degradation of model predictions for other variables that are otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.

Ensemble Data Assimilation Using a Unified Representation of Model Error

2015 ◽  
Vol 144 (1) ◽  
pp. 213-224 ◽  
Author(s):  
Chiara Piccolo ◽  
Mike Cullen
Keyword(s):  
Data Assimilation ◽  
Covariance Structure ◽  
Model Error ◽  
Model Errors ◽  
Stochastic Forcing ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Physically Based ◽  
Set Up ◽  
Initial Uncertainty

Abstract A natural way to set up an ensemble forecasting system is to use a model with additional stochastic forcing representing the model error and to derive the initial uncertainty by using an ensemble of analyses generated with this model. Current operational practice has tended to separate the problems of generating initial uncertainty and forecast uncertainty. Thus, in ensemble forecasts, it is normal to use physically based stochastic forcing terms to represent model errors, while in generating analysis uncertainties, artificial inflation methods are used to ensure that the analysis spread is sufficient given the observations. In this paper a more unified approach is tested that uses the same stochastic forcing in the analyses and forecasts and estimates the model error forcing from data assimilation diagnostics. This is shown to be successful if there are sufficient observations. Ensembles used in data assimilation have to be reliable in a broader sense than the usual forecast verification methods; in particular, they need to have the correct covariance structure, which is demonstrated.

Impacts of wildfires on the 2020 floods in southeast Australia: A vegetation data-assimilation case study  using the NASA coupled LIS/WRF-Hydro system

2021 ◽  
Author(s):  
Timothy Lahmers ◽  
Sujay Kumar ◽  
Aubrey Dugger ◽  
David Gochis ◽  
Joseph Santanello
Keyword(s):  
Data Assimilation ◽  
Hydrological Modeling ◽  
New South ◽  
New South Wales ◽  
Extreme Rainfall ◽  
Surface Energy Budget ◽  
Area Index ◽  
South Wales ◽  
Land Information System ◽  
The Impact

<div> <p>In late 2019 widespread wildfires impacted much of the New South Wales province in south east Australia, and this loss of vegetation contributed to increased surface runoff and consequently major flooding caused by extreme rainfall by early 2020. The recently developed NASA LIS/WRF-Hydro system enables the data assimilation (DA) capabilities of the NASA Land Information System (LIS) and the surface hydrological modeling capabilities of the WRF-Hydro model to be combined in a single model architecture. Combining the DA capabilities of the LIS system with WRF-Hydro, which has been used for both research and operational hydrologic simulations, we investigate the impacts of vegetation DA on the simulated floods in several basins across New South Wales, with varying degrees of burn severity from the 2019 fires. We also consider the impacts of the wildfires, as realized through vegetation DA on water partitioning and the surface energy budget, which both have implications for L-A interactions. For DA, we utilize the leaf area index retrievals from MODIS and vegetation optical depth from SMAP. For the present study, we will quantify the impact of the changes to the landscape brought about by the wildfires on hydrologic response, including flood severity, which would not be possible without the DA capabilities of the LIS/WRF-Hydro system.</p> </div>

scholarly journals StreamFlow 1.0: An extension to the spatially distributed snow model Alpine3D for hydrological modeling and deterministic stream temperature prediction

2016 ◽  
Author(s):  
Aurélien Gallice ◽  
Mathias Bavay ◽  
Tristan Brauchli ◽  
Francesco Comola ◽  
Michael Lehning ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Source Code ◽  
Stream Temperature ◽  
Object Oriented Programming ◽  
Temperature Prediction ◽  
Uncertainty Estimates ◽  
Physically Based ◽  
Snow Model ◽  
Alpine Catchments

Abstract. Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as a one of the promising approaches to reduce our uncertainty on future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically-based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface runoff, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82, and hourly mean temperature with a NSE of 0.78.

In Situ and In Transit Computing for Large Scale Geoscientific Simulation

2020 ◽  
Author(s):  
Sebastian Friedemann ◽  
Bruno Raffin ◽  
Basile Hector ◽  
Jean-Martial Cohard
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Large Scale ◽  
Transfer Model ◽  
Simulation Data ◽  
Computing Platform ◽  
Continental Scale ◽  
Physically Based ◽  
In Transit

<p>In situ and in transit computing is an effective way to place postprocessing and preprocessing tasks for large scale simulations on the high performance computing platform. The resulting proximity between the execution of preprocessing, simulation and postprocessing permits to lower I/O by bypassing slow and energy inefficient persistent storages. This permits to scale workflows consisting of heterogeneous components such as simulation, data analysis and visualization, to modern massively parallel high performance platforms. Reordering the workflow components gives a manifold of new advanced data processing possibilities for research. Thus in situ and in transit computing are vital for advances in the domain of geoscientific simulation which relies on the increasing amount of sensor and simulation data available.</p><p>In this talk, different in situ and in transit workflows, especially those that are useful in the field of geoscientific simulation, are discussed. Furthermore our experiences augmenting ParFlow-CLM, a physically based, state-of-the-art, fully coupled water transfer model for the critical zone, with FlowVR, an in situ framework with a strict component paradigm, are presented.<br>This allows shadowed in situ file writing, in situ online steering and in situ visualization.</p><p>In situ frameworks further can be coupled to data assimilation tools.<br>In the on going EoCoE-II we propose to embed data assimilation codes into an in transit computing environment. This is expected to enable ensemble based data assimilation on continental scale hydrological simulations with multiple thousands of ensemble members.</p>

Physically Based Hydrological Modeling of the 2002 Floods in San Antonio, Texas

2013 ◽  
Vol 18 (2) ◽  
pp. 228-236 ◽  
Author(s):  
Hatim O. Sharif ◽  
Singaiah Chintalapudi ◽  
Almoutaz A. Hassan ◽  
Hongjie Xie ◽  
Jon Zeitler
Keyword(s):  
Hydrological Modeling ◽  
San Antonio ◽  
Physically Based
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