Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model

Tian, Siyuan; Renzullo, Luigi J.; Lerat, Julien; Pipunic, Robert

doi:10.1029/2021wr031649

Cited by 2 publications

(3 citation statements)

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“…Tian et al. (2022) showed an example of such approach with a spatially explicit error covariance matrix used to assimilate streamflow in a gridded landscape model. A multi‐site configuration will be critical if DAISI is applied to areas where streamflow measurements are limited, hence reducing the effectiveness of data assimilation within DAISI.…”

Section: Discussionmentioning

confidence: 99%

“…ES was originally designed by van Leeuwen and Evensen (1996) to assimilate observations in large gridded models which suggests that DAISI could be extended to a multi-site approach where streamflow from all catchments are assimilated jointly. Tian et al (2022) showed an example of such approach with a spatially explicit error covariance matrix used to assimilate streamflow in a gridded landscape model. A multi-site configuration will be critical if DAISI is applied to areas where streamflow measurements are limited, hence reducing the effectiveness of data assimilation within DAISI.…”

Section: How Can Daisi Be Improved?mentioning

confidence: 99%

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Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Lerat,

Chiew,

Robertson

et al. 2024

Water Resources Research

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This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called “Data Assimilation Informed model Structure Improvement” (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes.

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Section: Discussionmentioning

confidence: 99%

Section: How Can Daisi Be Improved?mentioning

confidence: 99%

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Lerat,

Chiew,

Robertson

et al. 2024

Water Resources Research

Self Cite

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“…However, spatiotemporal state updating in distributed hydrological models via streamflow DA remains a challenge due to the large dimensional disparity between the model state space and observation space. One solution is to use spatiotemporal variance‒covariance from simulated climatology models as a surrogate for model error to spatially distribute constraints from gauge measurements (S. Tian et al., 2022).…”

Section: Applications Of Land Data Assimilation In Estimating Land Su...mentioning

confidence: 99%

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

Li,

Liu,

et al. 2024

Reviews of Geophysics

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Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies.

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Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model

Abstract: 2 of 20 conditions can play a major role for streamflow predictability for lead times up to 3 months on main Amazonian River tributaries. Over the last couple of decades, an abundance of gauge-measured streamflow (Abaza

Cited by 2 publications

References 73 publications

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Data Assimilation Informed Model Structure Improvement (DAISI) for Robust Prediction Under Climate Change: Application to 201 Catchments in Southeastern Australia

Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies

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