Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include dynamic simulation of vegetation and carbon to forty-three primary user-specified parameters. This is done using thirty-two years' worth of data from ten international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth

What Role Does Hydrological Science Play in the Age of Machine Learning?

ABSTRACT: This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished

On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process

It has been proposed that conservation laws might not be beneficial for accurate hydrological modelling due to errors in input (precipitation) and target (streamflow) data (particularly at the event time scale), and this might explain why deep learning models (which are not based on enforcing closure) can out-perform catchment-scale conceptual and process-based models at predicting streamflow. We test this hypothesis with two forcing datasets that disagree in total, long-term precipitation. We analyse the roll of strictly enforced mass conservation for matching a long-term mass balance between precipitation input and streamflow output using physics-informed (mass conserving) machine learning and find that: (1) enforcing closure in the rainfall-runoff mass balance does appear to harm the overall skill of hydrological models; (2) deep learning models learn to account for spatiotemporally variable biases in data (3) however this ‘closure’ effect accounts for only a small fraction of the difference in predictive skill between deep learning and conceptual models.12 month embargo; first published 03 March 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The impact of vertical measurement depth on the information content of soil moisture times series data

Natural Science Foundation of China 31171451；Key Project for the Strategic Science Plan in IGSNRR, CAS 2012ZD003；Chinese Scholarship Council；NASA Terrestrial Ecology Program NNH09ZDA001N<p class="FR_field"> Using a decade of ground-based soil moisture observations acquired from the United States Department of Agriculture&#39;s Soil Climate Analysis Network (SCAN), we calculate the mutual information (MI) content between multiple soil moisture variables and near-future vegetation condition to examine the existence of emergent drought information in vertically integrated (surface to 60 cm) soil moisture observations (theta(0-60) ([cm])) not present in either superficial soil moisture observations (theta(5) ([cm])) or a simple low-pass transformation of theta(5). Results suggest that while theta(0-60) is indeed more valuable than theta(5) for predicting near-future vegetation anomalies, the enhanced information content in theta(0-60) soil moisture can be effectively duplicated by the low-pass transformation of theta(5). This implies that, for drought monitoring applications, the shallow vertical penetration depth of microwave-based theta(5) retrievals does not represent as large a practical limitation as commonly perceived.</p