Het beeld van de Aarde

status: publishe

Global hydro-climatic biomes identified via multitask learning

© Author(s) 2018. The most widely used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the characteristic interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multitask learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based datasets indicate that, without the need to prescribe any land cover information, the identified regions of coherent climate-vegetation interactions agree well with the expectations derived from traditional global land cover maps. The resulting global hydro-climatic biomes can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate-vegetation interactions in Earth system models.status: publishe

Adaptive Soil Moisture Profile Filtering for Horizontal Information Propagation in the Independent Column-Based CLM2.0

Data assimilation aims to provide an optimal estimate of the overall system state, not only for an observed state variable or location. However, large-scale land surface models are typically column-based and purely random ensemble perturbation of states will lead to block-diagonal a priori (or background) error covariance. This facilitates the filtering calculations but compromises the potential of data assimilation to influence (unobserved) vertical and horizontal neighboring state variables. Here, a combination of an ensemble Kalman filter and an adaptive covariance correction method is explored to optimize the variances and retrieve the off-block-diagonal correlations in the a priori error covariance matrix. In a first time period, all available soil moisture profile observations in a small agricultural field are assimilated into the Community Land Model, version 2.0 (CLM2.0) to find the adaptive second-order a priori error information. After that period, only observations from single individual soil profiles are assimilated with inclusion of this adaptive information. It is shown that assimilation of a single profile can partially rectify the incorrectly simulated soil moisture spatial mean and variability. The largest reduction in the root-mean-square error in the soil moisture field varies between 7% and 22%, depending on the soil depth, when assimilating a single complete profile every two days during three months with a single time-invariant covariance correction. © 2009 American Meteorological Society.status: publishe

Assessment of model uncertainty for soil moisture through ensemble verification

The Community Land Model (CLM2.0) has been used to simulate land surface processes in a small corn field. The subdivision of grid cells into patches in the CLM2.0 was explored for the generation of Monte Carlo simulations for use in calibration and ensemble generation. A distributed multiobjective calibration was developed for the optimal estimation of parameters and initial state variables for 36 soil moisture profiles. Since the resulting parameter and initial state values did not lead to perfect simulations for soil moisture, and in order to better understand the forecast uncertainty, ensemble runs were generated. The ensembles generated by CLM2.0 have been verified by several methods that are commonly used in meteorology. It was shown that the perfect model approach cannot be applied for bounded hydrological applications and that perturbation of parameters is a necessity to obtain a realistic assessment of the forecast error. Perturbation of forcings only captures more of the model uncertainty than perturbation of initial conditions only, but also causes a too limited spread in the ensembles. The generation of ensemble members through perturbation of the parameter set, found through calibration, does not necessarily result in ensembles that surround the calibrated deterministic control run for soil moisture. This is partially due the nonlinearity of the model in the parameters. It may also indicate that some parameter sets are not robust and not appropriate to perturb for ensemble generation. Consequently, the resulting ensemble mean may not represent the best forecast or a priori state estimation. During periods of extreme drought or precipitation, the ensemble probability density function (pdf) deviates far from normality and the model behaves very nonlinearly. For state estimation, methods like the ensemble Kalman filter are best suited for the propagation of the first moments to account for the nonlinear dynamics during crucial events for hydrological simulations. However, the a posteriori estimate for this technique will only be optimal in the limited class of linear filters, since the underlying pdfs cannot be assumed to be Gaussian. Copyright 2006 by the American Geophysical Union.status: publishe

A copula-based stochastic generator for coupled precipitation and evaporation time series

status: publishe

Evaluating Bartlett-Lewis models for stochastic downscaling of regional climate model precipitation

status: publishe

State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency

[1] An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1-2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available. Copyright 2007 by the American Geophysical Union.status: publishe

Impact of soil hydraulic parameter uncertainty on soil moisture modeling

For simulations in basins where soil information is limited to soil type maps, a methodology is presented to quantify the uncertainty of soil hydraulic parameters arising from within-soil-class variability and to assess the impact of this uncertainty on soil moisture modeling. Continuous pedotransfer functions were applied to samples with different texture within each soil class to construct discrete probability distributions of the soil hydraulic parameters. When propagating the parameter distributions through a hydrologic model, a wide range of simulated soil moisture was generated within a single soil class. The pedotransfer function was found to play a crucial role in assessing the uncertainty in the modeled soil moisture, and the geographic origin of the pedotransfer function (region specific versus nonregion specific) highly affected the range and shape of the probability distribution of the soil hydraulic parameters. Furthermore, the modeled soil moisture distribution was found to be non-Gaussian. An accurate uncertainty assessment therefore requires the characterization of its higher-order moments. As an extension of this research, we have shown that applying continuous region-specific pedotransfer functions to the central point of a soil class is a better alternative to standard (often nonregion-specific) class pedotransfer functions for determining an average set of soil hydraulic parameters. Copyright 2011 by the American Geophysical Union.status: publishe

Optimization of a coupled hydrology-crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter

[1] It is well known that the presence and development stage of vegetation largely influences the soil moisture content. In its turn, soil moisture availability is of major importance for the development of vegetation. The objective of this paper is to assess to what extent the results of a fully coupled hydrology-crop growth model can be optimized through the assimilation of observed leaf area index (LAI) or soil moisture values. For this purpose the crop growth module of the World Food Studies (WOFOST) model has been coupled to a fully process based water and energy balance model (TOPMODEL-Based Land-Atmosphere Transfer Scheme (TOPLATS)). LAI and soil moisture observations from 18 fields in the loamy region in the central part of Belgium have been used to thoroughly validate the coupled model. An observing system simulation experiment (OSSE) has been performed in order to assess whether soil moisture and LAI observations with realistic uncertainties are useful for data assimilation purposes. Under realistic conditions (biweekly observations with a noise level of 5 volumetric percent for soil moisture and 0.5 for LAI) an improvement in the model results can be expected. The results show that the modeled LAI values are not sensitive to the assimilation of soil moisture values before the initiation of crop growth. Also, the modeled soil moisture profile does not necessarily improve through the assimilation of LAI values during the growing season. In order to improve both the vegetation and soil moisture state of the model, observations of both variables need to be assimilated. Copyright 2007 by the American Geophysical Union.status: publishe

Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter

Land surface models are usually biased in at least a subset of the simulated variables even after calibration. Bias estimation may therefore be needed for data assimilation. Here, in situ soil moisture profile observations in a small agricultural field were merged with Community Land Model (CLM2.0) simulations using different algorithms for state and forecast bias estimation with and without bias correction feedback. Simple state updating with the conventional ensemble Kalman filter (EnKF) allows for some implicit forecast bias correction. It is possible to estimate the soil moisture bias explicitly and derive superior soil moisture estimates with a generalized EnKF that uses a simple persistence model for the bias and assumes that the a priori bias error covariance is proportional to the a priori state error covariance. For the case of bi-weekly assimilation of the entire profile of soil moisture observations, bias estimation and correction typically reduces the RMSE in soil moisture (over the standard EnKF without bias correction) by around 60 percent. However, under the above assumptions, significant improvements are limited to state variables for which observations are available. Therefore, it is crucial to measure the state variables of interest. The best variant for state and bias estimation depends on the nature of the model bias and the output of interest to the user. In a model that is only biased for soil moisture, large and frequent increments for soil moisture updating may be required, which in turn may negatively impact the water balance and output fluxes. It is then better to post-process the soil moisture with the bias analysis without updating the model state. Copyright 2007 by the American Geophysical Union.status: publishe