Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data

Abstract

Soil moisture (SM) is a key variable of land surface‐atmosphere interactions. Data‐driven methods have been used to predict SM, but the predictability of SM has not been well evaluated. This study investigated what variables and methods can be used to better predict SM for leading times of 7 days or longer with a global coverage of FLUXNET site data for the first time. Three machine‐learning models, that is, Bayesian linear regression, random forest, and gradient boosting regression tree, are used for the prediction. Variables including atmospheric forcing, surface soil temperature, time variables (year, day of year, and hour), the Fourier transformation of time variables, and lagged SM (7‐ to 14‐day lagged) were sequentially added into models. A framework with five experiments is designed for factorial exploration of SM predictability. A stepwise method was used to build the best models for each site. The performance of regression models became better when adding more explaining variables in most cases. The results showed that from 50 to 95% of variation of the best models can be explained. The important explaining variables are lagged surface SM, followed by day of year, year, soil temperature, and atmospheric forcing. The predictability of SM depends highly on SM memory characteristics and the persistence of seasonality. The effect of SM memory characteristics on SM prediction as an initial condition question has been widely discussed in this paper. Our results also provide an insight that mechanisms of seasonality effects on SM should be also paid more attention to.

Multistep Forecasting of Soil Moisture Using Spatiotemporal Deep Encoder–Decoder Networks

“…To predict the variance in observations, TAC is a main factor and SAC has a limited impact on it. Thus, previous models that unaware of SAC also provided a relatively presentable performance (Pan et al 2019;Fang and Shen 2020). However, SAC is also a main factor to enhance predictive power, especially over some regions with high SAC and low TAC.…”

“…As we previously mentioned, it is considered that SAC and TAC have strong spatial variability related to the different soil texture, precipitation, and temperature (Pan et al 2019), which substantially influence the predicting ability. We used the local Moran index (MI) to represent the SAC, which is calculated as…”

“…LSTM-based models could adequately learn temporal autocorrelation (TAC) for SM prediction. However, the spatial autocorrelation (SAC) of SM is highly dynamic and changes over time, which may be also useful for SM predictions (Chen et al 2017;Pan et al 2019). Hu et al (2018) utilized a convolutional neural network (CNN) to predict SM, which could satisfy the requirements of learning SAC by convolutional (Conv) operation.…”

Estudios a micro y nanoescala de la respuesta de <i>Bordetella pertussis</i> a condiciones del entorno

“…No solo en biología molecular o ciencias de la salud se emplea este tipo de visualizaciones, sino que se aplica ampliamente en todo el ámbito científico. Por ejemplo, en un estudio de análisis de la humedad del suelo mediante diversas variables y métodos de predicción (Pan et al, 2019), utilizando gráficos violines reflejaron el desempeño de los modelos de regresión utilizados en el análisis y el mejoramiento aportado al agregar Este tipo de gráfico si bien hace años que se ha comenzado a utilizar, recientemente se ha convertido en una herramienta de representación de datos ampliamente utilizada en la ciencia en general. En conclusión, los violines son específicamente recomendables para reflejar de un modo adecuado y atractivo los resultados de trabajos científicos que abarcan una gran cantidad de información difícil de representar mediante los gráficos usados convencionalmente.…”

“…Los gráficos de violín muestran el rango completo y la distribución de los datos de la muestra. La línea horizontal central representa la mediana, (extraído de Pan et al, 2019). (B) Sección superior: gráficos violines que representan los ángulos de unión dependiendo de la conformación del anillo (eje x) y el ángulo de torsión glicosídico (color).…”