Abstract:The aim of this study was to follow the response to drought stress in a Poa pratensis canopy exposed to various levels of soil moisture deficit. We tracked the changes in the canopy reflectance (450-2450 nm) and retrieved vegetation properties (Leaf Area Index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm) and senescent material (Cs)) during a drought episode. Spectroscopic techniques and radiative transfer model (RTM) inversion were employed to monitor the gradual manifestation of drought effects in a laboratory setting. Plots of 21 cmˆ14.5 cm surface area with Poa pratensis plants that formed a closed canopy were divided into a well-watered control group and a group subjected to water stress for 36 days. In a regular weekly schedule, canopy reflectance and destructive measurements of LAI and Cab were taken. Spectral analysis indicated the first sign of stress after 4-5 days from the start of the experiment near the water absorption bands (at 1930 nm, 1440 nm) and in the red (at 675 nm). Spectroscopic techniques revealed plant stress up to 6 days earlier than visual inspection. Of the water stress-related vegetation indices, the response of Normalized Difference Water Index (NDWI_1241) and Normalized Photochemical Reflectance Index (PRI_norm) were significantly stronger in the stressed group than the control. To observe the effects of stress on grass properties during the drought episode, we used the RTMo (RTM of solar and sky radiation) model inversion by means of an iterative optimization approach. The performance of the model inversion was assessed by calculating R 2 and the Normalized Root Mean Square Error (RMSE) between retrieved and measured LAI (R 2 = 0.87, NRMSE = 0.18) and Cab (R 2 = 0.74, NRMSE = 0.15). All parameters retrieved by model inversion co-varied with soil moisture deficit. However, the first strong sign of water stress on the retrieved grass properties was detected as a change of Cw followed by Cab and Cdm in the earlier stages. The results from this study indicate that the spectroscopic techniques and RTMo model inversion have a promising potential of detecting stress on the spectral reflectance and grass properties before they become visibly apparent.
Abstract. Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods for estimating wtd anomalies (wtda) using other hydrometeorological observations routinely available near real time. In this work, we explore the potential of Long Short-Term
Memory (LSTM) networks for producing monthly wtda using monthly
precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11∘, ranging from the years 1996 to 2016. The data were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), soil type (St), and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ>0.15 m3 m−3, and Sw<10 mm, revealing a significant impact of the local factors
on the ability of the networks to process information. Furthermore, results
of the cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pra and wtda at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful for producing high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pra. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.
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