Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates

Sap flow encodes information about how plants regulate the opening and closing of stomata in response to varying soil water supply and atmospheric water demand. This study leverages this valuable information with model–data integration and deep learning to estimate canopy conductance in a hybrid catchment-scale model for more accurate hydrological simulations. Using data from three consecutive growing seasons, we first highlight that integrating canopy conductance inferred from sap flow data in a hydrological model leads to more realistic soil moisture estimates than using the conventional Jarvis–Stewart equation, particularly during drought conditions. The applicability of this first approach is, however, limited to the period where sap flow data are available. To overcome this limitation, we subsequently train a recurrent neural network (RNN) to predict catchment-averaged sap velocities based on standard hourly meteorological data. These simulated velocities are then used to estimate canopy conductance, allowing simulations for periods without sap flow data. We show that the hybrid model, which uses the canopy conductance from the machine learning (ML) approach, matches soil moisture and transpiration equally as well as model runs using observed sap flow data and has good potential for extrapolation beyond the study site. We conclude that such hybrid approaches open promising avenues for parametrizations of complex water–plant dynamics by improving our ability to incorporate novel or untypical data sets into hydrological models

Estimates of tree root water uptake from soil moisture profile dynamics

Root water uptake (RWU), as an important process in the terrestrial water cycle, can help us to better understand the interactions in the soil–plant–atmosphere continuum. We conducted a field study monitoring soil moisture profiles in the rhizosphere of beech trees at two sites with different soil conditions. We present an algorithm to infer RWU from step-shaped, diurnal changes in soil moisture. While this approach is a feasible, easily implemented method for moderately moist and homogeneously textured soil conditions, limitations were identified during drier states and for more heterogeneous soil settings. A comparison with the time series of xylem sap velocity underlines that RWU and sap flow (SF) are complementary measures in the transpiration process. The high correlation between the SF time series of the two sites, but lower correlation between the RWU time series, suggests that soil characteristics affect RWU of the trees but not SF

Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates

Sap flow encodes information about how plants regulate the opening and closing of stomata in response to varying soil water supply and atmospheric water demand. This study leverages this valuable information with model- data integration and deep learning to estimate canopy conductance in a hybrid catchment-scale model for more accurate hydrological simulations. Using data from three consecutive growing seasons, we first highlight that integrating canopy conductance inferred from sap flow data in a hydrological model leads to more realistic soil moisture estimates than using the conventional Jarvis-Stewart equation, particularly during drought conditions. The applicability of this first approach is, however, limited to the period where sap flow data are available. To overcome this limitation, we subsequently train a recurrent neural network (RNN) to predict catchment-averaged sap velocities based on standard hourly meteorological data. These simulated velocities are then used to estimate canopy conductance, allowing simulations for periods without sap flow data. We show that the hybrid model, which uses the canopy conductance from the machine learning (ML) approach, matches soil moisture and transpiration equally as well as model runs using observed sap flow data and has good potential for extrapolation beyond the study site. We conclude that such hybrid approaches open promising avenues for parametrizations of complex water-plant dynamics by improving our ability to incorporate novel or untypical data sets into hydrological models

Soil moisture: variable in space but redundant in time

Soil moisture at the catchment scale exhibits a huge spatial variability. This suggests that even a large amount of observation points would not be able to capture soil moisture variability. We present a measure to capture the spatial dissimilarity and its change over time. Statistical dispersion among observation points is related to their distance to describe spatial patterns. We analyzed the temporal evolution and emergence of these patterns and used the mean shift clustering algorithm to identify and analyze clusters. We found that soil moisture observations from the 19.4 km2 Colpach catchment in Luxembourg cluster in two fundamentally different states. On the one hand, we found rainfall-driven data clusters, usually characterized by strong relationships between dispersion and distance. Their spatial extent roughly matches the average hillslope length in the study area of about 500 m. On the other hand, we found clusters covering the vegetation period. In drying and then dry soil conditions there is no particular spatial dependence in soil moisture patterns, and the values are highly similar beyond hillslope scale. By combining uncertainty propagation with information theory, we were able to calculate the information content of spatial similarity with respect to measurement uncertainty (when are patterns different outside of uncertainty margins?). We were able to prove that the spatial information contained in soil moisture observations is highly redundant (differences in spatial patterns over time are within the error margins). Thus, they can be compressed (all cluster members can be substituted by one representative member) to only a fragment of the original data volume without significant information loss. Our most interesting finding is that even a few soil moisture time series bear a considerable amount of information about dynamic changes in soil moisture. We argue that distributed soil moisture sampling reflects an organized catchment state, where soil moisture variability is not random. Thus, only a small amount of observation points is necessary to capture soil moisture dynamics

V-FOR-WaTer - a virtual research environment for environmental research

Extent and diversity of environmental data are continuously increasing due to more sensor networks with higher spatial and temporal resolution. To find appropriate data for analyses and especially for large scale models and simulations in this data explosion can take up to several months. The preprocessing of these heterogeneous datasets from different research disciplines to acquire a coherent dataset, can be done with a wide range of algorithms and tools. The outcome is a base dataset that is not reproducible and in consequence, neither are the resulting analyses [3, 9]. The datasets therefore do not obey the FAIR principles [13]. The V-FOR-WaTer web portal [11] aims to improve this situation by collecting data and metadata from a wide variety of sources and by offering preprocessed data

Energy states of soil water – a thermodynamic perspective on soil water dynamics and storage-controlled streamflow generation in different landscapes

The present study confirms that a thermodynamic perspective on soil water is well suited to distinguishing the typical interplay of gravity and capillarity controls on soil water dynamics in different landscapes. To this end, we express the driving matric and gravity potentials by their energetic counterparts and characterize soil water by its free energy state. The latter is the key to defining a new system characteristic determining the possible range of energy states of soil water, reflecting the joint influences of soil physical properties and height over nearest drainage (HAND) in a stratified manner. As this characteristic defines the possible range of energy states of soil water in the root zone, it also allows an instructive comparison of top soil water dynamics observed in two distinctly different landscapes. This is because the local thermodynamic equilibrium at a given HAND and the related equilibrium storage allow a subdivision of the possible free energy states into two different regimes. Wetting of the soil in local equilibrium implies that free energy of soil water becomes positive, which in turn implies that the soil is in a state of storage excess, while further drying of the soil leads to a negative free energy and a state of storage deficit. We show that during 1 hydrological year the energy states of soil water visit distinctly different parts of their respective energy state spaces. The two study areas compared here exhibit furthermore a threshold-like relation between the observed free energy of soil water in the riparian zone and observed streamflow, while the tipping points coincide with the local equilibrium state of zero free energy. We found that the emergence of a potential energy excess/storage excess in the riparian zone coincides with the onset of storage-controlled direct streamflow generation. While such threshold behaviour is not unusual, it is remarkable that the tipping point is consistent with the underlying theoretical basis

Agroforestry : an appropriate and sustainable response to a changing climate in Southern Africa?

CITATION: Sheppard, Jonathan P. et al. 2020. Agroforestry : an appropriate and sustainable response to a changing climate in Southern Africa? Sustainability 12(17):6796, doi:10.3390/su12176796.The original publication is available at: https://www.mdpi.comENGLISH ABSTRACT: Agroforestry is often discussed as a strategy that can be used both for the adaptation to and the mitigation of climate change e ects. The climate of southern Africa is predicted to be severely a ected by such changes. With agriculture noted as the continent’s largest economic sector, issues such as food security and land degradation are in the forefront. In the light of such concerns we review the current literature to investigate if agroforestry systems (AFS) are a suitable response to the challenges besetting traditional agricultural caused by a changing climate. The benefits bestowed by AFS are multiple, o ering ecosystem services, influence over crop production and positive impacts on rural livelihoods through provisioning and income generation. Nevertheless, knowledge gaps remain. We identify outstanding questions requiring further investigation such as the interplay between trees and crops and their combination, with a discussion of potential benefits. Furthermore, we identify deficiencies in the institutional and policy frameworks that underlie the adoption and stimulus of AFS in the southern African region. We uphold the concept that AFS remains an appropriate and sustainable response for an increased resilience against a changing climate in southern Africa for the benefit of livelihoods and multiple environmental values.Publisher's versio

NFDI4Earth - OneStop4All proto-personas

The proto-personas were created as part of the development process of the NFDI4Earth OneStop4All portal. Personas can be defined as fictional characters created to represent typical users of a website, portal, service etc and are useful in order to better understand users' needs, experiences, behaviours and goals. The personas cover a diverse range of potential users who vary in needs, age, work experience and educational background. Within the NFDI4Earth, the proto-personas were created based on the experiences of NFDI4Earth partners with the target community according to Norman (2018). They are used to develop the functionalities of the OneStop4All portal, with the aim of making the portal efficient and user friendly for each persona.This work has been funded by the German Research Foundation (DFG) through the project NFDI4Earth ( TA2, M2.1, DFG project no.460036893, https://www.nfdi4earth.de/) within the German National Research Data Infrastructure (NFDI, https://www.nfdi.de/