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

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

Controlled oxygen vacancy induced p-type conductivity in HfO{2-x} thin films

We have synthesized highly oxygen deficient HfO$_{2-x}$ thin films by controlled oxygen engineering using reactive molecular beam epitaxy. Above a threshold value of oxygen vacancies, p-type conductivity sets in with up to 6 times 10^{21} charge carriers per cm3. At the same time, the band-gap is reduced continuously by more than 1 eV. We suggest an oxygen vacancy induced p-type defect band as origin of the observed behavior.Comment: 4 pages, 3 figure

On the origin of incoherent magnetic exchange coupling in MnBi/Fex_xCo1−x_{1-x} bilayer system

In this study we investigate the exchange coupling between the hard magnetic compound MnBi and the soft magnetic alloy FeCo including the interface structure between the two phases. Exchange spring MnBi-Fe$_x$Co$_{1-x}$ (x = 0.65 and 0.35) bilayers with various thicknesses of the soft magnetic layer were deposited onto quartz glass substrates in a DC magnetron sputtering unit from alloy targets. Magnetic measurements and density functional theory (DFT) calculations reveal that a Co-rich FeCo layer leads to more coherent exchange coupling. The optimum soft layer thickness is about 1 nm. In order to take into account the effect of incoherent interfaces with finite roughness, we have combined a cross-sectional High Resolution Transmission Electron Microscopy (HR-TEM) analysis with DFT calculations and micromagnetic simulations. The experimental results can be consistently described by modeling assuming a polycrystalline FeCo layer consisting of crystalline (110) and amorphous grains as confirmed by HR-TEM. The micromagnetic simulations show in general how the thickness of the FeCo layer and the interface roughness between the hard and soft magnetic phases both control the effectiveness of exchange coupling in an exchange spring system

Gradual reset and set characteristics in yttrium oxide based resistive random access memory

This paper addresses the resistive switching behavior in yttrium oxide based resistive random access memory (RRAM) (TiN/yttrium oxide/Pt) devices. We report the coexistence of bipolar and unipolar resistive switching within a single device stack. For bipolar DC operation, the devices show gradual set and reset behavior with resistance ratio up to two orders of magnitude. By using nanosecond regime pulses (20 to 100 ns pulse width) of constant voltage amplitude, this gradual switching behavior could be utilized in tuning the resistance during set and reset spanning up to two orders of magnitude. This demonstrates that yttrium oxide based RRAM devices are alternative candidates for multibit operations and neuromorphic applications

Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties

High landscape diversity is assumed to increase the number and level of ecosystem services. However, the interactions between ecosystem service provision, disturbance and landscape composition are poorly understood. Here we present a novel approach to include uncertainty in the optimization of land allocation for improving the provision of multiple ecosystem services. We refer to the rehabilitation of abandoned agricultural lands in Ecuador including two types of both afforestation and pasture rehabilitation, together with a succession option. Our results show that high compositional landscape diversity supports multiple ecosystem services (multifunction effect). This implicitly provides a buffer against uncertainty. Our work shows that active integration of uncertainty is only important when optimizing single or highly correlated ecosystem services and that the multifunction effect on landscape diversity is stronger than the uncertainty effect. This is an important insight to support a land-use planning based on ecosystem services

Compositional diversity of rehabilitated tropical lands supports multiple ecosystem services and buffers uncertainties

High landscape diversity is assumed to increase the number and level of ecosystem services. However, the interactions between ecosystem service provision, disturbance and landscape composition are poorly understood. Here we present a novel approach to include uncertainty in the optimization of land allocation for improving the provision of multiple ecosystem services. We refer to the rehabilitation of abandoned agricultural lands in Ecuador including two types of both afforestation and pasture rehabilitation, together with a succession option. Our results show that high compositional landscape diversity supports multiple ecosystem services (multifunction effect). This implicitly provides a buffer against uncertainty. Our work shows that active integration of uncertainty is only important when optimizing single or highly correlated ecosystem services and that the multifunction effect on landscape diversity is stronger than the uncertainty effect. This is an important insight to support a land-use planning based on ecosystem services

Accounting for multiple ecosystem services in a simulation of land‐use decisions: Does it reduce tropical deforestation?

Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio-economic objectives in decisionmaking. To test the effect of accounting for both ES and socio-economic objectives in land-use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi-objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decisionmakers, who may have different expectations about the future. When optimizing only socio-economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land-use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio-economic objectives has heterogeneous effects on land-use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non-natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies

Whole exome resequencing reveals recessive mutations in TRAP1 in individuals with CAKUT and VACTERL association

Congenital abnormalities of the kidney and urinary tract (CAKUT) account for approximately half of children with chronic kidney disease and they are the most frequent cause of end-stage renal disease in children in the US. However, its genetic etiology remains mostly elusive. VACTERL association is a rare disorder that involves congenital abnormalities in multiple organs including the kidney and urinary tract in up to 60% of the cases. By homozygosity mapping and whole exome resequencing combined with high-throughput mutation analysis by array-based multiplex PCR and next-generation sequencing, we identified recessive mutations in the gene TNF receptor-associated protein 1 (TRAP1) in two families with isolated CAKUT and three families with VACTERL association. TRAP1 is a heat shock protein 90-related mitochondrial chaperone possibly involved in antiapoptotic and endoplasmic reticulum-stress signaling. Trap1 is expressed in renal epithelia of developing mouse kidney E13.5 and in the kidney of adult rats, most prominently in proximal tubules and in thick medullary ascending limbs of Henle’s loop. Thus, we identified mutations in TRAP1 as highly likely causing CAKUT or CAKUT in VACTERL association

Oxygen Engineered Hafnium Oxide Thin Films grown by Reactive Molecular Beam Epitaxy

This study applies RMBE to grow thin films of hafnium oxide, a widely studied material which has found its way into commercialisation as a replacement of SiO2 in a field effect transistor. After investigating different substrates and probing various deposition conditions, RMBE-grown films of hafnium oxide yielded to epitaxial films of hafnia on c-cut sapphire. Having the ability to grow high-quality thin films of hafnium oxide allows studying the influence of defined oxygen deficiency on its physical properties, as the next step of this work. The optical properties changed dramatically from colourless and transparent for stoichiometric HfO2 to dark black and opaque for highly deficient films of HfO2-x. The optical band gap could be tuned within more than one eV, visualising the introduction of defects (oxygen vacancies) in situ during growth. In fact, Hafnia showed a metal to insulator transition as a function of the oxygen content, conductive HfO2-x exhibited electrical p-type conductivity with resistivities of 300 µWcm, charge carrier concentrations of 6 times 10 to the power of 21 cm-3 at mobilities of 2 cm²/(Vs). The observed conductivity seems to be intrinsic to oxygen deficient hafnia and not due to a percolation of a conducting phase in an insulating matrix, as evident from various characterisations. A simple defect band structure model has been developed based on the observations, covering the formation of defect bands within the band gap being responsible for electrical conductivity and absorption of radiation within the visible range. With respect to reports on high-Tc ferromagnetism, no evidence for d0-ferromagnetism and room temperature ferromagnetism in Ni-doped HfO2-x could be found