This study compares actual evapotranspiration (ETa) measurements by a set of six weighable lysimeters, ETa estimates obtained with the eddy covariance (EC) method, and evapotranspiration calculated ...with the full-form Penman-Monteith equation (ETPM) for the Rollesbroich site in the Eifel (western Germany). The comparison of ETa measured by EC (including correction of the energy balance deficit) and by lysimeters is rarely reported in the literature and allows more insight into the performance of both methods. An evaluation of ETa for the two methods for the year 2012 shows a good agreement with a total difference of 3.8% (19 mm) between the ETa estimates. The highest agreement and smallest relative differences (< 8%) on a monthly basis between both methods are found in summer. ETa was close to ETPM, indicating that ET was energy limited and not limited by water availability. ETa differences between lysimeter and EC were mainly related to differences in grass height caused by harvest and the EC footprint. The lysimeter data were also used to estimate precipitation amounts in combination with a filter algorithm for the high-precision lysimeters recently introduced by Peters et al. (2014). The estimated precipitation amounts from the lysimeter data differ significantly from precipitation amounts recorded with a standard rain gauge at the Rollesbroich test site. For the complete year 2012 the lysimeter records show a 16 % higher precipitation amount than the tipping bucket. After a correction of the tipping bucket measurements by the method of Richter (1995) this amount was reduced to 3%. With the help of an on-site camera the precipitation measurements of the lysimeters were analyzed in more detail. It was found that the lysimeters record more precipitation than the tipping bucket, in part related to the detection of rime and dew, which contribute 17% to the yearly difference between both methods. In addition, fog and drizzle explain an additional 5.5% of the total difference. Larger differences are also recorded for snow and sleet situations. During snowfall, the tipping bucket device underestimated precipitation severely, and these situations contributed also 7.9% to the total difference. However, 36% of the total yearly difference was associated with snow cover without apparent snowfall, and under these conditions snow bridges and snow drift seem to explain the strong overestimation of precipitation by the lysimeter. The remaining precipitation difference (about 33%) could not be explained and did not show a clear relation to wind speed. The variation of the individual lysimeters devices compared to the lysimeter mean are small, showing variations up to 3% for precipitation and 8% for evapotranspiration.
Soil water content is one of the key state variables in the soil‐vegetation‐atmosphere continuum due to its important role in the exchange of water and energy at the soil surface. A new promising ...method to measure integral soil water content at the field or small catchment scale is the cosmic‐ray probe (CRP). Recent studies of CRP measurements have mainly presented results from test sites located in very dry areas and from agricultural fields with sandy soils. In this study, distributed continuous soil water content measurements from a wireless sensor network (SoilNet) were used to investigate the accuracy of CRP measurements for soil water content determination in a humid forest ecosystem. Such ecosystems are less favorable for CRP applications due to the presence of a litter layer. In addition, lattice water and carbohydrates of soil organic matter and belowground biomass reduce the effective sensor depth and thus were accounted for in the calibration of the CRP. The hydrogen located in the biomass decreased the level of neutron count rates and thus also decreased the sensitivity of the cosmic‐ray probe, which in turn resulted in an increase of the measurement uncertainty. This uncertainty was compensated by using longer integration times (e.g., 24 h). For the Wüstebach forest site, the cosmic‐ray probe enabled the assessment of integral daily soil water content dynamics with a RMSE of about 0.03 cm3/cm3 without explicitly considering the litter layer. By including simulated water contents of the litter layer in the calibration, a better accuracy could be achieved.
Key Points
CRP is a promising method to measure soil water content at larger scales
Up to now, CRP have been in test sites other than humid forested ecosystem
CRP measurements are influenced by hydrogen located in in litter layers
Abstract
Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which ...are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period (
$$\sim $$
∼
32% groundwater RMSE reduction and soil moisture correlation increase from
$$\sim $$
∼
0.66 to
$$\sim $$
∼
0.85) but also during the forecast period (
$$\sim $$
∼
14% groundwater RMSE reduction and soil moisture correlation increase from
$$\sim $$
∼
0.69 to
$$\sim $$
∼
0.78) due to the effective impacts of the approach on both state and parameters.
Real‐time groundwater flow modeling with filter methods is interesting for dynamical groundwater flow systems, for which measurement data in real‐time are available. The Ensemble Kalman Filter (EnKF) ...approach is used here to update states together with parameters by adopting an augmented state vector approach. The performance of EnKF is investigated in a synthetic study with a two‐dimensional transient groundwater flow model where (1) only the recharge rate is spatiotemporally variable, (2) only transmissivity is spatially variable with σlnT2 = 1.0 or (3) with σlnT2 = 2.7, and (4) both recharge rate and transmissivity are uncertain (a combination of (1) and (3)). The performance of EnKF for simultaneous state and parameter estimation in saturated groundwater flow problems is investigated in dependence of the number of stochastic realizations, the updating frequency and updating intensity of log‐transmissivity, the amount of measurements in space and time, and the method (iterative versus noniterative EnKF), among others. Satisfactory results were also obtained if both transmissivity and recharge rate were uncertain. However, it was found that filter inbreeding is much more severe if hydraulic heads and transmissivities are jointly updated than if only hydraulic heads are updated. The filter inbreeding problem was investigated in more detail and could be strongly reduced with help of a damping parameter, which limits the intensity of the perturbation of the log‐transmissivity field. An additional reduction of filter inbreeding could be achieved by combining two measures: (1) inflating the elements of the predicted state covariance matrix on the basis of a comparison between the model uncertainty and the observed errors at the measurement points and (2) starting the flow simulations with a very large number of realizations and then sampling the desired number of realizations after one simulation time step by minimizing the differences between the local cpdfs (and bivariate cpdfs) of hydraulic head for the large ensemble and the corresponding cpdfs for the reduced ensemble. The two measures, which cause very limited CPU costs, allowed making 100 stochastic realizations for the reproduction of the states as efficient as 200–500 untreated stochastic realizations.
Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its ...susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0‐calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0‐calibration function was able to explain 95% of the overall variability in fast neutron intensity.
Key Points:
Soil water content was determined accurately with cosmic ray probes
An empirical linear correction for variable aboveground biomass was developed
The COSMIC operator, N0 and the hmf method were used for evaluation
Ensemble Kalman filters (EnKFs) are a successful tool for estimating state variables in atmospheric and oceanic sciences. Recent research has prepared the EnKF for parameter estimation in groundwater ...applications. EnKFs are optimal in the sense of Bayesian updating only if all involved variables are multivariate Gaussian. Subsurface flow and transport state variables, however, generally do not show Gaussian dependence on hydraulic log conductivity and among each other, even if log conductivity is multi‐Gaussian. To improve EnKFs in this context, we apply nonlinear, monotonic transformations to the observed states, rendering them Gaussian (Gaussian anamorphosis, GA). Similar ideas have recently been presented by Béal et al. (2010) in the context of state estimation. Our work transfers and adapts this methodology to parameter estimation. Additionally, we address the treatment of measurement errors in the transformation and provide several multivariate analysis tools to evaluate the expected usefulness of GA beforehand. For illustration, we present a first‐time application of an EnKF to parameter estimation from 3‐D hydraulic tomography in multi‐Gaussian log conductivity fields. Results show that (1) GA achieves an implicit pseudolinearization of drawdown data as a function of log conductivity and (2) this makes both parameter identification and prediction of flow and transport more accurate. Combining EnKFs with GA yields a computationally efficient tool for nonlinear inversion of data with improved accuracy. This is an attractive benefit, given that linearization‐free methods such as particle filters are computationally extremely demanding.
Key Points
Apparent non‐linearity of observed data removed by empirical transformation
This is an implicit pseudo‐linearization for improved EnKF results
First application of EnKFs to hydraulic tomography, unprecedented resolution
Mcl-1 is a member of the Bcl-2 family of proteins that promotes cell survival by preventing induction of apoptosis in many cancers. High expression of Mcl-1 causes tumorigenesis and resistance to ...anticancer therapies highlighting the potential of Mcl-1 inhibitors as anticancer drugs. Here, we describe AZD5991, a rationally designed macrocyclic molecule with high selectivity and affinity for Mcl-1 currently in clinical development. Our studies demonstrate that AZD5991 binds directly to Mcl-1 and induces rapid apoptosis in cancer cells, most notably myeloma and acute myeloid leukemia, by activating the Bak-dependent mitochondrial apoptotic pathway. AZD5991 shows potent antitumor activity in vivo with complete tumor regression in several models of multiple myeloma and acute myeloid leukemia after a single tolerated dose as monotherapy or in combination with bortezomib or venetoclax. Based on these promising data, a Phase I clinical trial has been launched for evaluation of AZD5991 in patients with hematological malignancies (NCT03218683).
Technological and methodological progress is essential to improve our understanding of fundamental processes in natural and engineering sciences. In this paper, we will address the potential of new ...technological and methodological advancements in soil hydrology to move forward our understanding of soil water related processes across a broad range of scales. We will focus on advancements made in quantifying root water uptake processes, subsurface lateral flow, and deep drainage at the field and catchment scale, respectively. We will elaborate on the value of establishing a science‐driven network of hydrological observatories to test fundamental hypotheses, to study organizational principles of soil hydrologic processes at catchment scale, and to provide data for the development and validation of models. Finally, we discuss recent developments in data assimilation methods, which provide new opportunities to better integrate observations and models and to improve predictions of the short‐term evolution of hydrological processes.
Key Points:
Improved description of small‐scale processes is a key to reduce uncertainty
Improve our understanding of hydrology through a network of observatories
Data assimilation provides a viable approach to integrate data and models
•We applied three parameterization methods for cosmic-ray soil moisture probes.•The methods were evaluated with independent measurements.•All three methods estimated areal average soil moisture with ...good accuracy.•Method specific calibration parameters correlated strongly with aboveground biomass.
The objective of this work was to assess the accuracy of soil water content determination from neutron flux measured by cosmic-ray probes under humid climate conditions. Ten cosmic-ray probes were set up in the Rur catchment located in western Germany, and calibrated by gravimetric soil sampling campaigns. Aboveground biomass was estimated at the sites to investigate the role of vegetation cover on the neutron flux and the calibration procedure. Three parameterization methods were used to generate site-specific neutron flux – soil water content calibration curves: (i) the N0-method, (ii) the hydrogen molar fraction method (hmf-method), and (iii) the COSMIC-method. At five locations, calibration measurements were repeated to evaluate site-specific calibration parameters obtained in two different sampling campaigns. At two locations, soil water content determined by cosmic-ray probes was evaluated with horizontally and vertically weighted soil water content measurements of two distributed in situ soil water content sensor networks. All three methods were successfully calibrated to determine field scale soil water content continuously at the ten sites. The hmf-method and the COSMIC-method had more similar calibration curves than the N0-method. The three methods performed similarly well in the validation and errors were within the uncertainty of neutron flux measurements despite observed differences in the calibration curves and variable model complexity. In addition, we found that the obtained calibration parameters NCOSMIC, N0 and NS showed a strong correlation with aboveground biomass.
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