Juliane Mai

Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins

AbstractAccurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical character...

Seasonal Soil Moisture Drought Prediction over Europe Using the North American Multi-Model Ensemble (NMME)

AbstractDroughts diminish crop yields and can lead to severe socioeconomic damages and humanitarian crises (e.g., famine). Hydrologic predictions of soil moisture droughts several months in advance are needed to mitigate the impact of these extreme events. In this study, the performance of a seasonal hydrologic prediction system for soil moisture drought forecasting over Europe is investigated. The prediction system is based on meteorological forecasts of the North American Multi-Model Ensemble (NMME) that are used to drive the mesoscale hydrologic model (mHM). The skill of the NMME-based forecasts is compared against those based on the ensemble streamflow prediction (ESP) approach for the hindcast period of 1983–2009. The NMME-based forecasts exhibit an equitable threat score that is, on average, 69% higher than the ESP-based ones at 6-month lead time. Among the NMME-based forecasts, the full ensemble outperforms the single best-performing model CFSv2, as well as all subensembles. Subensembles, however, ...

Computationally inexpensive identification of noninformative model parameters by sequential screening

Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobols global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models.

The German drought monitor

The 2003 drought event in Europe had major implications on many societal sectors, including energy production, health, forestry and agriculture. The reduced availability of water accompanied by high temperatures led to substantial economic losses on the order of 1.5 Billion Euros, in agriculture alone. Furthermore, soil droughts have considerable impacts on ecosystems, forest fires and water management. Monitoring soil water availability in near real-time and at high-resolution, i.e., 4 × 4 km2, enables water managers to mitigate the impact of these extreme events. The German drought monitor was established in 2014 as an online platform. It uses an operational modeling system that consists of four steps: (1) a daily update of observed meteorological data by the German Weather Service, with consistency checks and interpolation; (2) an estimation of current soil moisture using the mesoscale hydrological model; (3) calculation of a quantile-based soil moisture index (SMI) based on a 60 year data record; and (4) classification of the SMI into five drought classes ranging from abnormally dry to exceptional drought. Finally, an easy to understand map is produced and published on a daily basis on www.ufz.de/droughtmonitor. Analysis of the ongoing 2015 drought event, which garnered broad media attention, shows that 75% of the German territory underwent drought conditions in July 2015. Regions such as Northern Bavaria and Eastern Saxony, however, have been particularly prone to drought conditions since autumn 2014. Comparisons with historical droughts show that the 2015 event is amongst the ten most severe drought events observed in Germany since 1954 in terms of its spatial extent, magnitude and duration.

Are Assumptions about the Model Type Necessary in Reaction-Diffusion Modeling? A FRAP Application

At present, fluorescence recovery after photobleaching (FRAP) data are interpreted using various types of reaction-diffusion (RD) models: the model type is usually fixed first, and corresponding model parameters are inferred subsequently. In this article, we describe what we believe to be a novel approach for RD modeling without using any assumptions of model type or parameters. To the best of our knowledge, this is the first attempt to address both model-type and parameter uncertainties in inverting FRAP data. We start from the most general RD model, which accounts for a flexible number of molecular fractions, all mobile, with different diffusion coefficients. The maximal number of possible binding partners is identified and optimal parameter sets for these models are determined in a global search of the parameter-space using the Simulated Annealing strategy. The numerical performance of the described techniques was assessed using artificial and experimental FRAP data. Our general RD model outperformed the standard RD models used previously in modeling FRAP measurements and showed that intracellular molecular mobility can only be described adequately by allowing for multiple RD processes. Therefore, it is important to search not only for the optimal parameter set but also for the optimal model type.

Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets

Weather generators are used for spatiotemporal downscaling of climate model outputs (e.g., precipitation and temperature) to investigate the impact of climate change on the hydrological cycle. In this study, a multiplicative random cascade model is proposed for the stochastic temporal disaggregation of monthly to daily precipitation fields, which is designed to be applicable to grids of any spatial resolution and extent. The proposed method uses stationary distribution functions that describe the partitioning of precipitation throughout multiple temporal scales (e.g., weekly and biweekly scale). Moreover, it explicitly considers the intensity and spatial covariance of precipitation in the disaggregation procedure, but requires no assumption about the temporal relationship and spatial isotropy of precipitation fields. A split sampling test is conducted on a high-resolution (i.e., 4 × 4 km2 grid) daily precipitation data set over Germany (≈357,000 km2) to assess the performance of the proposed method during future periods. The proposed method has proven to consistently reproduce distinctive location-dependent precipitation distribution functions with biases less than 5% during both a calibration and evaluation period. Furthermore, extreme precipitation amounts and the spatial and temporal covariance of the generated fields are comparable to those of the observations. Consequently, the proposed temporal disaggregation approach satisfies the minimum conditions for a precipitation generator aiming at the assessment of hydrological response to climate change at regional and continental scales or for generating seamless predictions of hydrological variables.

Kinetic control of contaminant release from NAPLs – Information potential of concentration time profiles

Release of contaminants from non-aqueous phase liquids (NAPLs) is often limited by the dynamic exchange with aqueous solutions governed by a priori unknown kinetic laws. Release experiments require a thorough evaluation of the potential and limitations of kinetic models to reveal release processes. In this study, we investigated the characteristic concentration-time profiles of various models for the release of contaminants from an organic phase into an aqueous solution under no flow conditions. Criteria have been tested that allow for distinction of a first order one domain, a first order two domain, a spherical diffusion model, a spherical diffusion model with a time variable diffusion coefficient, a model for diffusion in a sphere with organic film, and a model for diffusion in a sphere with an aqueous film. The results can serve to evaluate the processes potentially governing release of organic contaminants from non-aqueous liquid phases.

Parameter Importance in FRAP Acquisition and Analysis: A Simulation Approach

Fluorescence recovery after photobleaching (FRAP) is a widespread technique used to determine intracellular reaction and diffusion parameters. In recent years, due to technical advances and an increasing number of mathematical models for analysis, there was a resurging interest in FRAP applications. However, care has to be taken when inverting parameters from such data. We study potential influences on FRAP acquisition and analysis like initial fluorescence distribution, membrane passage, and geometrical aspects. Monte Carlo simulations are employed for the investigation of reaction-diffusion processes to additionally include cases in which no analytical description is available. To assess the importance of influencing factors we apply a sensitivity method based on elementary effects providing an estimate for the global parameter space. The combination of simulations and sensitivity measure helps us to predict ranges of parameters used in acquisition and analysis for which a reliably inversion of reaction-diffusion parameters is possible. Using this approach, we show that FRAP data are highly susceptible to misinterpretation. However, by identifying the parameters of susceptibility, our analysis provides the means for taking measures to significantly improve FRAP data interpretation and analysis.

Extending theis' solution: Using transient pumping tests to estimate parameters of aquifer heterogeneity

A framework for interpreting transient pumping tests in heterogeneous transmissivity fields is developed to infer the overall geostatistical parameters of the medium without reconstructing the specific heterogeneous structure point wise. The methodology of Radial Coarse Graining is applied to deduce an effective radial description of multi-Gaussian transmissivity. It was used to derive an Effective Well Flow Solution for transient flow conditions including not only the storativity, but also the geometric mean, the variance, and the correlation length of log-transmissivity. This solution is shown to be appropriate to characterize the pumping test drawdown behavior in heterogeneous transmissivity fields making use of ensembles of simulated pumping tests with multiple combinations of statistical parameters. Based on the Effective Well Flow Solution, a method is developed for inferring heterogeneity parameters from transient pumping test drawdown data by inverse estimation. Thereby, the impact of statistical parameters on the drawdown is analyzed, allowing to determine the dependence of reliability of parameter estimates on location and number of measurements. It is shown, that the number of measurements can be reduced compared to steady state pumping tests. Finally, a sampling strategy for single aquifer analysis is developed, which allows to estimate the statistical parameters, in particular variance and correlation length for individual heterogeneous transmissivity fields making use of transient pumping test measurements at multiple locations. This article is protected by copyright. All rights reserved.

Spatial Retrieval of Broadband Dielectric Spectra

A broadband soil dielectric spectra retrieval approach (1 MHz–2 GHz) has been implemented for a layered half space. The inversion kernel consists of a two-port transmission line forward model in the frequency domain and a constitutive material equation based on a power law soil mixture rule (Complex Refractive Index Model—CRIM). The spatially-distributed retrieval of broadband dielectric spectra was achieved with a global optimization approach based on a Shuffled Complex Evolution (SCE) algorithm using the full set of the scattering parameters. For each layer, the broadband dielectric spectra were retrieved with the corresponding parameters thickness, porosity, water saturation and electrical conductivity of the aqueous pore solution. For the validation of the approach, a coaxial transmission line cell measured with a network analyzer was used. The possibilities and limitations of the inverse parameter estimation were numerically analyzed in four scenarios. Expected and retrieved layer thicknesses, soil properties and broadband dielectric spectra in each scenario were in reasonable agreement. Hence, the model is suitable for an estimation of in-homogeneous material parameter distributions. Moreover, the proposed frequency domain approach allows an automatic adaptation of layer number and thickness or regular grids in time and/or space.