TY - GEN A1 - Dobkowitz, Sophia A1 - Walz, Ariane A1 - Baroni, Gabriele A1 - Pérez-Marin, Aldrin M. T1 - Cross-Scale Vulnerability Assessment for Smallholder Farming BT - A Case Study from the Northeast of Brazil T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Climate change heavily impacts smallholder farming worldwide. Cross-scale vulnerability assessment has a high potential to identify nested measures for reducing vulnerability of smallholder farmers. Despite their high practical value, there are currently only limited examples of cross-scale assessments. The presented study aims at assessing the vulnerability of smallholder farmers in the Northeast of Brazil across three scales: regional, farm and field scale. In doing so, it builds on existing vulnerability indices and compares results between indices at the same scale and across scales. In total, six independent indices are tested, two at each scale. The calculated indices include social, economic and ecological indicators, based on municipal statistics, meteorological data, farm interviews and soil analyses. Subsequently, indices and overlapping indicators are normalized for intra- and cross-scale comparison. The results show considerable differences between indices across and within scales. They indicate different activities to reduce vulnerability of smallholder farmers. Major shortcomings arise from the conceptual differences between the indices. We therefore recommend the development of hierarchical indices, which are adapted to local conditions and contain more overlapping indicators for a better understanding of the nested vulnerabilities of smallholder farmers. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 978 KW - family farming KW - nested vulnerabilities KW - vulnerability indices KW - semi-arid regions KW - Paraíba Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-474703 SN - 1866-8372 IS - 978 ER - TY - JOUR A1 - Dobkowitz, Sophia A1 - Walz, Ariane A1 - Baroni, Gabriele A1 - Pérez-Marin, Aldrin M. T1 - Cross-Scale Vulnerability Assessment for Smallholder Farming BT - A Case Study from the Northeast of Brazil JF - Sustainability N2 - Climate change heavily impacts smallholder farming worldwide. Cross-scale vulnerability assessment has a high potential to identify nested measures for reducing vulnerability of smallholder farmers. Despite their high practical value, there are currently only limited examples of cross-scale assessments. The presented study aims at assessing the vulnerability of smallholder farmers in the Northeast of Brazil across three scales: regional, farm and field scale. In doing so, it builds on existing vulnerability indices and compares results between indices at the same scale and across scales. In total, six independent indices are tested, two at each scale. The calculated indices include social, economic and ecological indicators, based on municipal statistics, meteorological data, farm interviews and soil analyses. Subsequently, indices and overlapping indicators are normalized for intra- and cross-scale comparison. The results show considerable differences between indices across and within scales. They indicate different activities to reduce vulnerability of smallholder farmers. Major shortcomings arise from the conceptual differences between the indices. We therefore recommend the development of hierarchical indices, which are adapted to local conditions and contain more overlapping indicators for a better understanding of the nested vulnerabilities of smallholder farmers. KW - family farming KW - nested vulnerabilities KW - vulnerability indices KW - semi-arid regions KW - Paraíba Y1 - 2020 U6 - https://doi.org/10.3390/su12093787 SN - 2071-1050 VL - 12 IS - 9 PB - MDPI CY - Basel ER - TY - JOUR A1 - Baroni, Gabriele A1 - Francke, Till T1 - An effective strategy for combining variance- and distribution-based global sensitivity analysis JF - Environmental modelling & software with environment data news N2 - We present a new strategy for performing global sensitivity analysis capable to estimate main and interaction effects from a generic sampling design. The new strategy is based on a meaningful combination of varianceand distribution-based approaches. The strategy is tested on four analytic functions and on a hydrological model. Results show that the analysis is consistent with the state-of-the-art Saltelli/Jansen formula but to better quantify the interaction effect between the input factors when the output distribution is skewed. Moreover, the estimation of the sensitivity indices is much more robust requiring a smaller number of simulations runs. Specific settings and alternative methods that can be integrated in the new strategy are also discussed. Overall, the strategy is considered as a new simple and effective tool for performing global sensitivity analysis that can be easily integrated in any environmental modelling framework. KW - global sensitivity analysis KW - variance KW - distribution KW - generic sampling KW - design Y1 - 2020 U6 - https://doi.org/10.1016/j.envsoft.2020.104851 SN - 1364-8152 SN - 1873-6726 VL - 134 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Pilz, Tobias A1 - Francke, Till A1 - Baroni, Gabriele A1 - Bronstert, Axel T1 - How to Tailor my process-based hydrological model? BT - dynamic identifiability analysis of flexible model structures JF - Water resources research N2 - In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process-based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro-meteorological boundary conditions. For instance, we tested the Penman-Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in-depth analysis of process implementations and catchment functioning. KW - identifiability analysis KW - flexible model KW - numerics KW - model structure KW - WASA-SED KW - ECHSE Y1 - 2020 U6 - https://doi.org/10.1029/2020WR028042 SN - 0043-1397 SN - 1944-7973 VL - 56 IS - 8 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Erdal, Daniel A1 - Baroni, Gabriele A1 - Sánchez León, Eduardo Emilio A1 - Cirpka, Olaf A. T1 - The value of simplified models for spin up of complex models with an application to subsurface hydrology JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology N2 - Spinning up large-scale coupled surface-subsurface numerical models can be a time and resource consuming task. If an uninformed initial condition is chosen, the spin-up can easily require 20 years of repeated simulations on high-performance computing machines. In this paper we compare the classical approach of starting from a fixed shallow depth to groundwater (here 3 m) with three more informed approaches for the definition of initial conditions in the spin up. In the first of these three approaches, we start from a known-steady state groundwater table, calculated with a 2-D groundwater model and the yearly net recharge, and combine it with an unsaturated zone that assumes hydrostatic conditions. In the second approach, we start from the same groundwater table combined with vertical profiles in the unsaturated zone with uniform vertical flow identical to the groundwater recharge. In the third approach we calculate a dynamic steady state from a simplified subsurface model combining a transient 2-D groundwater model with a limited number of 1-D transient unsaturated zone columns on top. Results for spinning-up a 3-D Parflow-CLM model using the different initial conditions show that large gains can be made by considering states in groundwater and the vadose zone that are consistent, i.e. where groundwater recharge and the vertical flux in the vadose zone agree. By this, the spin-up time was reduced from about 10 years to about 3 years of simulated time. In the light of seasonal fluctuations of net recharge, using the transient approach showed more stable results. KW - Model spin-up KW - Groundwater-model KW - Unsaturated zone KW - 2.5-D model KW - Computation time Y1 - 2019 U6 - https://doi.org/10.1016/j.cageo.2019.01.014 SN - 0098-3004 SN - 1873-7803 VL - 126 SP - 62 EP - 72 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Baroni, Gabriele A1 - Drastig, Katrin A1 - Lichtenfeld, Anna-Ulrike A1 - Jost, Leonie A1 - Claas, Peter T1 - Assessment of irrigation scheduling systems in Germany BT - survey of the users and comparative study JF - Irrigation and drainage N2 - In Germany, the irrigation sector accounts for only 1% of water use. In recent years, however, this sector has attracted more attention due to the occurrence of severe drought periods. Irrigation scheduling systems could support adaptation strategies but little is known about current providers, performance and users. In this study we aimed to depict the current situation of the existence and functioning of irrigation scheduling systems available in Germany. Six methods were identified and assessed based on direct interviews with end-users and a comparative analysis. The results showed a positive feedback from the users. However, the recommendations were rarely implemented, while only the seasonal irrigation requirement was considered to support actual water abstraction. These results were corroborated by the comparative analysis. Five of the six irrigation scheduling systems estimated the seasonal irrigation amount consistently, while wider differences were found by looking at the irrigation season and at the number of irrigations. Overall, it is found that irrigation support systems are valuable tools for supporting adaptation strategies to fast changes in agro-environmental conditions. However, specific assessments based on real measurements should be considered in order to improve the performance of the systems and provide more consistent support to end-users. (c) 2019 John Wiley & Sons, Ltd. KW - irrigation KW - modelling systems KW - surveys KW - assessment KW - Germany Y1 - 2019 U6 - https://doi.org/10.1002/ird.2337 SN - 1531-0353 SN - 1531-0361 VL - 68 IS - 3 SP - 520 EP - 530 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Baroni, Gabriele A1 - Schalge, Bernd A1 - Rakovec, Oldrich A1 - Kumar, Rohini A1 - Schüler, Lennart A1 - Samaniego, Luis A1 - Simmer, Clemens A1 - Attinger, Sabine T1 - A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies JF - Water resources research N2 - The improvement of process representations in hydrological models is often only driven by the modelers' knowledge and data availability. We present a comprehensive comparison between two hydrological models of different complexity that is developed to support (1) the understanding of the differences between model structures and (2) the identification of the observations needed for model assessment and improvement. The comparison is conducted on both space and time and by aggregating the outputs at different spatiotemporal scales. In the present study, mHM, a process‐based hydrological model, and ParFlow‐CLM, an integrated subsurface‐surface hydrological model, are used. The models are applied in a mesoscale catchment in Germany. Both models agree in the simulated river discharge at the outlet and the surface soil moisture dynamics, lending their supports for some model applications (drought monitoring). Different model sensitivities are, however, found when comparing evapotranspiration and soil moisture at different soil depths. The analysis supports the need of observations within the catchment for model assessment, but it indicates that different strategies should be considered for the different variables. Evapotranspiration measurements are needed at daily resolution across several locations, while highly resolved spatially distributed observations with lower temporal frequency are required for soil moisture. Finally, the results show the impact of the shallow groundwater system simulated by ParFlow‐CLM and the need to account for the related soil moisture redistribution. Our comparison strategy can be applied to other models types and environmental conditions to strengthen the dialog between modelers and experimentalists for improving process representations in Earth system models. KW - hydrological models KW - assessments KW - monitoring strategies KW - improvements Y1 - 2019 U6 - https://doi.org/10.1029/2018WR023941 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 2 SP - 990 EP - 1010 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Schattan, Paul A1 - Köhli, Markus A1 - Schrön, Martin A1 - Baroni, Gabriele A1 - Oswald, Sascha Eric T1 - Sensing area-average snow water equivalent with cosmic-ray neutrons: the influence of fractional snow cover JF - Water resources research N2 - Cosmic-ray neutron sensing (CRNS) is a promising non-invasive technique to estimate snow water equivalent (SWE) over large areas. In contrast to preliminary studies focusing on shallow snow conditions (SWE <130 mm), more recently the method was shown experimentally to be sensitive also to deeper snowpacks providing the basis for its use at mountain experimental sites. However, hysteretic neutron response has been observed for complex snow cover including patchy snow-free areas. In the present study we aimed to understand and support the experimental findings using a comprehensive neutron modeling approach. Several simulations have been set up in order to disentangle the effect on the signal of different land surface characteristics and to reproduce multiple observations during periods of snow melt and accumulation. To represent the actual land surface heterogeneity and the complex snow cover, the model used data from terrestrial laser scanning. The results show that the model was able to accurately reproduce the CRNS signal and particularly the hysteresis effect during accumulation and melting periods. Moreover, the sensor footprint was found to be anisotropic and affected by the spatial distribution of liquid water and snow as well as by the topography of the nearby mountains. Under fully snow-covered conditions the CRNS is able to accurately estimate SWE without prior knowledge about snow density profiles or other spatial anomalies. These results provide new insights into the characteristics of the detected neutron signal in complex terrain and support the use of CRNS for long-term snow monitoring in high elevated mountain environments. KW - area-average snow monitoring KW - cosmic-ray neutron sensing KW - neutron simulations KW - spatial heterogeneity KW - fractional snow cover Y1 - 2019 U6 - https://doi.org/10.1029/2019WR025647 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 12 SP - 10796 EP - 10812 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Kayatz, Benjamin A1 - Baroni, Gabriele A1 - Hillier, Jon A1 - Lüdtke, Stefan A1 - Heathcote, Richard A1 - Malin, Daniella A1 - van Tonder, Carl A1 - Kuster, Benjamin A1 - Freese, Dirk A1 - Hüttl, Reinhard A1 - Wattenbach, Martin T1 - Cool farm tool water BT - A global on-line tool to assess water use in crop production JF - Journal of cleaner production N2 - The agricultural sector accounts for 70% of all water consumption and poses great pressure on ground water resources. Therefore, evaluating agricultural water consumption is highly important as it allows supply chain actors to identify practices which are associated with unsustainable water use, which risk depleting current water resources and impacting future production. However, these assessments are often not feasible for crop producers as data, models and experiments are required in order to conduct them. This work introduces a new on-line agricultural water use assessment tool that provides the water footprint and irrigation requirements at field scale based on an enhanced FAO56 approach combined with a global climate, crop and soil databases. This has been included in the Cool Farm Tool - an online tool which already provides metrics for greenhouse gas emissions and biodiversity impacts and therefore allows for a more holistic assessment of environmental sustainability in farming and agricultural supply chains. The model is tested against field scale and state level water footprint data providing good results. The tool provides a practical, reliable way to assess agricultural water use, and offers a means to engage growers and stakeholders in identifying efficient water management practices. (C) 2018 The Authors. Published by Elsevier Ltd. KW - Water footprint KW - FAO56 KW - Crop water use KW - Stakeholder involvement KW - Water resource management KW - Irrigation requirements Y1 - 2018 SN - 0959-6526 SN - 1879-1786 VL - 207 SP - 1163 EP - 1179 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Baroni, Gabriele A1 - Scheiffele, Lena M. A1 - Schrön, Martin A1 - Ingwersen, Joachim A1 - Oswald, Sascha Eric T1 - Uncertainty, sensitivity and improvements in soil moisture estimation with cosmic-ray neutron sensing JF - Journal of hydrology N2 - Cosmic-ray neutron sensing (CRNS) is a promising proximal soil sensing technique to estimate soil moisture at intermediate scale and high temporal resolution. However, the signal shows complex and non-unique response to all hydrogen pools near the land surface, providing some challenges for soil moisture estimation in practical applications. Aims of the study were 1) to assess the uncertainty of CRNS as a stand-alone approach to estimate volumetric soil moisture in cropped field 2) to identify the causes of this uncertainty 3) and possible improvements. Two experimental sites in Germany were equipped with a CRNS probe and point-scale soil moisture network. Additional monitoring activities were conducted during the crop growing season to characterize the soil-plant systems. This data is used to identify and quantify the different sources of uncertainty (factors). An uncertainty analysis, based on Monte Carlo approach, is applied to propagate these uncertainties to CRNS soil moisture estimations. In addition, a sensitivity analysis based on the Sobol’ method is performed to identify the most important factors explaining this uncertainty. Results show that CRNS soil moisture compares well to the soil moisture network when these point-scale values are weighted to account for the spatial sensitivity of the signal and other sources of hydrogen (lattice water and organic carbon) are added to the water content. However, the performance decreases when CRNS is considered as a stand-alone method to retrieve the actual (non-weighted) volumetric soil moisture. The support volume (penetration depth and radius) shows also a considerable uncertainty, especially in relatively dry soil moisture conditions. Four of the seven factors analyzed (the vertical soil moisture profile, bulk density, incoming neutron correction and the calibrated parameter N0) were found to play an important role. Among the possible improvements identified, a simple correction factor based on vertical point-scale soil moisture profiles shows to be a promising approach to account for the sensitivity of the CRNS signal to the upper soil layers. KW - Soil moisture KW - Cosmic-ray neutrons KW - Uncertainty analysis KW - Sensitivity analysis Y1 - 2018 U6 - https://doi.org/10.1016/j.jhydrol.2018.07.053 SN - 0022-1694 SN - 1879-2707 VL - 564 SP - 873 EP - 887 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Francke, Till A1 - Baroni, Gabriele A1 - Brosinsky, Arlena A1 - Foerster, Saskia A1 - Lopez-Tarazon, José Andrés A1 - Sommerer, Erik A1 - Bronstert, Axel T1 - What Did Really Improve Our Mesoscale Hydrological Model? BT - a Multidimensional Analysis Based on Real Observations JF - Water resources research N2 - Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases. KW - modeling KW - sensitivity analyses KW - model enhancement KW - sediment Y1 - 2018 U6 - https://doi.org/10.1029/2018WR022813 SN - 0043-1397 SN - 1944-7973 VL - 54 IS - 11 SP - 8594 EP - 8612 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Ghasemizade, Mehdi A1 - Baroni, Gabriele A1 - Abbaspour, Karim A1 - Schirmer, Mario T1 - Combined analysis of time-varying sensitivity and identifiability indices to diagnose the response of a complex environmental model JF - Environmental modelling & software with environment data news N2 - Sensitivity and identifiability analyses are common diagnostic tools to address over-parametrization in complex environmental models, but a combined application of the two analyses is rarely conducted. In this study, we performed a temporal global sensitivity analysis using the variance-based method of Sobol’ and a temporal identifiability analysis of model parameters using the dynamic identifiability method (DYNIA). We discuss the relationship between the two analyses with a focus on parameter identification and output uncertainty reduction. The hydrological model HydroGeoSphere was used to simulate daily evapotranspiration, water content, and seepage at the lysimeter scale. We found that identifiability of a parameter does not necessarily reduce output uncertainty. It was also found that the information from the main and total effects (main Sobol' sensitivity indices) is required to allow uncertainty reduction in the model output. Overall, the study highlights the role of combined temporal diagnostic tools for improving our understanding of model behavior. KW - Temporal sensitivity KW - Identifiability KW - Preferential flow KW - HydroGeoSphere KW - Output uncertainty Y1 - 2016 U6 - https://doi.org/10.1016/j.envsoft.2016.10.011 SN - 1364-8152 SN - 1873-6726 VL - 88 SP - 22 EP - 34 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Schroen, Martin A1 - Koehli, Markus A1 - Scheiffele, Lena A1 - Iwema, Joost A1 - Bogena, Heye R. A1 - Lv, Ling A1 - Martini, Edoardo A1 - Baroni, Gabriele A1 - Rosolem, Rafael A1 - Weimar, Jannis A1 - Mai, Juliane A1 - Cuntz, Matthias A1 - Rebmann, Corinna A1 - Oswald, Sascha Eric A1 - Dietrich, Peter A1 - Schmidt, Ulrich A1 - Zacharias, Steffen T1 - Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity JF - Hydrology and earth system sciences : HESS N2 - In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-5009-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 5009 EP - 5030 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Schattan, Paul A1 - Baroni, Gabriele A1 - Oswald, Sascha Eric A1 - Schoeber, Johannes A1 - Fey, Christine A1 - Kormann, Christoph A1 - Huttenlau, Matthias A1 - Achleitner, Stefan T1 - Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing JF - Water resources research N2 - The characteristics of an aboveground cosmic-ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point-scale snow depth (SD) and snow-water-equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230-270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m(-3) and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point-scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain. KW - cosmic-ray neutron sensing KW - snow hydrology KW - continuous snowpack monitoring KW - alpine environment Y1 - 2017 U6 - https://doi.org/10.1002/2016WR020234 SN - 0043-1397 SN - 1944-7973 VL - 53 SP - 3615 EP - 3634 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Baroni, Gabriele A1 - Zink, Matthias A1 - Kumar, Rohini A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 545 KW - global sensitivity analysis KW - hydraulic conductivity KW - pedotransfer functions KW - parameter uncertainty KW - physical properties KW - solute transport KW - model KW - rainfall KW - Evapotranspiration KW - impact Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419174 SN - 1866-8372 IS - 545 ER - TY - JOUR A1 - Baroni, Gabriele A1 - Zink, Matthias A1 - Kumar, Rohini A1 - Samaniego, Luis A1 - Attinger, Sabine T1 - Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales JF - Hydrology and earth system sciences : HESS N2 - Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications. Y1 - 2017 U6 - https://doi.org/10.5194/hess-21-2301-2017 SN - 1027-5606 SN - 1607-7938 VL - 21 SP - 2301 EP - 2320 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Schrön, Martin A1 - Köhli, Markus A1 - Scheiffele, Lena A1 - Iwema, Joost A1 - Bogena, Heye R. A1 - Lv, Ling A1 - Martini, Edoardo A1 - Baroni, Gabriele A1 - Rosolem, Rafael A1 - Weimar, Jannis A1 - Mai, Juliane A1 - Cuntz, Matthias A1 - Rebmann, Corinna A1 - Oswald, Sascha Eric A1 - Dietrich, Peter A1 - Schmidt, Ulrich A1 - Zacharias, Steffen T1 - Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 636 KW - forested headwater catchment KW - moisture observing system KW - soil-water content KW - parameterization methods KW - scale KW - field KW - dynamics KW - observatories KW - networks Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-419134 IS - 636 SP - 5009 EP - 5030 ER - TY - JOUR A1 - Baroni, Gabriele A1 - Oswald, Sascha Eric T1 - A scaling approach for the assessment of biomass changes and rainfall interception using cosmic-ray neutron sensing JF - Journal of hydrology N2 - Cosmic-Ray neutron sensing (CRS) is a unique approach to measure soil moisture at field scale filling the gap of current methodologies. However, CRS signal is affected by all the hydrogen pools on the land surface and understanding their relative importance plays an important role for the application of the method e.g., validation of remote sensing products and data assimilation. In this study, a soil moisture scaling approach is proposed to estimate directly the correct CRS soil moisture based on the soil moisture profile measured at least in one position within the field. The approach has the advantage to avoid the need to introduce one correction for each hydrogen contribution and to estimate indirectly all the related time-varying hydrogen pools. Based on the data collected in three crop seasons, the scaling approach shows its ability to identify and to quantify the seasonal biomass water equivalent. Additionally, the analysis conducted at sub-daily time resolution is able to quantify the daily vertical redistribution of the water biomass and the rainfall interception, showing promising applications of the CRS method also for these types of measurements. Overall, the study underlines how not only soil moisture but all the specific hydrological processes in the soil-plant-atmosphere continuum should be considered for a proper evaluation of the CRS signal. For this scope, the scaling approach reveals to be a simple and pragmatic analysis that can be easily extended to other experimental sites. (C) 2015 Elsevier B.V. All rights reserved. KW - Cosmic-ray KW - Soil moisture KW - Scaling KW - Interception KW - Biomass water KW - Agricultural field Y1 - 2015 U6 - https://doi.org/10.1016/j.jhydrol.2015.03.053 SN - 0022-1694 SN - 1879-2707 VL - 525 SP - 264 EP - 276 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Villarreyes, Carlos Andres Rivera A1 - Baroni, Gabriele A1 - Oswald, Sascha Eric T1 - Inverse modelling of cosmic-ray soil moisture for field-scale soil hydraulic parameters JF - European journal of soil science N2 - We used inverse modelling techniques and soil moisture measured by the cosmic-ray neutron sensing (CRS) to estimate root-zone soil hydraulic properties at the field scale. A HYDRUS-1D model was developed for inverse modelling and calibrated with parameter estimation software (PEST) using a global optimizer. Integral CRS measurements recorded from a sunflower farm in Germany comprised the model input. Data were transformed to soil water storage to enable direct model calibration with a HYDRUS soil-water balance. Effective properties at the CRS scale were compared against local measurements and other inversely estimated soil properties from independent soil moisture profiles. Moreover, CRS-scale soil properties were tested on the basis of how field soil moisture (vertical distribution) and soil water storage were reproduced. This framework provided good estimates of effective soil properties at the CRS scale. Simulated soil moisture at different depths at the CRS scale agreed with field observations. Moreover, simulated soil water storage at the CRS scale compared well with calculations from point-scale profiles, despite their different support volumes. The CRS-scale soil properties estimated with the inverse model were within the range of variation of properties identified from all inverse simulations at the local scale. This study demonstrates the potential of CRS for inverse estimation of soil hydraulic properties. Y1 - 2014 U6 - https://doi.org/10.1111/ejss.12162 SN - 1351-0754 SN - 1365-2389 VL - 65 IS - 6 SP - 876 EP - 886 PB - Wiley-Blackwell CY - Hoboken ER - TY - JOUR A1 - Baroni, Gabriele A1 - Tarantola, S. T1 - A general probabilistic framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study JF - Environmental modelling & software with environment data news N2 - The present study proposes a General Probabilistic Framework (GPF) for uncertainty and global sensitivity analysis of deterministic models in which, in addition to scalar inputs, non-scalar and correlated inputs can be considered as well. The analysis is conducted with the variance-based approach of Sobol/Saltelli where first and total sensitivity indices are estimated. The results of the framework can be used in a loop for model improvement, parameter estimation or model simplification. The framework is applied to SWAP, a 113 hydrological model for the transport of water, solutes and heat in unsaturated and saturated soils. The sources of uncertainty are grouped in five main classes: model structure (soil discretization), input (weather data), time-varying (crop) parameters, scalar parameters (soil properties) and observations (measured soil moisture). For each source of uncertainty, different realizations are created based on direct monitoring activities. Uncertainty of evapotranspiration, soil moisture in the root zone and bottom fluxes below the root zone are considered in the analysis. The results show that the sources of uncertainty are different for each output considered and it is necessary to consider multiple output variables for a proper assessment of the model. Improvements on the performance of the model can be achieved reducing the uncertainty in the observations, in the soil parameters and in the weather data. Overall, the study shows the capability of the GPF to quantify the relative contribution of the different sources of uncertainty and to identify the priorities required to improve the performance of the model. The proposed framework can be extended to a wide variety of modelling applications, also when direct measurements of model output are not available. KW - Global sensitivity analysis KW - Non-scalar input factors KW - Hydrological model KW - Multi-variables Y1 - 2014 U6 - https://doi.org/10.1016/j.envsoft.2013.09.022 SN - 1364-8152 SN - 1873-6726 VL - 51 SP - 26 EP - 34 PB - Elsevier CY - Oxford ER -