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Item type:Publication, Access status: with files , Evaporation dynamics from flowing water surfacesIn contrast to the wealth of information on evaporation dynamics from placid water surfaces such as lakes and reservoirs, estimating water evaporation from turbulent surfaces of streams remains a challenge. Evidence suggests considerable differences in evaporation rates from flowing water bodies relative to placid surfaces. Here, we experimentally investigate how flow characteristics (velocity and turbulence) and boundary conditions (wind and radiation) regulate evaporation from flowing water surfaces by using a controlled laboratory flume to isolate key hydrodynamic–atmospheric interactions relevant to streams and rivers. We used a closed flume (7.6 m length, 0.31 m width, and 0.5 m depth) imposing different boundary conditions over the test section of 1.5 m in length while other parts of the flume were covered to prevent evaporative losses. Our results demonstrate that the effect of turbulence on evaporation depends on the temperature profile of the water body and redistribution of radiative heat with turbulent mixing highlighting that evaporation emerges from a nonlinear coupling between hydrodynamic mixing and energy availability. Under solar radiation, increased water flow and turbulence can reduce evaporation by suppressing the formation of a warm surface layer and redistributing absorbed heat in depth of the water column. Without shortwave radiation flux, however, enhanced surface velocity and turbulent mixing may cause significant enhancement in evaporation rates up to 2–5 times that of placid water surfaces. Numerical simulations indicate that enhanced surface turbulence and wave activity increase interfacial roughness and shear, regulating the vapor boundary layer and producing spatially heterogeneous and intermittent vapor fluxes. The study offers novel insights into evaporation from wavy and turbulent flowing water surfaces for a better understanding of evaporation from riverine networks across flow regimes and climatic conditions.Publicationtype: Journal Article20 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Convection-permitting climate models can support observations to generate rainfall return levelsInformation about the frequency and intensity of extreme precipitation is generally derived from fitting extreme value models using point-observations, but the regionalization of these models is challenging. Here we propose using high-resolution convection-permitting climate model output as covariates for the estimation of observation-based spatial rainfall return levels. We apply the Weather and Forecasting Research (WRF) model at a 1.5 km resolution driven by ERA5 reanalysis data over southern Germany, where 1,132 rain gauges provide observations of daily rainfall. For this complex topography, we build three different smooth spatial Generalized Extreme Value (GEV) models: (a) a reference model using latitude, longitude and elevation as covariates; (b) a model adding mean annual precipitation from the WRF; (c) a model adding extreme value statistical model estimates using WRF output. We show that the additional information provided by the WRF model can improve the representation of 10-year and 100-year return levels of daily rainfall by lowering the percentage bias, mean absolute error, and root-mean-square error. Furthermore, we conduct an extensive cross-validation, where only 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all rain gauges are considered when building spatial GEV models. Again, the additional information provided by the WRF model can improve results here. This cross-validation study also highlights the robustness of our approach, showing great potential for use in data-scarce regions.Publicationtype: Journal Article7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , An analytical framework to investigate groundwater‐atmosphere interactions influenced by soil propertiesThe interaction between climate and groundwater forms a complex, coupled system that affects land‐atmosphere feedback processes and thus local climatic parameters. We propose an analytical framework that integrates local groundwater information and soil hydrophysical characteristics to identify regions with bidirectional (two‐way) coupling where groundwater is influenced by climatic factors (e.g., precipitation) and may affect local climate (e.g., through surface fluxes). The framework capitalizes on the concept of the evaporation characteristic length to quantify the hydraulic connection of groundwater to the soil surface. To evaluate the framework, we calculate the maximum depth of hydraulic connection Dmax between groundwater and the soil surface in Hamburg, Germany. For regions with Dmax exceeding the groundwater depth (d), a bidirectional mode of coupling is defined, while Dmax < d implies a unidirectional coupling mode. Our results indicate that climate driven evaporation changes potentially alter the coupling between groundwater and climate depending on soil texture. Moreover, soil hydraulic properties and shallow groundwater tables could play a crucial role in shifting land‐atmosphere feedback processes by influencing the coupling mode. This research provides insights into the groundwater‐climate interactions under various climatic conditions and soil textures which could contribute to sustainable land‐use management practices, particularly in regions characterized by bidirectional coupling.Publicationtype: Journal Article21 51 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An improved copula-based framework for efficient global sensitivity analysis(Wiley, 2024-01-22); ; ; Global sensitivity analysis (GSA) enhances our understanding of computational models and simplifies model parameter estimation. VarIance-based Sensitivity analysis using COpUlaS (VISCOUS) is a variance-based GSA framework. The advantage of VISCOUS is that it can use existing model input-output data (e.g., water model parameters-responses) to estimate the first- and total-order Sobol’ sensitivity indices. This study improves VISCOUS by refining its handling of marginal densities of the Gaussian mixture copula model (GMCM). We then evaluate VISCOUS using three types of generic functions relevant to water system models. We observe that its performance depends on function dimension, input-output data size, and non-identifiability. Function dimension refers to the number of uncertain input factors analyzed in GSA, and non-identifiability refers to the inability to estimate GMCM parameters. VISCOUS proves powerful in estimating first-order sensitivity with a small amount of input-output data (e.g., 200 in this study), regardless of function dimension. It always ranks input factors correctly in both first- and total-order terms. For estimating total-order sensitivity, it is recommended to use VISCOUS when the function dimension is not very high (e.g., less than 20) due to the challenge of producing sufficient input-output data for accurate GMCM inferences (e.g., more than 10,000 data). In cases where all input factors are equally important (a rarity in practice), VISCOUS faces non-identifiability issues that impact its performance. We provide a didactic example and an open-source Python code, pyVISCOUS, for broader user adoption.Publicationtype: Journal Article6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The impact of meteorological forcing uncertainty on hydrological modeling: a gobal analysis of cryosphere basins(Wiley, 2023-06-01); ; 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Large-domain multisite precipitation generation: operational blueprint and demonstration for 1,000 sitesStochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water-related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large-domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large-domain simulations in short time. The proposed approach focuses on correlated processes with mixed (zero-inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed-Uniform space enables a substantial simplification of the so-called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1,000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non-Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, and the use of advection, anisotropy, and nonstationary spatiotemporal correlation functions.Publicationtype: Journal Article10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Rainfall generation revisited: introducing CoSMoS-2s and advancing copula-based intermittent time series modelingWhat elements should a parsimonious model reproduce at a single scale to precisely simulate rainfall at many scales? We posit these elements are: (a) the probability of dry and linear correlation structure of the wet/dry sequence as a proxy reproducing the distribution of wet/dry spells, and (b) the marginal distribution of nonzero rainfall and its correlation structure. We build a two-state rainfall model, the CoSMoS-2s, that explicitly reproduces these elements and is easily applicable at any timescale. Additionally, the paper: (a) introduces the Generalized Exponential ((Formula presented.)) distribution system comprising six flexible distributions with desired properties to describe nonzero rainfall and facilitate time series generation; (b) extends the CoSMoS framework to allow simulations with negative correlations; (c) simplifies the generation of binary sequences with any correlation structure by analytical approximations; (d) introduces the rank-based CoSMoS-2s that preserves Spearman's correlations, has an analytical formulation, and is also applicable for infinite variance time series, (e) introduces the copula-based CoSMoS-2s enabling intermittent times series generation with nonzero values having the dependence structure of any desired copula, and (f) offers conceptual generalizations for rainfall modeling and beyond, with specific ideas for future improvements and extensions. The CoSMoS-2s is tested using four long hourly rainfall records; the simulations reproduce rainfall properties at multiple scales including the wet/dry spells, probability of dry, characteristics of nonzero rainfall, and the behavior of extremes.Publicationtype: Journal Article4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The abuse of popular performance metrics in hydrologic modeling(Wiley, 2021-09-01); ; The goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large-sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.Publicationtype: Journal Article4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Advancing space-time simulation of random fields: from storms to cyclones and beyondRealistic stochastic simulation of hydro-environmental fluxes in space and time, such as rainfall, is challenging yet of paramount importance to inform environmental risk analysis and decision making under uncertainty. Here, we advance random fields simulation by introducing the concepts of general velocity fields and general anisotropy transformations. This expands the capabilities of the so-called Complete Stochastic Modeling Solution (CoSMoS) framework enabling the simulation of random fields (RF's) preserving: (a) any non-Gaussian marginal distribution, (b) any spatiotemporal correlation structure (STCS), (c) general advection expressed by velocity fields with locally varying speed and direction, and (d) locally varying anisotropy. We also introduce new copula-based STCS's and provide conditions guaranteeing their positive definiteness. To illustrate the potential of CoSMoS, we simulate RF's with complex patterns and motion mimicking rainfall storms moving across an area, spiraling fields resembling weather cyclones, fields converging to (or diverging from) a point, and colliding air masses. The proposed methodology is implemented in the freely available CoSMoS R package.Publicationtype: Journal Article4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , VISCOUS: a variance-based sensitivity analysis using copulas for efficient identification of dominant hydrological processes(Wiley, 2021-07-01); ; ; Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.Publicationtype: Journal Article5