Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: a Case Study for the Contiguous USA

2021 ◽  
Author(s):  
Ashley E. Van Beusekom ◽  
Lauren E. Hay ◽  
Andrew R. Bennett ◽  
Young-Don Choi ◽  
Martyn P. Clark ◽  
...  
Keyword(s):  
Longwave Radiation ◽  
Latin Hypercube Sampling ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Model Parameters ◽  
Model Output ◽  
Regional Variability ◽  
Model Sensitivity

Abstract Surface meteorological analyses are an essential input (termed ‘forcing’) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily-average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin Hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

scholarly journals Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

2021 ◽  
Author(s):  
Daniel Bittner ◽  
Beatrice Richieri ◽  
Gabriele Chiogna
Keyword(s):  
Groundwater Recharge ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Time Dependent ◽  
Model Parameters ◽  
Lumped Parameter ◽  
Hydrodynamic Processes ◽  
Input Model ◽  
Karst Model ◽  
Input Uncertainties

AbstractUncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

scholarly journals Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

2019 ◽  
Vol 19 (20) ◽  
pp. 13227-13241 ◽  
Author(s):  
Stephan Nyeki ◽  
Stefan Wacker ◽  
Christine Aebi ◽  
Julian Gröbner ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Longwave Radiation ◽  
Ground Temperature ◽  
Surface Radiation ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Radiative Effect ◽  
Cloud Properties ◽  
Cloud Radiative Effect ◽  
Downward Longwave Radiation ◽  
Integrated Water Vapour

Abstract. The trends of meteorological parameters and surface downward shortwave radiation (DSR) and downward longwave radiation (DLR) were analysed at four stations (between 370 and 3580 m a.s.l.) in Switzerland for the 1996–2015 period. Ground temperature, specific humidity, and atmospheric integrated water vapour (IWV) trends were positive during all-sky and cloud-free conditions. All-sky DSR and DLR trends were in the ranges of 0.6–4.3 W m−2 decade−1 and 0.9–4.3 W m−2 decade−1, respectively, while corresponding cloud-free trends were −2.9–3.3 W m−2 decade−1 and 2.9–5.4 W m−2 decade−1. Most trends were significant at the 90 % and 95 % confidence levels. The cloud radiative effect (CRE) was determined using radiative-transfer calculations for cloud-free DSR and an empirical scheme for cloud-free DLR. The CRE decreased in magnitude by 0.9–3.1 W m−2 decade−1 (only one trend significant at 90 % confidence level), which implies a change in macrophysical and/or microphysical cloud properties. Between 10 % and 70 % of the increase in DLR is explained by factors other than ground temperature and IWV. A more detailed, long-term quantification of cloud changes is crucial and will be possible in the future, as cloud cameras have been measuring reliably at two of the four stations since 2013.

scholarly journals Tropical thin cirrus and relative humidity observed by the Atmospheric Infrared Sounder

2008 ◽  
Vol 8 (6) ◽  
pp. 1501-1518 ◽  
Author(s):  
B. H. Kahn ◽  
C. K. Liang ◽  
A. Eldering ◽  
A. Gettelman ◽  
Q. Yue ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Climate Models ◽  
Temporal Variations ◽  
Ocean Basin ◽  
Specific Humidity ◽  
Future Climate Change ◽  
Effective Diameter ◽  
Regional Variability ◽  
Atmospheric Infrared Sounder

Abstract. Global observations of cloud and humidity distributions in the upper troposphere within all geophysical conditions are critically important in order to monitor the present climate and to provide necessary data for validation of climate models to project future climate change. Towards this end, tropical oceanic distributions of thin cirrus optical depth (τ), effective diameter (De), and relative humidity with respect to ice (RHi) within cirrus (RHic) are simultaneously derived from the Atmospheric Infrared Sounder (AIRS). Corresponding increases in De and cloud temperature are shown for cirrus with τ>0.25 that demonstrate quantitative consistency to other surface-based, in situ and satellite retrievals. However, inferred cirrus properties are shown to be less certain for increasingly tenuous cirrus. In-cloud supersaturation is observed for 8–12% of thin cirrus and is several factors higher than all-sky conditions; even higher frequencies are shown for the coldest and thinnest cirrus. Spatial and temporal variations in RHic correspond to cloud frequency while regional variability in RHic is observed to be most prominent over the N. Indian Ocean basin. The largest cloud/clear sky RHi anomalies tend to occur in dry regions associated with vertical descent in the sub-tropics, while the smallest occur in moist ascending regions in the tropics. The characteristics of RHic frequency distributions depend on τ and a peak frequency is located between 60–80% that illustrates RHic is on average biased dry. The geometrical thickness of cirrus is typically less than the vertical resolution of AIRS temperature and specific humidity profiles and thus leads to the observed dry bias, shown with coincident cloud vertical structure obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The joint distributions of thin cirrus microphysics and humidity derived from AIRS provide unique and important regional and global-scale insights on upper tropospheric processes not available from surface, in situ, and other contemporary satellite observing platforms.

scholarly journals What Drives the Variability of Evaporative Demand across the Conterminous United States?

2012 ◽  
Vol 13 (4) ◽  
pp. 1195-1214 ◽  
Author(s):  
Michael Hobbins ◽  
Andrew Wood ◽  
David Streubel ◽  
Kevin Werner
Keyword(s):  
United States ◽  
Wind Speed ◽  
Time Scales ◽  
Land Surface ◽  
Longwave Radiation ◽  
Mean Value ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Evaporative Demand ◽  
System Variables

Abstract To understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980–2009. This evaporative demand measure is from the “PenPan” model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales.

scholarly journals Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1177 ◽  
Author(s):  
Shuai Zhou ◽  
Yimin Wang ◽  
Jianxia Chang ◽  
Aijun Guo ◽  
Ziyan Li
Keyword(s):  
Bulk Density ◽  
Hydrological Model ◽  
Latin Hypercube Sampling ◽  
Soil Bulk Density ◽  
Hydrologic Model ◽  
Factorial Analysis ◽  
Interactive Effects ◽  
Model Parameters ◽  
Analysis Method ◽  
Runoff Simulation

Hydrological model parameters are generally considered to be simplified representations that characterize hydrologic processes. Therefore, their influence on runoff simulations varies with climate and catchment conditions. To investigate the influence, a three-step framework is proposed, i.e., a Latin hypercube sampling (LHS-OAT) method multivariate regression model is used to conduct parametric sensitivity analysis; then, the multilevel-factorial-analysis method is used to quantitatively evaluate the individual and interactive effects of parameters on the hydrologic model output. Finally, analysis of the reasons for dynamic parameter changes is performed. Results suggest that the difference in parameter sensitivity for different periods is significant. The soil bulk density (SOL_BD) is significant at all times, and the parameter Soil Convention Service (SCS) runoff curve number (CN2) is the strongest during the flood period, and the other parameters are weaker in different periods. The interaction effects of CN2 and SOL_BD, as well as effective hydraulic channel conditions (CH_K2) and SOL_BD, are obvious, indicating that soil bulk density can impact the amount of loss generated by surface runoff and river recharge to groundwater. These findings help produce the best parameter inputs and improve the applicability of the model.

scholarly journals MetSim v2.0.0: A flexible and extensible framework for the estimation and disaggregation of meteorological data

2019 ◽  
Author(s):  
Andrew R. Bennett ◽  
Joseph J. Hamman ◽  
Bart Nijssen
Keyword(s):  
Meteorological Data ◽  
Longwave Radiation ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Automated Testing ◽  
Maximum Temperature ◽  
Temperature And Precipitation ◽  
Spatially Distributed ◽  
Temporal Disaggregation ◽  
Meteorological Simulation

Abstract. MetSim is a freely available, open source Python based model for simulation and disaggregation of meteorological variables with applications in the environmental and Earth sciences. MetSim can be used to generate spatially distributed sub-daily timeseries of incoming shortwave radiation, outgoing longwave radiation, air pressure, specific humidity, relative humidity, vapor pressure, precipitation, and air temperature given daily timeseries of minimum temperature, maximum temperature, and precipitation. Based on previously developed algorithms, we demonstrate that MetSim is able to closely reproduce their results while providing a number of advantages and improvements. We implemented automated testing to decrease errors during development and to improve reproducibility, modularized the algorithms to allow for extensions and modifications, and implemented robust single and multi-node parallelism. We describe the overall architecture, algorithms, and capabilities of MetSim by describing its four major modules. These are split into a model driver, solar geometry module, meteorological simulation module, and temporal disaggregation module. We also describe the available options and parameters that MetSim exposes to its users and analyze MetSim's scalability for large datasets.

scholarly journals Comparing impacts of parameter and spatial data uncertainty for a grid-based distributed watershed model

2016 ◽  
Vol 18 (6) ◽  
pp. 961-974 ◽  
Author(s):  
Younggu Her ◽  
Conrad Heatwole
Keyword(s):  
Land Cover ◽  
Parameter Uncertainty ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Model Output ◽  
Model Errors ◽  
Land Cover Data ◽  
The Impact

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

scholarly journals Tropical thin cirrus and relative humidity observed by the Atmospheric Infrared Sounder

2007 ◽  
Vol 7 (6) ◽  
pp. 16185-16225 ◽  
Author(s):  
B. H. Kahn ◽  
C. K. Liang ◽  
A. Eldering ◽  
A. Gettelman ◽  
Q. Yue ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Climate Models ◽  
Temporal Variations ◽  
Ocean Basin ◽  
Specific Humidity ◽  
Future Climate Change ◽  
Effective Diameter ◽  
Regional Variability ◽  
Atmospheric Infrared Sounder

Abstract. Global observations of cloud and humidity distributions in the upper troposphere within all geophysical conditions are critically important in order to monitor the present climate and to provide necessary data for validation of climate models to project future climate change. Towards this end, tropical oceanic distributions of thin cirrus optical depth (τ), effective diameter (De), and relative humidity with respect to ice (RHi) within cirrus (RHic) are simultaneously derived from the Atmospheric Infrared Sounder (AIRS). Corresponding increases in De and cloud temperature are shown for cirrus with τ>0.25 that demonstrate quantitative consistency to other surface-based, in situ and satellite retrievals of cirrus. However, inferred cirrus properties are shown to be less certain for increasingly tenuous cirrus. In-cloud supersaturation is observed for 8–12% of thin cirrus and is several factors higher than all-sky conditions; even higher frequencies are shown for the coldest and thinnest cirrus. Spatial and temporal variations in RHic correspond to cloud frequency while regional variability in RHic is observed to be most prominent over the N. Indian Ocean basin. The largest cloud/clear sky RHi anomalies tend to occur in dry regions associated with vertical descent in the sub-tropics, while the smallest occur in moist ascending regions in the tropics. The characteristics of RHic frequency distributions depend on τ and a peak frequency is located between 60–80% that illustrates RHic is on average biased dry. The geometrical thickness of cirrus is typically less than the vertical resolution of AIRS temperature and specific humidity profiles and thus leads to the observed dry bias, shown with coincident cloud vertical structure obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The joint distributions of thin cirrus microphysics and humidity derived from AIRS provide unique and important regional and global-scale insights on upper tropospheric processes not available from surface, in situ, and other contemporary satellite observing platforms.

scholarly journals MOESHA: A Genetic Algorithm for Automatic Calibration and Estimation of Parameter Uncertainty and Sensitivity of Hydrologic Models

2017 ◽  
Vol 60 (4) ◽  
pp. 1259-1269 ◽  
Author(s):  
Bradley L. Barnhart ◽  
Keith A. Sawicz ◽  
Darren L. Ficklin ◽  
Gerald W. Whittaker
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Hydrologic Modeling ◽  
Hydrologic Model ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Output ◽  
Discharge Data

Abstract. Characterization of the uncertainty and sensitivity of model parameters is an essential facet of hydrologic modeling. This article introduces the multi-objective evolutionary sensitivity handling algorithm (MOESHA) that combines input parameter uncertainty and sensitivity analyses with a genetic algorithm calibration routine to dynamically sample the parameter space. This novel algorithm serves as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating model parameters to a hydrologic model, MOESHA determines the optimal distribution of model parameters that maximizes model robustness and minimizes error, and the results provide an estimate for model uncertainty due to the uncertainty in model parameters. Subsequently, we compare the model parameter distributions to the distribution of a dummy variable (i.e., a variable that does not affect model output) to differentiate between impactful (i.e., sensitive) and non-impactful parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of model parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrologic model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol’s global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters, demonstrate the uncertainty of model results with respect to uncertainties in model parameters, and achieve excellent calibration results. A major drawback of the algorithm is that it is computationally expensive; therefore, parallelized methods should be used to reduce the computational burden. Keywords: Genetic algorithm, Hydrologic modeling, Model calibration, Sensitivity analysis, Uncertainty.

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