scholarly journals Assimilation of Piezometric Data to Calibrate Parsimonious Daily Hydrological Models

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2342
Author(s):  
Axel Flinck ◽  
Nathalie Folton ◽  
Patrick Arnaud
Keyword(s):  
Time Series ◽  
River Flow ◽  
Hydrological Model ◽  
Water Levels ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Time Step ◽  
Processing Methods ◽  
Analysis System ◽  
Set Up

Low water levels are a seasonal phenomenon, which can be long, short, and more or less intense, affecting entire watercourses. This phenomenon has become a concern for many countries who seek better understanding of the processes that affect it and learn how to optimally manage water resources (pumping, irrigation). Consequently, a lumped rainfall model at daily time step (GR) has been defined, calibrated, and regionalised over French territories. The input data come from SAFRAN, the distributed mesoscale atmospheric analysis system, which provides daily solid and liquid precipitation and temperature data throughout the French territory. This model could be improved, in particular to more accurately simulate the hydrological response of watersheds interacting with groundwater. The idea is to use piezometric data from the ADES bank, available in France, and to use it for the calibration phase of the hydrological model. The analysis was carried out across ten French catchments that are representative of various hydrometeorological behaviours and are located in a diverse hydrogeological context. Each catchment must be represented by a piezometer that closely represents the main aquifer that interacts with the basin. This piezometer is located on part of the watershed that is most covered in terms of its drainage network, and closest to its outlet. Different signal processing methods are used to characterise the relationship between the fluctuation of river flow, piezometric levels and rainfall time series. Potential processing methods will be carried out in the temporal domain. To quantify groundwater table inertia and that of the catchment area, correlograms were calculated from daily chronicles of flows and piezometric levels. A cross-correlatory analysis was set up to see, in more detail, the correlations between the flow rates (especially base flows) and piezometric level time series. This type of analysis makes it possible to study relationships between various observations, and tests were carried out to take this information into account during the phase of the calibration of hydrological model parameters. These different analyses will hopefully help us to use piezometric data to consolidate the quality and robustness of the modelling.

scholarly journals Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

2010 ◽  
Vol 7 (1) ◽  
pp. 1103-1141 ◽  
Author(s):  
X. Fang ◽  
J. W. Pomeroy ◽  
C. J. Westbrook ◽  
X. Guo ◽  
A. G. Minke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Accumulation ◽  
Model Parameters ◽  
Field Observations ◽  
Runoff Generation ◽  
Stream Channels ◽  
Physically Based ◽  
Set Up ◽  
Spot 5

Abstract. The eastern Canadian Prairies are dominated by cropland, pasture, woodland and wetland areas. The region is characterized by many poor and internal drainage systems and large amounts of surface water storage. Consequently, basins here have proven challenging to hydrological model predictions which assume good drainage to stream channels. The Cold Regions Hydrological Modelling platform (CRHM) is an assembly system that can be used to set up physically based, flexible, object oriented models. CRHM was used to create a prairie hydrological model for the externally drained Smith Creek Research Basin (~400 km2), east-central Saskatchewan. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were used and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland as derived from a supervised classification of SPOT 5 imagery. Two types of modelling approaches calibrated and uncalibrated, were set up for 2007/08 and 2008/09 simulation periods. For the calibrated modelling, only the surface depression capacity of upland area was calibrated in the 2007/08 simulation period by comparing simulated and observed hydrographs; while other model parameters and all parameters in the uncalibrated modelling were estimated from field observations of soils and vegetation cover, SPOT 5 imagery, and analysis of drainage network and wetland GIS datasets as well as topographic map based and LiDAR DEMs. All the parameters except for the initial soil properties and antecedent wetland storage were kept the same in the 2008/09 simulation period. The model performance in predicting snowpack, soil moisture and streamflow was evaluated and comparisons were made between the calibrated and uncalibrated modelling for both simulation periods. Calibrated and uncalibrated predictions of snow accumulation were very similar and compared fairly well with the distributed field observations for the 2007/08 period with slightly poorer results for the 2008/09 period. Soil moisture content at a point during the early spring was adequately simulated and very comparable between calibrated and uncalibrated results for both simulation periods. The calibrated modelling had somewhat better performance in simulating spring streamflow in both simulation periods, whereas the uncalibrated modelling was still able to capture the streamflow hydrographs with good accuracy. This suggests that prediction of prairie basins without calibration is possible if sufficient data on meteorology, basin landcover and physiography are available.

Comparing runoff statistics simulated with a hydrological model utilizing stochastically generated lumped catchment rainfall and multiple point rainfall

2020 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Hydrological Modelling ◽  
Flood Frequency ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Catchment Scale ◽  
Time Step ◽  
Rainfall Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. In the past, when using stochastic rainfall time series for hydrological modelling purposes, catchment rainfall for use in hydrological modelling was calculated from the multiple point rainfall time series. As an alternative to this approach, it will be tested whether catchment rainfall can be modelled directly, negating the drawbacks (and need) encountered in generating spatially consistent time series. An Alternating Renewal rainfall model (ARM) will be used to generate multiple point and lumped catchment rainfall time series in hourly resolution. The generated rainfall time series will be used to drive the rainfall-runoff model HBV-IWW with an hourly time step for mesoscale catchments in Germany. Validation will be performed by comparing modelled runoff regarding runoff and flood statistics using stochastically generated lumped catchment rainfall versus multiple point rainfall. It would be advantageous if the results based on catchment rainfall are comparable to those using multiple point rainfall, so catchment rainfall could be generated directly with the stochastic rainfall models. Extremes at the catchment scale may also be better represented if catchment rainfall is generated directly.</p>

scholarly journals Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides

2013 ◽  
Vol 30 (3) ◽  
pp. 569-589 ◽  
Author(s):  
Pascal Matte ◽  
David A. Jay ◽  
Edward D. Zaron
Keyword(s):  
Time Series ◽  
Harmonic Analysis ◽  
River Flow ◽  
Water Levels ◽  
Spectral Lines ◽  
Noise Model ◽  
Analysis Tool ◽  
Tidal Analysis ◽  
Nonstationary Signals ◽  
Analysis Methods

Abstract One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis tool has been developed, NS_TIDE, adapted to the study of nonstationary signals, in this case, river tides. It builds the nonstationary forcing directly into the tidal basis functions. It is implemented by modification of T_TIDE; however, certain concepts, particularly the meaning of a constituent and the Rayleigh criterion, are redefined to account for the smearing effects on the tidal spectral lines by nontidal energy. An error estimation procedure is included that constructs a covariance matrix of the regression coefficients, based on either an uncorrelated or a correlated noise model. The output of NS_TIDE consists of time series of subtidal water levels [mean water level (MWL)] and tidal properties (amplitudes and phases), expressed in terms of external forcing functions. The method was tested using records from a station on the Columbia River, 172 km from the ocean entrance, where the tides are strongly altered by river flow. NS_TIDE hindcast explains 96.4% of the signal variance with a root-mean-square error of 0.165 m obtained from 288 parameters, far better than traditional harmonic analysis (38.5%, 0.604 m, and 127 parameters). While keeping the benefits of harmonic analysis, its advantages compared to existing tidal analysis methods include its capacity to distinguish frequencies within tidal bands without losing resolution in the time domain or data at the endpoints of the time series.

scholarly journals Identification of Hydrologic Models, Optimized Parameters, and Rainfall Inputs Consistent with In Situ Streamflow and Rainfall and Remotely Sensed Soil Moisture

2018 ◽  
Vol 19 (8) ◽  
pp. 1305-1320 ◽  
Author(s):  
Ashley J. Wright ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Input Data ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Input ◽  
Rainfall Estimates

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

scholarly journals Temporal rainfall disaggregation using a micro-canonical cascade model: Possibilities to improve the autocorrelation

2019 ◽  
Author(s):  
Hannes Müller-Thomy
Keyword(s):  
Time Series ◽  
Extreme Event ◽  
A Priori ◽  
Extreme Rainfall ◽  
Cascade Model ◽  
Special Focus ◽  
Rainfall Time Series ◽  
Lower Saxony ◽  
Time Step ◽  
Daily Data

Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated so is the underestimation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation. Both modifications are based on a state-of-the-art reference cascade model. In the first modification, a position-dependency is introduced in the first disaggregation step. In the second modification the position of a wet time step is redefined in addition. Both modifications led to an improvement of the autocorrelation, especially the position redefinition. Simultaneously, two approaches are investigated to avoid the generation of time steps with too small rainfall intensities, the conservation of a minimum rainfall amount during the disaggregation process itself and the mimicry of a measurement device after the disaggregation process. The mimicry approach shows slight better results for the autocorrelation and hence was kept for a subsequent resampling investigation using Simulated Annealing. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant. Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.

scholarly journals A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1849 ◽  
Author(s):  
Mahmood Mahmoodian ◽  
Jairo Arturo Torres-Matallana ◽  
Ulrich Leopold ◽  
Georges Schutz ◽  
Francois H. L. R. Clemens
Keyword(s):  
Time Series ◽  
Storage Tank ◽  
Data Driven ◽  
Urban Drainage ◽  
Rainfall Time Series ◽  
Short Term ◽  
Time Step ◽  
Speed Up ◽  
Error Indicators

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

Assessing the impact of the development of an urban district on shallow groundwater using the integrated urban hydrological model URBS

2020 ◽  
Author(s):  
emmanuel berthier ◽  
jérémie sage ◽  
emmanuel dumont ◽  
marie-laure mosini ◽  
fabrice rodriguez ◽  
...  
Keyword(s):  
Water Table ◽  
Water Levels ◽  
Integrated Modelling ◽  
Model Parameters ◽  
Good Representation ◽  
Surprising Result ◽  
Time Step ◽  
Urban District ◽  
The Impact ◽  
Underground Constructions

<p>The urbanisation leads to modifications in the water budget, not only at the surface but in groundwater as well. Few urban modelling studies deal with this topic, due to the lack of appropriate models. The URBS (Urban Runoff Branching Structure) model has been developed since several decades to simulate water transfers at the scale of an urban district. An integrated modelling approach is deliberately adopted to account for the numerous elements that influence urban hydrology: the spatial distribution of the sealed surfaces, interactions between the urban soil and water networks or underground, sustainable drainage systems…. In URBS, the spatial discretization of a catchment is based on Urban Hydrologic Elements (UHE) constituted by cadastral parcels and the adjacent streets, connected to the drainage network. URBS is able to perform continuous and long-period simulations (typically several years) of water fluxes in urban districts for small time-steps (typically few-minutes), with rainfall and potential evapotranspiration as input data.</p><p>The URBS model is adopted to study the hydrological impact of the Moulon district layout, a 200 ha development operation of the Paris-Saclay Cluster (currently underway). The project should result in an increase of sealed surfaces from 14% to 35% and a densification of underground constructions such as networks and basements. A shallow unconfined aquifer extends on the whole area. The fluctuations of ground-water levels have been monitored at an hourly time-step with 8 piezometers since 2012. Water-table levels exhibit significant variations, with near-saturation levels during winter and several meters depths during summer, although the piezometers do not all exhibit the same dynamics.</p><p>A calibration of the URBS model is first conducted for a 2-year period using only piezometric data and no flowrate data. The calibration is solely performed for the parameters influencing the soil compartment: soil permeability and parameters of the sewer infiltration process. Model performances are rather satisfactory with good representation of the observed levels for several piezometers, despite some difficulties for two piezometers exhibiting atypical variations. Once the URBS model is calibrated for the initial situation, simulations are conducted for the project layout (accounting for land-use modification and underground constructions) so as to evaluate the hydrological impacts of the development. Simulation results suggest that an increase of water table levels might be expected after the development of the district (this somehow surprising result may partly originate from the decrease of evapotranspiration fluxes associated with the increased of sealed surfaces).</p><p>The analysis of these first simulations also suggests that large uncertainties might be expected regarding the water levels computed by URBS. A simplified uncertainty analysis (based on Monte-Carlo simulations) is thus conducted to evaluate and distinguish uncertainties associated with model parameters and the total uncertainties in model outputs. While the results clearly evidence the importance of total uncertainties (although the uncertainties due to the model parameters remain low), they also confirm that groundwater depths could be reduced by the construction of the Moulon district.</p>

Model adequacy tests for improving predictions in ungauged basins

2020 ◽  
Author(s):  
Cristina Prieto ◽  
Nataliya Le Vine ◽  
Dmitri Kavetski ◽  
César Álvarez ◽  
Raúl Medina
Keyword(s):  
Time Series ◽  
Hydrological Model ◽  
Flow Time ◽  
Flow Index ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Ungauged Catchments ◽  
Subsequent Work ◽  
Study Results

<p>Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. Meeting this challenge is made difficult by the uncertainty in the “regionalization” model used to transpose hydrological data (e.g., flow indices) from gauged to ungauged basins, and by the uncertainty in the hydrological model used to predict streamflow in the ungauged basin. This study combines recent advances in flow index selection, regionalization via machine learning methods, and a Bayesian inference framework. In addition, it proposes two new statistical metrics, “DistanceTest” and “InfoTest”, to assess the adequacy of a model before estimating its parameters. “DistanceTest” quantifies whether a model (hydrological or regionalization) is likely to reproduce the available hydrological information in a catchment. “InfoTest” is based on Bayes Factors and quantifies the information added by a model (hydrological or regionalization) over prior knowledge about the available hydrological information in a catchment). The proposed adequacy tests can be seen as a prerequisite for a model (hydrological or regionalization) being considered capable of providing meaningful and high quality flow time series predictions in ungauged catchments. If a model is found inadequate a priori and rejected, the modeler is spared the effort in estimating the model parameters, which can be a substantial saving.</p><p>The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is found that (1) a small number of PCs capture approximately 87% of variability in the flow indices, and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. In addition, the case study results suggest that the hydrological model is the main source of uncertainty in comparison to the regionalization model, and hence should receive the main priority in subsequent work at the case study catchments.</p>

scholarly journals The sensitivity of catchment runoff models to rainfall data at different spatial scales

2000 ◽  
Vol 4 (4) ◽  
pp. 653-667 ◽  
Author(s):  
V. A. Bell ◽  
R. J. Moore
Keyword(s):  
Time Series ◽  
Spatial Scales ◽  
Rainfall Data ◽  
Scale Model ◽  
Distributed Model ◽  
Rainfall Time Series ◽  
Time Step ◽  
Stratiform Rain ◽  
Convective Rain ◽  
Catchment Runoff

Abstract. The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment) dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911) but significantly better during convective rain (R2 of 0.953 compared to 0.909). The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed model is explored. The need to recalibrate the model for use with rainfall data of a given resolution, particularly for periods of convective rain, is highlighted. Again, best performance is obtained using lower resolution rainfall data. This is interpreted as evidence for the need to improve the distributed model structure to make better use of the higher resolution information on rainfall and topographic controls on runoff. Degrading the resolution of rainfall data, model or both to achieve the smoothing apparently needed is not seen as wholly appropriate. Keywords: rainfall, runoff, sensitivity, scale, model, flood

scholarly journals The Cache la Poudre river basin snow water equivalent modeling with NewAge-JGrass

2013 ◽  
Vol 6 (3) ◽  
pp. 4447-4474 ◽  
Author(s):  
G. Formetta ◽  
S. K. Kampf ◽  
O. David ◽  
R. Rigon
Keyword(s):  
Time Series ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Automatic Calibration ◽  
Snow Melting ◽  
Time Step ◽  
Equivalent Time ◽  
Cache La Poudre River ◽  
Snow Water

Abstract. The paper presents a snow water equivalent model as part of the hydrological modeling system NewAge-JGrass. The model take in account of the main physical processes influencing the snow melting (precipitation form separation, melting and freezing modeling) coupled with the snowpack mass conservation equation. The snow melting depends not only on the air temperature but also on the radiation received by the pixel. The model is perfectly integrated in the NewAge-JGrass modeling system and uses many of its components such as shortwave radiation balance, krigings and automatic calibration algorithms. As all the NewAge-JGrass components, the presented model can be executed both in raster and in vector mode and the simulation time step can be daily, hourly or sub-hourly as the user needs. The model is applied on the Cache la Poudre river basin (CO, USA). Three are the applications presented in the paper. Firstly, the simulation of snow water equivalent in three different measurement stations is performed. Model parameters are calibrated and model performances are quantitatively computed by comparing simulated and measured snow water equivalent time series. Indices of goodness of fit such as Kling–Gupta Efficiency, Index of Agreement and Percentage Bias are computed. Secondly, the representativeness of the model parameters in different locations is discussed. Finally a raster mode application is performed: snow water equilvalent maps on the whole Cache la Poudre river are computed. In all the applications the model performance are satisfactory in term of goodness of fitting measured snow water equivalent time series. The integration of the model in the NewAge-JGrass system allows the used to o enjoy all the component of the system: input data computation, output maps visualizetion in the GIS JGrass, model parameters automatic calibration.

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