Diagnostic calibration of a hydrological model in an alpine area

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

Download Full-text

Incorporating Advanced Scatterometer Surface and Root Zone Soil Moisture Products into the Calibration of a Conceptual Semi-Distributed Hydrological Model

Water ◽

10.3390/w13233366 ◽

2021 ◽

Vol 13 (23) ◽

pp. 3366

Author(s):

Martin Kubáň ◽

Juraj Parajka ◽

Rui Tong ◽

Isabella Pfeil ◽

Mariette Vreugdenhil ◽

...

Keyword(s):

Soil Moisture ◽

Forest Cover ◽

Hydrological Model ◽

Root Zone ◽

Retrieval Algorithm ◽

Multiple Objective ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Runoff Generation ◽

Alpine Catchments

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff.

Download Full-text

Diagnostic calibration of a hydrological model in a mountain area by hydrograph partitioning

Hydrology and Earth System Sciences ◽

10.5194/hess-19-1807-2015 ◽

2015 ◽

Vol 19 (4) ◽

pp. 1807-1826 ◽

Cited By ~ 23

Author(s):

Z. H. He ◽

F. Q. Tian ◽

H. V. Gupta ◽

H. C. Hu ◽

H. P. Hu

Keyword(s):

Hydrological Modeling ◽

Hydrological Model ◽

Water Source ◽

Water Sources ◽

Model Parameters ◽

Runoff Generation ◽

Runoff Water ◽

Mountain Areas ◽

Glacier Meltwater ◽

Diagnostic Potential

Abstract. Hydrological modeling can exploit informative signatures extracted from long time sequences of observed streamflow for parameter calibration and model diagnosis. In this study we explore the diagnostic potential of hydrograph partitioning for model calibration in mountain areas, where meltwater from snow and glaciers is an important source for river runoff (in addition to rainwater). We propose an index-based method to partition the hydrograph according to dominant runoff water sources, and a diagnostic approach to calibrate a mountain hydrological model. First, by accounting for the seasonal variability of precipitation and the altitudinal variability of temperature and snow/glacier coverage, we develop a set of indices to indicate the daily status of runoff generation from each type of water source (i.e., glacier meltwater, snow meltwater, rainwater, and groundwater). Second, these indices are used to partition a hydrograph into four parts associated with four different combinations of dominant water sources (i.e., groundwater, groundwater + snow meltwater, groundwater + snow meltwater + glacier meltwater, and groundwater + snow meltwater + glacier meltwater + rainwater). Third, the hydrological model parameters are grouped by the associated runoff sources, and each group is calibrated to match the corresponding hydrograph partition in a stepwise and iterative manner. Similar to use of the regime curve to diagnose seasonality of streamflow, the hydrograph partitioning curve based on a dominant runoff water source (more briefly called the partitioning curve, not necessarily continuous) can serve as a diagnostic signature that helps relate model performance to model components. The proposed methods are demonstrated via application of a semi-distributed hydrological model (THREW, Tsinghua Representative Elementary Watershed) to the Tailan River basin (TRB) (1324 km2) in the Tianshan Mountains of China. Results show that the proposed calibration approach performed reasonably well. Cross-validation and comparison to an automatic calibration method indicated its robustness.

Download Full-text

A meta-model assisted framework of optimization of the Hydrological model parameters for accurate calibration

10.5194/egusphere-egu2020-21083 ◽

2020 ◽

Author(s):

Ammara Nusrat ◽

Hamza Farooq Gabriel ◽

Sajjad Haider ◽

Muhammad Shahid

Keyword(s):

Sensitivity Analysis ◽

Parameter Optimization ◽

Hydrological Modeling ◽

Hydrological Model ◽

Flood Forecasting ◽

Large River ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Dimensional Parameter ◽

Input Parameters

&#160;Increase in frequency of the floods is one of the noticeable climate change impacts. The efficient and optimized flood analysis system needs to be used for the reliable flood forecasting. The credibility and the reliability of the flood forecasting system is depending upon the framework used for its parameter optimization. Comprehensive framework has been presented to optimize the input parameters of the computationally extensive distributed hydrological model. A large river basin has the high spatio-temporal heterogeneity of aquifer and surface properties. &#160;Estimating the parameters in fully distributed hydrological model is a challenging task. The parameter optimization becomes computationally more demanding when the model input parameters (30 to 100 even greater) have multi-dimensional parameter space, many output parameters which make the optimization problem multi-objective and large number of model simulations requirement for the optimization. Aforementioned challenges are met by introducing the methodology to optimize the input parameters of fully distributed hydrological model, following steps are included (1) screening of the parameters through Morris sensitivity analysis method in different flow periods, so that optimization would be performed for sensitive parameters, different scalar output functions are used in this regard (2) to emulate the hydrologic response of the dynamic model, surrogate models or meta-models are used (3) sampling of parameters values using the optimized ranges obtained from the meta-models; the results are evident that the parameter optimization using the proposed framework is efficient can be effectively performed.&#160; The effectiveness and efficiency of the proposed framework has been demonstrated through the accurate calibration of the model with fewer model runs. This study also demonstrates the importance and use of scalar functions in calculating sensitivity indices, when the model output is temporally variable. In addition, the parameter optimization using the proposed framework is efficient and present study can be used as reference for optimization of distributed hydrological model.&#160;&#160;Keywords: Calibration, parameter ranking, Sensitivity analysis, Hydrological modeling, optimization

Download Full-text

Diagnostic calibration of a hydrological model in an alpine area by hydrograph partitioning

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-13385-2014 ◽

2014 ◽

Vol 11 (12) ◽

pp. 13385-13441 ◽

Cited By ~ 1

Author(s):

Z. H. He ◽

F. Q. Tian ◽

H. V. Gupta ◽

H. C. Hu ◽

H. P. Hu

Keyword(s):

Hydrological Modeling ◽

Hydrological Model ◽

Water Source ◽

Water Sources ◽

Model Parameters ◽

Tianshan Mountain ◽

Runoff Generation ◽

Runoff Water ◽

Glacier Meltwater ◽

Diagnostic Potential

Abstract. Hydrological modeling can exploit informative signatures extracted from long time sequences of observed streamflow for parameter calibration and model diagnosis. In this study we explore the diagnostic potential of hydrograph partitioning for model calibration in alpine areas, where meltwater from snow and glaciers are important sources for river runoff (in addition to rainwater). We propose an index-based method to partition the hydrograph according to dominant runoff water sources, and a diagnostic approach to calibrate an alpine hydrological model. First, by accounting for the seasonal variability of precipitation and the altitudinal variability of temperature and snow/glacier coverage, we develop a set of indices to indicate the daily status of runoff generation from each type of water source (i.e. glacier meltwater, snow meltwater, rainwater, and groundwater). Second, these indices are used to partition a hydrograph into four parts associated with four different combinations of dominant water sources (i.e. groundwater, groundwater + snow meltwater, groundwater + snow meltwater + glacier meltwater, groundwater + snow meltwater + glacier meltwater + rainwater). Third, the hydrological model parameters are grouped by the associated runoff generation mechanism, and each group is calibrated to match the corresponding hydrograph partition in a stepwise and iterative manner. Similar to use of the regime curve to diagnose seasonality of streamflow, the hydrograph partitioning curve based on a dominant runoff water source (more briefly called the partitioning curve, not necessarily continuous) can serve as a diagnostic signature that helps relate model performance to model components. The proposed methods are demonstrated via application of a semi-distributed hydrological model (THREW) to the Tailan River basin (1324 km2) in the Tianshan Mountain of China.

Download Full-text

Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-1103-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 1103-1141 ◽

Cited By ~ 2

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.

Download Full-text

Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

Hydrology and Earth System Sciences ◽

10.5194/hess-18-3923-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 3923-3936 ◽

Cited By ~ 20

Author(s):

X. Xie ◽

S. Meng ◽

S. Liang ◽

Y. Yao

Keyword(s):

Real Time ◽

Land Surface ◽

Hydrological Model ◽

Similarity Measures ◽

State Parameter ◽

Model Parameters ◽

Prediction Errors ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Short Period

Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

Download Full-text

Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites

Hydrology and Earth System Sciences ◽

10.5194/hess-14-2577-2010 ◽

2010 ◽

Vol 14 (12) ◽

pp. 2577-2594 ◽

Cited By ~ 35

Author(s):

M. Shrestha ◽

L. Wang ◽

T. Koike ◽

Y. Xue ◽

Y. Hirabayashi

Keyword(s):

Snow Depth ◽

Hydrological Model ◽

Snow Water Equivalent ◽

Phase 1 ◽

Forest Site ◽

Distributed Hydrological Model ◽

Snowmelt Runoff ◽

Runoff Generation ◽

Basin Scale ◽

Snow Physics

Abstract. In this study, the snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is significantly improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics is hereafter termed WEB-DHM-S. Since the in-situ observations of spatially-distributed snow variables with high resolution are currently not available over large regions, the new distributed system (WEB-DHM-S) is at first rigorously tested with comprehensive point measurements. The stations used for evaluation comprise the four open sites of the Snow Model Intercomparison Project (SnowMIP) phase 1 with different climate characteristics (Col de Porte in France, Weissfluhjoch in Switzerland, Goose Bay in Canada and Sleepers River in USA) and one open/forest site of the SnowMIP phase 2 (Hitsujigaoka in Japan). The comparisons of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff at the SnowMIP1 sites reveal that WEB-DHM-S, in general, is capable of simulating the internal snow process better than the original WEB-DHM. Sensitivity tests (through incremental addition of model processes) are performed to illustrate the necessity of improvements over WEB-DHM and indicate that both the 3-layer snow module and the new albedo scheme are essential. The canopy effects on snow processes are studied at the Hitsujigaoka site of the SnowMIP2 showing that the snow holding capacity of the canopy plays a vital role in simulating the snow depth on ground. Through these point evaluations and sensitivity studies, WEB-DHM-S has demonstrated the potential to address basin-scale snow processes (e.g., the snowmelt runoff), since it inherits the distributed hydrological framework from the WEB-DHM (e.g., the slope-driven runoff generation with a grid-hillslope scheme, and the flow routing in the river network).

Download Full-text

Modeling the Hydrological Effect on Local Gravity at Moxa, Germany

Journal of Hydrometeorology ◽

10.1175/jhm488.1 ◽

2006 ◽

Vol 7 (3) ◽

pp. 346-354 ◽

Cited By ~ 24

Author(s):

Shaakeel Hasan ◽

Peter A. Troch ◽

J. Boll ◽

C. Kroner

Keyword(s):

Transfer Function ◽

Hydrological Modeling ◽

Hydrological Model ◽

Water Storage ◽

Superconducting Gravimeter ◽

Hydrological Processes ◽

Earth Tides ◽

Time Averaging ◽

Distributed Hydrological Model ◽

High Positive Correlation

Abstract A superconducting gravimeter has observed with high accuracy (to within a few nm s−2) and high frequency (1 Hz) the temporal variations in the earth’s gravity field near Moxa, Germany, since 1999. Hourly gravity residuals are obtained by time averaging and correcting for earth tides, polar motion, barometric pressure variations, and instrumental drift. These gravity residuals are significantly affected by hydrological processes (interception, infiltration, surface runoff, and subsurface redistribution) in the vicinity of the observatory. In this study time series analysis and distributed hydrological modeling techniques are applied to understand the effect of these hydrological processes on observed gravity residuals. It is shown that the short-term response of gravity residuals to medium- to high-rainfall events can be efficiently modeled by means of a linear transfer function. This transfer function exhibits an oscillatory behavior that indicates fast redistribution of stored water in the upper layers (interception store, root zone) of the catchment surrounding the instrument. The relation between groundwater storage and gravity residuals is less clear and varies according to the season. High positive correlation between groundwater and gravity exists during winter months when the freezing of the upper soil layers immobilizes water stored in the unsaturated zone of the catchment. To further explore the spatiotemporal dynamics of the relevant hydrological processes and their relation to observed gravity residuals, a GIS-based distributed hydrological model is applied for the Silberleite catchment. Driven by observed atmospheric forcings (precipitation and potential evapotranspiration), the model allows the authors to compute the variation of water storage in three different layers: the interception store, the snow cover store, and the soil moisture store. These water storage dynamics are then converted to predicted gravity variation at the location of the superconducting gravimeter and compared to observed gravity residuals. During most of the investigated period (January 2000 to January 2004) predictions are in good agreement with the observed patterns of gravity dynamics. However, during some winter months the distributed hydrological model fails to explain the observations, which supports the authors’ conclusion that groundwater variability dominates the hydrological gravity signal in the winter. More hydrogeological research is needed to include groundwater dynamics in the hydrological model.

Download Full-text

Extending flood forecasting lead time in large watershed by coupling WRF QPF with distributed hydrological model

10.5194/hess-2016-512 ◽

2016 ◽

Author(s):

Ji Li ◽

Yangbo Chen ◽

Huanyu Wang ◽

Jianming Qin ◽

Jie Li

Keyword(s):

Lead Time ◽

Hydrological Model ◽

Southern China ◽

Weather Forecast ◽

Forecast Model ◽

Flood Forecasting ◽

Rain Gauge ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Flood Warning

Abstract. Long lead time flood forecasting is very important for large watershed flood mitigation as it provides more time for flood warning and emergency responses. Latest numerical weather forecast model could provide 1–15 days quantitative precipitation forecasting products at grid format, by coupling this product with distributed hydrological model could produce long lead time watershed flood forecasting products. This paper studied the feasibility of coupling the Liuxihe Model with the WRF QPF for a large watershed flood forecasting in southern China. The QPF of WRF products has three lead time, including 24 hour, 48 hour and 72 hour, the grid resolution is 20 km × 20 km. The Liuxihe Model is set up with freely downloaded terrain property, the model parameters were previously optimized with rain gauge observed precipitation, and re-optimized with WRF QPF. Results show that the WRF QPF has bias with the rain gauge precipitation, and a post-processing method is proposed to post process the WRF QPF products, which improves the flood forecasting capability. With model parameter re-optimization, the model's performance improves also, it suggests that the model parameters be optimized with QPF, not the rain gauge precipitation. With the increasing of lead time, the accuracy of WRF QPF decreases, so does the flood forecasting capability. Flood forecasting products produced by coupling Liuxihe Model with WRF QPF provides good reference for large watershed flood warning due to its long lead time and rational results.

Download Full-text

Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

MATEC Web of Conferences ◽

10.1051/matecconf/201824601108 ◽

2018 ◽

Vol 246 ◽

pp. 01108

Author(s):

Lili Wang ◽

Hongjun Bao

Keyword(s):

Overland Flow ◽

Hydrological Model ◽

Weather Prediction ◽

Flood Forecasting ◽

False Alarms ◽

Generation Model ◽

Flood Forecast ◽

Distributed Hydrological Model ◽

Runoff Generation ◽

Numerical Weather

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.

Download Full-text