Water balance study of a high altitude catchment in Indus basin of Himalayas: application of physics-based distributed hydrologic model-MIKE SHE

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives which arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for a distributed hydrologic model MOBIDIC, which combines two sensitivity analysis techniques (Morris method and State Dependent Parameter method) with a multiobjective optimization (MOO) approach &varepsilon;-NSGAII. This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina with three objective functions, i.e., standardized root mean square error of logarithmic transformed discharge, water balance index, and mean absolute error of logarithmic transformed flow duration curve, and its results were compared with those with a single objective optimization (SOO) with the traditional Nelder–Mead Simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show: (1) the two sensitivity analysis techniques are effective and efficient to determine the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization; (2) both MOO and SOO lead to acceptable simulations, e.g., for MOO, average Nash–Sutcliffe is 0.75 in the calibration period and 0.70 in the validation period; (3) evaporation and surface runoff shows similar importance to watershed water balance while the contribution of baseflow can be ignored; (4) compared to SOO which was dependent of initial starting location, MOO provides more insight on parameter sensitivity and conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provides an alternative way for future MOBIDIC modelling.

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Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4101-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 4101-4112 ◽

Cited By ~ 18

Author(s):

J. Yang ◽

F. Castelli ◽

Y. Chen

Keyword(s):

Sensitivity Analysis ◽

Water Balance ◽

Surface Runoff ◽

Hydrologic Model ◽

Optimization Approach ◽

Objective Functions ◽

Analysis Techniques ◽

Distributed Hydrologic Model ◽

Conflicting Objectives ◽

Initial Starting

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach ε-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more insight into parameter sensitivity and the conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provide an alternative way for future MOBIDIC modeling.

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Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales

Water ◽

10.3390/w12051279 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1279

Author(s):

Tyler Madsen ◽

Kristie Franz ◽

Terri Hogue

Keyword(s):

Local Population ◽

Model Performance ◽

Forecast Model ◽

Hydrologic Model ◽

Laboratory Research ◽

Discharge Data ◽

Distributed Hydrologic Model ◽

Verification Period ◽

Silver Spring

Demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. In the current study, the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (NOAA/NWS, Silver Spring, MD, USA) was used to explore the accuracy of a distributed hydrologic model to simulate discharge at watershed scales ranging from 20 to 2500 km2. The model was calibrated and validated using observed discharge data at the basin outlets, and discharge at uncalibrated subbasin locations was evaluated. Two precipitation products with nominal spatial resolutions of 12.5 km and 4 km were tested to characterize the role of input resolution on the discharge simulations. In general, model performance decreased as basin size decreased. When sub-basin area was less than 250 km2 or 20–40% of the total watershed area, model performance dropped below the defined acceptable levels. Simulations forced with the lower resolution precipitation product had better model evaluation statistics; for example, the Nash–Sutcliffe efficiency (NSE) scores ranged from 0.50 to 0.67 for the verification period for basin outlets, compared to scores that ranged from 0.33 to 0.52 for the higher spatial resolution forcing.

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Spatio‐temporal evaluation of gridded precipitation products for the high‐altitude Indus basin

International Journal of Climatology ◽

10.1002/joc.7073 ◽

2021 ◽

Author(s):

Zakir Hussain Dahri ◽

Fulco Ludwig ◽

Eddy Moors ◽

Shakil Ahmad ◽

Bashir Ahmad ◽

...

Keyword(s):

High Altitude ◽

Indus Basin ◽

Spatio Temporal

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Continuous streamflow simulation with the HRCDHM distributed hydrologic model

Journal of Hydrology ◽

10.1016/j.jhydrol.2004.03.032 ◽

2004 ◽

Vol 298 (1-4) ◽

pp. 61-79 ◽

Cited By ~ 37

Author(s):

Theresa M. Carpenter ◽

Konstantine P. Georgakakos

Keyword(s):

Hydrologic Model ◽

Distributed Hydrologic Model ◽

Streamflow Simulation

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On the benefit of the Canadian Small Lake Model to better represent the impact of small natural lakes on GEM-Hydro streamflow simulations.

10.5194/egusphere-egu21-3471 ◽

2021 ◽

Author(s):

Etienne Gaborit ◽

Murray MacKay ◽

Camille Garnaud ◽

Vincent Fortin

Keyword(s):

Water Balance ◽

Grid Point ◽

Hydrologic Model ◽

Surface Fluxes ◽

Dew Point ◽

Multiple Model ◽

Small Lake ◽

Surface Component ◽

Lake Model ◽

The Impact

This study aims at assessing the impact of a new lake model on streamflow simulations performed with the GEM-Hydro hydrologic model developed at ECCC. GEM-Hydro is at the heart of the National Surface and River Prediction System (NSRPS) which ECCC uses to forecast river flows over most of Canada. The GEM-Hydro model mainly consists of the GEM-Surf component to represent surface processes, and of the Watroute model to represent river and lake routing, in order to perform streamflow simulations and forecasts. The surface component of GEM-Hydro can simulate 5 different types of surfaces.&#160; Currently, the water tile consists of a very simple algorithm which, in terms of water balance, consists of producing runoff fluxes simply equal to precipitation minus evaporation. This runoff over water surfaces is then provided as input, along with runoff and drainage generated over other surface tiles, to the Watroute model. The Watroute version used in GEM-Hydro currently only represents major lakes (area greater than 100km2) along the river networks, and does not represent the impact that small lakes can have on streamflow, which mainly consists in slowing down runoff before it reaches the main streams of the network.Recently, the Canadian Small Lake Model (CSLM) was implemented in the surface component of GEM-Hydro to represent the energy and water balance over water tiles more accurately. So far, CSLM simulations have been shown promising in terms of evaporation, ice cover, absolute and dew point temperature simulations, compared with the former algorithm used over water. However, the impact of CSLM on the resulting streamflow simulations performed with GEM-Hydro has not been evaluated yet. This study aims first at evaluating the impact of CSLM on streamflow simulations, and secondly at testing different CSLM configurations as well as different coupling strategies with Watroute, with the objective of finding the best set up for the prediction of streamflow in Canada. For example, overland runoff generated by the land tile can be provided to the water tile of the same grid point in different ways, and the outflow computed at the outlet of the water tile can be computed with different parameters. Moreover, different outflow computations have to be taken into account depending on if the water tile of a grid point represents subgrid-scale lakes, or if on the contrary it belongs to a lake spanning over multiple model grid points.To do so, different GEM-Hydro open-loop simulations have been performed on the Lake of the Woods watershed, located in Canada, with and without CSLM to represent water tiles. The CSLM configurations leading to the best results are presented here. CSLM simulations are also evaluated in terms of surface fluxes, to ensure that the main purpose of the model, which is to improve surface fluxes to ultimately improve atmospheric forecasts, is preserved, compared to the default configuration of the model. Ideas for further improving the coupling between the GEM-Hydro surface and routing components, in terms of lake processes, are also presented and will be tested in future work.

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