Hydrological modeling in data sparse environments

2021 ◽  
Author(s):  
Faizan Anwar ◽  
András Bárdossy ◽  
Jochen Seidel
Keyword(s):  
Input Data ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Heavy Rain ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Daily Data ◽  
Time Period ◽  
Main Driver ◽  
Model Parameter Uncertainty

<p>We demonstrate that in data sparse environments, model parameter uncertainty is not the only cause of concern. To get a meaningful outcome, input data uncertainty has to be taken into account as well. The procedure involved calibration of a hydrological model using recent daily data rich time period along with validation. A historical flood was simulated (after warmup) for which the input data were relatively sparse in space, namely precipitation and temperature, using the calibrated model parameters. Precipitation was assumed to be the main driver of this event. Results showed that by only using interpolated precipitation (e.g. IDW or Kriging), the magnitude and timing of the peak were incorrect, even after using very many different parameter vectors that performed equally well for the recent times. Subsequently, the model was inverted for precipitation i.e. input fields that produced the correct timing, magnitude, dependence in space and distributions were searched for. This was done using a previously developed simulation algorithm. The new fields showed that the same hydrograph could have been produced by two main types of conditions, namely, early snow cover that melted and heavy rain. The plausibility of the simulated fields was also assessed by comparing their structure in space to events in recent times.</p>

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

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).

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

scholarly journals Calibration of the hydrological model GR4J from potential evapotranspiration estimates by the Penman-Monteith and Oudin methods in the Ouémé watershed (West Africa)

2020 ◽  
Vol 383 ◽  
pp. 163-169
Author(s):  
Domiho Japhet Kodja ◽  
Arsène J. Sègla Akognongbé ◽  
Ernest Amoussou ◽  
Gil Mahé ◽  
E. Wilfrid Vissin ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Daily Rainfall ◽  
Performance Criteria ◽  
Economic Activities ◽  
Wet Season ◽  
Daily Data ◽  
The Difference ◽  
And Performance

Abstract. The Ouémé watersheds at Bétérou and Bonou has been recently facing increased sensitivity to extreme hydroclimatic phenomena that occurred by flooding or drought events. In the same time, the population growth and the related socio-economic activities increased the pressure state on water resources. In this context, hydrological modeling is an important issue and this study aims at analyzing the calibration of the hydrological model GR4J based on PET Penman-Monteith and Oudin methods. Daily rainfall, Penman-Monteith and Oudin evapotranspiration and daily data flow from the Bétérou and Bonou hydrometric stations on the Ouémé Basin have been implemented in the GR4J model over the period 1971 to 2010. Oudin PET values are slightly higher than the Penman-Monteith PET ones. However, the difference between the two PET methods have only few impacts on the optimization and performance criteria of the GR4J model. The Nash values ranges from 0.83 to 0.91 in Bonou, and 0.52 to 0.70 in Bétérou for the calibration in dry period, while in validation, they are 0.59 to 0.78 in Bétérou, and 0.56 to 0.88 in Bonou in wet season. In view of these results, with the two PET methods used which do not result from the same climatic variables, it should be said that the formulation of PET has only few impacts on the results of GR4J for these tropical basins.

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

2015 ◽  
Vol 19 (4) ◽  
pp. 1807-1826 ◽  
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.

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

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

<p> 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.  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.  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. </p><p> </p><p><strong>Keywords: </strong>Calibration, parameter ranking, Sensitivity analysis, Hydrological modeling, optimization</p>

scholarly journals Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches

2018 ◽  
Vol 22 (9) ◽  
pp. 5021-5039 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
John F. Burkhart ◽  
Thomas V. Schuler
Keyword(s):  
Uncertainty Analysis ◽  
Parameter Uncertainty ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Behavioral Model ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Observation Error ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Parameter Uncertainty Analysis

Abstract. Parameter uncertainty estimation is one of the major challenges in hydrological modeling. Here we present parameter uncertainty analysis of a recently released distributed conceptual hydrological model applied in the Nea catchment, Norway. Two variants of the generalized likelihood uncertainty estimation (GLUE) methodologies, one based on the residuals and the other on the limits of acceptability, were employed. Streamflow and remote sensing snow cover data were used in conditioning model parameters and in model validation. When using the GLUE limit of acceptability (GLUE LOA) approach, a streamflow observation error of 25 % was assumed. Neither the original limits nor relaxing the limits up to a physically meaningful value yielded a behavioral model capable of predicting streamflow within the limits in 100 % of the observations. As an alternative to relaxing the limits, the requirement for the percentage of model predictions falling within the original limits was relaxed. An empirical approach was introduced to define the degree of relaxation. The result shows that snow- and water-balance-related parameters induce relatively higher streamflow uncertainty than catchment response parameters. Comparable results were obtained from behavioral models selected using the two GLUE methodologies.

scholarly journals Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

2018 ◽  
Vol 378 ◽  
pp. 11-16 ◽  
Author(s):  
Nadia Oosthuizen ◽  
Denis A. Hughes ◽  
Evison Kapangaziwiri ◽  
Jean-Marc Mwenge Kahinda ◽  
Vuyelwa Mvandaba
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Water Use ◽  
Southern Africa ◽  
Input Data ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Predictive Uncertainty ◽  
Surface Water Resources ◽  
Large Irrigation

Abstract. The demand for water resources is rapidly growing, placing more strain on access to water and its management. In order to appropriately manage water resources, there is a need to accurately quantify available water resources. Unfortunately, the data required for such assessment are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In this study, the uncertainty related to the estimation of water resources of two sub-basins of the Limpopo River Basin – the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe – is assessed. Input data (and model parameters) are significant sources of uncertainty that should be quantified. In southern Africa water use data are among the most unreliable sources of model input data because available databases generally consist of only licensed information and actual use is generally unknown. The study assesses how these uncertainties impact the estimation of surface water resources of the sub-basins. Data on farm reservoirs and irrigated areas from various sources were collected and used to run the model. Many farm dams and large irrigation areas are located in the upper parts of the Mogalakwena sub-basin. Results indicate that water use uncertainty is small. Nevertheless, the medium to low flows are clearly impacted. The simulated mean monthly flows at the outlet of the Mogalakwena sub-basin were between 22.62 and 24.68 Mm3 per month when incorporating only the uncertainty related to the main physical runoff generating parameters. The range of total predictive uncertainty of the model increased to between 22.15 and 24.99 Mm3 when water use data such as small farm and large reservoirs and irrigation were included. For the Shashe sub-basin incorporating only uncertainty related to the main runoff parameters resulted in mean monthly flows between 11.66 and 14.54 Mm3. The range of predictive uncertainty changed to between 11.66 and 17.72 Mm3 after the uncertainty in water use information was added.

scholarly journals Evaluation of Uncertainties in Input Data and Parameters of a Hydrological Model Using a Bayesian Framework: A Case Study of a Snowmelt–Precipitation-Driven Watershed

2016 ◽  
Vol 17 (8) ◽  
pp. 2333-2350 ◽  
Author(s):  
J. L. Zhang ◽  
Y. P. Li ◽  
G. H. Huang ◽  
C. X. Wang ◽  
G. H. Cheng
Keyword(s):  
Input Data ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Bayesian Framework ◽  
Model Parameters ◽  
Precipitation Data ◽  
Snow Melting ◽  
Simulation Performance ◽  
Data Errors ◽  
Temperature And Precipitation

Abstract In this study, a Bayesian framework is proposed for investigating uncertainties in input data (i.e., temperature and precipitation) and parameters in a distributed hydrological model as well as their effects on the runoff response in the Kaidu watershed (a snowmelt–precipitation-driven watershed). In the Bayesian framework, the Soil and Water Assessment Tool (SWAT) is used for providing the basic hydrologic protocols. The Delayed Rejection Adaptive Metropolis (DRAM) algorithm is employed for the inference of uncertainties in input data and model parameters with global and local adaptive strategies. The advanced Bayesian framework can help facilitate the exploration of variation of model parameters due to input data errors, as well as propagation from uncertainties in data and parameters to model outputs in both snow-melting and nonmelting periods. A series of calibration cases corresponding to data errors under different periods are examined. Results show that 1) input data errors can affect the distributions of model parameters as well as parameters’ correlation, implying that data errors could influence the related hydrologic processes as well as their relations; 2) considering input data errors could improve the hydrologic simulation ability for peak streamflows; 3) considering errors of temperature and precipitation data as well as uncertainties of model parameters can provide the best modeling simulation performance in the snow-melting period; and 4) accounting for uncertainties in precipitation data and model parameters can provide the best modeling performance during the nonmelting period. The findings will help enhance hydrological model’s capability for simulating/predicting water resources during different seasons for snowmelt–precipitation-driven watersheds.

The impact of interpolation method on the accuracy of meteorological variables in distributed hydrological model

2020 ◽  
Author(s):  
Jiajia Liu ◽  
Zuhao Zhou ◽  
Ziqi Yan ◽  
Yangwen Jia ◽  
Hao Wang
Keyword(s):  
Input Data ◽  
Hydrological Model ◽  
Interpolation Method ◽  
Stable State ◽  
Meteorological Station ◽  
Data Uncertainty ◽  
Meteorological Variables ◽  
Distributed Hydrological Model ◽  
Centroid Point ◽  
The Impact

<p>Precipitation and other meteorological variables are very important input data for distributed hydrological models, which determine the simulation accuracy of the models. It is a normal way to subdivide the large area watershed into numerous subbasins to reflect the spatial variation, and the value is usually unique within each subbasin. In most model application, the values of meteorological variables are interpolated from meteorological station observed data to the centroid point of the subbasin with interpolation method (called one-cell interpolation). Because the centroid point could not represent the whole subbasin, the one-cell interpolation will bring input data uncertainty to the model. In this study, a new method is introduced to analysis this uncertainty, which firstly interpolate the values into numerous cells smaller than the subbasin then sum up to the subbasin (called multi-cells interpolation). The results show that one-cell interpolation way is not always consistent with the results of multi-cells interpolation, and the variance is greater in summer than in winter. The consistency grows with the increase of the number of the cells, which indicates that dozens of the cells could got the stable state. The variance is also influenced by the density of meteorological station, but the minimal cell number is almost the same. Thus, in the interpolation of the meteorological variables in distributed hydrological model, it recommends to interpolate the values to numerous smaller cells then sum up to the subbasins, rather than only interpolate to the centroid point.</p>

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.

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