Hydrological Modeling in Agricultural Intensive Watershed: The Case of Upper East Fork White River, USA

Identifying the core hydrological processes of catchments is a critical step for operative hydrological modeling. This study attempts to assess the long-term alterations in streamflow in three adjacent catchments of Upper East Fork White River, Indiana USA, by employing the SWAT hydrological model. The model simulations are spanning from 1980 up to 2015 and distributed in three configurations periods to identify monthly alterations in streamflow. For this purpose, water abstraction, land use, tillage, and agricultural field drainage practices have been incorporated in the model to provide accurate data input. The model setup also integrates spatially disaggregated sectorial water use data from surface and groundwater resources integrating the significant increases of water abstractions mainly for agricultural and public water supply purposes. The land cover of the study area is governed by rotating crops, while agricultural practices and tile drainage are crucial model parameters affecting the regional hydrological balance. Streamflow prediction is based on the SUFI-2 algorithm and the SWAT-CUP interface has been used for the monthly calibration and validation phases of the model. The evaluation of model simulations indicate a progressively sufficient hydrological model setup for all configuration periods with NSE (0.87, 0.88, and 0.88) and PBIAS (14%, −7%, and −2.8%) model evaluation values at the Seymour outlet. Surface runoff/precipitation as well as percolation/precipitation ratios have been used as indicators to identify trends to wetter conditions. Model outputs for the upstream areas, are successful predictions for streamflow assessment studies to test future implications of land cover and climate change.

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Diagnostic calibration of a hydrological model in an alpine area

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-1253-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 1253-1300 ◽

Cited By ~ 4

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).

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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.

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Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

E3S Web of Conferences ◽

10.1051/e3sconf/202016301001 ◽

2020 ◽

Vol 163 ◽

pp. 01001

Author(s):

Georgy Ayzel ◽

Liubov Kurochkina ◽

Eduard Kazakov ◽

Sergei Zhuravlev

Keyword(s):

Deep Learning ◽

Hydrological Model ◽

State Of The Art ◽

Flash Flood ◽

Early Warning Systems ◽

Model Parameters ◽

Ungauged Basins ◽

Streamflow Prediction ◽

Basin Scale ◽

The Impact

Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.

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Impact of alternative land cover descriptions on urban hydrological model simulations

Urban Water Journal ◽

10.1080/1573062x.2019.1634742 ◽

2019 ◽

Vol 16 (2) ◽

pp. 103-113

Author(s):

Teemu Kokkonen ◽

Lassi Warsta ◽

Tero J. Niemi ◽

Maija Taka ◽

Nora Sillanpää ◽

...

Keyword(s):

Land Cover ◽

Hydrological Model ◽

Model Simulations

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Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

10.5194/hess-2019-464 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aynom T. Teweldebrhan ◽

John F. Burkhart ◽

Thomas V. Schuler ◽

Morten Hjorth-Jensen

Keyword(s):

Machine Learning ◽

Parameter Identification ◽

Hydrological Model ◽

Response Surfaces ◽

Model Parameters ◽

Large Set ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Main Challenge ◽

Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

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Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Hydrology and Earth System Sciences ◽

10.5194/hess-24-4641-2020 ◽

2020 ◽

Vol 24 (9) ◽

pp. 4641-4658 ◽

Cited By ~ 1

Author(s):

Aynom T. Teweldebrhan ◽

Thomas V. Schuler ◽

John F. Burkhart ◽

Morten Hjorth-Jensen

Keyword(s):

Machine Learning ◽

Parameter Identification ◽

Hydrological Model ◽

Response Surfaces ◽

Model Parameters ◽

Large Set ◽

Distributed Hydrological Model ◽

Streamflow Prediction ◽

Main Challenge ◽

Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months – especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash–Sutcliffe efficiency value of 0.89 and 0.83, respectively.

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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.

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Applicability of Different Hydrological Model Concepts on Small Catchments: Case Study of Bükkös Creek, Hungary

Acta Silvatica et Lignaria Hungarica ◽

10.2478/aslh-2014-0006 ◽

2014 ◽

Vol 10 (1) ◽

pp. 77-90 ◽

Cited By ~ 2

Author(s):

Péter Torma ◽

Borbála Széles ◽

Géza Hajnal

Keyword(s):

Model Comparison ◽

Hydrological Model ◽

Model Performance ◽

Digital Terrain Model ◽

Model Parameters ◽

Lumped Model ◽

Terrain Model ◽

Distributed Approach ◽

Model Setup ◽

And Performance

Abstract This study aims to test and compare the applicability and performance of two different hydrological model concepts on a small Hungarian watershed. The lumped model of HEC-HMS and the semi-distributed TOPMODEL have been implemented to predict streamflow of Bükkös Creek. Models were calibrated against the highest flood event recorded in the basin in May, 2010. Validation was done in an extended interval when smaller floods were observed. Acceptable results can be achieved with the semi-distributed approach. Model comparison is made by means of sensitivity analysis of model parameters. For TOPMODEL the effect of spatial resolution of the digital terrain model while for HMS the complexity of the model setup was further explored. The results were quantified with model performance indices.

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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

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

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5021-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 5021-5039 ◽

Cited By ~ 14

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.

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