A method to reduce the computational requirement while assessing uncertainty of complex hydrological models

Athira, P.; Sudheer, K. P.

doi:10.1007/s00477-014-0958-4

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…It is capable of simulating hydrological processes, impacts of climate and land use changes, water use management, water quality and quantity assessments (Gassman et al 2007;Wu and Chen 2013;Athira and Sudheer 2015). The model was used in this study because it has been successfully applied in other catchments in Africa e.g.…”

Section: Model Descriptionmentioning

confidence: 99%

“…They can also be used to investigate the impacts of land use change, climate change and agricultural activities at a catchment scale (Wu and Chen 2013;Athira and Sudheer 2015;Zhou et al 2015;Shi et al 2016), making them a useful tool to help decision makers better understand environmental problems and design appropriate mitigation strategies. Given the current technological advancement in the acquisition and storage of hydro-meteorological data, the use of spatially distributed, physically based CHMs to enhance management decisions at basin scale is receiving increasing attention from the scientific community (Golmohammadi et al 2014;Leta et al 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

Nkiaka

Nawaz

Lovett

2017

Stoch Environ Res Risk Assess

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Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R 2 ) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed streamflow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique outperformed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.

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Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

Nkiaka

Nawaz

Lovett

2017

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…We used a large and computationally intensive parameter space as described in (Moustakas & Evans, 2015) and supplementary material therein . The model's sensitivity to input parameters had been tested with Latin Hypercube Sampling a robust method for sweeping out parameter space (McKay et al, 1979;Athira & Sudheer, 2015). Two amendments in the input parameters of (Moustakas & Evans, 2015) were made here: (i) the initial number of infected badgers, and (ii) initial number of infected cattle.…”

Section: Model Parameterisationmentioning

confidence: 99%

A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Moustakas

Evans

2016

Stoch Environ Res Risk Assess

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Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.

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“…Over the past several decades, many approaches have been developed to study the spatial variation of rainfall and numerous mathematical formulations have been proposed to identify the errors and uncertainties from rainfall measurements, including data-driven and analytical methods [18,19]. An important question is what spatial-temporal scales of rainfall measurement are required for estimating rainfall with an acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

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Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

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A method to reduce the computational requirement while assessing uncertainty of complex hydrological models

Cited by 10 publications

References 44 publications

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

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