Macro-scale grid-based and subbasin-based hydrologic modeling: joint simulation and cross-calibration

Chu, Xuefeng; Lin, Zhulu; Nasab, Mohsen Tahmasebi; Zeng, Lan; Grimm, Kendall; Bazrkar, Mohammad Hadi; Wang, Ning; Liu, Xingwei; Zhang, Xiaodong; Zheng, Haochi

doi:10.2166/hydro.2018.026

Cited by 13 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The variable thresholds for HADI and SHADI were close. This result was in accordance with the finding by Bazrkar & Chu (2021), where the output from a grid-based hydrologic model (GHM) (Chu et al 2019) in a different study period (2003)(2004)(2005)(2006)(2007) was used. The clustering method was used to derive these variable thresholds based on the probabilities of occurrences of those values considering both spatial and temporal distributions of droughts.…”

Section: Selection Of the Best Categorization Methodssupporting

confidence: 90%

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Bazrkar

Chu

2022

Journal of Hydroinformatics

Self Cite

View full text Add to dashboard Cite

Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction.

show abstract

Section: Selection Of the Best Categorization Methodssupporting

confidence: 90%

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Bazrkar

Chu

2022

Journal of Hydroinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Figure 5 a indicates that CS1 did not provide accurate estimates of peak flows for both calibration and validation periods. Other studies have also highlighted that the traditional calibration of the SWAT model may not be able to provide satisfactory simulations of peak flows in the RRB [ 48 ]. In contrast, if the wet and dry years were separated for model calibration (i.e., CS2), a significant improvement was achieved.…”

Section: Resultsmentioning

confidence: 99%

“…Similar to other SWAT studies for depression-dominated areas, 25 SWAT parameters were selected for sensitivity analysis [ 17 , 28 , 48 ]. The most sensitive water quantity and quality parameters were selected for evaluating the impacts of different calibration schemes.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

SWAT Modeling of Non-Point Source Pollution in Depression-Dominated Basins under Varying Hydroclimatic Conditions

Nasab

Grimm

Bazrkar

et al. 2018

IJERPH

Self Cite

View full text Add to dashboard Cite

Non-point source (NPS) pollution from agricultural lands is the leading cause of various water quality problems across the United States. Particularly, surface depressions often alter the releasing patterns of NPS pollutants into the environment. However, most commonly-used hydrologic models may not be applicable to such depression-dominated regions. The objective of this study is to improve water quantity/quality modeling and its calibration for depression-dominated basins under wet and dry hydroclimatic conditions. Specifically, the Soil and Water Assessment Tool (SWAT) was applied for hydrologic and water quality modeling in the Red River of the North Basin (RRB). Surface depressions across the RRB were incorporated into the model by employing a surface delineation method and the impacts of depressions were evaluated for two modeling scenarios, MS1 (basic scenario) and MS2 (depression-oriented scenario). Moreover, a traditional calibration scheme (CS1) was compared to a wet-dry calibration scheme (CS2) that accounted for the effects of hydroclimatic variations on hydrologic and water quality modeling. Results indicated that the surface runoff simulation and the associated water quality modeling were improved when topographic characteristics of depressions were incorporated into the model (MS2). The Nash–Sutcliffe efficiency (NSE) coefficient indicated an average increase of 30.4% and 19.6% from CS1 to CS2 for the calibration and validation periods, respectively. Additionally, the CS2 provided acceptable simulations of water quality, with the NSE values of 0.50 and 0.74 for calibration and validation periods, respectively. These results highlight the enhanced capability of the proposed approach for simulating water quantity and quality for depression-dominated basins under the influence of varying hydroclimatic conditions.

show abstract

“…The normal spatial interoperation may not represent the features that may have important impacts on the local climate. The scale mismatch problem can be overcome by using Regional Climate Model (RCM) or by using the Statistical Downscaling techniques [2,[20][21][22]. The RCM develops a finer resolution regional climate model that is driven by boundary conditions simulated by the global GCMs at coarser scales.…”

Section: Applied Environmental Researchmentioning

confidence: 99%

Change in Rainfall Patterns in the Hilly Region of Uttarakhand due to the Impact of Climate Change

Kumar

Bhattacharjya

2020

App. Envi. Res.

View full text Add to dashboard Cite

Uttarakhand, a Himalayan state of India, may experience an increase in temperature of 1.4°C to 5.8°C by 2100 due to global warming. The rise in temperature may melt the glaciers of the state and may have some significant impact on the rainfall. In this study, we have quantified the changes in the rainfall of the state. Also, an attempt has been made to evaluate the impact of climate change on rainfall. The future rainfall can be estimated by using a global circulation model (GCM). However, due to the very coarse spatial resolution of the different GCM, we cannot use them directly. For matching this spatial inequality between the GCM output and historical precipitation data, we used the statistical downscaling technique. In the present study, we have examined the suitability of the artificial neural network with principal component analysis for downscaling the rainfall for different hilly districts of the state. We used the GCM model developed by Canadian Earth System Model, and the Indian metrological department gridded rainfall data. We performed the analysis for the different scenarios to visualize the impact of climate change on rainfall trends for all nine hilly districts of Uttarakhand. Results show that there was a clear indication of climate change in upper Himalayan Districts like Pithoragarh, Rudraprayag, and Chamoli, which was observed from the peak of monthly rainfall. The percentage change of monsoon rainfall in the future may go up to 200 % in the case of RCP8.5, and the change maybe around 180% for RCP4. Also, the volume of rainfall may increase in the case of RCP8.5 from July to September as compared to the historical data, i.e., there may be a shifting of monsoon rainfall in the future.

show abstract

Macro-scale grid-based and subbasin-based hydrologic modeling: joint simulation and cross-calibration

Cited by 13 publications

References 27 publications

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

Development of category-based scoring support vector regression (CBS-SVR) for drought prediction

SWAT Modeling of Non-Point Source Pollution in Depression-Dominated Basins under Varying Hydroclimatic Conditions

Change in Rainfall Patterns in the Hilly Region of Uttarakhand due to the Impact of Climate Change

Contact Info

Product

Resources

About