Watershed model calibration framework developed using an influence coefficient algorithm and a genetic algorithm and analysis of pollutant discharge characteristics and load reduction in a TMDL planning area

Cho, Jae Heon; Lee, Jong Ho

doi:10.1016/j.jenvman.2015.07.049

Cited by 17 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is broad recognition of the primary regulation of runoff on pollutant loads [ 19 , 54 ]. For instance, the LDC put forward by the EPA establishes the maximum allowable load variation according to different flow regimes [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load–Duration Curve Method—A Case Study in Data-Scarce Luanhe River Basin

Jin

Chen

Zhao

et al. 2022

IJERPH

View full text Add to dashboard Cite

A better understanding of river capacity for contaminants (i.e., water environmental capacity, WEC) is essential for the reasonable utilization of water resources, providing government’s with guidance about sewage discharge management, and allocating investments for pollutant reduction. This paper applied a new framework integrating a modified hydro-environmental model, Soil and Water Assessment Tool (SWAT) model, and load–duration curve (LDC) method for the dynamic estimation of the NH3-N WEC of the data-scarce Luanhe River basin in China. The impact mechanisms of hydrological and temperature conditions on WEC are discussed. We found that 77% of the WEC was concentrated in 40% hydrological guarantee flow rates. While the increasing flow velocity promoted the pollutant decay rate, it shortened its traveling time in streams, eventually reducing the river WEC. The results suggest that the integrated framework combined the merits of the traditional LDC method and the mechanism model. Thus, the integrated framework dynamically presents the WEC’s spatiotemporal distribution under different hydrological regimes with fewer data. It can also be applied in multi-segment rivers to help managers identify hot spots for fragile water environmental regions and periods at the basin scale.

show abstract

Section: Discussionmentioning

confidence: 99%

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load–Duration Curve Method—A Case Study in Data-Scarce Luanhe River Basin

Jin

Chen

Zhao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The exceedance frequencies of the TP for the high-flow, moist and mid-range conditions were high and the exceedance rate for the high-flow condition was particularly high. Most of the data from the high-flow conditions exceeded the WQSs (Cho and Lee, 2015).…”

Section: Introductionmentioning

confidence: 91%

Comparing Water Quantity between Korean and Japanese River

Noh¹,

An²,

Shinogi³

et al. 2017

Journal of the Faculty of Agriculture, Kyushu University

View full text Add to dashboard Cite

Two watersheds with similar area were selected to compare water qualities between Korea and Japan. The one from Korea is called the Yongdam dam watershed in the Geum river basin, and the other from Japan is called the Nakama watershed in the Onga river basin. The 2 water quality stations from Korea and 2 stations from Japan within each watershed were selected and water quality concentration data were collected to determine load durations, respectively. The relationships between discharge and water pollution load on BOD, COD, SS, TN, and TP were derived, and water pollution loads were estimated on a daily basis in each water quality station. And means of LDCs in each country were drawn and compared with each other. Summarizing the estimated daily water pollution loads and the results of LDCs, it was concluded that TMDL showed 1.49 times higher BOD, 1.11 times higher COD, 4.95 times higher SS, 1.43 times higher TN in Korean and the same TP, and 1 st load in LDC showed 1.81 to 6.14 times higher in Korean, and remaining 95 th , 185 th , 275 th , and 285 th loads in LDC showed 1.30 to 15.43 times higher in Japanese. Specific loads were expressed with daily mean in the period of each flow duration interval, and were compared with each other between Korea and Japan, of which results were shown with 1.836~8.063 times higher to Korea in high flows, and with 1.119~8.169 times higher to Japan in other flows except SS, TN values in moist conditions. Comparing an annual sum, BOD load was 1.16 times higher in Korea, COD 1.113 in Korea, SS 4.891 in Korea, TN 1.446 in Korea, and TP was same. Evaluating with 10 year frequency, Korea showed 1.041~2.360 times higher loads than Japan except TP in high flows, Japan 1.175~14.226 times higher than Korea except TN in low flows. Annual sum showed that BOD load was 1.517 times higher in Japan, COD 1.564 in Japan, SS 1.408 in Korea, TN 1.008 in Korea, and TP 2.383 in Japan. From the above result, it was concluded that Korean river was getting more water pollution loads than Japanese river in high flow interval, but in other flow interval was higher to Japan in general.

show abstract

“…Xin'anjiang model is a method based on regression prediction. Big hydrological data contain the hydrological evolution pattern; recently, the introduction of artificial intelligence methods to the hydrological data prediction has gained much attention, and many artificial intelligence relevance methods have been applied to hydrological prediction, such as neural networks, [5][6][7][8] fuzzy theory, [9][10][11] and genetic algorithms, [12][13][14] which have greatly promoted the development of rainfall prediction. Support vector machine (SVM) [15][16][17] holds the advantage of highly generalized and is widely used in machine learning context, while its prediction result is biased due to the incomplete hydrological data.…”

Section: Related Workmentioning

confidence: 99%

Hybrid model of generative adversarial network and Takagi‐Sugeno for multidimensional incomplete hydrological big data prediction

Liu

Song

2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary The processing of rainfall‐runoff transmission in the catchment area is a very complex phenomenon, such as temporal and spatial changes of the catchment characteristics and uncertainties of rainfall patterns. To handle this challenge, data‐driven hydrologic models emerge rapidly for the rainfall runoff prediction. However, the incomplete hydrological data constrain the development of digital hydrologic model. This article proposes a rainfall‐runoff prediction method of coupling generative adversarial network (GAN) and Takagi‐Sugeno (T‐S) fuzzy model, in which the GAN is used to generate the data for completing the incomplete hydrological data, and the T‐S fuzzy model is utilized for forecasting the rainfall runoff. The presented method is examined by real data in Huaihe basin just considering the precipitation and streamflow. Experiments show that the model combined GAN and T‐S can achieve satisfactory prediction results.

show abstract

Watershed model calibration framework developed using an influence coefficient algorithm and a genetic algorithm and analysis of pollutant discharge characteristics and load reduction in a TMDL planning area

Cited by 17 publications

References 24 publications

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load–Duration Curve Method—A Case Study in Data-Scarce Luanhe River Basin

New Framework for Dynamic Water Environmental Capacity Estimation Integrating the Hydro-Environmental Model and Load–Duration Curve Method—A Case Study in Data-Scarce Luanhe River Basin

Comparing Water Quantity between Korean and Japanese River

Hybrid model of generative adversarial network and Takagi‐Sugeno for multidimensional incomplete hydrological big data prediction

Contact Info

Product

Resources

About