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Author: Grafiati
Published: 14 March 2023

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1

ANDO, Yosihisa, and Yosihiko ORISAKA. "Hydrological Model for Mountainous Basin." PROCEEDINGS OF THE JAPANESE CONFERENCE ON HYDRAULICS 33 (1989): 37–42. http://dx.doi.org/10.2208/prohe1975.33.37.

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2

Taur, Cheng‐Kang, Greg Toth, George E. Oswald, and Larry W. Mays. "Austin Detention Basin Optimization Model." Journal of Hydraulic Engineering 113, no. 7 (July 1987): 860–78. http://dx.doi.org/10.1061/(asce)0733-9429(1987)113:7(860).

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3

Ricardo, Nomensen, Hendra Amijaya, and Salahuddin Husein. "Basin Evolution Palispatic Model of Bonaparte Basin, Australia Northwest Shelf." Journal of Applied Geology 2, no. 2 (October 23, 2018): 83. http://dx.doi.org/10.22146/jag.39988.

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This research area is located on the Australian NW Shelf close to the westernedge of the Sahul Platform. This research is aimed to generate the palispatic basin model of Bonaparte Basin, Australian Northwest Shelf. It is to predict the impact of Neogene collision on the petroleum system distribution on Australian Northwest Shelf. The main data used in this research are seismic data using qualitative method analysis. The well data is used to well-seismic tied. After data acquisition, the seismic data are interpreted based on the horizon and structure interpretation. These interpretation are to reconstruct the basin evolution thorough geologic time. According to data analysis, the basin evolution palispatic model are divided into Paleo-proterozoic, Paleozoic, Triassic, Early Jurassic, Middle Jurassic, Late Jurassic, Early Cretaceous, Late Cretaceous, Early Eocene, Late Miocene and Recent condition. Regional tectonically there are at least three important events in NW Shelf: Middle Triassic-Jurassic NNE–SSW extension phase, Late Jurassic NE–SW extension phase and the Neogen collision phase; the Neogen collision effects on Northwest Shelf Australia. These three events contributed in forming and disturbing the Paleozoic and Mesozoic petroleum system in Bonaparte basin especially.
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4

Chinnasamy, Pennan. "Inference of basin flood potential using nonlinear hysteresis effect of basin water storage: case study of the Koshi basin." Hydrology Research 48, no. 6 (December 5, 2016): 1554–65. http://dx.doi.org/10.2166/nh.2016.268.

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Abstract Current flood forecasting tools for river basins subject to extreme seasonal monsoon rainfall are of limited value because they do not consider nonlinearity between basin hydrological properties. The goal of this study is to develop models that account for nonlinearity relationships in flood forecasting, which can aid future flood warning and evacuation system models. Water storage estimates from the Gravity Recovery and Climate Experiment, along with observed discharge and rainfall data were used to develop two multivariate autoregressive monthly discharge models. Model-I was based on rainfall only, while Model-II was based on rainfall and water storage estimates for the Koshi subbasin within the Ganges River basin. Results indicate that the saturation of water storage units in the basin play a vital role in the prediction of peak floods with lead times of 1 to 12 months. Model-II predicted monthly discharge with Nash–Sutcliffe efficiency (NSE) ranging from 0.66 to 0.87, while NSE was 0.4 to 0.85 for Model-I. Model-II was then tested with a 3-month lead to predict the 2008 Koshi floods – with NSE of 0.75. This is the first study to use ‘fixed effects’ multivariate regression in flood prediction, accounting for the nonlinear hysteresis effect of basin storage on floods.
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5

Jeong, Dong Kug. "Re-Analysis of Clark Model Based on Drainage Structure of Basin." Journal of the Korean Society of Civil Engineers 33, no. 6 (2013): 2255. http://dx.doi.org/10.12652/ksce.2013.33.6.2255.

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6

Elorza, F. J., J. Mira, and J. Icke. "A sensitivity study for the Amstel River Basin water quality model." River Systems 17, no. 1-2 (July 28, 2006): 245–63. http://dx.doi.org/10.1127/lr/17/2006/245.

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7

Wellmann, J. Florian, and Lynn B. Reid. "Basin-scale Geothermal Model Calibration: Experience from the Perth Basin, Australia." Energy Procedia 59 (2014): 382–89. http://dx.doi.org/10.1016/j.egypro.2014.10.392.

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8

Hutchins, M., K. Urama, E. Penning, J. Icke, C. Dilks, T. Bakken, C. Perrin, T. Saloranta, L. Candela, and J. Kämäri. "The model evaluation tool: guidance for applying benchmark criteria for models to be used in river basin management." River Systems 17, no. 1-2 (July 28, 2006): 23–48. http://dx.doi.org/10.1127/lr/17/2006/23.

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9

Schmitz, Gerd H., and Günther J. Seus. "Analytical Model of Level Basin Irrigation." Journal of Irrigation and Drainage Engineering 115, no. 1 (February 1989): 78–95. http://dx.doi.org/10.1061/(asce)0733-9437(1989)115:1(78).

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10

Rinaldo, Andrea, and Alessandro Marani. "Basin scale model of solute transport." Water Resources Research 23, no. 11 (November 1987): 2107–18. http://dx.doi.org/10.1029/wr023i011p02107.

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11

Prairie, James R., and Balaji Rajagopalan. "A basin wide stochastic salinity model." Journal of Hydrology 344, no. 1-2 (September 2007): 43–54. http://dx.doi.org/10.1016/j.jhydrol.2007.06.029.

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12

Piper, B. S., C. Chawalit, and V. Pantheep. "Chi river basin irrigation demand model." Water Resources Management 3, no. 2 (1989): 155–63. http://dx.doi.org/10.1007/bf00872470.

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13

Willgoose, Garry, Rafael L. Bras, and Ignacio Rodriguez-Iturbe. "A model of river basin evolution." Eos, Transactions American Geophysical Union 71, no. 47 (1990): 1806. http://dx.doi.org/10.1029/90eo00349.

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14

Judson, Arthur, Rudy M. King, and Glen E. Brink. "Multi-basin avalanche simulation: A model." Cold Regions Science and Technology 13, no. 1 (October 1986): 35–47. http://dx.doi.org/10.1016/0165-232x(86)90005-4.

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15

Croley, Thomas E., and Chansheng He. "Distributed-Parameter Large Basin Runoff Model. I: Model Development." Journal of Hydrologic Engineering 10, no. 3 (May 2005): 173–81. http://dx.doi.org/10.1061/(asce)1084-0699(2005)10:3(173).

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16

Choi, Y. "Empirical Model for Basin Effects Accounts for Basin Depth and Source Location." Bulletin of the Seismological Society of America 95, no. 4 (August 1, 2005): 1412–27. http://dx.doi.org/10.1785/0120040208.

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17

Nielsen, S. B., O. R. Clausen, and E. McGregor. "basin%Ro: A vitrinite reflectance model derived from basin and laboratory data." Basin Research 29 (October 24, 2015): 515–36. http://dx.doi.org/10.1111/bre.12160.

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18

Tari, G., T. Báldi, and M. Báldi-Beke. "Paleogene retroarc flexural basin beneath the Neogene Pannonian Basin: A geodynamic model." Tectonophysics 226, no. 1-4 (November 1993): 433–55. http://dx.doi.org/10.1016/0040-1951(93)90131-3.

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19

Chen, Gang, Chuanhai Wang, Xing Fang, Xiaoning Li, Pingnan Zhang, and Wenjuan Hua. "Distributed-Framework Basin Modeling System: IV. Application in Taihu Basin." Water 13, no. 5 (February 26, 2021): 611. http://dx.doi.org/10.3390/w13050611.

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This paper presents the application of a distributed-framework basin modeling system (DFBMS) in Taihu Basin, China. The concepts of professional modeling systems and system integration/coupling have been summarized in the first three series papers. This study builds a hydrologic and hydrodynamic model for Taihu Basin, which is in the lowland plain areas with numerous polder areas. Digital underlying surface area data agree with the survey results from the water resource development and utilization. The runoff generated in each cell was calculated with the model based on the digital underlying surface data. According to the hydrological feature units (HFU) concept from the DFBMS, Taihu Basin was conceptualized into six different HFUs. The basic data of rainfall, evaporation, water surface elevation (WSE), discharge, tide level, and water resources for consumption and discharge in 2000 were used to calibrate the model. The simulated results of WSE and discharge matched the observed data well. The observed data of 1998, 1999, 2002, and 2003 were used to validate the model, with good agreement with the simulation results. Finally, the basic data from 2003 were used to simulate and evaluate the management scheme of water diversion from the Yangtze River to Taihu Lake. Overall, the DFBMS application in Taihu Basin showed good performance and proved that the proposed structure for DFBMS was effective and reliable.
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20

Lee, Byong-Ju, and Deg-Hyo Bae. "Streamflow Forecast Model on Nakdong River Basin." Journal of Korea Water Resources Association 44, no. 11 (November 30, 2011): 853–61. http://dx.doi.org/10.3741/jkwra.2011.44.11.853.

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21

Proedrou, P., and M. C. Papaconstantinou. "PRIMUS BASIN - A MODEL FOR OIL EXPLORATION." Bulletin of the Geological Society of Greece 36, no. 1 (January 1, 2004): 327. http://dx.doi.org/10.12681/bgsg.16675.

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An extentional tectonic led to the formation of the basin along marginal listric faults. The fast subsidence created the frame for the accumulation of a thick section of miocene, pliocene and quaternary deposits amounted to 5000 meters and resulted to the good preservation of the organic matter as source for the oil generation. The existence of anticlines and other types of traps around the deepest part of the basin where the oil generation took place is another important factor for discovering hydrocarbons. The growth fault activation led to the formation of roll over anticlines in front of them. Stratigraphie oil bearing traps do also exist. The thick salt layers that were deposited during the upper miocene following the isolation of the basin from the open sea contributed to the reduction conditions in it. Moreover this salt cap rock holds the whole oil migration below it and prescribes the stratigraphie level for the prospects. The short distance between the generation area and the surrounded fault and statigraphic traps accelerated the migration and trapping of the hydrocarbons. The strong relief of the basin due to the fast subsidence led to the extend deposition of turbiditic sediments that form the reservoirs for the majority of the fields.The good knowledge of the geological evolution of the basin and the geochemical processes which take place states the best prepositions for a successful hydrocarbons exploration.
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22

Bradford, Scott F., and Nikolaos D. Katopodes. "Finite Volume Model for Nonlevel Basin Irrigation." Journal of Irrigation and Drainage Engineering 127, no. 4 (August 2001): 216–23. http://dx.doi.org/10.1061/(asce)0733-9437(2001)127:4(216).

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23

Pereira, Helga, José Rui Figueira, and Rui Cunha Marques. "Multiobjective Irrigation Model: Alqueva River Basin Application." Journal of Irrigation and Drainage Engineering 145, no. 7 (July 2019): 05019006. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0001396.

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24

McEwen, Sarah Smith. "Restoration Required: The Mississippi River Basin Model." Civil Engineering Magazine Archive 90, no. 3 (March 2020): 38–41. http://dx.doi.org/10.1061/ciegag.0001477.

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25

Lamarche, Juliette, and Magdalena Scheck-Wenderoth. "3D structural model of the Polish Basin." Tectonophysics 397, no. 1-2 (March 2005): 73–91. http://dx.doi.org/10.1016/j.tecto.2004.10.013.

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26

Kochergin, V. P., S. N. Sklyar, and R. K. Sultanov. "Multilevel hydrothermodynamic model of a deep basin." Physical Oceanography 6, no. 3 (May 1995): 167–76. http://dx.doi.org/10.1007/bf02197515.

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27

Chaturvedi, M. C., Peter Rogers, and Shyang-Lai Kung. "The coordinating model of the ganga basin." Sadhana 8, no. 1 (February 1985): 93–121. http://dx.doi.org/10.1007/bf02811273.

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28

Yao, Huaxia, and Akira Terakawa. "Distributed Hydrological Model for Fuji River Basin." Journal of Hydrologic Engineering 4, no. 2 (April 1999): 108–16. http://dx.doi.org/10.1061/(asce)1084-0699(1999)4:2(108).

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29

Xu, Z. X., and J. Y. Li. "Estimating Basin Evapotranspiration Using Distributed Hydrologic Model." Journal of Hydrologic Engineering 8, no. 2 (March 2003): 74–80. http://dx.doi.org/10.1061/(asce)1084-0699(2003)8:2(74).

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30

Halliwell, A. Roy. "Engineering Model for Well‐Mixed Tidal Basin." Journal of Waterway, Port, Coastal, and Ocean Engineering 112, no. 5 (September 1986): 572–90. http://dx.doi.org/10.1061/(asce)0733-950x(1986)112:5(572).

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31

Gupta, A. Das, J. Premchitt, and S. A. Hussain. "Groundwater Basin Response: Simulation with Mathematical Model." Water International 16, no. 1 (January 1991): 17–26. http://dx.doi.org/10.1080/02508069108686095.

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32

del Mar Gallardo, María, Pedro Merino, Laura Panizo, and Alberto Salmerón. "Integrating river basin DSSs with model checking." International Journal on Software Tools for Technology Transfer 20, no. 5 (October 24, 2017): 499–514. http://dx.doi.org/10.1007/s10009-017-0478-x.

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33

Virbulis, Janis, Uldis Bethers, Tomas Saks, Juris Sennikovs, and Andrejs Timuhins. "Hydrogeological model of the Baltic Artesian Basin." Hydrogeology Journal 21, no. 4 (March 26, 2013): 845–62. http://dx.doi.org/10.1007/s10040-013-0970-7.

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34

Hofmann, Peter, Michael Urbat, Andreas Hensel, and Peter Schäfer. "Age model for the Late Oligocene Kärlich Blauton of the Neuwied Basin, Germany." Neues Jahrbuch für Geologie und Paläontologie - Monatshefte 2003, no. 5 (May 12, 2003): 283–96. http://dx.doi.org/10.1127/njgpm/2003/2003/283.

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35

Sang, Guo Qing, Sheng Le Cao, and Ze Biao Wei. "Research and Application of the Combined of SWMM and Tank Model." Applied Mechanics and Materials 166-169 (May 2012): 593–99. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.593.

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Hydrologic Model is the basic tool of simulation of the runoff generation and confluence of the basin. It is widely used in the research of hydrologic process of the drainage basin. In this paper, the characteristics of SWMM Model and Tank Model were introduced. The SWMM-Tank model was established that combined with advantages of the SWMM and Tank model. This model was used in hydrological simulation of the Big Ning river basin. The Surface and Underground runoff was simulated respectively using the SWMM model and Tank model. The simulation results show that the SWMM-Tank model can meet the requirement of accuracy and the combined model is simple. The combined model can be used in hydrologic simulated of the drainage basin that the underground information is lacked.
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36

Burns, James R., and Pinyarat Sirisomboonsuk. "Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin." International Journal of Business Analytics 9, no. 1 (January 1, 2022): 1–22. http://dx.doi.org/10.4018/ijban.314223.

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In this paper, the authors create, justify, and document a system dynamics model of the oil and gas production within the Permian Basin of Texas. Then the researchers show how to fit the model to historical time series data (big data). The authors use the model to better understand the process structure, the production dynamics, and to explore the deleterious consequences of limited pipeline capacity in the Permian Basin. The model is also employed to better understand how to increase revenues derived from the basin. From this model, numerous suggestions are made as to how to improve the overall revenue and profitability coming from the Permian Basin. The model's ultimate purposes and its associated big data are to foster a basic appreciation of the causality inherent in the ‘system' and how basic model parameters affect and influence measures of model performance.
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37

He, Chansheng, and Thomas E. Croley. "Application of a distributed large basin runoff model in the Great Lakes basin." Control Engineering Practice 15, no. 8 (August 2007): 1001–11. http://dx.doi.org/10.1016/j.conengprac.2007.01.011.

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38

Zhang, Peng, Lianfu Mei, Ping Xiong, Xiaolin Hu, Renyuan Li, and Huaning Qiu. "Structural features and proto-type basin reconstructions of the Bay of Bengal Basin: A remnant ocean basin model." Journal of Earth Science 28, no. 4 (June 8, 2017): 666–82. http://dx.doi.org/10.1007/s12583-017-0750-8.

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39

Sridharan, K., N. S. Lakshmana Rao, M. S. Mohan Kumar, and V. Ramesam. "Computer model for vedavati ground water basin. Part 2. Regional model." Sadhana 9, no. 1 (February 1986): 43–55. http://dx.doi.org/10.1007/bf02812175.

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40

Liu, Chen-Wuing, and Wen-Sheng Lin. "A Smectite Dehydration Model in a Shallow Sedimentary Basin: Model Development." clays and clay minerals 53, no. 1 (February 1, 2005): 55–70. http://dx.doi.org/10.1346/ccmn.2005.0530107.

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41

Vikhliaev, Yury, Paul Schopf, Tim DelSole, and Ben Kirtman. "Finding Multiple Basin Modes in a Linear Ocean Model." Journal of Atmospheric and Oceanic Technology 24, no. 6 (June 2007): 1033–49. http://dx.doi.org/10.1175/jtech2020.1.

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A method for finding the most unstable eigenmodes in linear models using the breeding technique was developed. The breeding technique was extended to allow for the calculation of complex eigenvalues and eigenvectors of the linear model operator without involving computationally expensive matrix manipulations. While the breeding method finds the most unstable modes, multiple planetary basin modes may be found by removing the leading modes using the adjoint model. To test the sensitivity of basin modes to model formulation, the method was applied for the calculation of the gravest planetary basin modes in a reduced-gravity linear shallow water model with complex basin geometry and background circulation. It was found that the leading basin modes are not sensitive to the form of the dissipation or model resolution, suggesting that the decadal modes are robust. However, the properties of the low-frequency modes are strongly affected by the basin geometry and the mean flow.
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42

Yang, Shan Shan, Zheng He Xu, and Ke Kong. "The Flow Simulation Based on SWAT Model in Wohushan Reservoir Basin." Applied Mechanics and Materials 353-356 (August 2013): 2637–40. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.2637.

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In order to study the applicability of SWAT model in small and medium-sized basin, the author established SWAT model to simulate monthly runoff in Wohushan Reservoir basin. According to a lot of basic data, the spatial database and the attribute database were constructed. The entire study area was divided into 51 catchments including 126 hydrological response units. The parameters were validated by the measured data from 2007 to 2009 and calibrated from 2010 to 2011. The correlation coefficient and the Nash efficiency coefficient of monthly runoff simulation are higher than 0.70, and the relative error is lower than 15%. Considering some potential errors between the measured data and the simulated data, SWAT model can simulate the runoff process of Wohushan Reservoir basin well.
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43

Hamidi Machekposhti, Karim, Hossein Sedghi, Abdolrasoul Telvari, and Hossein Babazadeh. "Modeling Climate Variables of Rivers Basin using Time Series Analysis (Case Study: Karkheh River Basin at Iran)." Civil Engineering Journal 4, no. 1 (February 7, 2018): 78. http://dx.doi.org/10.28991/cej-030970.

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Stochastic models (time series models) have been proposed as one technique to generate scenarios of future climate change. Precipitation, temperature and evaporation are among the main indicators in climate study. The goal of this study is the simulation and modeling of climatic parameters such as annual precipitation, temperature and evaporation using stochastic methods (time series analysis). The 40-year data of precipitation and 37-year data of temperature and evaporation at Jelogir Majin station (upstream of Karkheh dam reservoir) in western of Iran has been used in this study and based on ARIMA model, The auto-correlation and partial auto-correlation methods, assessment of parameters and types of model, the suitable models to forecast annual precipitation, temperature and evaporation were obtained. After model validation and evaluation, the Predicting was made for the ten future years (2006 to 2015). In view of the Predicting made, the precipitation amounts will be decreased than recent years. As regards the mean of annual temperature and evaporation, the findings of the Predicting show an increase in temperature and evaporation.
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44

Kaiglová, Jana, and Jakub Langhammer. "Analysis of efficiency of pollution reduction measures in rural basin using MIKE Basin model. Case study: Olšava River Basin." Journal of Hydrology and Hydromechanics 62, no. 1 (March 1, 2014): 43–54. http://dx.doi.org/10.2478/johh-2014-0007.

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Abstract This paper presents the results of testing the applicability of the MIKE Basin model for simulating the efficiency of scenarios for reducing water pollution. The model has been tested on the Olšava River Basin (520 km2) which is a typical rural region with a heterogeneous mix of pollution sources with variable topography and land use. The study proved that the model can be calibrated successfully using even the limited amount of data typically available in rural basins. The scenarios of pollution reduction were based on implementation and intensification of municipal wastewater treatment and conversion of arable land on fields under the risk of soil erosion to permanent grassland. The application of simulation results of these scenarios with proposed measures proved decreasing concentrations in downstream monitoring stations. Due to the practical applicability of proposed measures, these could lead to fulfilment of the water pollution limits required by the Czech and EU legislation. However, there are factors of uncertainty that are discussed that may delay or limit the effect of adopted measures in small rural basins.
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45

Xu, Chaowei, Jiashuai Yang, and Lingyue Wang. "Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation." Sustainability 14, no. 14 (July 13, 2022): 8576. http://dx.doi.org/10.3390/su14148576.

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Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.
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46

Sun, Dong. "The Research on Cooperation Coordination Model of Transboundary Water Pollution." Applied Mechanics and Materials 380-384 (August 2013): 4207–11. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4207.

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At first, this paper introduced some related domestic and global researches which are about lake basin transboundary water pollution, and put forward several basic assumptions combining the lake basin situation. Based on this foundation, this paper constructed the minimum cost of the whole region, and proposed the cooperation coordination model of the lake basin transboundary water pollution. Then it took advantage of Poyang lake for empirical analysis, and got the function of pollutant reduction cost and function of environmental damage cost by means of multiple regression analysis with the five main data of Poyang lake from 2001 to 2010. Next, through cooperation coordination model establishment and solution, the paper could get the conclusion that compared with the actual pollution reduction plan the model solution is better. At last, the paper talked about the deficiencies and improvement.
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47

Chen, Meiqiu, Xiaohua Wei, Hongsheng Huang, and Tiangui Lü. "Poyang Lake basin: a successful, large-scale integrated basin management model for developing countries." Water Science and Technology 63, no. 9 (May 1, 2011): 1899–905. http://dx.doi.org/10.2166/wst.2011.413.

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Protection of water environment while developing socio-economy is a challenging task for lake regions of many developing countries. Poyang Lake is the largest fresh water lake in China, with its total drainage area of 160,000 km2. In spite of rapid development of socio-economy in Poyang Lake region in the past several decades, water in Poyang Lake is of good quality and is known as the “last pot of clear water” of the Yangtze River Basin in China. In this paper, the reasons of “last pot of clear water” of Poyang Lake were analysed to demonstrate how economic development and environmental protection can be coordinated. There are three main reasons for contributing to this coordinated development: 1) the unique geomorphologic features of Poyang Lake and the short water residence time; 2) the matching of the basin physical boundary with the administrative boundary; and 3) the implementation of “Mountain-River-Lake Program” (MRL), with the ecosystem concept of “mountain as source, river as connection flow, and lake as storage”. In addition, a series of actions have been taken to coordinate development, utilisation, management and protection in the Poyang Lake basin. Our key experiences are: considering all basin components when focusing on lake environment protection is a guiding principle; raising the living standard of people through implementation of various eco-economic projects or models in the basin is the most important strategy; preventing soil and water erosion is critical for protecting water sources; and establishing an effective governance mechanism for basin management is essential. This successful, large-scale basin management model can be extended to any basin or lake regions of developing countries where both environmental protection and economic development are needed and coordinated.
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48

McPherson, Brian J. O. L., and John D. Bredehoeft. "Overpressures in the Uinta Basin, Utah: Analysis using a three-dimensional basin evolution model." Water Resources Research 37, no. 4 (April 2001): 857–71. http://dx.doi.org/10.1029/2000wr900260.

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49

Garg, N. K., and Shambhu Azad. "A framework model for water-sharing among co-basin states of a river basin." Journal of Hydrology 560 (May 2018): 289–300. http://dx.doi.org/10.1016/j.jhydrol.2018.03.037.

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50

Kumar, Pushpendra, A. K. Lohani, and A. K. Nema. "Rainfall Runoff Modeling Using MIKE 11 Nam Model." Current World Environment 14, no. 1 (April 25, 2019): 27–36. http://dx.doi.org/10.12944/cwe.14.1.05.

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Abstract:
River basin planning and management are primarily based on the accurate assessment and prediction of catchment runoff. A continuous effort has been made by the various researchers to accurately assess the runoff generated from precipitation by developing various models. In this paper conceptual hydrological MIKE 11 NAM approach has been used for developing a runoff simulation model for Arpasub-basin of Seonath river basin in Chhattisgarh, India. NAM model has been calibrated and validated using discharge data at Kota gauging site on Arpa basin. The calibration and validation results show that this model is capable to define the rainfall runoff process of the basin and thus predicting daily runoff. The ability of the NAM model in rainfall runoff modelling of Arpa basin was assessed using Nash–Sutcliffe Efficiency Index (EI), coefficient of determination (R2) and Root Mean Square Error (RMSE). This study demonstrates the usefulness of the developed model for the runoff prediction in the Arpa basin which acts as a useful input for the integrated water resources development and management at the basin scale.
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