scholarly journals MANAGEMENT OF BAI HASSAN UNCONFINED AQUIFER, LESSER ZAB RIVER BASIN, KURDISTAN REGION, IRAQ USING A MODELING APPROACH

2020 ◽  
Vol 53 (2B) ◽  
pp. 1-23
Author(s):  
Moutaz Al-Dabbas
2015 ◽  
Vol 6 (4) ◽  
pp. 880-890 ◽  
Author(s):  
Rajeev Saran Ahluwalia ◽  
S. P. Rai ◽  
S. K. Jain ◽  
D. P. Dobhal ◽  
Amit Kumar

In the present study, an attempt has been made to estimate the snow/glacier melt contribution in the head water region of the Beas Basin using a conventional hydrograph approach and a modeling (SNOWMOD) technique. The discharge and other meteorological data from 1996 to 2008 of the Manali site were used for the study. The results of SNOWMOD modeling reveal that snow/glacier melt contribution to the Beas River in the head water region varied between 52 (minimum) and 56% (maximum) with an annual average of 54% during the study period. The results obtained using the conventional approach showed the contribution of snow/glacier melt varied between 48 (minimum) and 52% (maximum) with an annual average of 50%. Results obtained using both techniques corroborate each other. This study reveals that the Beas River is mainly sustained by the snow/glacier melt contribution in the head water region.


2015 ◽  
Vol 29 (9) ◽  
pp. 3265-3289 ◽  
Author(s):  
Chengcheng Huang ◽  
Guoqiang Wang ◽  
Xiaogu Zheng ◽  
Jingshan Yu ◽  
Xinyi Xu

Chemosphere ◽  
2016 ◽  
Vol 143 ◽  
pp. 50-56 ◽  
Author(s):  
YoonKyung Cha ◽  
Young Mo Kim ◽  
Jae-Woo Choi ◽  
Suthipong Sthiannopkao ◽  
Kyung Hwa Cho

2015 ◽  
Vol 18 (3) ◽  
pp. 446-465 ◽  
Author(s):  
Golnazalsadat Mirfenderesgi ◽  
S. Jamshid Mousavi

Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.


Author(s):  
J. R. Santillan ◽  
A. M. Amora ◽  
M. Makinano-Santillan ◽  
J. T. Marqueso ◽  
L. C. Cutamora ◽  
...  

In this paper, we present a combined geospatial and two dimensional (2D) flood modeling approach to assess the impacts of flooding due to extreme rainfall events. We developed and implemented this approach to the Tago River Basin in the province of Surigao del Sur in Mindanao, Philippines, an area which suffered great damage due to flooding caused by Tropical Storms Lingling and Jangmi in the year 2014. The geospatial component of the approach involves extraction of several layers of information such as detailed topography/terrain, man-made features (buildings, roads, bridges) from 1-m spatial resolution LiDAR Digital Surface and Terrain Models (DTM/DSMs), and recent land-cover from Landsat 7 ETM+ and Landsat 8 OLI images. We then used these layers as inputs in developing a Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS)-based hydrologic model, and a hydraulic model based on the 2D module of the latest version of HEC River Analysis System (RAS) to dynamically simulate and map the depth and extent of flooding due to extreme rainfall events. The extreme rainfall events used in the simulation represent 6 hypothetical rainfall events with return periods of 2, 5, 10, 25, 50, and 100 years. For each event, maximum flood depth maps were generated from the simulations, and these maps were further transformed into hazard maps by categorizing the flood depth into low, medium and high hazard levels. Using both the flood hazard maps and the layers of information extracted from remotely-sensed datasets in spatial overlay analysis, we were then able to estimate and assess the impacts of these flooding events to buildings, roads, bridges and landcover. Results of the assessments revealed increase in number of buildings, roads and bridges; and increase in areas of land-cover exposed to various flood hazards as rainfall events become more extreme. The wealth of information generated from the flood impact assessment using the approach can be very useful to the local government units and the concerned communities within Tago River Basin as an aid in determining in an advance manner all those infrastructures (buildings, roads and bridges) and land-cover that can be affected by different extreme rainfall event flood scenarios.


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