Xinjiang Water Resources Research Institute

Urunchi, China

Xinjiang Water Resources Research Institute

Urunchi, China

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Abudu S.,Xinjiang Water Resources Research Institute | Abudu S.,New Mexico State University | Cui C.-L.,Xinjiang Water Resources Research Institute | Saydi M.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University
Water Science and Engineering | Year: 2012

The snowmelt runoff model (SRM) has been widely used in simulation and forecast of streamflow in snow-dominated mountainous basins around the world. This paper presents an overall review of worldwide applications of SRM in mountainous watersheds, particularly in data-sparse watersheds of northwestern China. Issues related to proper selection of input climate variables and parameters, and determination of the snow cover area (SCA) using remote sensing data in snowmelt runoff modeling are discussed through extensive review of literature. Preliminary applications of SRM in northwestern China have shown that the model accuracies are relatively acceptable although most of the watersheds lack measured hydro-meteorological data. Future research could explore the feasibility of modeling snowmelt runoff in data-sparse mountainous watersheds in northwestern China by utilizing snow and glacier cover remote sensing data, geographic information system (GIS) tools, field measurements, and innovative ways of model parameterization. Copyright © 2012 Editorial Office of Water Science and Engineering.


Abudu S.,Xinjiang Water Resources Research Institute | Abudu S.,New Mexico State University | Cevik S.Y.,New Mexico State University | Bawazir S.,New Mexico State University | And 2 more authors.
Water History | Year: 2011

This article evaluates the vitality of the ancient karez systems in various aspects in modern society by providing examples from Turpan region of China. These aspects include the historical and cultural importance, socio-economic impacts, interactions with the surrounding environment, contribution to agricultural biodiversity in arid lands, and unique regional characteristics of karezes. The results show that the karez systems are not only economically feasible but also a sustainable water supply for irrigation and domestic uses. Furthermore, karezes have invaluable historical, cultural, and social significance. However, karez systems are facing abandonment due to the introduction of new technologies as a result of an increase in water demand. Karezes are particularly susceptible to impairment by groundwater withdrawals with modern wells and pumps. In such regions, the proper conservation and maintenance of karez systems will help sustainable water management and contribute to economic development. © 2011 Springer Science + Business Media B.V.


Abudu S.,New Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | Bawazir A.S.,New Mexico State University
Journal of Hydrologic Engineering | Year: 2011

Monthly streamflow forecasting during spring-summer runoff season using snow telemetry (SNOTEL) precipitation and snow water equivalent (SWE) as predictors in the Rio Grande Headwaters Basin in Colorado was investigated. The transfer-function noise (TFN) models with SNOTEL precipitation input were built for monthly streamflow. Then, one-month-ahead forecasts of TFN models for the springsummer runoff season were modified with SWE using an artificial neural networks (ANN) technique denoted in this study as hybrid TFN + ANN. The results indicated that the hybrid TFN + ANN approach not only demonstrated better generalization capability but also improved one-month-ahead forecast accuracy significantly when compared with single TFN and ANN models. The normalized root mean squared errors (NRMSE) of one-month-ahead forecasts of TFN, ANN, and TFN + ANN approaches for spring-summer runoff season were 0.38, 0.30, and 0.25. These findings accentuate that the presented stochastic hybrid modeling approach is an advantageous option to improve one-month-ahead forecast accuracy of monthly streamflow in spring-summer runoff season in the Rio Grande Headwaters Basin. © 2011 American Society of Civil Engineers.


Abudu S.,Xinjiang Water Resources Research Institute | Abudu S.,New Mexico State University | Cui C.-L.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | Abudukadeer K.,Xinjiang Water Resources Bureau
Water Science and Engineering | Year: 2010

This paper presents the application of autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and Jordan-Elman artificial neural networks (ANN) models in forecasting the monthly streamflow of the Kizil River in Xinjiang, China. Two different types of monthly streamflow data (original and deseasonalized data) were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors. The one-month-ahead forecasting performances of all models for the testing period (1998-2005) were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River. The Jordan-Elman ANN models, using previous flow conditions as inputs, resulted in no significant improvement over time series models in one-month-ahead forecasting. The results suggest that the simple time series models (ARIMA and SARIMA) can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models. Copyright © Editorial Office of Water Science and Engineering.


Cui C.,Xinjiang Water Resources Research Institute | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | Sheng Z.,Xinjiang Water Resources Research Institute | Sheng Z.,Texas AgriLife Research Center
World Environmental and Water Resources Congress 2012: Crossing Boundaries, Proceedings of the 2012 Congress | Year: 2012

One of the largest world karez water supply systems located in the Turpan oasis, Xinjiang Uyghur Autonomous Region, China is facing challenges as water demand increases and overexploitation of groundwater by deep wells. In this paper we evaluated the vitality of the ancient karez systems in various aspects in modern society by providing examples from Turpan region of China. These aspects include the historical and cultural importance, socio-economic impacts, interactions with the surrounding environment, contribution to agricultural biodiversity in arid lands, and the unique regional characteristics of karezes. The results show that the karez systems are not only economically feasible but also a sustainable water supply for irrigation and domestic uses. Furthermore, karezes have invaluable historical, cultural and social significance. In such regions, the proper conservation and maintenance of karez systems will help sustain water supplies and contribute to economic development. © 2012 ASCE.


Zamani Sabzi H.,New Mexico State University | Humberson D.,New Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | King J.P.,Stanford University
Expert Systems with Applications | Year: 2016

In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches - such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) - can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches - ANFIS, GA, and PSO - within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally, appropriate initial variables for the optimization process were introduced through the Takagi-Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi-Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them. © 2015 Elsevier Ltd. All rights reserved.


Abudu S.,Xinjiang Water Resources Research Institute | Abudu S.,New Mexico State University | Cui C.,Xinjiang Water Resources Research Institute | King J.P.,New Mexico State University | And 2 more authors.
Science China Technological Sciences | Year: 2011

This study presented the application of partial least squares regression (PLSR) in estimating daily pan evaporation by utilizing the unique feature of PLSR in eliminating collinearity issues in predictor variables. The climate variables and daily pan evaporation data measured at two weather stations located near Elephant Butte Reservoir, New Mexico, USA and a weather station located in Shanshan County, Xinjiang, China were used in the study. The nonlinear relationship between climate variables and daily pan evaporation was successfully modeled using PLSR approach by solving collinearity that exists in the climate variables. The modeling results were compared to artificial neural networks (ANN) models with the same input variables. The results showed that the nonlinear equations developed using PLSR has similar performance with complex ANN approach for the study sites. The modeling process was straightforward and the equations were simpler and more explicit than the ANN black-box models. © 2011 Science China Press and Springer-Verlag Berlin Heidelberg.


Wang F.,Xinjiang Water Resources Research Institute | Liu Z.,Gezhouba Xinjiang Engineering Bureau
Advances in Science and Technology of Water Resources | Year: 2013

The special long-distance drilling rigs cannot be used in the site of Neelum-Jhelum Hydropower Project in Pakistan due to their large volume. The horizontal drilling method is adopted based on the experimental and economical analyses by using light common rigs. The drilling rigs are improved by installing some stabilizing devices, and some modifications are performed by transforming the hoisting swivel elevator, the loading and unloading frame for orifice, the reaming device, the blocker and other associated components. The drilling parameters for different strata are determined based on experiments. A series of drilling components and control parameters suitable for engineering practices of the horizontal directional drilling with common drilling rigs are proposed through continuous improvement, adjustment and exploration. They are successfully applied in Neelum-Jhelum Hydropower Project and provide basis for the optimization parameters and construction schemes of excavation sections and culvert bracings.


Abudu S.,Mexico State University | Abudu S.,Xinjiang Water Resources Research Institute | King J.P.,Mexico State University | Sheng Z.,Texas AgriLife Research Center
Journal of the American Water Resources Association | Year: 2012

This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function-noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross-correlation statistical tests. The chi-square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one- to three-month-ahead model forecasts for the testing period of 1984-1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one-month-ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two-month-ahead and three-month-ahead forecasts. For three-month-ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one- to three-month-ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin. © 2011 American Water Resources Association.

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