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| 1 | Effects of emitter discharge rates on soil salinity distribution and cotton(Gossypium hirsutum L.) yield under drip irrigation with plastic mulch in an arid region of Northwest China显示文摘A field experiment was carried out to investigate the effects of different emitter discharge rates under drip irrigation on soil salinity distribution and cotton yield in an extreme arid region of Tarim River catchment in Northwest China.Four treatments of emitter discharge rates,i.e.1.8,2.2,2.6 and 3.2 L/h,were designed under drip irrigation with plastic mulch in this paper.The salt distribution in the range of 70-cm horizontal distance and 100-cm vertical distance from the emitter was measured and analyzed during the cotton growing season.The soil salinity is expressed in terms of electrical conductivity(dS/m) of the saturated soil extract(EC e),which was measured using Time Domain Reflector(TDR) 20 times a year,including 5 irrigation events and 4 measured times before/after an irrigation event.All the treatments were repeated 3 times.The groundwater depth was observed by SEBA MDS Dipper 3 automatically at three experimental sites.The results showed that the order of reduction in averaged soil salinity was 2.6 L/h > 2.2 L/h > 1.8 L/h > 3.2 L/h after the completion of irrigation for the 3-year cotton growing season.Therefore,the choice of emitter discharge rate is considerably important in arid silt loam.Usually,the ideal emitter discharge rate is 2.4-3.0 L/h for soil desalinization with plastic mulch,which is advisable mainly because of the favorable salt leaching of silt loam and the climatic conditions in the studied arid area.Maximum cotton yield was achieved at the emitter discharge rate of 2.6 L/h under drip irrigation with plastic mulch in silty soil at the study site.Hence,the emitter discharge rate of 2.6 L/h is recommended for drip irrigation with plastic mulch applied in silty soil in arid regions. | Sulitan DANIERHAN Abudu SHALAMU Hudan TUMAERBAI DongHai GUAN | 2013 | Journal of Arid Land2013，5，1： | 15 |
| 2 | Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China显示文摘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. | Shalamu ABUDU Chun-liang CUI James Phillip KING Kaiser ABUDUKADEER | 2010 | Water Science and Engineering2010，3，3： | 8 |
| 3 | 水稻控制灌溉下华东稻麦轮作农田N_2O排放模拟显示文摘基于田间小区试验,利用DNDC模型模拟了水稻控制灌溉下的华东稻麦轮作农田N_2O排放情况,分析了DNDC模型在该地区以及水稻控制灌溉条件下的适用性。结果表明,DNDC模型能较好地模拟控制灌溉稻田N_2O排放规律,模拟值与实测值的相关系数为0.79(n=39,p<0.001);同时能较好地模拟控制灌溉稻田N_2O排放通量与土壤水分调控及施肥的关系。但模型对土壤脱水程度的响应不够敏感,导致部分峰值出现时间稍有滞后。后茬麦田N_2O排放通量的模拟值多低于实测,模拟主峰值较实测值增大了14.96%(p<0.05),模拟次峰值比实测值减小了18.10%(p<0.05)。稻季、麦季及稻麦轮作期的N_2O排放总量的模拟值与实测值的相对误差分别为5.86%、-20.17%(p<0.05)和-4.97%,可见,DNDC模型能较好地模拟控制灌溉稻田N_2O排放总量,但明显低估了后茬冬小麦田的N_2O排放总量,稻麦轮作农田N_2O排放总量的模拟值和实测值总量相差不大。因此,DNDC模型可以用来模拟华东地区控制灌溉稻田N_2O排放,但不能准确地模拟后茬冬小麦田的N_2O排放。 | 侯会静 Shalamu Abudu 陈慧 杨士红 | 2016 | 农业机械学报2016，47，12： | 8 |
| 4 | Application of snowmelt runoff model(SRM) in mountainous watersheds:A review显示文摘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. | Shalamu ABUDU Chun-liang CUI Muattar SAYDI James Phillip KING | 2012 | Water Science and Engineering2012，5，2： | 7 |
| 5 | Cou- pled GSI-SVAT model with groundwater-surface water interac- tion in the riparian zone of Tarim River 显示文摘 | DANIERHAN Sulitan ABUDU Shalamu GUAN Donghai | 2013 | Journal of Hydrologic Engineering2013，10，： | 1 |
| 6 | A coupled GSI-SVAT model with groundwater-surface water interaction in the riparian zone of Tarim River 显示文摘 | SULITAN Danierhan SHALAMU Abudu GUAN Donghai | 2013 | Journal of Hydrologic Engineering2013，18，10： | 1 |
| 7 | Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed显示文摘This study assessed the performances of the traditional temperature-index snowmelt runoff model(SRM) and an SRM model with a finer zonation based on aspect and slope(SRM + AS model) in a data-scarce mountain watershed in the Urumqi River Basin,in Northwest China.The proposed SRM + AS model was used to estimate the melt rate with the degree-day factor(DDF) through the division of watershed elevation zones based on aspect and slope.The simulation results of the SRM + AS model were compared with those of the traditional SRM model to identify the improvements of the SRM + AS model's performance with consideration of topographic features of the watershed.The results show that the performance of the SRM + AS model has improved slightly compared to that of the SRM model.The coefficients of determination increased from 0.73,0.69,and 0.79 with the SRM model to 0.76,0.76,and 0.81 with the SRM + AS model during the simulation and validation periods in 2005,2006,and 2007,respectively.The proposed SRM + AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization,careful input data selection,and data preparation. | Shalamu Abudu Zhu-ping Sheng Chun-liang Cui Muatter Saydi Hamed-Zamani Sabzi James Phillip King | 2016 | Water Science and Engineering2016，9，4： | 1 |
| 8 | Modeling of daily pan evaporation using partial least squares regression显示文摘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. | ABUDU Shalamu CUI ChunLiang J. Phillip KING Jimmy MORENO A. Salim BAWAZIR | 2011 | Science China(Technological Sciences)2011，54，1： | 0 |
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