Lanzhou Regional Climate Center

Lanzhou, China

Lanzhou Regional Climate Center

Lanzhou, China
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Ma P.-L.,Institute of Arid Meteorology | Ma P.-L.,Lanzhou Regional Climate Center | Pu J.-Y.,Tianshui Agrometeo rological Experiment Station | Wang W.-T.,Xifeng Agrometeorological Experiment Station
Chinese Journal of Applied Ecology | Year: 2010

To explore the influence of light and temperature factors on the biomass accumulation of winter wheat at its development stages and in different organs, this paper analyzed the variation patterns of the biomass accumulation and the influence of TEP (thermal effectiveness photosynthetically active radiation) on the accumulation at each development stage, based on the observation data from the Xifen Agrometeorological Experiment Station in Gansu Province, including winter wheat phenophase and yield factors in 1981-2008, biomass at three-leaf, over-wintering, jointing, head-ing, milky maturity, and maturity stages in 1995-2008, and meteorological data in 1995-2008. The biomass accumulation of winter wheat in its whole growth period presented " S" curve, with the maximum value at heading-milky maturity stage. Since 1981, the TEP at heading-milky maturity stage increased with a rate of 3. 314 MJ . m -2 . a -1, and the TEP at other stages varied as parable curves. The TEP at turning green-jointing and milky maturity-maturity stages had a higher value in the 1990s and a lower value in the 1980s and early 21st century, while that at jointing-heading stage had a lower value in the 1990s but a higher value in the 1980s and early 21st century. There was a significant correlation between the TEP at each development stage and the actual yield. The LAI (leaf area index) at each development stage also had a significant correlation with the utilization rate of TEP at corresponding stage. When the LAI at jointing and heading stages was increased by 1, the utilization rate of TEP was correspondingly increased by 0.049 and 0.259 g . MJ, re-spectively.

Wang J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang J.,Lanzhou Institute of Arid Meteorology | Li X.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Lu L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Fang F.,Lanzhou Regional Climate Center
European Journal of Agronomy | Year: 2013

Regional crop yield estimations play important roles in the food security of a society. Crop growth models can simulate the crop growth process and predict crop yields, but significant uncertainties can be derived from the input data, model parameters and model structure, especially when applied at the regional scale. Abundant observational information provides the relative true value of surface conditions, and this information includes those areal data from remote sensors and ground observations. Data fusion technology integrates the advantages of crop growth models and multi-source observations, and it provides an innovative means for making precise regional corn yield estimations. A regional corn yield estimation framework based on two types of observation-model fusion methods is recounted in this paper. First, a 2008 application of the WOrld FOod Studies (WOFOST) growth model to the Yingke Oasis of Gansu province in northwest China suggested this method of simulating corn growth trends and yields, with attention to carbon absorption in particular. Second, this study applied a simulated annealing algorithm to obtain an optimized vector of parameters for the WOFOST model by using local multi-source data. After parameter estimation, the root mean square error (RMSE) of the simulated yield decreased from 1676.00kgha-1 to 4.00kgha-1. Moreover, the correlation coefficients between simulated and observed gross primary production (GPP) from 2009 to 2011 were 0.941, 0.967 and 0.962. Validation showed that a parameter estimation algorithm can reduce parameter uncertainties. Afterwards, the optimized model was used in a sequence data assimilation algorithm together with regional CHRIS leaf area index (LAI) data to incorporate spatial heterogeneity and evaluate model performance in estimating the near future regional corn yields. The general crop growth curve and final yield prediction were adjusted by using a real-time LAI variable update of the WOFOST model in each simulation unit. Numerical experiments on the sequence filter showed that the assimilation process can provide accurate regional estimations of crop growth and final yield on the basis of yield statistics from 50 sample points. The RMSE of the regional yield estimation at 50 sample points was 339.14kgha-1. Finally, by fusing a whole CHRIS-LAI image over the corn planting region of Yingke Oasis, a precise spatial distribution map of the estimated corn yield was obtained. © 2013 Elsevier B.V.

Wang J.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Wang J.,Lanzhou Institute of Arid Meteorology | Li X.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Lu L.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Fang F.,Lanzhou Regional Climate Center
Environmental Modelling and Software | Year: 2013

Sensitivity analysis (SA) has become a basic tool for the understanding, application and development of models. However, in the past, little attention has been paid to the effects of the parameter sample size and parameter variation range on the parameter SA and its temporal properties. In this paper, the corn crop planted in 2008 in the Yingke Oasis of northwest China is simulated based on meteorological observation data for the inputs and statistical data for the parameters. Furthermore, using the extended Fourier Amplitude Sensitivity (EFAST) algorithm, SA is performed on the 47 crop parameters of the WOrld FOod STudies (WOFOST) crop growth models. A deep analysis is conducted, including the effects of the parameter sample size and variation range on the parameter SA, the temporal properties and the multivariable output issues of SA. The results show that sample size highly affects the convergence of the sensitivity indices. Two types of parameter variation ranges are used for the analysis, and the results show that the sensitive parameters of the two parameter spaces are distinctly different. In addition, taking the storage organ biomasses at the different growth stages as the objective output, the time-dependent characteristics of the parameter sensitivity are discussed. The results show that several sensitive parameters exist in the grain biomass throughout the entire development stage. In addition, analyzing the twelve sensitive parameters has proven that although certain parameters have no effect on the final yield, they play key roles in certain growth stages, and the importance of these parameters gradually increases. Finally, the sensitivity analyses of different state variable outputs are performed, including the biomass, yield, leaf area index, and transpiration coefficient. The results suggest that the sensitive parameters of various variable processes differ. This study highlights the importance of considering multiple characteristics of the model parameters and the responses of the models in specific phenological stages. © 2013 Elsevier Ltd.

Matin M.A.,University of New Brunswick | Bourque C.P.-A.,University of New Brunswick | Bourque C.P.-A.,Lanzhou Regional Climate Center
Journal of Hydrology | Year: 2013

Water vapor generated locally by actual evapotranspiration (AET) is important both to the recycling of water regionally and to the long term sustainability of desert-oases in the semi-arid-to-arid region of northwest (NW) China. An accurate assessment of AET is central to describing the hydrologic status of watersheds. Conventional methods of estimating AET from meteorological point data are generally not appropriate for regions with high spatial variability, particularly with respect to landcover and topography. Insufficient monitoring stations make it particularly difficult to estimate AET that is spatially representative of large areas. The objective of this study was to estimate spatially-distributed monthly AET for a complex landscape, consisting of deserts, oases, and mountains, with climate and landcover data generated primarily from remote sensing (RS) data. In this study, we used two complementary relationship (CR)-based methods to estimate monthly reference evapotranspiration (ETo) and AET over a 10-year period (2000-2009) for two large watersheds in NW China. In evaluating the performance of CR-based methods, we compared point-estimates of ETo and AET generated with the two methods (generated either by using climate-station data or by extracting point-estimates from end products produced from RS-data) against (i) climate-station-based estimates of ETo calculated with the FAO Penman-Monteith (P-M) equation and from pan-evaporation data, and (ii) geographically-corresponding point-estimates of AET extracted from the MODIS global product of AET (MOD16) recently developed by Mu et al. (2011, Remote Sensing of Environment, 115, 1781-1800). Point-extractions of AET from MOD16-products were the least representative, when compared to ETo and AET calculated with the other methods. Between CR-based methods, the Venturini et al. (2008, Remote Sensing of Environment, 112, 132-141) method provided the best comparison with ETo calculated with the P-M equation and from pan-evaporation data. Due to its independence from wind velocity, the Venturini method is rated the most suitable for regional application, especially for the complex landscapes of NW China. © 2013 Elsevier B.V.

Bourque C.P.-A.,Lanzhou Regional Climate Center | Bourque C.P.-A.,University of New Brunswick | Mir M.A.,University of New Brunswick
Journal of Hydrology | Year: 2012

Back and forth exchange of water vapour and liquid water from oases at the base of the Qilian Mountains (NW China) and from the Qilian Mountains to oases as surface and shallow subsurface flow has been previously shown by model simulation to be a potentially important mechanism in the long-term stabilisation of oases in westcentral Gansu (Bourque and Hassan, 2009). In a subsequent re-examination of oasis self-support, we use monthly snow-cover patterns in the Qilian Mountains to determine the extent oasis vegetation and evapotranspiration in the low-lying portions of the upper and middle Shiyang and Hei River watersheds control snowfall dynamics in the Qilian Mountains. Monthly snow-cover area (SCA) in the watersheds is simulated with a spatially-distributed model designed to address differences in (i) topography along the prevailing wind direction, (ii) water-vapour production and transport, (iii) in-mountain production of precipitation, and (iv) precipitation phase changes. Seasonal variations in oasis vegetation, surface temperature (for model input), and SCA (for model validation) are described as separate timeseries of monthly composites of enhanced vegetation index, land surface temperature, and normalised difference snow index generated from MODIS optical reflectance and thermal emission data. Comparisons of modelled and snow-index-based estimates of SCA in the Shiyang and Hei River watersheds for the hydrological year, from August 2004 to July 2005, provide nearly similar spatiotemporal patterns; overlap between SCA's exceeds 60% for most months. An exception to this is in mid-summer of 2004, where overlap between SCA's is <30%. Agreement between monthly SCA's reinforces the importance of oasis-vegetation dynamics and mass transfer of water vapour to the atmosphere in guiding seasonal formation of precipitation and snow-cover dynamics in the Qilian Mountains. © 2012 Elsevier B.V.

Zhang C.,CAS Lanzhou Cold and Arid Regions Environmental and Engineering Research Institute | Zhang C.,Lanzhou Regional Climate Center | Bourque C.P.-A.,Lanzhou Regional Climate Center | Bourque C.P.-A.,University of New Brunswick | And 2 more authors.
Advances in Atmospheric Sciences | Year: 2010

This paper outlines a methodology to estimate monthly precipitation surfaces at 1-km resolution for the Upper Shiyang River watershed (USRW) in northwest China. Generation of precipitation maps is based on the application of a four-variable genetic algorithm (GA) trained on 10 years of weather and ancillary data, i.e., surface air temperature, relative humidity, Digital Elevation Model-derived estimates of elevation, and time of year collected at 29 weather stations in west-central Gansu and northern Qinghai province. An observed-to-GA predicted data comparison of 10 years of precipitation collected at the 29 weather stations showed that about 84% of the variability in observed values could be explained by the trained GA, including variability in two independent datasets. Point-comparisons of observed and modeled precipitation along an elevation-rainfall gradient demonstrated near-similar spatiotemporal patterns. A precipitation surface for USRW for July, 2005, was developed with the trained GA and input surfaces of surface air temperature and relative humidity generated from Moderate Resolution Imaging Spectroradiometer sensor (MODIS) products of land surface temperature. Spatial tendencies in predicted maximum and minimum values of surface air temperature, relative humidity, and precipitation within a 2-km radius circle around selected weather stations were in close agreement with the values measured at the weather stations. © Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer Berlin Heidelberg 2010.

Matin M.A.,University of New Brunswick | Bourque C.P.-A.,University of New Brunswick | Bourque C.P.-A.,Lanzhou Regional Climate Center | Bourque C.P.-A.,Beijing Forestry University
Remote Sensing of Environment | Year: 2013

Seasonal water flow to semi-arid to arid lands worldwide is regularly controlled by precipitation and snowmelt processes in nearby mountain ranges, where few monitoring stations exist. In this paper we present a new approach to assess the seasonal and inter-annual development of precipitation and associated snow-cover area (SCA) and snow-water storage in the Qilian Mountains of NW China. The approach addresses the spatial enhancement and calibration of remote sensing (RS) data acquired from coarse-resolution TRMM (Tropical Rainfall Measuring Mission; ~26-km resolution) and MODIS images in the development of image-surfaces of in-mountain precipitation and surface air temperature at 250-m resolution for input to a new spatially-distributed monthly snow-accumulation and snowmelt model. Spatially-enhanced precipitation and air temperature surfaces were subsequently calibrated using either geographically-weighted regression or simple linear regression and point-data from climate stations. When point-extractions from the calibrated products were compared against a new set of independent climate-station data, their respective values were comparable, giving an overall r2 of 0.87 for precipitation (RMSE=10mmmonth-1) and 0.96 for surface air temperature. With input from calibrated surfaces, monthly SCA and snow-water equivalence (SWE) in the Qilian Mountains were subsequently modeled over a 10-year period (2000-2009). Seasonal values of SCA and SWE were compared with MODIS-based (optical) and AMSR-E passive microwave-based estimates of SCA and SWE. In most cases, comparisons revealed suitable agreement across the various evaluations of SCA. Minor discrepancy between MODIS- and AMSR-E-based estimates of SCA resulted in a mean overlap of 73% when modeled SCA was compared to MODIS-based SCA and 84% overlap, when compared to AMSR-E-based SCA. Modeled and AMSR-E-based estimates of SWE at lower- to upper-mountain elevations (≤3900m above mean sea level; AMSL) compared fairly well. At elevations >3900m AMSL, discrepancies between estimates were largely attributed to an overestimation of local precipitation. © 2013 Elsevier Inc.

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