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ABSTRACT: Fluxes of carbon dioxide, water vapor, and heat were measured above crop canopy using the eddy covariance method during the
2008 maize growing season, over an agricultural field within an oasis located in the middle reaches of Heihe River basin,
northwest China. The values for friction velocity, the Monin–Obukhov stability parameter, and energy balance closure indicated
that the eddy covariance system at this study site provided reliable flux estimates. Results from measurements showed that
the mean sensible heat flux was 70Wm−2 with a maximum value of 164Wm−2 (May) and a minimum value of 45Wm−2 (July) during the maize growing season. In contrast, the mean latent heat was 278Wm−2 with a maximum value of 383Wm−2 (July) and minimum of 101Wm−2 (May). The mean downward soil heat flux was 55Wm−2 with a maximum value of 127Wm−2 (May) and minimum of 49Wm−2 (July). The magnitude of mean daytime net CO2 uptake was −11.50μmolm−2s−1 with a maximum value of −28.32μmolm−2s−1 (18 and 19 July) and a minimum values of −0.32μmolm−2s−1 (18 and 19 May). Correlation was observed between daytime half-hourly carbon dioxide flux and canopy conductance. In addition,
the relationship between carbon dioxide flux and photosynthetically active radiation for selected days during different stages
of maize growing season indicated the carbon dioxide flux uptake by the canopy was controlled by actual photosynthetic activity
related to the variation of green leaf area index for the different growing stages.
KeywordsArid region–CO2 flux–Eddy covariance–Heat flux–Turbulent flux
Environmental earth sciences 05/2012; 64(3):619-629. · 1.06 Impact Factor
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ABSTRACT: It is absolutely necessary to quantify the hydrological processes in earth surface by numerical models in the cold regions
where although most Chinese large rivers acquire their headstreams, due to global warming, its glacier, permafrost and snow
cover have degraded seriously in the recent 50years. Especially in an arid inland river basin, where the main water resources
come from mountainous watershed, it becomes an urgent case. However, frozen ground’s impact to water cycle is little considered
in the distributed hydrological models for a watershed. Took Heihe mountainous watershed with an area of 10,009km2, as an example, the authors designed a distributed heat-water coupled (DWHC) model by referring to SHAW and COUP. The DWHC
model includes meteorological variable interception model, vegetation interception model, snow and glacier melting model,
soil water-heat coupled model, evapotransporation model, runoff generation model, infiltration model and flow concentration
model. With 1km DTM grids in daily scale, the DWHC model describes the basic hydrological processes in the research watershed,
with 3∼5 soil layers for each of the 18 soil types, 9 vegetation types and 11 landuse types, according to the field measurements,
remote sensing data and some previous research results. The model can compute the continuous equation of heat and water flow
in the soil and can estimate them continuously, by numerical methods or by some empirical formula, which depends on freezing
soil status. However, the model still has some conceptual parameters, and need to be improved in the future. This paper describes
only the model structure and basic equations, whereas in the next papers, the model calibration results using the data measured
at meteorological stations, together with Mesoscale Model version 5 (MM5) outputs, will be further introduced.
Environmental Geology 04/2012; 53(6):1299-1309. · 1.13 Impact Factor
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ABSTRACT: A distributed water–heat coupled model (DWHC) is calibrated by using daily precipitation data from 26 hydrological and meteorological
stations: daily averaged air temperature data from the 11 stations and daily pan evaporation data (E601) from the 15 stations
in 2000. Six tests by using different spatial interpolation methods to calculate the above daily meteorological data in each
1km×1km grid, are designed to simulate the mean daily runoff generated from the research Heihe mountainous watershed in
2000. Due to spatial sparseness and asymmetry of the hydrological and meteorological stations, the results of the six tests
have little differences. The interpolation method in 3-D mode considering altitude is not better than those taking no account
of altitude, nor are the model results when the daily meteorological data at the two stations far from the research watershed
are complemented. At last, a nearest neighbor interpolation method in 2-D mode is used to calibrate the DWHC model, in which
the revised Nash-Sutcliffe Efficiency NSE, balance error B, determinate coefficient R
2, root mean square error RMSE and average absolute error MAE is about 0.61, 0.08%, 0.73, 25.0 and 15.8m3s−1, respectively. However, by using the daily data in 1999 to validate the model, the NSE, B, R
2, RMSE and MAE are, respectively, 0.63, −2.98%, 0.77, 34.9 and 20.3m3s−1. The reason that the model result is not favorable is mainly because of the lack of detailed soil information, meteorological
data and vegetation data; even worse, the basic equations for runoff generation processes are mainly derived from the research
results in other regions and meanwhile, its flow concentration method should be improved too. The water balance of the research
watershed in 2000 is also discussed in this paper. Though the runoff simulation results are not favorable, the estimated evapotranspiration
and runoff components are in accordance with the usual knowledge qualitatively, parts of which meet with the field measurements.
According to the model results, the runoff is mainly generated from the land surfaces and shallow soil layers in this cold
mountainous watershed. The alpine meadow has evident water conservation function based on the model results, field investigation
and field observation results. The DWHC model also reproduces the formation processes of the thick-layered ground ice to some
extent, though it is suppositional due to lack of detailed soil, vegetation and meteorological information.
Environmental Geology 04/2012; 55(1):17-28. · 1.13 Impact Factor
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ABSTRACT: The distributed water-heat coupled (DWHC) model is calibrated, with the help of the Mesoscale model version 5 (MM5), by calculating
the daily precipitation, the daily average air temperature at the 2.0m heights and the daily potential evaporation in Heihe
mountainous watershed area and its vicinity (96.786°∼102.284°E, 37.328°∼40.601°N, 17×104km2), from February 11 to June 30, 2003. The MM5 model periodically ran every 10days in 3km×3km grid resolution with an
integral time step of 3s. In the MM5 model, many scheme or options are consulted or adopted, such as the Grell scheme cumulus
parameterization method, the Dudhia option, the cloud-radiation scheme, MRF PBL option and the modified Oregon State University
Land-surface model (OSULSM). According to the projection transform methods, the MM5 outputs are interpolated to the 1km×1km
grid in Alberts projection by using triangle-based cubic interpolation (Cubic) and nearest neighbor interpolation (Nearest)
methods, with which the DWHC model shares the same method. The result shows that, when the Nearest method is used, the Nash-Sutcliffe
equation value of the daily average runoff is 0.79, the balance error is −0.79% and the goodness of fit R
2 value is 0.81. Meanwhile, when the Cubic method is used, the Nash-Sutcliffe equation value, the balance error and the R
2 value are 0.79, −0.65% and 0.80, respectively. Though the runoff simulation result is not favorable, it is still better than
that using measured data at the meteorological and hydrological stations; the latter has a Nash-Sutcliffe equation value of
0.61. The MM5-DWHC model results also show that runoff mainly occurs on land surfaces and from shallow soil layers. According
to model calibration results, certain outputs of MM5 are singular to some extent and the DWHC model is very sensitive to the
initial values.
Environmental Geology 04/2012; 53(4):763-768. · 1.13 Impact Factor
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Agricultural Water Management 02/2007; 87(3):337-346. · 2.00 Impact Factor
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ABSTRACT: Atmospheric CO2 concentration (CC) near land surface and meteorological variables have been measured at four sites, named Yeniugou (alpine meadow and permafrost), Xishui (mountainous forest), Linze (oasis edge) and Ejina (lower desert), respectively, in Heihe River Basin, northwest China. The results showed that, the half hourly CC at night was larger than in daytime, and the daily averaged CC was the largest in winter. The averaged CC of 932 d at the Linze was about 418 ppm, was about 366 ppm in the 762 d at the Ejina. In the same period from September 23 to November 9, 2004, the averaged CC was about 625, 334, 436 and 353 ppm, at Yeniugou, Xishui, Linze and Ejina, respectively. The linear relationship between daily averaged CC and air temperature T was negative, between CC and relative humidity (RH) was positive. The linear CC-atmospheric pressure (AP) relationship was negative at the Linze and Yeniugou, was positive at the Ejina. The relationship between CC and global radiation R was exponent, and soil temperature Ts was negative linear, and soil water content was complex. The correlation between CC and wind speed was not existent. Using meteorological variables together to simulate CC, could give good results.
Journal of Environmental Sciences 02/2006; 18(4):708-15. · 1.66 Impact Factor
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ABSTRACT: There are many definitions for the phrase ‘Cold Regions’, albeit it has been widely used. In China, Cold Regions should include all the permafrost area, the glacier area, and the great majority of stable seasonal snow cover area, in which the vegetation type and climate type are, to some extent, unique compared with those in the other regions. The air temperature, according to the literal meaning of ‘Cold Regions’, is the major factor to judge whether a region belongs to Cold Regions or not. In this essay, the averaged monthly air temperature data, which were derived from 4-time daily data, at the 571 stations in China, from 1961 to 1998, were used. The averaged monthly air temperature at the each 1 km grid in Alberts projection was calculated by using the regressing equation, in which altitude, Y axis and X axis in Alberts projection of the 571 stations were input and R2 was of 0.92–0.97. The results show that the Woo's definition and Yang's definition for the Cold Regions are not appropriate in China. A new definition of the Cold Regions in China should be that the averaged air temperature of the coldest month is lower than − 3.0 °C, the number of months of which the averaged air temperature is higher than 10 °C is not more than 5, and the averaged yearly temperature does not exceed 5.0 °C. The new partition by the definition is nearly in accordance with the borders of the defacto area of permafrost, seasonal stable snow cover, vegetation distribution and climate regionalization. Therefore, the area of the Cold Regions in China by the new definition is about 417.4 × 104 km2, 43.5% of the country's land area.
Cold Regions Science and Technology.
