In order to quantify evaportranspiration and separate it into evaporation and transpiration, the two-layer model was applied. The aerodynamic and rice canopy resistance were estimated based on the two-layer model with measured meteorological data, water surface evaporation beneath the rice canopy and transpiration from the canopy measured by lysimeters. The aerodynamic resistances included aerodynamic resistance between the rice canopy and the air (rac), aerodynamic resistance between the water surface beneath the rice canopy and the air (rag), and the total aerodynamic resistance (ra). It was found that the aerodynamic resistances decreased with the increase in wind speed, while they increased with the increase in plant height. The relationships between these aerodynamic resistances and the wind speed above the canopy were individually parameterized by plant height. The rice canopy resistance (rc) was mainly influenced by the global solar radiation (SR) and the vapor pressure deficit of the air (VPD). The rc decreased with the increase in SR, while it increased with the increase in the VPD. The rc model was developed by a hyperbolic function with SR for the three growing stages and the model parameter was decided by the VPD. By incorporating these resistance sub-models into the two-layer model, evaporation from the water surface beneath the canopy and transpiration by the rice canopy were reproduced successfully.
We constructed a simple statistical model to predict the minimum temperature in spring from evening dew point temperature (Td) and relative humidity (H) data, and then compared it with a Brunt-type semi-empirical heat balance model. In general, statistical models are considered to have only local validity and to relate well only to calm, clear skies. However, there has been a lack of detailed analyses on the universal validity of these statistical models and the effects of weather conditions on them. We used hourly meteorological data collected in March, April, and May in Aomori, Yamagata, Fukushima, and Matsumoto to construct both models. The minimum temperature on calm, clear nights (Tm) was expressed as Tm = Td-10.29 ln (H)+42.0. The equation was accurate at all 4 locations, with a low root mean square error (RMSE) of 1.39°C. On all calm nights, the RMSE was 1.80°C. Under all weather conditions, the RMSE was 2.16°C. Two important factors in the Brunt-type model-the net longwave radiation and the thermal inertia of soil-were empirically determined from data collected on calm, clear nights. The Brunt-type model gave less accurate results, with an RMSE of 1.88°C. Applying the statistical model with all-weather parameters at 18 locations nationwide in April 2001, when severe frosts occurred, gave RMSE values of 1.5 to 2.7°C. These results indicate that the statistical model has good universal validity, with more accurate estimates of minimum temperatures than the Brunt-type model under calm, clear skies, and relatively good results under all-weather conditions.
This study evaluated energy balance closure in the eddy covariance method in a cool-temperate evergreen coniferous forest on steeply sloping topography in a region that experiences snowfall. We investigated the ratio of the sum of sensible and latent heat fluxes to available energy (the energy balance ratio; EBR) and the slope of the regression line of the eddy fluxes against available energy (closure ratio; CR). The 3 yr of continuous data were divided into snowfall (November-April) and snow-free periods (May-October). Uncorrected EBR and CR values at this site were 0.56 and 0.44 during the snowfall period and 0.69 and 0.60 during the snow-free period, respectively. Corrections of the storage term, the AOA error, and the inclined surface improved EBR by 2.0-2.6%, 8.3-8.9% and 4.9-5.9%, and improved CR by 5.3-5.8%, 5.6-7.9%, and 4.0-5.4%. Consequently, EBR and CR reached 0.86 and 0.79 during the snow-free period. By also considering the heat of fusion, EBR reached 0.79 during the snowfall period. Our findings suggest that heat fluxes of eddy-covariance measurements over a forest on steep slopes have a similar accuracy as those over other topography.
The aim of this study was to investigate whether daily quantitative nutrient management (QNM) consumed less nutrients without decreasing yields than conventional electrical conductivity (EC)-based management at a given leaf area index (LAI) in tomato rockwool hydroponics. Plants in a greenhouse were supplied with low (QNM-L) or high (QNM-H) daily amounts of nutrients at a given daily water uptake for QNM treatments and with a nutrient solution at a constant EC with an open system for EC-based management from April to August 2010. Variation of LAI was obtained by varying plant density in each nutrient supply treatment. When compared at the same plant density, the amount of nutrients supplied during the experiment and cumulative total fruit yield were significantly lower in QNM treatments than in EC-based management associated with a lower LAI. In the relationship between fruit yields or nutrient supply per unit ground area and LAI, the total and marketable fruit yields and nutrient supply increased linearly with the increasing LAI in all treatments. At a given LAI, QNM-L supplied the least nutrients per ground area followed by QNM-H, and EC-based management supplied the most. In the total or marketable fresh fruit yield per unit ground area, there was no significant difference in slope or intercept among the three treatments, indicating that fruit yields on a ground area basis can be accounted for by LAI irrespective of the nutrient supply treatment. The conclusion is that daily QNM was more nutrient-efficient than EC-based management without lowering fruit yields if the same LAI was achieved. Although QNM and EC-based management gave a comparable marketable yield at a given LAI, QNM treatments yielded more marketable fruits with smaller individual fruits than EC-based management, implying that the relationship between the fruit number and individual fruit weight is a trade-off at a given LAI.
A portable, easy-to-install chamber system for rapid measurement of soil CO2 efflux is needed for the immediate analysis of CO2 flushes after events such as tillage and grassland renovation. We conducted field measurements and simple simulations to validate the effectiveness of a portable chamber system with an infrared CO2 gas analyzer (IRGA) and a temperature/humidity sensor (the SIR system). We confirmed that a measurement period of 60-180 s after the closure of the chamber was sufficient for the calculation of soil CO2 efflux. We compared the SIR system with a closed static chamber system and an automated open/closed chamber system. The efflux data obtained with the SIR system were linearly related to the data obtained with the other systems, and the relationships between the soil temperature and efflux were similar in the three systems. These results show that the SIR system provided soil CO2 efflux values with a reasonable accuracy relative to the other systems. We also examined how the calculated efflux values were affected by the internal compensation of the IRGA for environmental parameters and water vapor dilution in the chamber. The potential error caused by the default internal compensation of the IRGA was within 3.1% under general environmental conditions. The effect of water vapor dilution was large (>20%) for small CO2 effluxes. The combined effects of the default internal compensation and water vapor dilution were larger for small CO2 effluxes than for large CO2 effluxes, and the effects became larger under low air pressure conditions. We recommend that accurate environmental compensation and water vapor correction be applied for analysis of small CO2 effluxes when there is a rapid increase in water vapor, especially under low air pressure conditions.
Drastic temperature decreases after precipitation events in the cold season in Mongolia can harm livestock, often leading to high stock mortality. We investigated seasonal and regional temperature changes before and after precipitation in Mongolia. We conducted a time-series analysis of changes of temperature relative to daily mean temperature at 25 weather stations before, on, and after days of precipitation. We categorized the relative temperature time series into three types: peak shaped (P), valley shaped (V), and gradually decreasing (D), which characterized spring-summer, winter, and autumn, respectively. We produced 11-day time series of relative temperature centered on precipitation days for each precipitation event at each station from 1961 to 2007 and applied principal component analysis to the relative temperature time series. Our results show that the first principle component (PC1) pattern is V-shaped, and the principal component analysis scores tended to be negative in winter and positive in spring. The PC2 pattern was closely related to the D-shaped trend of relative temperature, and the scores were positive from autumn to early winter and negative from spring to summer. Synoptic weather pattern analysis before and after precipitation days showed that, in general, both the P- and V-shaped trends accompanied the passage of a cold front; whether were patterns are P- or V-shaped was determined by the thermal conditions of the background air mass into which the contrasting air mass invaded to produce a precipitation-bearing front.
Our main objective was to elucidate how snow cover and soil frost influenced CO2 dynamics over agricultural land. We observed the CO2 flux above the soil or snow surface continuously using the commonly used static-chamber method and the CO2 concentration in the soil on snow-removal plot and untreated control plot over agricultural land in northern Japan from September 25, 2009, to May 31, 2010. The recorded largest CO2 flux was 3.9 μmol m-2 s-1 and CO2 concentration in soil was 390-5000 ppm. Little CO2 flux was observed during the soil-freezing and snow-covered periods. The CO2 concentration had been increasing about 10 ppm day-1 during the soil-freezing period at the snow-removal plot. At the beginning of April, the CO2 flux increased temporarily up to 0.19 μmol m-2 s-1 after the snow melted entirely at the untreated control plot and up to 0.52 μmol m-2 s-1 after the soil had thawed at the snow-removal plot. Snow-melting and soil-thawing largely influenced on CO2 flux, irrespective of soil temperature. The data were not explained by conventionally used temperature response functions for CO2 fluxes in these periods.
Micrometeorological heat, water, and CO2 fluxes have been widely monitored with the eddy covariance (EC) method. Its uncertainty information should be as important as the flux measurement itself and is indispensable to studies addressing site intercomparison, model validation, and model-data synthesis. Therefore, we estimated the fractional uncertainty φ using EC data collected over various types of land cover and at different instrumental heights. We also investigated φ characteristics according to a spatiotemporal scale and the flux averaging interval τ. As a result, we suggest that if φ is estimated under the EC measurement conditions satisfying stationarity, it is stable and uniform regardless of the land cover, spatiotemporal scale, and kind of flux. Based on the constancy of φ, we determined the baseline as a function of τ for a stationarity or a heterogeneity index of the EC measurement.
To elucidate effects of tillage systems and organic-matter applications on N2O emissions, we assessed seasonal patterns and magnitudes of N2O emissions from Andosol upland soil growing wheat and soybeans using different tillage systems (conventional tillage and no-tillage) and organic-matter applications (crop-residue, crop-residue and cattle manure, and none). Mean N2O emissions during June 2008-October 2010 were 41-134 mgN m-2 yr-1. The N2O emissions were observed immediately after manure application, fertilizer application, and during the later growing season in soybean-cropped soil. ANOVA revealed that the kind of crop and the organic-matter applications affect N2O emissions, unlike tillage systems. Greater N2O emissions in soil with residues and manure suggest that NO3--N determines N2O production by denitrification. Additionally, N2O emissions are greater in soybean-cropped soil, probably because of denitrification. The presence of fresh organic matter such as shed roots and degraded root nodules, NO3--N, or NO2--N under wet soil conditions is necessary for N2O production by denitrification. The water-filled pore space (WFPS) in no-tilled soil is consistently greater than that in conventionally tilled soil, but it does not affect N2O emissions, suggesting that soil wetness changes are the cause: tillage is not the sole limiting factor for N2O production in well-aerated soil. Greater N2O emissions during warm, wet periods of the growing season imply that emissions are related to climatic conditions that affect the soil environment and the resultant microbial activity. Nevertheless, N2O emissions from the study site with light-colored Andosol resembled those from Andosol, but were less than those from the other soil types such as those of Brown Forest soil, Brown Lowland soil, Grey Lowland soil, Wet Andosol, and Peat soil. Light-colored Andosol of this study site has lower background emissions.
Management of rice straw is of environmental concern because it significantly affects methane (CH4) emissions from rice paddies. To evaluate straw-application effects on paddies those have different cultivation histories, we measured CH4 emissions in continuously cultivated rice paddy (CP) and recently converted paddy (RP) (from soybean cultivation) with rice straw treatments (+S). Further, we hypothesized that changes in i) soil Fe (III) reduction and ii) population dynamics of methanogens were responsible for the different responses of CH4 emissions due to straw application between CP and RP. Methane (CH4) emission from CP+S was 2-fold larger than that from RP+S, although relative enhancement was higher in later (492%) than the former (289%) compared with no straw application. Stoichiometric evaluation revealed that applied rice straw acted as an exogenous source of electron donor for CH4 production, especially in CP. Our results showed that the increase in CH4 emissions by straw application was much greater in continuous than short term paddy.
A supersonic pan-evaporimeter was developed for performing dynamic analysis of evaporative demand in a greenhouse. The pan-evaporimeter comprises two parts: a small-sized cylindrical container (a diameter of 27 cm, a height of 15 cm) filled with water and a supersonic water-level detector. The pan evaporation rate (Epan) can be evaluated for an arbitrary time interval on the basis of the decrease in the water level due to evaporation from the pan-evaporimeter. The performance and validity of the panevaporimeter were examined by analyzing the characteristics of Epan on an hourly basis and its relation with the solar radiation, air temperature and humidity, wind speed, in addition, the transpiration rate of a tomato plant (Tr) in a greenhouse. A positive linear relationship was clearly obtained between the hourly Epan and the environmental elements, and then sensitivity of Epan to the environmental elements was revealed. Furthermore, a good correlation was found between the Epan and the Tr on hourly basis for one day. These results suggest that the developed pan-evaporimeter is a useful tool for analyzing the dynamics of evaporative demand and for estimating the transpiration rate in a greenhouse.
The operations of a compact Doppler radar rain gauge (R2S; Rufft, FRG) and optical disdrometer (LPM; THIES, FRG) are based on raindrop size distribution (DSD) measurements. We checked the instrumental error of these sensors and compared each sensor with a reference tipping-bucket rain gauge. This is because both rain gauges can detect fine particles and so they can function as rain sensors. The R2S has a measuring bias of rainfall intensity when the drop size distribution differs from the assumed statistical DSD model. The instrumental error on the LPM is small; in fact, the LPM shows good agreement with the reference rain gauge. Where the atmospheric density differs remarkably from the standard elevation, as is the case in highland areas, the R2S requires calibration using a reference rain gauge. The resultant calibration coefficient of the R2S to convert the reading into a reference tipping-bucket rain-gauge equivalent was 0.51 in a forest at an elevation of 1380 m. Further gathering of calibration coefficients obtained at different elevations will improve the R2S's applicability.