Half-hourly Time Scale Research Articles

Improved estimation of gross primary productivity (GPP) using solar-induced chlorophyll fluorescence (SIF) from photosystem II

Solar-induced chlorophyll fluorescence (SIF) contains contributions from both photosystem I (PSI) and photosystem II (PSII). In theory, SIF emitted from PSII (SIFPSII) should be extracted from at-sensor SIF to quantify photosynthetic CO2 assimilation, as PSI fluorescence yield is nearly insensitive to changes in photochemical yield. In many SIF-related studies, the fraction of chlorophyll-absorbed energy allocated to PSII (β2), a key factor controlling the flux of excitation energy for SIFPSII, is simply assigned a fixed value. However, β2 is regulated in response to variations in environmental conditions to avoid an energy imbalance between PSI and PSII. By quantifying the regulating effect of the cytochrome b6f complex (Cyt b6f) on the electron transport from PSII to PSI, and its interaction with energy dissipation in both photosystems, we develop a framework to estimate β2 and PSII fluorescence yield (ΦF2), two key determinants for SIFPSII, from SIF emission, PAR, air temperature, the maximum carboxylase activity of Rubisco (Vcmax), and the maximum activity of Cyt b6f (JCB6F_max). The framework is equipped with a two-leaf scheme, enabling us to estimate SIFPSII for sunlit and shaded leaves from top-of-canopy SIF observations (SIFTOC). Our simulation results showed that β2 and ΦF2 tend to change in opposite directions with varying PAR, making the contribution of PSII to SIFTOC relatively constant. We compare gross primary productivity (GPP) mechanistically estimated from SIFPSII obtained with fixed (GPPSIF_Fβ) and dynamic β2 (GPPSIF_Dβ) against the eddy-tower-derived GPP (GPPEC) at a winter-wheat experiment site. At a half-hourly time scale, GPPSIF_Dβ is better than GPPSIF_Fβ, showing higher correlations with GPPEC (R2 = 0.74 versus R2 = 0.64 and RMSE = 6.61 μmol m−2 s−1 versus RMSE = 7.67 μmol m−2 s−1). The study provides a practical way to estimate the contribution of SIFPSII to SIFTOC, giving a better theoretical basis to SIF-based GPP estimation models.

Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem

Abstract. Accurate and efficient modeling of stomatal conductance (gs) has been a key challenge in vegetation models across scales. Current practice of most land surface models (LSMs) assumes steady-state gs and predicts stomatal responses to environmental cues as immediate jumps between stationary regimes. However, the response of stomata can be orders of magnitude slower than that of photosynthesis and often cannot reach a steady state before the next model time step, even on half-hourly timescales. Here, we implemented a simple dynamic gs model in the vegetation module of an LSM developed within the Climate Modeling Alliance and investigated the potential biases caused by the steady-state assumption from leaf to canopy scales. In comparison with steady-state models, the dynamic model better predicted the coupled temporal response of photosynthesis and stomatal conductance to changes in light intensity using leaf measurements. In ecosystem flux simulations, while the impact of gs hysteresis response may not be substantial in terms of monthly integrated fluxes, our results highlight the importance of considering this effect when quantifying fluxes in the mornings and evenings, as well as interpreting diurnal hysteresis patterns observed in ecosystem fluxes. Simulations also indicate that the biases in the integrated fluxes are more significant when stomata exhibit different speeds for opening and closure. Furthermore, prognostic modeling can bypass the A-Ci iterations required for steady-state simulations and can be robustly run with comparable computational costs. Overall, our study demonstrates the implications of dynamic gs modeling for improving the accuracy and efficiency of LSMs and for advancing our understanding of plant–environment interactions.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m−2 d−1 °C−1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

Which adjustment methods are suitable for the wind-induced errors of Geonor T-200BM3 precipitation weighing gauges in a periglacial site?

Single Alter shielded T-200BM3 weighing precipitation gauges are widely used in the measurement of all precipitation types (rainfall, snow and mixed precipitation) in unattended boreal or alpine regions, but their original datasets must be adjusted for undercatch errors caused by wind in snowy, windy and harsh environments. Therefore, previous researchers have developed many adjustment methods for all precipitation types on different time scales. However, which adjustment method is suitable for T-200BM3 weighing gauge wind-induced error adjustment in harsh alpine regions is unclear. Therefore, precipitation measurement intercomparison experiments were conducted in the Qilian Mountains from July 2018 to July 2021, and eight adjustment methods; were evaluated for wind-induced errors for daily, individual precipitation event, hourly, and half-hourly time scales. Z2004 outperformed the other adjustment methods in regard to the daily measurements of snow and mixed precipitation. Regarding individual snowfall events, M2007 reduced the absolute value of RMSE (bias) from 1.44 to 1.32 mm (0.77–0.24 mm) and could be recommended for snowfall event adjustment. K2017-1 attained a better performance than K2017-2 in regard to half-hourly snowfall and mixed sample adjustment and was more suitable for half-hourly snowfall sample adjustment. K2017-1 reduced the absolute value of bias from 0.07 to 0.00 mm for snowfall. Finally, Z2004, M2007, and K2017-1 yielded better adjustment results for the daily accumulation precipitation amount (>2 mm d−1), individual snowfall events (>2 mm per event), and half-hourly accumulation snowfall or mixed samples (>1 mm 30 min−1), respectively. However, further intercomparison in different climate regions is needed for trace precipitation samples.

Interactive impacts of atmospheric, soil, and vegetation conditions on land surface energy partitioning over a coastal wetland in Northern China

Elucidating environmental controls on land surface energy partitioning at ecosystem levels is critical for better understanding water and energy cycles across landscapes; however, the underlying effects of changing atmospheric, soil, and vegetation conditions on the energy partitioning in coastal wetlands are not well understood. Here, the temporal variations in surface evaporative fraction (EF; defined as latent heat flux-LE divided by the sum of LE and sensible heat flux-H) and its responses to environmental conditions were analyzed at half-hourly and daily timescales, using measurements from a Bowen ratio tower situated in an herbaceous coastal wetland in Tianjin, Northern China. Results showed that net radiation (Rn) was mainly allocated to H with an average H/Rn of 0.90 and daily EF was averaged at 0.14 during the study period. The EF patterns in the studied coastal wetland were significantly different from those in terrestrial ecosystems, which was mainly attributed to the complex interactions in local atmospheric, soil, and vegetation conditions. Specifically, significant directional relationships among environmental variables were detected through the Granger causality analysis (GCA), especially at half-hourly timescales. The GCA and boosted regression tree methods showed that energy-related factors (i.e., air temperature and Rn) were the dominant controls on EF. Poor vegetation status, which was affected by salinity and flood stress, primarily limited LE and subsequently temporal EF variability. During the wet period, atmospheric conditions changed with the prevailing onshore winds, resulting in the dominant control of relative humidity on EF variations. Overall, the EF variability in coastal wetlands was not solely controlled by energy but was more tightly constrained by atmospheric conditions and vegetation dynamics, which offers new insights into energy exchanges in coastal wetland ecosystems, especially for determining the coupling strength between atmosphere and land surface processes.

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling.

Can a physically-based land surface model accurately represent evapotranspiration partitioning? A case study in a humid boreal forest

One of the roles of Land Surface Models (LSMs) is to partition evapotranspiration (E) into overstory transpiration (ET), understory evapotranspiration (EG), and wet canopy evaporation (EC). Unfortunately, only a handful of studies have evaluated the performance of LSMs with E partitioning. Unlike dry canopies which are dominated by transpiration, wet canopies lead to the evaporation of intercepted water. In this respect, there is no better testing site for LSMs than the humid boreal forest, which is characterized by frequent precipitation and sustained evapotranspiration. This study assesses the performance of the Canadian Land Surface Scheme (CLASS) in simulating evapotranspiration and its components by applying detailed observations of water pathways and residence times in a forest canopy using a variety of methods (eddy covariance; sap flow, throughfall and stemflow measurements; and the stem compression approach). The study site was located in Montmorency Forest in Québec, Canada, a balsam fir boreal forest with ≈1600 mm of annual precipitation. The field campaign was conducted during the summers of 2017 and 2018. To improve the accuracy of the simulations, we adjusted the definition of maximum canopy water storage, S=c×LAI, by using c=0.49 mm instead of the default value of 0.20 mm. This simple modification improved the performance of CLASS when simulating components of evapotranspiration, indicated by the increase in the modified Kling-Gupta efficiency (KGE′) with rises between 0.01 and 0.23. Overall, CLASS performed well in simulating evapotranspiration and overstory transpiration in dry canopy conditions at half-hourly time steps with KGE′ values between 0.67 and 0.81. The contribution of simulated ET,EG, and EC to total E during the two sampling periods were 46%,24%, and 30%, respectively, which were comparable with the field observations (35%,22%, and 25%, respectively). In conclusion, CLASS was able to simulate E partitioning in dry and wet canopy conditions reasonably well at both seasonal and half-hourly time scales.

Simulation of evapotranspiration and its components for the mobile dune using an improved dual-source model in semi-arid regions

Accurate estimation of evapotranspiration (ET) and its components is essential for a deep understanding of hydrological processes in semi-arid regions. In this study, an improved Shuttleworth-Wallace model (i.e., SWH model) was applied to simulate ET and its components over the growing season in four consecutive years, 2013 to 2016, for mobile dunes in China’s Horqin Sandy Land. The performance of the model was validated using the measurements of eddy covariance (EC) technology and micro-lysimeter; the modeled ET was in good agreement with the measured ET at both half-hourly and daily timescales, and the modeled soil evaporation (E) was consistent with the micro-lysimeter measurements. E accounted for the major fraction of ET in the mobile dune ecosystem, with E/ET being 0.62, 0.84, 0.82, and 0.61, for the 2013 to 2016 growing seasons, respectively. During the vigorous growing stage, the monthly E/ET was mainly controlled by the leaf area index (LAI) in wet years, while water became the limiting factor for E/ET in dry years. At a half-hourly timescale, E/ET was primarily controlled by canopy stomatal conductance and soil moisture content, but when soil moisture content was lower than the wilting point, E/ET depended solely on the soil moisture content. When soil moisture content was between the wilting point and the field capacity, E/ET was mainly controlled by soil moisture content in the early and late growing stages, but by canopy stomatal conductance in the vigorous growing stage. When soil moisture content exceeded the field capacity, E/ET was primarily controlled by canopy stomatal conductance.

Radiance-based NIRv as a proxy for GPP of corn and soybean

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv,Rad and absorbed photosynthetically active radiation by green leaves (APARgreen), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

Using the maximum entropy production approach to integrate energy budget modelling in a hydrological model

Abstract. Total terrestrial evaporation, also referred to as evapotranspiration, is a key process for understanding the hydrological impacts of climate change given that warmer surface temperatures translate into an increase in the atmospheric evaporative demand. To simulate this flux, many hydrological models rely on the concept of potential evaporation (PET), although large differences have been observed in the response of PET models to climate change. The maximum entropy production (MEP) model of land surface fluxes offers an alternative approach for simulating terrestrial evaporation in a simple way while fulfilling the physical constraint of energy budget closure and providing a distinct estimation of evaporation and transpiration. The objective of this work is to use the MEP model to integrate energy budget modelling within a hydrological model. We coupled the MEP model with HydroGeoSphere (HGS), an integrated surface and subsurface hydrologic model. As a proof of concept, we performed one-dimensional soil column simulations at three sites of the AmeriFlux network. The coupled model (HGS-MEP) produced realistic simulations of soil water content (root-mean-square error – RMSE – between 0.03 and 0.05 m3 m−3; NSE – Nash–Sutcliffe efficiency – between 0.30 and 0.92) and terrestrial evaporation (RMSE between 0.31 and 0.71 mm d−1; NSE between 0.65 and 0.88) under semi-arid, Mediterranean and temperate climates. At the daily timescale, HGS-MEP outperformed the stand-alone HGS model where total terrestrial evaporation is derived from potential evaporation, which we computed using the Penman–Monteith equation, although both models had comparable performance at the half-hourly timescale. This research demonstrated the potential of the MEP model to improve the simulation of total terrestrial evaporation in hydrological models, including for hydrological projections under climate change.

Estimation of soil temperature from meteorological data using different machine learning models

Soil temperature (Ts) plays a key role in physical, biological and chemical processes in terrestrial ecosystems. Accurate estimation of Ts at various soil depths is crucial for land-atmosphere interactions. This study investigated the applicability of four different machine learning models, extreme learning machine (ELM), generalized regression neural networks (GRNN), backpropagation neural networks (BPNN) and random forests (RF), for modeling half-hourly Ts at four different depths of 2 cm, 5 cm, 10 cm, and 20 cm on the Loess Plateau of China. A field experiment was conducted to measure half-hourly Ts and meteorological variables. Air temperature, wind speed, relative humidity, solar radiation, and vapor pressure deficit were used as inputs to train the models for estimation of half-hourly Ts. The results showed ELM, GRNN, BPNN and RF models provided desirable performance in modeling half-hourly Ts at all depths, with root mean square error values ranging 2.26–2.95, 2.36–3.10, 2.32–3.04 and 2.31–3.00 °C, mean absolute error values ranging 1.76–2.26, 1.83–2.31, 1.80–2.32 and 1.79–2.26 °C, Nash–Sutcliffe coefficient values ranging 0.856–0.930, 0.841–0.924, 0.847–0.927 and 0.850–0.927, and concordance correlation coefficient values ranging 0.925–0.965, 0.925–0.963, 0.928–0.963, and 0.924–0.961 for the ELM, GRNN, BPNN, and RF models, respectively. There was a statistically significant agreement (P < 0.001) between the measured and modeled values at both half-hour and daily timescales, and the box plots showed the distributional differences between the measured and modeled values were small. Generally, the ELM model had slightly better performance with much better computation speed than GRNN, BPNN as well as RF models at half-hourly timescales, thus the ELM model was highly recommended to estimate Ts at different soil depths.

Impact of CO2 storage flux sampling uncertainty on net ecosystem exchange measured by eddy covariance

Complying with several assumption and simplifications, most of the carbon budget studies based on eddy covariance (EC) measurements quantify the net ecosystem exchange (NEE) by summing the flux obtained by EC (FC) and the storage flux (SC). SC is the rate of change of a scalar, CO2 molar fraction in this case, within the control volume underneath the EC measurement level. It is given by the difference in the quasi-instantaneous profiles of concentration at the beginning and end of the EC averaging period, divided by the averaging period. The approaches used to estimate SC largely vary, from measurements based on a single sampling point usually located at the EC measurement height, to measurements based on profile sampling. Generally a single profile is used, although multiple profiles can be positioned within the control volume. Measurement accuracy reasonably increases with the spatial sampling intensity, however limited resources often prevent more elaborated measurement systems. In this study we use the experimental dataset collected during the ADVEX campaign in which turbulent and non-turbulent fluxes were measured in three forest sites by the simultaneous use of five towers/profiles. Our main objectives are to evaluate both the uncertainty of SC that derives from an insufficient sampling of CO2 variability, and its impact on concurrent NEE estimates.Results show that different measurement methods may produce substantially different SC flux estimates which in some cases involve a significant underestimation of the actual SC at a half-hourly time scales. A proper measuring system, that uses a single vertical profile of which the CO2 sampled at 3 points (the two closest to the ground and the one at the lower fringe of the canopy layer) is averaged with CO2 sampled at a certain distance and at the same height, improves the horizontal representativeness and reduces this (proportional) bias to 2–10% in such ecosystems. While the effect of this error is minor on long term NEE estimates, it can produce significant uncertainty on half-hourly NEE fluxes.

The Use of the Deviation Angle Variance Technique on Geostationary Satellite Imagery to Estimate Tropical Cyclone Size Parameters

Abstract This study extends past research based on the deviation angle variance (DAV) technique that utilizes digital brightness temperatures from longwave infrared satellite images to objectively measure the symmetry of a tropical cyclone (TC). In previous work, the single-pixel DAV values were used as an objective estimator of storm intensity while maps of the DAV values indicated areas where tropical cyclogenesis was occurring. In this study the spatial information in the DAV maps is utilized along with information from the Cooperative Institute for Research in the Atmosphere’s extended best-track archive and the Statistical Hurricane Intensity Prediction Scheme model to create multiple linear regression models of wind radii parameters for TCs in the North Atlantic basin. These models are used to estimate both symmetric, and by quadrant, 34-, 50-, and 64-kt wind radii (where 1 kt = 0.51 m s−1 1) on a half-hourly time scale. The symmetric model assumes azimuthal symmetry and has mean absolute errors of 38.5, 23.2, and 13.5 km (20.8, 12.5, and 7.3 n mi) for the 34-, 50-, and 64-kt wind radii, respectively, which are lower than results for most other techniques except for those based on AMSU. The asymmetric model independently estimates radii in each quadrant and produces mean absolute errors for the wind radii that are generally highest in the northwest quadrant and lowest in the southwest quadrant similar to other techniques. However, as a percentage of the average wind radii from aircraft reconnaissance, all quadrants have similar errors.

Evaluating the complementary relationship for estimating evapotranspiration using the multi-site data across north China

The ability to predict actual evapotranspiration flux (λEa) by physically based evaporanspiration (ET) model is limited globally due to the difficulty in validating the site-specific model parameters. Thus, the approaches for estimating λEa using only routine meteorological variables play a critical role in understanding and predicting hydrological cycle in the context of climate change. In this study, the performance of a complementary relationship (CR) method (Granger and Gray, 1989; GG model) on different timescales (daily and half-hourly) was evaluated using a high-quality dataset of selected 12 eddy covariance flux towers, which encompassed a number of cropland, grassland, evergreen needleleaf forest, desert shrub and wetland sites across northern China. The results indicated that the GG model is applicable in estimating daily λEa for most ecosystems across northern China. However, significant underestimations of daily λEa were found for the croplands (Daman and Dunhuang sites) and the desert shrub (Ejina) in the arid northwest China, which may be attributed to the enhanced λEa by horizontal advection and the deep root water-uptake, respectively. By using the Monin-Obukhov similarity theory with a surface energy balance constraint, the model performance on half-hourly timescale was satisfactory for the 12 tower sites with R2 ranging from 0.54 to 0.81 and the slopes of Deming regression line between measured and simulated λEa from 0.77 to 1.14. Indeed, the study highlights the need for further investigation of the timescale dependence of the CR-based ET models.

Energy exchange and evapotranspiration over irrigated seed maize agroecosystems in a desert-oasis region, northwest China

Investigating the dynamics of energy and water vapor exchange in oasis agroecosystems is important to improve scientific understanding of land surface processes in desert-oasis regions. In this study, water vapor and energy fluxes were obtained by using an eddy covariance technique for two similar irrigated seed maize fields at Yingke and Pingchuan, in northwest China. Seasonal variabilities of evapotranspiration (ET) and relevant environmental and biophysical factors were explored. Results showed that the energy balance closures were reasonable, with energy balance ratio of 0.99 and 0.79 for a half-hourly time scale at Yingke and Pingchuan, respectively. The seasonal changes in net radiation (Rn), latent heat flux (LE), and sensible heat flux (H) of Yingke and Pingchuan were similar. Net radiation was 11.27MJm−1day−1 during the growing season. Latent heat flux accounted for 67.5% of net radiation, sensible heat flux was 25.0%, and soil heat flux was 7.5%. A reverse seasonal change was found in partitioning energy flux into LE and H. The seasonal variation in energy flux partitioning was significantly related to the phenology of maize. During the growing season, ET was 467 and 545mm, and mean daily ET 2.84 and 3.35mmday−1 at Pingchuan and Yingke, respectively. “Non-growing” season ET was 15% of the annual ET in the bare field (during October–March) and 85% of the annual ET for maize (during April–September). Daily ET was mainly controlled by net radiation and air temperature, and was significantly affected by leaf area index (<3.0m2m−2) and canopy conductance (<10mms−1). Furthermore, irrigation promoted daily ET greatly during the growing season. Accurate estimation of seed maize ET and determination the controlling factors helps to develop exact irrigation scheduling and improve water resource use in desert-oasis agroecosystems.

Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: impact of soil hydraulic and vegetation properties

Abstract. Evapotranspiration has been recognized as one of the most uncertain terms in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs (Interaction Sol–Biosphere–Atmosphere) simulations of evapotranspiration are assessed at the field scale over a 12-year Mediterranean crop succession. The model is evaluated in its standard implementation which relies on the use of the ISBA pedotransfer estimates of the soil properties. The originality of this work consists in explicitly representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamics of simulated and measured evapotranspiration over a long period of time. The analysis focuses on key parameters which drive the simulation of ET, namely the rooting depth, the soil moisture at saturation, the soil moisture at field capacity and the soil moisture at wilting point. A sensitivity analysis is first conducted to quantify the relative contribution of each parameter on ET simulation over 12 years. The impact of the estimation method used to retrieve the soil parameters (pedotransfer function, laboratory and field methods) on ET is then analysed. The benefit of representing the variations in time of the rooting depth and wilting point is evaluated. Finally, the propagation of uncertainties in the soil parameters on ET simulations is quantified through a Monte Carlo analysis and compared with the uncertainties triggered by the mesophyll conductance which is a key above-ground driver of the stomatal conductance. This work shows that evapotranspiration mainly results from the soil evaporation when it is continuously simulated over a Mediterranean crop succession. This results in a high sensitivity of simulated evapotranspiration to uncertainties in the soil moisture at field capacity and the soil moisture at saturation, both of which drive the simulation of soil evaporation. Field capacity was proved to be the most influencing parameter on the simulation of evapotranspiration over the crop succession. The evapotranspiration simulated with the standard surface and soil parameters of the model is largely underestimated. The deficit in cumulative evapotranspiration amounts to 24 % over 12 years. The bias in daily daytime evapotranspiration is −0.24 mm day−1. The ISBA pedotransfer estimates of the soil moisture at saturation and at wilting point are overestimated, which explains most of the evapotranspiration underestimation. The use of field capacity values retrieved from laboratory methods leads to inaccurate simulation of ET due to the lack of representativeness of the soil structure variability at the field scale. The most accurate simulation is achieved with the average values of the soil properties derived from the analysis of field measurements of soil moisture vertical profiles over each crop cycle. The representation of the variations in time of the wilting point and the maximum rooting depth over the crop succession has little impact on the simulation performances. Finally, we show that the uncertainties in the soil parameters can generate substantial uncertainties in ET simulated over 12 years (the 95 % confidence interval represents 23 % of cumulative ET over 12 years). Uncertainties in the mesophyll conductance have lower impact on ET. Measurement random errors explain a large part of the scattering between simulations and measurements at half-hourly timescale. The deficits in simulated ET reported in this work are probably larger due to likely underestimation of ET by eddy-covariance measurements. Other possible model shortcomings include the lack of representation of soil vertical heterogeneity and root profile along with inaccurate energy balance partitioning between the soil and the vegetation at low leaf area index.

Daily underlying water use efficiency for AmeriFlux sites

Water use efficiency (WUE) is a crucial parameter to describe the interrelationship between gross primary production (GPP) and evapotranspiration (ET). Incorporating the nonlinear effect of vapor pressure deficit (VPD), underlying WUE (uWUE = GPP · VPD0.5/ET) is better than inherent WUE (IWUE = GPP · VPD/ET) at the half-hourly time scale. However, appropriateness of uWUE has not yet been evaluated at the daily time scale. To determine whether uWUE is better than IWUE, daily data for seven vegetation types from 34 AmeriFlux sites were used to validate uWUE at the daily time scale. First, daily mean VPD was shown to be a good substitute for the effective VPD that was required to preserve daily GPP totals. Second, an optimal exponent, k*, corresponding to the best linear relationship between GPP · VPDk* and ET, was about 0.55 both at half-hourly and daily time scales. Third, correlation coefficient between GPP · VPDk and ET showed that uWUE (k = 0.5 and r = 0.85) was a better approximation of the optimal WUE (k = k* and r = 0.86) than IWUE (k = 1 and r = 0.81) at the daily scale. Finally, when yearly uWUE was used to predict daily GPP from daily ET and mean VPD, uWUE worked considerably better than IWUE. Comparing observed and predicted daily GPP, the average correlation coefficient and Nash-Sutcliffe coefficient of efficiency were 0.81 and 0.59, respectively, using yearly uWUE, and only 0.59 and −0.83 using yearly IWUE. As a nearly optimal WUE, uWUE consistently outperformed IWUE and could be used to evaluate the effects of global warming and elevated atmosphere CO2 on carbon assimilation and evapotranspiration.

Optimisation of combined heat and power production for buildings using heat storage

Reducing carbon emissions from buildings is vital to achieve goals for avoiding dangerous climate change, and supplying them with low-carbon heat is essential. In the UK, the development of heat networks for supplying low-carbon heat is being encouraged for urban areas where there is high heat demand density. This paper investigates heat demand variability, the role of heat networks and combined heat and power (CHP) in satisfying this demand, and finally the advantages of using heat storage in the system.Building heat demands from 50 buildings were analysed at a half-hour resolution with modelling to determine CHP operation patterns with and without heat storage. Daily total heat demand was found to vary from 25% of the full-year average in summer months up to 235% of the average on the coldest days in winter. The heat demand was shown to correlate to outdoor temperatures measured with the degree-day parameter, except for approximately 100days during the warmest part of the year falling outside the heating season. Sharp spikes in heat demand were seen at the half-hourly time scale coinciding with the switching on of heating systems in some buildings with consequences for building energy supply options.It was shown that for an annual heat demand of 40,000MWh, the use of thermal storage can significantly increase the running time of a CHP energy centre with 4MW capacity designed to supply this demand. The cost savings resulting from increased on-site heat and electricity production resulted in a payback period for heat storage investment of under four years with further benefits if it can assist other heat sources on the heat network. Environmental advantages of using heat storage included further carbon dioxide emission reductions of 1000–1500tonnes per year depending upon the CHP configuration.

Reflections on the surface energy imbalance problem

The ‘energy imbalance problem’ in micrometeorology arises because at most flux measurement sites the sum of eddy fluxes of sensible and latent heat (H+λE) is less than the available energy (A). Either eddy fluxes are underestimated or A is overestimated. Reasons for the imbalance are: (1) a failure to satisfy the fundamental assumption of one-dimensional transport that is necessary for measurements on a single tower to represent spatially-averaged fluxes to/from the underlying surface, and (2) measurement errors in eddy fluxes, net radiation and changes in energy storage in soils, air and biomass below the measurement height. Radiometer errors are unlikely to overestimate A significantly, but phase lags caused by incorrect estimates of the energy storage terms can explain why H+λE systematically underestimates A at half-hourly time scales. Energy closure is observed at only 8% of flux sites in the La Thuile dataset (http://www.fluxdata.org/DataInfo/default.aspx) with half-hourly averages but this increases to 45% of sites using 24h averages because energy entering the soil, air and biomass in the morning is returned in the afternoon and evening. Unrealistically large and positive horizontal gradients in temperature and humidity are needed for advective flux divergences to explain the energy imbalance at half-hourly time scales. Imbalances between H+λE and A still occur in daily averages but the small residual energy imbalances are explicable by horizontal and vertical advective flux divergences. Systematic underestimates of the vertical heat flux also occur if horizontal u′T′ covariances contaminate the vertical w′T′ signal due to incorrect coordinate rotations. Closure of the energy balance is possible at half-hourly time scales by careful attention to all sources of measurement and data processing errors in the eddy covariance system and by accurate measurement of net radiation and every energy storage term needed to calculate available energy.