Large Aperture Scintillometer Research Articles

Long-term measurement and characterization of boundary layer optical turbulence

The United States Naval Academy long-term scintillation measurement campaign was a multi-year effort to characterize optical turbulence in the near-maritime atmospheric boundary layer (ABL). At its core, the field experiment consists of in situ measurements of bulk atmospheric and oceanographic parameters, as well as path-averaged measurements of the refractive index structure parameter, C n 2, collected using a large-aperture scintillometer. The field experiment ran from January 1st, 2020, through September 26th, 2023, representing the most comprehensive collection of optical turbulence measurements in the near-maritime ABL to date. Long-term measurements enable researchers to evaluate existing theory and develop new models applicable to this environment. The present study characterizes some of the physical relationships that affect optical turbulence. This characterization focuses on the relationship between local optical turbulence and select atmospheric and oceanographic parameters. The impact of temperature gradients on the extent of optical turbulence was analyzed, along with its interactions with relative humidity and wind speed. The diurnal and seasonal variations in measured C n 2 were examined, with comparisons drawn against other environments. Further information and the full dataset are publicly available through the optical turbulence benchmark repository [Jellen et al., GitHub, 2023].

2013–2020年海河流域农田水热碳通量及气象要素观测数据集

The present study area was located in the cropland ecosystem near Guanting Reservoir in Haihe River Basin. We conducted long-term observations of the multi-scale fluxes and meteorological elements, using various observation systems, including lysimeter, eddy covariance, large aperture scintillometer and automatic weather station/gradient of meteorological elements. This dataset consists of multi-scale fluxes (meter, hundred-meter, and kilometer-meter) and meteorological elements data. Observation items include ecosystem net carbon exchange, latent heat flux/evapotranspiration, sensible heat flux, air temperature, relative humidity, wind speed, wind direction, downward/upward shortwave radiation, downward/upward longwave radiation, net radiation, atmospheric pressure, precipitation, infrared radiation temperature, photosynthetic effective radiation, soil temperature, soil moisture, soil heat flux, and average soil temperature, etc. This dataset is processed carefully and can be used to study the effects of reservoir expansion and shrinkage on the circulation of carbon and water or other substances in the surrounding ecosystem. Additionally, it can serve as a powerful data basis for the research on relevant remote sensing estimation and model simulation.

Surface energy fluxes and energy balance closure using large aperture scintillometer-based ET station on heterogeneous agricultural landscape in north India

This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as ‘Scintillometry’ which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape.

Variation and Transformation of Evapotranspiration at Different Scales in a Desert Steppe

Water resources are severely scarce in desert steppes, and precipitation rarely collects in rivers or is transformed into groundwater. Evapotranspiration (ET) is the primary “export” of precipitation conversion and is the main mechanism for water vapor exchange between the underlying surface and atmosphere. ET changes have certain scale effects. This study focused on the natural grasslands in the Xilamuren Desert Steppe and analyzed and estimated the ET patterns at different scales, including micro-, point-, and surface scales, using observational data from instruments such as a photosynthetic meter, Eddy-covariance system (EC), and large-aperture scintillometer (LAS) from the Ecological Hydrology National Field Science Observation Station in the northern foothills of the Yin Mountains, Inner Mongolia. The spatial scale was extended based on this analysis. The results showed that at the microscale, the diurnal variation in the photosynthetic and transpiration rates of Leymus chinensis followed a bimodal curve. In July and August (high-temperature months), photosynthetic and transpiration rates were almost synchronous. In May and October, when the temperature was moderate, the transpiration rate was delayed compared to the photosynthetic rate at the first peak, and the second peak was significantly smaller than the first peak. At the point-scale, the daily average ET during the growing season was 1.37 mm·d−1 and the total cumulative ET was 251 mm. Transpiration levels exhibited significant seasonal variation in the following order: July > August > June > September > May > October. At the surface-scale, the daily average ET during the growing season was 1.60 mm·d−1 and the total cumulative ET was 294 mm, which was 17% higher than that of the point-scale. The surface-scale ET was estimated using the observed values of the EC and the scale relationship formula and was optimized using different spatial scales of crop coefficients. This well reflected the ET patterns at the surface-scale. Therefore, this study proposes a spatial scale expansion method for a homogeneous underlying surface, verifies its value, and provides methodological support for estimating ET in cases of data scarcity.

Estimation of atmospheric refractive index structure constant using an InGaAs/InP single-photon detector.

Remote sensing of atmospheric refractive index structure constant ($\boldsymbol{C}_{\boldsymbol{n}}^2$) using lidar incorporating a single-photon detector (SPD) is proposed. The influence of turbulence on the fiber coupling efficiency with different fiber modes is analyzed. $\boldsymbol{C}_{\boldsymbol{n}}^2$ can be derived from the ratio of the backscattering signals counted on single-mode and multimode fiber-coupling channels of the SPD. In the experiment, by eliminating the shot noise effect on the fluctuation of the ratio, the lowest coupling ratio is used to retrieve $\boldsymbol{C}_{\boldsymbol{n}}^2$ and demonstrated by comparing to the results measured from a large aperture scintillometer (LAS). Good agreement between results from the LAS and the lidar is achieved. The correlation coefficients are 0.90, 0.89, and 0.89, under three different weather conditions.

Water vapor-heat-carbon fluxes and meteorological observation matrix dataset in 2012 over Zhangye oasis-desert area

The oasis-desert ecosystem is a unique landscape in arid/semi-arid regions, and the observation and research of its water vapor, heat, and carbon fluxes are of great significance to the stability and sustainable development of oases. This study focuses on the oasis-desert area of Zhangye City, Gansu Province, in the middle reaches of the Heihe River Basin. Based on the state-of-art flux observation matrix experiment conducted in the region in 2012, we processed the water vapor-heat-carbon fluxes and meteorological data from the experiment in two nested matrices (30 km × 30 km and 5.5 km × 5.5 km). There are a total of 22 eddy covariance systems and 21 automatic weather stations within the 21 observation sites as well as 4 group large aperture scintillometer and 3 group thermal dissipation probe observations in the two nested matrices. The observation parameters encompass a wide range of elements, including ecosystem net carbon exchange, latent heat flux, sensible heat flux, air temperature, relative humidity, wind speed, wind direction, downward/upward short wave radiation, downward/upward long wave radiation, net radiation, atmospheric pressure, precipitation, infrared radiation temperature, photosynthetic effective radiation, soil temperature, soil moisture, soil heat flux and average soil temperature, tree transpiration, etc. This meticulously processed dataset can be used to study the characteristics and impact mechanisms of water vapor, heat, and carbon changes in oasis-desert areas. Moreover, it can provide a strong data foundation for model simulation or remote sensing estimation results.

Changes in energy balance and total evaporation with age, and between two commercial forestry species in South Africa

Expansion of the area planted to eucalypts has been observed in the last two decades due to an improvement in markets for products from this tree species. This has raised concerns over the management of freshwater resources as other species are replaced by Eucalyptus, which has been shown to use more water than other commercial forestry species. The energy balance (EB) and total evaporation (ET) over Acacia mearnsii was previously monitored at the Two Streams research catchment, and the site harvested in 2018 with subsequent re-planting of E. dunnii. This presented an opportunity to measure the two-year-old E. dunnii (Edun2) EB and ET for comparison on the same site with the previously planted A. mearnsii with results from two-year-old A. mearnsii (Amear2) and six-year-old A. mearnsii (Amear6) crops. ET and EB measurements on Amear2 were obtained using a large aperture scintillometer, while eddy covariance was used for Amear6 and Edun2. Measurements were conducted in October 2007 to September 2008, October 2012 to September 2013 and October 2019 to September 2020 for Amear2, Amear6 and Edun2. The leaf area index (LAI) was measured using a LAI 2200 plant canopy analyser for all crops. The annual plantation water productivity (PWPWOOD) was calculated as a ratio of productive stand volume to ET for Amear2, Amear6 and Edun2. Results showed that latent energy fluxes dominated the EB in all crops for both summer and winter seasons, indicating a possibility that trees were not limited by plant available water in winter (dry season). The Edun2 and Amear2 annual ET was statistically (p > 0.05) similar, while ET of the younger crops (Amear2 and Edun2) was 12% greater than Amear6. High ET in Edun2 was caused by high LAI while Amear2 was caused by high transpiration per unit leaf area in young trees than in mature trees. Monthly crop factors were derived from FAO ETo and ET for all three crops, providing a convenient and transferable method of estimating ET from meteorological data over a large scale. The Edun2 PWPWOOD was greater than Amear2, while Amear6 was greater than both the young crops. This study provides insight into the total water-use by different species at different stages of growth at the same site. It is recommended that catchment water balance measurements be continued on the current E. dunnii crop for the full crop rotation to assess the long-term impact of E. dunnii on streamflow.

An evaluation of three evapotranspiration models to determine water fluxes over hillslopes encroached by invasive alien plants in Eastern Cape Province, South Africa

Ecosystem services in the South African grassland biome are being impacted by the presence of invasive alien plants (IAPs), particularly from the Australian genus Acacia. IAPs have elevated landscape water use and determining water fluxes is crucial to defining suitable interventions. This study evaluates three models of water flux over IAP-invaded grassland against evapotranspiration (ET) measured by a large-aperture scintillometer (LAS). ET was modelled using an energy balance model (MEDRUSH), a biophysical model (Penman-Monteith-Leuning (PML)) and a remotely sensed product (MOD16), and their results compared with ET measured by the LAS. ETLAS was measured during an abbreviated field campaign in November 2019 over a dense silver wattle (A. dealbata) stand associated with hillslope seeps. Of the three models tested against the LAS campaign, MEDRUSH performed well-enough to be used to model ET over the continuous scientific-grade micro-meteorological record (315 days). We used MEDRUSH to model ET over the invaded hillslope seeps and compared this with the expected ET that would occur over the adjacent, un-invaded grassland. Total ET over the IAP-invaded area was approximately 536 mm (60% of reference ET) as compared with the grassland (202 mm), showing that woody encroachment significantly increases landscape water use. To estimate the local impact of this, we used earth observation to determine the area of woody invasion in a 2 664 km2 regional window. Approximately 10% (274 km2) of this window was invaded by IAPs and, assuming the geographic window was representative of all the areas invaded by wattle, this represents ~ 89 000 ML of water used annually by the IAPs. Removal of wattle, followed by suitable restoration of landscape functionality, would greatly enhance rangeland productivity and water production from hillslope seeps.

Detailed urban roughness parametrization for anthropogenic heat flux estimation using earth observation data

Anthropogenic Heat (AH) emissions modify the energy balance in urban areas and is crucial for urban microclimate modelling and improved weather forecast modelling. Therefore, the present study conducted on Delhi and its surroundings firstly aims to estimate AH using Earth Observation (EO) data of Landsat 8 then, evaluate the impact of detailed urban roughness parameterization on the estimation of AH and further validate the obtained flux values with ground based observations of Large Aperture Scintillometer (LAS) setup. The study has been conducted over three time periods for October 2017, March 2018 and June 2018 by processing six Landsat tiles. Three methods have been employed on EO data for AH computation i.e. single urban roughness value for entire study area (Method 1), LULC based roughness values adopted from the literature (Method 2) and lastly, detailed pixel-by-pixel varying roughness values calculated from fine scale urban parameterization (Method 3) for each time period, The average AH values are higher for the month of June 2018: 359.91, 368.57 and 359.16 W/m2 as compared to month of March 2018 (322.44, 330.84 and 298.35 W/m2) and October 2017 (318.00, 331.04 and 306.71 W/m2) for method 1, method 2 and method 3 respectively. Net radiation and Sensible Heat Flux shows a good correspondence with in-situ measurements for most of the tiles and method 3 shows better spatial distribution of fluxes as compared to other two methods. However, due to difference in approach for estimation of fluxes (LAS setup computes latent heat flux as residual while EO based approach computes AH as residual), conclusive results could not be drawn with respect to aptness of a single method. Accurate estimation of AH in urban areas can assist further in formulating policies, regulations and action plans related to mitigation and control of heat stress, climate change and improved weather forecasting.

Improving regional climate simulations based on a hybrid data assimilation and machine learning method

Abstract. The energy and water vapor exchange between the land surface and atmospheric boundary layer plays a critical role in regional climate simulations. This paper implemented a hybrid data assimilation and machine learning framework (DA-ML method) into the Weather Research and Forecasting (WRF) model to optimize surface soil and vegetation conditions. The hybrid method can integrate remotely sensed leaf area index (LAI), multi-source soil moisture (SM) observations, and land surface models (LSMs) to accurately describe regional climate and land–atmosphere interactions. The performance of the hybrid method on the regional climate was evaluated in the Heihe River basin (HRB), the second-largest endorheic river basin in Northwest China. The results show that the estimated sensible (H) and latent heat (LE) fluxes from the WRF (DA-ML) model agree well with the large aperture scintillometer (LAS) observations. Compared to the WRF (open loop – OL), the WRF (DA-ML) model improved the estimation of evapotranspiration (ET) and generated a spatial distribution consistent with the ML-based watershed ET (ETMap). The proposed WRF (DA-ML) method effectively reduces air warming and drying biases in simulations, particularly in the oasis region. The estimated air temperature and specific humidity from WRF (DA-ML) agree well with the observations. In addition, this method can simulate more realistic oasis–desert boundaries, including wetting and cooling effects and wind shield effects within the oasis. The oasis–desert interactions can transfer water vapor to the surrounding desert in the lower atmosphere. In contrast, the dry and hot air over the desert is transferred to the oasis from the upper atmosphere. The results show that the integration of LAI and SM will induce water vapor intensification and promote precipitation in the upstream of the HRB, particularly on windward slopes. In general, the proposed WRF (DA-ML) model can improve climate modeling by implementing detailed land characterization information in basins with complex underlying surfaces.

Comparison of the Sensible Heat Flux Determined by Large-Aperture Scintillometer and Eddy Covariance Measurements with Respect to the Energy Balance Problem in the Taklimakan Desert

Comparison of the Sensible Heat Flux Determined by Large-Aperture Scintillometer and Eddy Covariance Measurements with Respect to the Energy Balance Problem in the Taklimakan Desert

Improving predictions of evapotranspiration by integrating multi-source observations and land surface model

Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, and carbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture (SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter (EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. A machine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remote sensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from the hybrid model are 29.06%, 42.76%, and 50.00% lower than Noah-MP at the Arou, Daman, and Sidaoqiao sites, respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.

Turbulence Detection in the Atmospheric Boundary Layer Using Coherent Doppler Wind Lidar and Microwave Radiometer

The refractive index structure constant (Cn2) is a key parameter used in describing the influence of turbulence on laser transmissions in the atmosphere. Three different methods for estimating Cn2 were analyzed in detail. A new method that uses a combination of these methods for continuous Cn2 profiling with both high temporal and spatial resolution is proposed and demonstrated. Under the assumption of the Kolmogorov “2/3 law”, the Cn2 profile can be calculated by using the wind field and turbulent kinetic energy dissipation rate (TKEDR) measured by coherent Doppler wind lidar (CDWL) and other meteorological parameters derived from a microwave radiometer (MWR). In a horizontal experiment, a comparison between the results from our new method and measurements made by a large aperture scintillometer (LAS) is conducted. The correlation coefficient, mean error, and standard deviation between them in a six-day observation are 0.8073, 8.18 × 10−16 m−2/3 and 1.27 × 10−15 m−2/3, respectively. In the vertical direction, the continuous profiling results of Cn2 and other turbulence parameters with high resolution in the atmospheric boundary layer (ABL) are retrieved. In addition, the limitation and uncertainty of this method under different circumstances were analyzed, which shows that the relative error of Cn2 estimation normally does not exceed 30% under the convective boundary layer (CBL).

Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models

Quantification of actual crop evapotranspiration (ETa) over large areas is a critical issue to manage water resources, particularly in semi-arid regions. In this study, four models driven by high resolution remote sensing data were intercompared and evaluated over an heterogeneous and complex traditional irrigated area located in the piedmont of the High Atlas mountain, Morocco, during the 2017 and 2018 seasons: (1) SAtellite Monitoring of IRrigation (SAMIR) which is a software-based on the FAO-56 dual crop coefficient water balance model fed with Sentinel-2 high-resolution Normalized Difference Vegetation Index (NDVI) to derive the basal crop coefficient (Kcb); (2) Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) which is a surface energy balance model fed with land surface temperature (LST) derived from thermal data provided from Landsat 7 and 8; (3) a modified version of the Shuttleworth–Wallace (SW) model which uses the LST to compute surface resistances and (4) METRIC-GEE which is a version of METRIC model (“Mapping Evapotranspiration at high Resolution with Internalized Calibration”) that operates on the Google Earth Engine platform, also driven by LST. Actual evapotranspiration (ETa) measurements from two Eddy-Covariance (EC) systems and a Large Aperture Scintillometer (LAS) were used to evaluate the four models. One EC was used to calibrate SAMIR and SPARSE (EC1) which were validated using the second one (EC2), providing a Root Mean Square Error (RMSE) and a determination coefficient (R) of 0.53 mm/day (R=0.82) and 0.66 mm/day (R=0.74), respectively. SW and METRIC-GEE simulations were obtained respectively from a previous study and Google Earth Engine (GEE), therefore no calibration was performed in this study. The four models predict well the seasonal course of ETa during two successive growing seasons (2017 and 2018). However, their performances were contrasted and varied depending on the seasons, the water stress conditions and the vegetation development. By comparing the statistical results between the simulation and the measurements of ETa it has been shown that SAMIR and METRIC-GEE are the less scattered and the better in agreement with the LAS measurements (RMSE equal to 0.73 and 0.68 mm/day and R equal to 0.74 and 0.82, respectively). On the other hand, SPARSE is less scattered (RMSE = 0.90 mm/day, R = 0.54) than SW which is slightly better correlated (RMSE = 0.98 mm/day, R = 0.60) with the observations. This study contributes to explore the complementarities between these approaches in order to improve the evapotranspiration mapping monitored with high-resolution remote sensing data

Forecasting surface-layer optical turbulence above the Tibetan Plateau using the WRF model

This study investigates the performance of forecasting the surface-layer optical turbulence (Cn2) using the Weather Research and Forecasting (WRF) model. The forecasts are compared with the measurements of two instruments, a large aperture scintillometer and a micro-thermometer. The short- and medium-range WRF-predicted Cn2 vary little under strong turbulence conditions (generally during daytime when strong wind speed and unstable atmosphere occur inside the surface layer), but are relatively divergent for weak turbulence. The predicted Cn2 agree well with the trends of measurements among different forecast times (in most cases the correlation coefficients exceed 0.7), but the short-range forecasting shows slightly better performance than the medium-range forecasting.

The Namib Turbulence Experiment: Investigating Surface–Atmosphere Heat Transfer in Three Dimensions

Abstract The Namib Turbulence Experiment (NamTEX) was a multinational micrometeorological campaign conducted in the central Namib Desert to investigate three-dimensional surface layer turbulence and the spatiotemporal patterns of heat transfer between the subsurface, surface, and atmosphere. The Namib provides an ideal location for fundamental research that revisits some key assumptions in micrometeorology that are implicitly included in the parameterizations describing energy exchange in weather forecasting and climate models: homogenous flat surfaces, no vegetation, little moisture, and cloud-free skies create a strong and consistent diurnal forcing, resulting in a wide range of atmospheric stabilities. A novel combination of instruments was used to simultaneously measure variables and processes relevant to heat transfer: a 3-km fiber-optic distributed temperature sensor (DTS) was suspended in a pseudo-three-dimensional array within a 300 m × 300 m domain to provide vertical cross sections of air temperature fluctuations. Aerial and ground-based thermal imagers recorded high-resolution surface temperature fluctuations within the domain and revealed the spatial thermal imprint of atmospheric structures responsible for heat exchange. High-resolution soil temperature and moisture profiles together with heat flux plates provided information on near-surface soil dynamics. Turbulent heat exchange was measured with a vertical array of five eddy-covariance point measurements on a 21-m mast, as well as by collocated small- and large-aperture scintillometers. This contribution first details the scientific goals and experimental setup of the NamTEX campaign. Then, using a typical day, we demonstrate (i) the coupling of surface layer, surface, and soil temperatures using high-frequency temperature measurements, (ii) differences in spatial and temporal standard deviations of the horizontal temperature field using spatially distributed measurements, and (iii) horizontal anisotropy of the turbulent temperature field.

Using a large aperture scintillometer for determining evapotranspiration of mountain foothills agriculture in the Rheraya catchment, Morocco

ISHS IX International Symposium on Irrigation of Horticultural Crops Using a large aperture scintillometer for determining evapotranspiration of mountain foothills agriculture in the Rheraya catchment, Morocco

An optimized surface aerodynamic temperature approach to estimate maize sensible heat flux and evapotranspiration

Typical errors for single-source sensible heat flux estimation models range from 15-40% for vegetated surfaces, with 10 to 20% errors introduced by atmosphere stability functions and remaining errors due to the quality of the input data and assumptions regarding the surface aerodynamic temperature (To). This paper describes the development and evaluation of an optimized To model, which serves as key input to estimate and improve sensible heat fluxes for maize fields. The best resulting optimized linear model included four variables: air temperature, surface temperature, fractional vegetation cover, and a proposed new variable that accounts for wind speed and the relative angle of attack between wind direction and crop row orientation. During the 2017 to 2019 maize growing seasons, on two different fields located at a Research Farm near Greeley, Colorado, USA, field data were collected. Micrometeorological data were measured in situ at the height of 3.30 m above the ground surface. Nadir-looking stationary infrared thermometers measured surface temperature. Multispectral surface reflectance data were collected on-site weekly using a handheld multispectral radiometer. Net radiation and soil heat fluxes were acquired at approximately ¼ and ½ of the field's length. Sensible heat fluxes were measured with two sets of Large Aperture Scintillometers to calibrate the To model. A one-way Analysis of Variance was performed to model To for different ranges of maize leaf area index values. A local sensitivity analysis was performed to identify the most influential explanatory variables in the To optimization model. The sensitivity analysis indicated that uncertainty on air temperature and surface temperature measurements were responsible for the most variability in sensible heat flux estimates when using the developed optimized To model. Overall, results indicated that the optimized To model improved the estimation of maize sensible heat fluxes by 31%; which resulted in an improvement on latent heat flux (or evapotranspiration) by 9% compared to a non-optimized linear To model. The interactions between the crop row layout and wind direction promote more turbulent mixing for heat transfer mechanisms.

Reconstruction of remotely sensed daily evapotranspiration data in cloudy-sky conditions

The unavailability of thermal infrared satellite observations in cloudy conditions has limited the spatial distribution and temporal coverage of remotely sensed evapotranspiration (ET) data. As a result, a number of approaches have been developed to reconstruct remotely sensed daily ET data in cloudy-sky conditions. Despite the wide application of these approaches, no work has been conducted to compare their performance over a wide variety of climatic and vegetative conditions. In this study, three commonly used ET reconstruction approaches namely, reference ET fraction (EToF), land surface temperature reconstruction (LSTR), and variational data assimilation (VDA) are used to obtain daily ET data under cloudy conditions. The abovementioned three approaches are applied to the Heihe River Basin (HRB) in the northwest of China during the growing season in 2015. The HRB covers an area of approximately 1,432,000 km2 and contains various land covers. The large aperture scintillometer (LAS) and eddy covariance (EC) measurements are used to evaluate performance of the abovementioned three methods over the grassland, cropland, and riparian forest sites in the HRB. The results show that the EToF approach underestimates ET at the grassland and riparian sites in cloudy days because of the negatively biased ET/ETo (where ETo is the reference evapotranspiration) in clear-sky days. The VDA approach overestimates ET in cloudy-sky conditions because of the overpredicted evaporative fraction values. The LSTR approach overestimates ET due to the under-reconstructed LST in cloudy day. The mean absolute percentage difference (MAPD) and root mean square error (RMSE) statistical metrics are used to compare performance of the three approaches The LSTR (MAPD = 43.1% and RMSE = 1.3 mm/day) and VDA (MAPD = 46.7% and RMSE = 1.4 mm/day) approaches slightly outperform EToF (MAPD = 46.6% and RMSE = 1.5 mm/day). The RMSEs of reconstructed ET values by EToF, VDA, and LSTR increase respectively from 1.6, 1.2, and 1.5 (mm/day) to 2.2, 1.9, and 2.0 (mm/day) as the number of consecutive cloudy days increases from 1 to 3. These outcomes suggest that synergistic use of space-borne microwave and thermal infrared LST observations into remote sensing-based ET methods can improve the reconstruction of ET data under cloudy conditions.

Hydrologic modeling of reach scale fluxes from flood irrigated fields

Agricultural water is of considerable interest to water managers and policymakers as irrigation—particularly flood irrigation—accounts for the largest portion of freshwater use. However, characterization of how and when flood applied water contributes to storage and adjacent surface water bodies via return flow remains limited, particularly relative to the volume of studies addressing surface processes. This study focuses on improving our understanding of return flow paths and explicitly quantifying subsurface return flow contributions using hydro-geophysical observations coupled with a vadose zone transport flow model. Our approach, which simulates return flow in agricultural fields in a small, heavily instrumented field site in northern Wyoming, leverages daily measured values of evapotranspiration obtained from a large aperture scintillometer and atmospheric conditions, and uses seismic data to characterize heterogeneous structure of the soil profiles at field scale. We use these high-resolution, site specific datasets to parameterize a process-based vadose zone flow model that quantifies hydrologic response to flood irrigation along the stream reach, allowing us to identify return flow fluxes out of the fields. Using our approach, we found that the majority of applied irrigation water (60%) left the fields through subsurface flow paths, while only 13% was consumed by vegetation. Subsurface runoff over less-permeable soil layers, governed by the subsurface structural characteristics, was identified as the dominant subsurface flow process contributing to fluxes out of the fields. Importantly, our findings about the importance of subsurface return flow paths nuance previous research which has emphasized surface flow paths. By improving our understanding of return flow processes through a high- resolution vadose zone model that leverages climatological, geophysical, and hydrological observations, this study advances our understanding of return flow processes. Understanding how subsurface characteristics influence return flow generation in semi-arid agriculturally productive regions will help managers and policy makers balance desires for improved irrigation efficiency against concerns about irrigation-dependent ecosystem services.