A Field Experiment on the Small-Scale Variability of Rainfall Based on a Network of Micro Rain Radars and Rain Gauges

Small-scale summer rainfall variability in a semiarid zone was studied by deploying five vertically pointing Micro Rain Radars (MRRs) along a nearly straight line and by using 12 rain gauges in the study area of the Xilin River catchment in China. The spatial scales of 4 and 9 km correspond to the resolution of precipitation radar and rainfall products from satellites. The dataset of the MRRs and rain gauges covers two months in the summer of 2009. Three parameters, that is, spatial correlation, intermittency, and the coefficient of variation (CV), were used to describe the rainfall variability as based on the data from the MRRs and rain gauges. The probability of partial beamfilling in a 4-km (9 km) pixel over a 30-min temporal scale was 17%–20% (28%–37%). More accurate equipment can measure lower rainfall intermittency. For scales of 4 and 9 km, the median CV of the accumulation times that were longer than 3 h with rainfall > 1 mm was 0.17–0.42. The accuracy of areal rainfall measured by different quantities of equipment was also evaluated. One MRR was sufficient for measuring the daily areal rainfall at a 4-km scale, with a fraction of prediction within a factor of 2 of observations of 1.0 and a correlation coefficient of ≥0.58 when daily mean rainfall was >1 mm.

Spatially explicit regionalization of airborne flux measurements using environmental response functions

The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River catchment (&amp;approx;3670 km2) are accurate to ≤18% (1 σ). The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River catchment. Agreement of the land cover specific Bowen ratios to within 12 &amp;pm; 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.

Spatial resolution and regionalization of airborne flux measurements using environmental response functions

The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River Catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical- (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River Catchment (&amp;approx; 3670 km2) are accurate to ≤ 18%. The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤ 5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River Catchment. Agreement of the land cover specific Bowen ratios to within 12 ± 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.