Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications

Franz, T., Wahbi, A., Zhang, J., Vreugdenhil, M., Heng, L., Dercon, G., Strauss, P., Brocca, L., Wagner, W., 2020, Practical Data Products From Cosmic-Ray Neutron Sensing for Hydrological Applications, Frontiers (Los Angel. Calif.) (2020): 2–9. doi_10.3389/frwa.2020.00009,
URL: http://www.cnr.it/prodotto/i/426427

The Cosmic-Ray Neutron Sensor (CRNS) technique for estimating landscape average soil water content (SWC) is now a decade old and includes many practical methods for implementing measurements, such as identification of detection area and depth and determining crop biomass water equivalent. However, in order to maximize the societal relevance of CRNS SWC data, practical value-added products need to be developed that can estimate both water flux (i.e., rainfall, deep percolation, evapotranspiration) and root zone SWC changes. In particular, simple methods that can be used to estimate daily values at landscape average scales are needed by decision makers and stakeholders interested in utilizing this technique. Moreover, landscape average values are necessary for better comparisons with remote sensing products. In this work we utilize three well-established algorithms to enhance the usability of the CRNS data. The algorithms aim to_ (1) temporally smooth the neutron intensity and SWC time series, (2) estimate a daily rainfall product using the Soil Moisture 2 Rain (SM2RAIN) algorithm, and (3) estimate daily root zone SWC using an exponential filter algorithm. The algorithms are tested on the CRNS site at the Hydrological Open Air Laboratory experiment in Petzenkirchen, Austria over a 3 years period. Independent observations of rainfall and point SWC data are used to calibrate the algorithms. With respect to the neutron filter, we found the Savitzky-Golay (SG) had the best results in preserving the amplitude and timing of the SWC response to rainfall as compared to the Moving Average (MA), which shifted the SWC peak by 2-4 h. With respect to daily rainfall using the SM2RAIN algorithm, we found the MA and SG filters had similar results for a range of temporal windows (3-13 h) with cumulative errors of <9% against the observations. With respect to daily root zone SWC, we found all filters behaved well (Kling-Gupta-Efficiency criteria > 0.9). A methodological framework is presented that summarizes the different processes, required data, algorithms, and products.

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