Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

A Possible Explanation for the Z-R Parameter Inconsistency when Comparing Stratiform and Convective Rainfall

The well-known Z-R power law Z A R(sup b) uses two parameters, A and b, in order to relate rainfall rate R to measured weather radar reflectivity Z. A common method used by researchers is to compute Z and R from disdrometer data and then extract the A-b parameter pair from a log-linear line fit to a scatter plot of Z-R pairs. Even though it may seem far more truthful to extract the parameter pair from a fit of radar Z(sub R) versus gauge rainfall rate R(sub G), the extreme difference in spatial and temporal sampling volumes between radar and rain gauge creates a slew of problems that can generally only be solved by using rain gauge arrays and long sampling averages. Disdrometer derived A-b parameters are easily obtained and can provide information for the study of stratiform versus convective rainfall. However, an inconsistency appears when comparing averaged A-b pairs from various researchers. Values of b range from 1.26 to 1.51 for both stratiform and convective events. Paradoxically the values of A fall into three groups: 150 to 200 for convective; 200 to 400 for stratiform; and 400 to 500 again for convective. This apparent inconsistency can be explained by computing the A-b pair using the gamma DSD coupled with a modified drop terminal velocity model, v(D) alpha D(sup beta) - w, where w is a somewhat artificial constant vertical velocity of the air above the disdrometer. This model predicts three regions of A, corresponding to w less than 0, w = 0, and w greater than 0, which approximately matches observed data

A phenomenological relationship between vertical air motion and disdrometer derived A-b coefficients

Using the well-known Z-R power law, Z = A R^b, A-b parameters derived from a single disdrometer are readily found and can provide useful information to study rainfall drop size distributions (DSDs). However, large variations in values are often seen when comparing A-b sets from various researchers. Values of b typically range from 1.25 to 1.55 for both stratiform and convective events. The values of A approximately fall into three groups: 150 to 200 for convective, 200 to 400 for stratiform, and 400 to 500 for convective. Computing the A-b parameters using the gamma DSD, coupled with a modified drop terminal velocity model, vD(D) = vT(D) - w, where D is drop diameter, vT(D) is still air drop terminal velocity, and w is an estimate of vertical velocity of the air well above the disdrometer, shows an interesting result. This model predicts three regions of A, corresponding to w 0. Additional models that incorporate a constant vertical air velocity are also investigated. A-b sets derived from a Joss Waldvogel (JW) disdrometer and DSD data acquired near Athalassa, Cyprus, using selected 24 hour data sets from 2011 to 2014, are compared to the above models. The data is separated into two main groups: stratiform events defined by rainfall rates that did not exceed 10 mm/h at any time during the 24-hour period, and convective events defined by rainfall rates not flagged as stratiform. The convective rainfall is further separated into two groups: A-b pairs that fall to the left of the stratiform pairs and pairs that fall to the right. This procedure is repeated with data from other researchers that corresponds to seasonal averages. In all cases, the three vertical groupings of the A-b parameter plot seem to correlate to DSD simulations where various values of positive and negative vertical velocities are used

Detecting Underground Military Structures Using Field Spectroscopy

Satellite remote sensing is considered as an increasingly important technology for detecting underground structures. It can be applied to a wide range of applications, as shown by various researchers. However, there is a great need to integrate information from a variety of sources, sent at different times and of different qualities using remote sensing tools. A SVC-HR1024 field spectroradiometer could be used, and in-band reflectance’s are determined for medium- and high-resolution satellite sensors, including Landsat. Areas covered by natural soil where underground structures are present or absent can easily be detected, as a result of the change in the spectral signature of the vegetation throughout the phenological stages; in this respect, vegetation indices (VIs) such as the normalized difference vegetation index (NDVI), simple ratio (SR), and enhanced vegetation index (EVI) may be used for this purpose. Notably, the SR vegetation index is useful for determining areas where military underground structures are present

D7.1 Report on the ECoE research clusters and research groups: management, function and technical capacity

This deliverable focuses on the formation of the Eratosthenes Centre of Excellence thematic research clusters of Environment & Climate, the Resilient Society and Big Earth Data Analytics in terms of the operations, research collaborations, tools to facilitate research, agreeing internal structures and allocating staff responsibilities. This deliverable will focus on the integration of recruited research personnel, research equipment and the Strategic Partners’ expertise to meet the needs of the research groups

Smart Water Management for Irrigation Purposes: The SWSOIP Pproject

It seems that the future scenarios for water resources management are characterized by increasing demand and by the short-term unsustainability of many reservoirs in the Mediterranean basin. To address these scenarios, improved management of water resources was needed for water economy, and water recycling policies. Furthermore, agriculture characterized as the largest water user worldwide and the monitoring of the agriculture via remote sensing techniques is an enormous subject where it used for special scientific applications such as irrigation, precision farming, yield prediction, estimation of evapotranspiration etc. The main objective of this paper is to present the current situation of water resources in the Mediterranean region and present the methodology and main objectives of the SWSOIP project which aims to develop a smart watering system for the irrigation process based on the estimation of evapotranspiration using both in-situ data (spectroradiometric, LAI, CH and meteorological) and Sentinel satellite data

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year