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

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.JRC.H.5-Land Resources Managemen