No abstract

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.

No abstract

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

This study reports on the development of remote sensing methods for estimation of biophysical parameters in olive orchards. Field and airborne campaigns were conducted in 2003 and 2004 in two orchards located in southern Spain. Ground measurements of crown transmittance and leaf area index (LAI) of individual olive trees (Olea europaea L.) were done using the LAI‐2000 Plant Canopy Analyzer. Hyperspectral images were acquired with a compact airborne spectrographic imager (CASI) at 1‐m spatial and with QuickBird satellite sensor at 2.5‐m spatial resolution. The panchromatic 0.6‐m spatial resolution image was also acquired with QuickBird. These images enabled the application of automatic algorithms for olive crown identification and delineation, to determine tree crown size and LAI. These methods proved successful for determining projected olive crown area, obtaining determination coefficients (r 2) in the range of 0.82 to 0.65, and root mean square errors (RMSE) of 4.8 and 6.2 m2 for CASI hyperspectral and QuickBird panchromatic images, respectively. The olive crown volume was estimated using image‐estimated projected crown area values, yielding r 2 ranging between 0.87 and 0.70, with RMSE of 8.4 and 11.3 m3 for CASI hyperspectral and QuickBird panchromatic images, respectively. Olive crown transmittance and LAI of individual olive trees were evaluated using spectral vegetation indices (normalized difference vegetation index [NDVI], renormalized difference vegetation index [RDVI], simple ratio index [SR], modified simple ratio [MSR]) yielding better correlations with CASI images, r 2 in the range 0.71 to 0.75 (P < 0.0001) and 0.57 to 0.62 (P < 0.0001) for crown transmittance and LAI, respectively. These methods enable obtaining maps of biophysical parameters in olive trees at farm scale in an operational way demonstrating the validity of the methodology used.