Soft Water Level Sensors for Characterizing the Hydrological Behaviour of Agricultural Catchments

Abstract

An innovative soft water level sensor is proposed to characterize the hydrological behaviour of agricultural catchments by measuring rainfall and stream flows. This sensor works as a capacitor coupled with a capacitance to frequency converter and measures water level at an adjustable time step acquisition. It was designed to be handy, minimally invasive and optimized in terms of energy consumption and low-cost fabrication so as to multiply its use on several catchments under natural conditions. It was used as a stage recorder to measure water level dynamics in a channel during a runoff event and as a rain gauge to measure rainfall amount and intensity. Based on the Manning equation, a method allowed estimation of water discharge with a given uncertainty and hence runoff volume at an event or annual scale. The sensor was tested under controlled conditions in the laboratory and under real conditions in the field. Comparisons of the sensor to reference devices (tipping bucket rain gauge, hydrostatic pressure transmitter limnimeter, Venturi channels…) showed accurate results: rainfall intensities and dynamic responses were accurately reproduced and discharges were estimated with an uncertainty usually acceptable in hydrology. Hence, it was used to monitor eleven small agricultural catchments located in the Mediterranean region. Both catchment reactivity and water budget have been calculated. Dynamic response of the catchments has been studied at the event scale through the rising time determination and at the annual scale by calculating the frequency of occurrence of runoff events. It provided significant insight into catchment hydrological behaviour which could be useful for agricultural management perspectives involving pollutant transport, flooding event and global water balance.

Automatic Estimation of Urban Waterlogging Depths from Video Images Based on Ubiquitous Reference Objects

“…The comparison with existing methods is discussed in the following. Water level sensors have high monitoring accuracy [5], but they are expensive. Usually only a small number of sensors are distributed in limited urban areas [3].…”

“…Currently, there are four main types of data sources for urban flood depth extraction, including water level sensors [5], remote sensing [6][7][8][9], social-media/crowdsourcing data [3,10], and video surveillance data [4]. Among these data sources, video surveillance data can record the entire process of urban flooding and has the advantages of continuity in time, high resolution, wide coverage in space, and low cost, which makes it the most attractive data source.…”

The Use of Photogrammetry to Construct Time Series of Vegetation Permeability to Water and Seed Transport in Agricultural Waterways

“…The water level inside the channel after rainfall events was monitored with a capacity sensor with centimetric precision [47]. Rainfall, temperature, wind velocity, and global radiation were measured by a meteorological station located 1.5 km from the study site.…”

Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning

“…Currently, there are three main types of methods for urban flood depth extraction, which can be classified according to the data sources used, e.g., water level sensors, remote sensing, and social media and crowdsourcing data. Traditional methods use water level sensors as the data sources [10]. However, because of the high costs of water level sensors, limited numbers of waterlogging points can be monitored.…”