Water Level Measurements Research Articles

Demonstrating the Potential of Low-Cost GNSS Receiver for tidal monitoring, storms, and flood detecting: example of 2022 Noru Storm in Thua Thien Hue province, Vietnam

Extreme hydrological events such as tsunamis, high tides, or storm surges seriously threaten coastal communities. These events result in flooding, property damage, loss of life, and long-term economic and social impacts. Therefore, monitoring and detecting extreme hydrological events significantly affect coastal areas in disaster response efforts. However, the cost of installing and maintaining these stations can be a significant challenge for developing countries. The objective of this study is to use a low-cost GNSS receiver to monitor tides and detect extreme coastal hydrological phenomena by analyzing changes in water level, using analysis of the signal-to-noise ratio (SNR) data. Data used in this study were collected from a GNSS station located in the Tam Giang Lagoon area, Thua Thien Hue, Vietnam, from September to October 2022. The water level based on GNSS-R is compared with the sensor's measured water level, with the Pearson correlation coefficient reaching 0.96 and RMSE of 0.08m. Continuous Wavelet Transform analysis demonstrated the relationship between water levels and extreme hydrological events. The results show that distinct signatures in the data correspond to the Noru typhoon from September 27-29, 2022, and the inundation from October 14-19, 2022, in Thua Thien Hue. This information is the basis for forecasting and early warning of extreme events and informing disaster response and management efforts.

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

Study of the interdigital electrode sensor at resonance frequency during water transition

This paper uses the co-planar interdigital electrode (IDE) sensor to measure water level. The researchers generally characterize the interdigital electrode sensor as a fringe field capacitor sensor developed on the printed circuit board and utilize the capacitor sensor's properties for liquid-level measurement. The interdigital electrode sensors illustrate more than one resonance at the higher frequencies, and in this study, the first resonance frequency fr -has been utilized for the water level measurement. Three water types are examined here: distilled, tap, and river. The study assesses that with the transition of water, the permittivity between the electrodes is changed and, it leads to a change in capacitance hence, the change in resonance frequency was observed. The proposed sensor can be represented by the lumped element equivalent series RLC circuit. The developed IDE sensor has good repeatability, small variability, and small hysteresis error. The maximum standard error for distilled, tap, and river water are 0.02833, 0.02503, and 0.02618, respectively, and the hysteresis error is less than 1.903% of full-scale output variation. The maximum error for the fr estimation is about ±2 Hz.

Diverse response of shallow lake water levels to decadal weather patterns in a heterogeneous glacial Boreal Plains landscape

AbstractTo examine the relative controls of landscape and climate on spatial variability, we measured water level dynamics of shallow lakes over two decades that represent both the heterogeneity of surficial geology classifications, and thus the potential range in surface and groundwater connectivity, and the long‐term weather patterns of the Boreal Plain hydrogeoclimatic setting. Large ranges in shallow lakes water levels (between 0.25 and 2 m) were observed corresponding to extremes in precipitation relative to the long‐term mean precipitation over the study period. We found low concurrence in water level dynamics among four detailed study lakes that received the same meteorological weather signal, but were located in different surficial geology texture classifications that incorporated important landscape parameters associated with lake water balance and storage. Surficial geology classification alone did not, however, distinguish between different ranges in lake water level measured in a broader synoptic survey of 26 lakes across the region. Thus, simple surficial geology classifications cannot alone be applied to classify Boreal Plain lake water level dynamics and other controls, notably landscape position, must also be considered. We further show that inter‐annual variability in lake water levels was significantly greater than seasonal variability in this hydrogeoclimatic setting. This emphasizes the need for studies of sufficient length to capture weather extremes that include periods of wetting and drying, and demonstrates how observed magnitudes of water level variability, and lake function, can be an artefact of study length and initiation date. These findings provide a foundation to test and calibrate conceptual understanding of the wider controls of lake water levels to form holistic frameworks to mitigate ecological and societal impacts due to hydrological changes under climate and anthropogenic disturbance within and between hydrogeoclimatic settings.

Integrated monitoring method of flood free surface and surface velocity in a laboratory compound channel

River flooding can pose a significant threat to people's lives and properties on the floodplain. Due to the difficulty and potential danger of monitoring field river floods, laboratory experiments have become the main and key method for exploring flood propagation and evaluating potential flood risks. However, the complex three-dimensional flow state of floods in the compound channel makes current measurement techniques inadequate for meeting monitoring requirements. To this end, a new technique is developed in this study that can simultaneously monitor the flood free surface and the surface velocity in the laboratory compound channel. The developed measurement technique combines stereoscopy and the particle tracking velocimetry (PTV) method through the pixel coordinate fitting method, so that the coordinates of the tracer particles can be reconstructed in the three-dimensional state. This method can not only increase the basic data in the free surface construction but can also capture the complex three-dimensional flood flow behavior. The proposed technology was applied to measure the free surface of flood propagation in a laboratory compound channel. Water level, flow velocity, wave velocity, and wave height were simultaneously measured. The measurement accuracy was verified using wave probes, spatial planes, and fringe projection techniques. The results indicate that the water level measurement error is approximately 1.6 mm, and the flow velocity measurement error is around 3 %. The developed measurement system is simpler than previous methods based on light reflection or absorption. Most importantly, it can simultaneously measure both the flood free surface and surface velocity by only sowing tracer particles on the water surface. The current measurement technology offers a new tool for studying flood propagation in a laboratory compound channel.

Assessing the impacts of ice penetration on monitoring water levels of high-latitude and -altitude lakes from CryoSat-2 altimetry

The CryoSat-2 satellite provides high-precision and long-term monitoring of global lake levels, offering advantages for water resource management, ecological protection, and disaster warning. However, the lake ice penetration by radar electromagnetic waves of CryoSat-2 altimetry tends to result in underestimation of water levels for lakes in high latitudes and altitudes. By contrast, the laser altimetry of ICESat-2 enables us to obtain the height measurements from lake ice surface and the ice penetration impacts on lake levels can be nearly ignored in the ICESat-2 elevation product. After converting the CryoSat-2 and ICESat-2 altimetry data to the same datum and height reference system, the water level difference of (near-) synchronous observations between the two satellites could be mainly attributed to the influences by the lake ice penetration from CryoSat-2 data. In this study, totally 257 lakes in high-latitude (north of 50°N) and high-altitude (the Tibetan Plateau, TP) regions that are covered by both CryoSat-2 and ICESat-2 satellite tracks are examined to explore the characteristics of ice penetration impacts on water level measurements. The altimetry data during unfreezing seasons (June to October) are used to correct the systematic height biases of these two satellite measurements, thus their water level differences during freezing seasons (November to May of the following year) are deemed as the influences of ice penetration on lake level measurements by CryoSat-2 radar altimetry. The results show that the maximum underestimation depth of water level for each lake varies within the range of 0.01–3.47 m, with an average of 0.90 m. Particularly in North America with a dense distribution of lakes, the underestimation of lake levels by CryoSat-2 gradually ascends with increasing latitude. CryoSat-2 underestimates lake levels in Europe and the TP less than in North America. Among different months, the lake ice leads to the maximum underestimation on the water level by CryoSat-2 altimetry in March. The spatial and temporal characteristics of lake ice impacts on the water level measurements by CryoSat-2 altimetry would be an essential reference for quantifying and reducing the uncertainties of satellite radar altimetry for monitoring inland water dynamics.

The calibration of sharp-crested weirs with a horizontal edge used for measuring flows in partially full pipes

This paper presents the results of a laboratory study on the discharge capacity of sharp-crested weirs fitted with a horizontal edge in pipes during open-channel flow conditions and clean water used to measure the outflow. Such sharp-crested weirs are mounted in pipes and are used to control the inflow to separators. The stream profile does not correspond to the profile given by Bazin for sharp crested weirs in channels. A desired location of the water level measurement point for flow rate calculations was provided. Discharge curves were identified for three sharp-crested weirs of 0.0465, 0.0634 and 0.0771 m in height, installed in the pipe of 0.1534 m in diameter and inclinations of 0.5 and 1.0%. The discharge curves for weir flow with free nappe does not show a significant effect of the pipe slope on the weir discharge capacity. The non-dimensional formulas for the discharge capacity of the sharp-crested weir were found as general polynomial regressions. The results indicate that the calibrated sharp-crested weir with a horizontal edge placed in a pipe can be used to control the flow. Due to the scale effect, relationships obtained from the calibration cannot be generalised to other pipe diameters and weirs heights than those analysed.

Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning

Traditional methods for water-level measurement usually employ permanent structures, such as a scale built into the water system, which is costly and laborious and can wash away with water. This research proposes a low-cost, automatic water-level estimator that can appraise the level without disturbing water flow or affecting the environment. The estimator was developed for urban areas of a volcanic island water channel, using machine learning to evaluate images captured by a low-cost remote monitoring system. For this purpose, images from over one year were collected. For better performance, captured images were processed by converting them to a proposed color space, named HLE, composed of hue, lightness, and edge. Multiple residual neural network architectures were examined. The best-performing model was ResNeXt, which achieved a mean absolute error of 1.14 cm using squeeze and excitation and data augmentation. An explainability analysis was carried out for transparency and a visual explanation. In addition, models were developed to predict water levels. Three models successfully forecasted the subsequent water levels for 10, 60, and 120 min, with mean absolute errors of 1.76 cm, 2.09 cm, and 2.34 cm, respectively. The models could follow slow and fast transitions, leading to a potential flooding risk-assessment mechanism.

Satellite-Derived Indicators of Drought Severity and Water Storage in Estuarine Reservoirs: A Case Study of Qingcaosha Reservoir, China

Estuarine reservoirs are critical for freshwater supply and security, especially for regions facing water scarcity challenges due to climate change and population growth. Conventional methods for assessing drought severity or monitoring reservoir water level and storage are often limited by data availability, accessibility and quality. We present an approach for monitoring estuarine reservoir water levels, storage and extreme drought via satellite remote sensing and waterline detection. Based on the CoastSat algorithm, Landsat-8 and Sentinel-2 images from 2013 to 2022 were adopted to extract the waterline of Qingcaosha Reservoir, the largest estuarine reservoir in the world and a key source of freshwater for Shanghai, China. This study confirmed the accuracy of the satellite-extracted results through two main methods: (1) calculating the angle of the central shoal slope in the reservoir using the extracted waterline data and measured water levels and (2) inverting the time series of water levels for comparison with measured data. The correlation coefficient of the estimated water level reached ~0.86, and the root mean square error (RMSE) of the estimated shoal slope was ~0.2°, indicating that the approach had high accuracy and reliability. We analyzed the temporal and spatial patterns of waterline changes and identified two dates (21 February 2014 and 15 October 2022) when the reservoir reached the lowest water levels, coinciding with periods of severe saltwater intrusions in the estuary. The extreme drought occurrences in the Qingcaosha Reservoir were firstly documented through the utilization of remote sensing data. The results also indicate a strong resilience of the Qingcaosha Reservoir and demonstrate that the feasibility and utility of using satellite remote sensing and waterline detection for estuarine reservoir storage can provide timely and accurate information for water resource assessment, management and planning.

Identifying Flood Prediction using Machine Learning Techniques

Flood is the most devastating and destructive that can destroy everything on land. These floods will cause further flooding in affected areas. Flood prediction models are being researched to reduce risk, think strategically, reduce human life and reduce property damage from floods. Over the last two years, AI techniques have improved the forecasting process, resulting in better execution and financial planning stability. First of all, these events can take everyone's feelings into account. Artificial intelligence models for flood prediction are crucial for flood warning, flood mitigation or prediction. Machine learning programs have become ubiquitous due to their computational needs for limited information. We believe that collecting only a small amount of data can help representative vector, best scores. The selected tree was successful due to better than expected accuracy and best score. Machine learning algorithms used in this flood prediction are decision trees, logistic regression, etc. For evaluation and comparison. Logistic regression can provide more accurate results than other algorithms and provide high efficiency and improvement. Floods are perhaps the most destructive event in the world, can cause irreversible damage and cause great suffering to humanity. Generally, most farmers are the most disturbed people in the world because their hard work can suddenly fail, causing their hearts to become melancholy. To measure water level and velocity over a large area, it is important to provide an exposure model that includes safety. These models can be aimed to improve the prediction by using different methods. Additionally, these models provide accurate predictions of flood events in a year, but do not provide much understanding and detail of the options needed.

Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors—the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T—were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0–5.0 m range, with a standard deviation of 0.44–1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22–0.23 m within the 2.0–5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

Distinctive Patterns of Water Level Change in Swedish Lakes Driven by Climate and Human Regulation

AbstractDespite having approximately 100,000 lakes, Sweden has limited continuous gauged lake water level data. Although satellite radar altimetry (RA) has emerged as a popular alternative to measure water levels in inland water bodies, it has not yet been used to understand the large‐scale changes in Swedish lakes. Here, we quantify the changes in water levels in 144 lakes using RA data and in situ gauged measurements to examine the effects of flow regulation and hydroclimatic variability. We use data from several RA missions, including ERS‐2, ENVISAT, JASON‐1,2,3, SARAL, and Sentinel‐3A/B. We found that during 1995–2022, around 52% of the lakes exhibited an increasing trend and 43% a decreasing trend. Most lakes exhibiting an increasing trend were in the north of Sweden, while most lakes showing a decreasing trend were in the south. Regarding the potential effects of regulation, we found that unregulated lakes had smaller trends in water level and dynamic storage than regulated ones. While the seasonal patterns of water levels in the lakes in the north are similar in regulated and unregulated lakes, in the south, they differ substantially. This study highlights the need to continuously monitor lake water levels for adaptation strategies in the face of climate change and understand the downstream effects of water regulatory schemes.

Review of Automatic Water Tank Filling System Using Arduino

Abstract: This project revolves around an Arduino- based automatic water level controller and indicator, employing ultrasonic sensors for water level measurement. These sensors operate on the "echo" principle, wherein transmitted sound waves return after striking an obstacle. Utilizing this principle enables the calculation of travel time for both outgoing and returning waves, facilitating the determination of distance. This fundamental concept is integrated into the project design. The system activates the motor pump automatically when the water level is low, addressing the pressing issue of drinking water scarcity prevalent in India and other countries. The project aims to contribute to water conservation efforts, given the prevalent crisis. Many households in India use overhead tanks, resulting in water overflow and substantial wastage of both water and electricity. To combat this, the project introduces an automatic water level controller, eliminating the need for manual switching ON and OFF of the motor pump. The device continuously monitors the water level and triggers a relay, subsequently activating the motor pump. This automated process not only minimizes water and electricity wastage but also reduces the reliance on manual operation, thereby optimizing efficiency.

Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model

Abstract. Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km2). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.

Quantitative Groundwater Modelling under Data Scarcity: The Example of the Wadi El Bey Coastal Aquifer (Tunisia)

The Grombalia aquifer constitutes a complex aquifer system formed by shallow, unconfined, semi-deep, and deep aquifers at different exploitation levels. In this study, we focused on the upper aquifer, the Wadi El Bey coastal aquifer. To assess natural aquifer recharge, we used a novel physiography-based method that uses soil texture-dependent potential infiltration coefficients and monthly rainfall data. The developed transient flow model was then applied to compute the temporal variation in the groundwater level in 34 observation wells from 1973 to 2020, taking into account the time series of spatially variable groundwater recharge, artificial groundwater recharge from 5 surface infiltration basins, pumping rates on 740 wells, and internal prescribed head cells to mimic water exchange between the wadis and aquifer. The quantified deviations in the computed hydraulic heads from measured water levels are acceptable because the database used to construct a scientifically sound and reliable groundwater model was limited. Further work is required to collect field data to quantitatively assess the local inflow and outflow rates between surface water and groundwater. The simulation of 12 climate scenarios highlighted a bi-structured north‒south behaviour in the hydraulic heads: an increase in the north and a depletion in the south. A further increase in the pumping rate would, thus, be severe for the southern part of the Wadi El Bey aquifer.

Evaluation of deep learning computer vision for water level measurements in rivers

Image-based gauging stations offer the potential for substantial enhancement in the monitoring networks of river water levels. Nonetheless, the majority of camera gauges fall short in delivering reliable and precise measurements because of the fluctuating appearance of water in the rivers over the course of the year. In this study, we introduce a method for measuring water levels in rivers using both the traditional continuous image subtraction (CIS) approach and a SegNet neural network based on deep learning computer vision. The historical images collected from on-site investigations were employed to train three neural networks (SegNet, U-Net, and FCN) in order to evaluate their effectiveness, overall performance, and reliability. The research findings demonstrated that the SegNet neural network outperformed the CIS method in accurately measuring water levels. The root mean square error (RMSE) between the water level measurements obtained by the SegNet neural network and the gauge station's readings ranged from 0.013 m to 0.066 m, with a high correlation coefficient of 0.998. Furthermore, the study revealed that the performance of the SegNet neural network in analyzing water levels in rivers improved with the inclusion of a larger number of images, diverse image categories, and higher image resolutions in the training dataset. These promising results emphasize the potential of deep learning computer vision technology, particularly the SegNet neural network, to enhance water level measurement in rivers. Notably, the quality and diversity of the training dataset play a crucial role in optimizing the network's performance. Overall, the application of this advanced technology holds great promise for advancing water level monitoring and management in river systems.

Development of a hazard risk map for assessing pedestrian risk in urban flash floods: A case study in Cúcuta, Colombia

AbstractThe rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5‐s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer‐based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time‐varying comparison of the results was done. Results show that the regions of high‐water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high‐risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.

Development of an IoT-Based System for River Siren Control and Height Detection Utilizing LoRa and Solar Cell Technology

We need a tool that can measure river water levels automatically based on IoT so that water level data can be received in real time. This tool uses an Arduino microcontroller which functions to convert analog data into digital by measuring water level using an ultrasonic sensor so that the data can be converted into digital water level data and displayed via the internet and a buzzer or siren as a danger warning when the water level rises high as well as water level data. can be monitored as a whole. Apart from that, this tool is equipped with LoRa communication. This LoRa can send signals as far as 15 km without obstructions, so even if there is no internet, this tool can still work and can be used in areas without electricity because it uses solar cells as a power supply. If the water reaches the maximum limit, the siren will sound and stop again when the water in the river is at normal level.

ArduHydro: a low-cost device for water level measurement and monitoring

In this paper, we present ArduHydro, a low-cost device for water level measurement and monitoring designed for employment in controlled and outdoor environments. It measures the water level through an ultrasonic sensor and elaborates the signals through an Arduino microcontroller. The small size of this device, its robustness and accuracy make ArduHydro properly versatile for different applications in the field of water control and management. This article describes the design, the components, the costs, and the performance of ArduHydro. The performance was assessed with a laboratory test inside an open-channel flume and comparing ArduHydro measurements with those obtained with a traditional ultrasonic sensor. Furthermore, an example of ArduHydro application for detecting the wavefront evolution during surface irrigation of a maize crop is presented. The results revealed that ArduHydro measurements were, on average, very consistent with those obtained by the traditional ultrasonic sensor in all different flow conditions. The application of ArduHydro during a surface watering of an agricultural field allowed us to obtain important spatiotemporal information about the water depth along the longitudinal direction of the field, paying the way for a real comprehension of the dynamics of wavefront evolution in a real-world case study.

Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing

Small water storages (≤ 500 ha surface area) enhance water supply for agricultural, human and livestock consumption. Their oftentimes large numbers and wide geographic spread, plus inaccurate or absent in situ metering, make their inclusion in water resource management difficult. This research assessed the capabilities of satellite optical remote sensing and LiDAR altimetry to characterise small water storages used for irrigation, so they can be included in water resources assessments. Landsat and Sentinel-2 water maps were integrated with airborne and satellite LiDAR altimetry to assess: (i) locations; (ii) elevation-area-volume relations; (iii) dates of construction/decommissioning; (iv) actual evaporation losses; and (v) storage volume changes of floodplain on-farm water storages (FoFWS) used primarily for cotton irrigation in the semi-arid Barwon-Darling region (142,173 km2), Australia. These remotely sensed characteristics would enable the representation of FoFWS in river system models and in regional water management plans, thus enhancing socioeconomic and environmental outcomes. Results from Landsat water maps showcased the growth in FoFWS surface area. In January 1988 there were 17 FoFWS with a total maximum surface area of 101.89 ha. By December 2021, the then 105 FoFWS maximum surface area was 5587.68 ha; a 55-times increase. For the first time in a regional context, mean annual (2000/01–2020/21) standing water evaporation losses were estimated for all FoFWS. These accounted to 52.47 MCM/year, which is a substantial ∼27% of the estimated long-term mean regional irrigation diversion limit (189 MCM/year). Elevation measurements from satellite LiDAR altimetry data, specifically GEDI and ICESat-2, were evaluated to assess if water volumes in FoFWS can be estimated through elevation-area-volume relations from a 1 m digital elevation model derived from airborne LiDAR data. Although GEDI's spatially variable footprint and ICESat-2's 91-day repeat cycle render them insufficient to monitor changes in water volumes in FoFWS, both satellite LiDAR altimeters provided accurate water level observations, with root mean squared differences (RMSD) of 0.38 m for GEDI and 0.12 m for ICESat-2, when compared to in situ water level measurements. ICESat-2's 0.7 m along-track sampling interval also detected changes of FoFWS structural features, such as increases in wall heights in recent years. This study demonstrated the value of integrating high-resolution airborne LiDAR data with satellite altimetry (LiDAR and/or radar interferometry), along with satellite optical water maps and rates of actual evaporation, to monitor FoFWS and other similar individually small yet collectively large reservoirs. Where similar data are available globally, then accurate information about small and dispersed water storages across poorly metered irrigated agricultural landscapes can be retrieved. Doing so will improve water resource management in the Barwon-Darling region, and in similar semi-arid and arid areas globally where irrigated agriculture is present.