Carlos A. Oroza

Design of a network of robotic Lagrangian sensors for shallow water environments with case studies for multiple applications

This article describes the design methodology for a network of robotic Lagrangian floating sensors designed to perform real-time monitoring of water flow, environmental parameters, and bathymetry of shallow water environments (bays, estuarine, and riverine environments). Unlike previous Lagrangian sensors which passively monitor water velocity, the sensors described in this article can actively control their trajectory on the surface of the water and are capable of inter-sensor communication. The addition of these functionalities enables Lagrangian sensing in obstacle-encumbered environments, such as rivers. The Ishikawa cause and effect design framework is used to ensure that the final system synthesizes the diverse operational and functional needs of multiple end-user groups to arrive at a broadly applicable system design. A summary of potential applications for the system is given including completed projects performed on behalf of the Department of Homeland Security, Office of Naval Research, and the California Bay-Delta Authority.

Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR018896 Key Points: A machine-learning algorithm for optimizing snow sensor placements is compared to expert placements in an existing sensor network The spatial and temporal transferability of the algorithm is then assessed in 14 total LiDAR surveys in two uninstrumented catchments The accuracy of the snow depth estimated from the sensor measurements is higher than expert and randomized placements in each of the surveys Correspondence to: C. A. Oroza, coroza@berkeley.edu Citation: Oroza, C. A., Z. Zheng, S. D. Glaser, and D. Tuia (2016), Optimizing embedded sensor network design for catchment- scale snow-depth estimation using LiDAR and machine learning, Water Resour. Res., 52, 8174–8189, doi:10.1002/2016WR018896. Received 11 MAR 2016 Accepted 23 SEP 2016 Accepted article online 27 SEP 2016 Published online 22 OCT 2016 Optimizing embedded sensor network design for catchment-scale snow-depth estimation using LiDAR and machine learning Carlos A. Oroza 1 , Zeshi Zheng 1 , Steven D. Glaser 1 , Devis Tuia 2 , and Roger C. Bales 1,3 Department of Civil and Environmental Engineering, University of California, Berkeley, California, USA, 2 Department of Geography, University of Zurich, Zurich, Switzerland, 3 Sierra Nevada Research Institute, University of California, Merced, California, USA Abstract We evaluate the accuracy of a machine-learning algorithm that uses LiDAR data to optimize ground-based sensor placements for catchment-scale snow measurements. Sampling locations that best represent catchment physiographic variables are identified with the Expectation Maximization algorithm for a Gaussian mixture model. A Gaussian process is then used to model the snow depth in a 1 km 2 area sur- rounding the network, and additional sensors are placed to minimize the model uncertainty. The aim of the study is to determine the distribution of sensors that minimizes the bias and RMSE of the model. We com- pare the accuracy of the snow-depth model using the proposed placements to an existing sensor network at the Southern Sierra Critical Zone Observatory. Each model is validated with a 1 m 2 LiDAR-derived snow-depth raster from 14 March 2010. The proposed algorithm exhibits higher accuracy with fewer sensors (8 sensors, RMSE 38.3 cm, bias 5 3.49 cm) than the existing network (23 sensors, RMSE 53.0 cm, bias 5 15.5 cm) and randomized placements (8 sensors, RMSE 63.7 cm, bias 5 24.7 cm). We then evaluate the spatial and temporal transferability of the method using 14 LiDAR scenes from two catchments within the JPL Airborne Snow Observatory. In each region, the optimized sensor placements are determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys is then com- pared to 100 configurations of sensors selected at random. We find the error statistics (bias and RMSE) to be more consistent across the additional surveys than the average random configuration. 1. Introduction A major challenge of hydrologic science in montane regions relates to estimating the spatial variability of snow cover [Bales et al., 2006; Trujillo and Lehning, 2015; McCreight et al., 2014; Guan et al., 2013]. Multiple independent variables govern the distribution of snow cover, including elevation, slope, aspect, and the dis- tribution of canopy [Faria et al., 2000; Musselman et al., 2008; Lehning et al., 2011; Helfricht et al., 2014]. Non- stationary effects such as climate warming and changes in vegetation structure may significantly alter the timing and magnitude of storage and runoff in these watersheds [Goulden and Bales, 2014; Flanner et al., 2009]. Existing regression-based hydrologic models, which use statistical relations from historical hydro- graphs to predict runoff and inform allocation decisions [Perkins et al., 2009; Rosenberg et al., 2011; Rango and Martinec, 1995] will have limited accuracy as conditions deviate from historical norms and thus may prove to be inadequate for predictions in water management. C 2016. American Geophysical Union. V All Rights Reserved. OROZA ET AL. Recent research has focused on improving hydrologic models by assimilating remote-sensing and in situ measurements with distributed energy-balance models to better estimate storage and runoff [Guan et al., 2013]. These methods use well-developed remote-sensing [Painter et al., 2003; Rosenthal and Dozier, 1996; € lli et al., 2002; Egli et al., 2012] and energy-balance models [Marks et al., 1992; Link and Marks, Dozier, 1989; P a 1999; Brubaker et al., 1996] to estimate snow and snowmelt processes across basins. In situ measurements for these methods are presently limited to snow pillows and snow courses, which largely sample flat, open terrain [Molotch and Bales, 2006], yet the distribution of snow cover can vary considerably as a function of topographic features. To address this, in situ sensor measurements can be deployed to capture the mean and variance of the snow depth, which can be used to inform models that use these statistics as inputs [e.g., Essery and Pomeroy, 2004]. Alternatively, individual sensor measurements can be used together with OPTIMIZING SNOW SENSOR PLACEMENTS

Mobile phone based drifting lagrangian flow sensors

Mobile phone based drifters offer distinct advantages over those using custom electronic circuit boards. They leverage the inexpensive and modern hardware provided by the mobile phone market to supply water resource scientists with a new solution to sensing water resources. Mobile phone based drifters strategically address in situ sensing applications in order to focus on the large scale use of mobile phones dealing with communications, software, hardware, and system reliability. We have demonstrated that a simple design of a drifter built around an Android phone robustly survives many hours of experimental usage. In addition to the positioning capabilities of the phone via GPS, we also use the accelerometer of the phone to filter out samples when the drifter is in storage. The success of these drifters as passive mobile phone sensors has also led us to develop motorized mobile phone drifters.

Real-time Alpine Measurement System Using Wireless Sensor Networks

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

A Machine-Learning-Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments

We evaluate the accuracy of a machine-learning-based path loss model trained on 42 157 324 RSSI samples collected over one year from an environmental wireless-sensor network using 2.4 GHz radios. The 2218 links in the network span a 2000 km2 basin and are deployed in a complex environment, with large variations of terrain attributes and vegetation coverage. Four candidate machine-learning algorithms were evaluated in order to find the one with lowest error: 1) Random Forest; 2) AdaBoost; 3) Neural Networks; and 4)

Valuing year‐to‐go hydrologic forecast improvements for a peaking hydropower system in the Sierra Nevada

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Heterogeneous Fleets of Active and Passive Floating Sensors for River Studies

-Nearest-Neighbors. Of the candidate models, Random Forest showed the lowest error. The independent variables used in the model include path distance, canopy coverage, terrain variability, and path angle. We compare the accuracy of this model to several well-known canonical (free space, plane earth) and empirical propagation models (Weissberger, ITU-R, COST235). Unlike canonical models, machine-learning algorithms are not problem-specific: they rely on an extensive dataset and a flexible model architecture to make predictions. We show how this model achieves a 37% reduction in the average prediction error compared to the canonical/empirical model with the best performance. This paper presents an in-depth discussion on the strengths and limitations of the proposed approach as well as opportunities for further research.

SierraNet: monitoring the snowpack in the Sierra Nevada: demo

Author(s): Rheinheimer, DE; Bales, RC; Oroza, CA; Lund, JR; Viers, JH | Abstract:

SOL: An end-to-end solution for real-world remote monitoring systems

Lagrangian sensing for tracing hydrodynamic trajectories is an innovative approach for studying estuarial environments. Actuated Lagrangian sensors are capable of avoiding obstacles and navigating when active and retain a passive hydrodynamic profile that is suited for Lagrangian sensing when passive. A heterogeneous fleet of actuated and passive drifting sensors is presented. Data assimilation using a high-performance computing HPC cluster that runs the ensemble Kalman filter EnKF is an essential component of the estuarial state estimation system. The performance of the mixed capability fleet and the data assimilation backend is evaluated in the context of a landmark 96-unit river study in the Sacramento-San Joaquin Delta region of California.

Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction

Next-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture, air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring.