This work-in-progress paper details a project designed to engage Coastal Virginia middle-school students in climate resilience by deploying Internet of Things (IoT) sensors that collect real-time water quantity and quality data. By incorporating Dragino LoRaWAN technology and hands-on, classroom-friendly Arduino experiments, the project provides students with tangible skills in sensor technology and data analysis, connecting theoretical learning to real-world applications. Simultaneously, the data supports local efforts to improve regional hydrologic and hydraulic models.

As coastal cities are increasingly vulnerable to climate- related flooding, the need for accurate and timely flood predictions is pressing, particularly for emergency response planning. This study presents an approach to predict flood events at high spatial resolution, leveraging inundation maps and 15-minute rainfall data. We employ Fourier Neural Operator (FNO), a method that learns efficient approximations of solutions to parametric partial differential equations. By incorporating multi-modal data types, our FNO model captures the complex dynamics of flood events including water levels in affected areas and long-range spatial and temporal dependencies. This interdisciplinary research contributes to the development of generalizable methods for predicting floods in coastal cities.

While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location.

With recent experience in multiple large-scale disasters, it has been widely confirmed that the severity of a disaster is greatly dependent on the effectiveness of ambulance dispatching during disaster phase. However, previous base station (i.e., temporary or permanent hospital) based ambulance redeployment methods and dynamic ambulance scheduling methods cannot handle the ambulance dispatching problem in catastrophic situations. In this paper, we present MobiAmbulance: a human Mobility based Ambulance dispatching system that aims to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delay of the fulfilled requests. We studied a state-scale human mobility dataset and found that the change of vehicle flow rate can be utilized to determine the connection status between road segments, and the distribution of people in catastrophic situations is drastically different from that in normal situations. Then, we develop a method to determine the road network connection status and the set of road segments that can still be driven through by ambulances after disaster. Based on the updated road network graph, we develop an ambulance dispatching method based on weighted driving route to maximize the total number of fulfilled patient pick-up requests, and minimize the driving delays of the fulfilled requests. Our trace-driven experiments demonstrate the superior performance of MobiAmbulance over other comparison methods.

Watershed delineation is a process to compute the drainage area for a point on the land surface, which is a critical step in hydrologic and water resources analysis. However, existing watershed delineation tools are still insufficient to support hydrologists and watershed researchers due to the lack of essential capabilities such as fully leveraging scalable and high performance computing infrastructure (public cloud), and providing predictable performance for the delineation tasks. To solve these problems, this paper reports on WDCloud, which is a system for large-scale watershed delineation on public cloud. For the design and implementation of WDCloud, we employ three main approaches: 1) an automated catchment search mechanism for a public data set, 2) three performance improvement strategies (Data-reuse, parallel-union, and MapReduce), and 3) local linear regression-based execution time estimator for watershed delineation. Moreover, WDCloud extensively utilizes several compute and storage capabilities from Amazon Web Services in order to maximize the performance, scalability, and elasticity of watershed delineation system. Our evaluations on WDCloud focus on two main aspects of WDCloud; the performance improvement for watershed delineation via three strategies and the estimation accuracy for watershed delineation time by local linear regression. The evaluation results show that WDCloud can achieve 18x-111x of speed-ups for delineating any scale of watersheds in the contiguous United States as compared to commodity laptop environments, and accurately predict execution time for watershed delineation with 85.6% of prediction accuracy, which is 23%-13% higher than other state-of-the-art approaches.

Understanding hydrologic systems at the scale of large watersheds and river basins is critically important to society when faced with extreme events, such as floods and droughts, or with concerns about water quality. A critical requirement of watershed modeling is model calibration, in which the computational model's parameters are varied during a search algorithm in order to find the best match against physically-observed phenomena such as streamflow. Because it is generally performed on a laptop computer, this calibration phase can be very time-consuming, significantly limiting the ability of a hydrologist to experiment with different models. In this paper, we describe our system for watershed model calibration using cloud computing, specifically Microsoft Windows Azure. With a representative watershed model whose calibration takes 11.4 hours on a commodity laptop, our cloud-based system calibrates the watershed model in 43.32 minutes using 16 cloud cores (15.78x speedup), 11.76 minutes using 64 cloud cores (58.13x speedup), and 5.03 minutes using 256 cloud cores (135.89x speedup). We believe that such speed-ups offer the potential toward real-time interactive model creation with continuous calibration, ushering in a new paradigm for watershed modeling.