@article{ford_wang_kumar_sankarasubramanian_2024, title={Characterization of the urban heat Island effect from remotely sensed data based on a hierarchical model}, volume={25}, ISSN={["2589-9155"]}, url={https://doi.org/10.1016/j.hydroa.2024.100184}, DOI={10.1016/j.hydroa.2024.100184}, abstractNote={This study attempts to statistically characterize the Urban Heat Island Intensity (UHII) (ΔT) for 55 cities under three climate regimes – arid, snow and temperate – across the US. The study uses remotely sensed data products, daily temperature from MODIS and daily evapotranspiration from SSEBop model, to calculate the urban–rural difference in daily-mean temperature and daily-mean evapotranspiration (ΔT and ΔET respectively) for the selected cities. By developing a hierarchical model that explains UHII using temporally-varying ΔET and spatially-varying urban morphometric characteristics (total urban area and percentage impervious area) available for each city, we find that 89% of the spatio-temporal variability in annual ΔT can be explained. The relationship between ΔT and ΔET is found to be negative indicating increased difference in daily means of ET (ΔET) result in increased difference in daily means of temperature (ΔT) between urban and rural paracels The variation of ΔT per unit ΔET is found to be highest in arid and snowy environments and smallest in temperate environments in the south-southeast US. The relation between ΔT and ΔET is negative for most cities, except Madison (WI) and Sacramento (CA), across the US. Both the selected urban morphometric properties are found to be statistically significant in explaining the spatial variability in UHII, but the difference in urban–rural difference in evapotranspiration is the primary driver for UHII.}, journal={JOURNAL OF HYDROLOGY X}, author={Ford, Lucas and Wang, Dingbao and Kumar, Mukesh and Sankarasubramanian, A.}, year={2024}, month={Dec} }
 @article{ford_sankarasubramanian_2023, title={Generalizing Reservoir Operations Using a Piecewise Classification and Regression Approach}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034890}, DOI={10.1029/2023WR034890}, abstractNote={AbstractInflow anomalies at varying temporal scales, seasonally varying storage mandates, and multipurpose allocation requirements contribute to reservoir operational decisions. The difficulty of capturing these constraints across many basins in a generalized framework has limited the accuracy of streamflow estimates in land‐surface models for locations downstream of reservoirs. We develop a Piecewise Linear Regression Tree to learn generalized daily operating policies from 76 reservoirs from four major basins across the coterminous US. Reservoir characteristics, such as residence time and maximum storage, and daily state variables, such as storage and inflow, are used to group similar observations across all reservoirs. Linear regression equations are then fit between daily state variables and release for each group. We recommend two models—Model 1 (M1) that performs the best when simulating untrained records but is complex and Model 2 (M2) that is nearly as performant as M1 but more parsimonious. The simulated release median root mean squared error is 49.7% (53.2%) of mean daily release with a median Nash‐Sutcliffe efficiency of 0.62 (0.52) for M1 (M2). Long‐term residence time is shown to be useful in grouping similar operating reservoirs. Release from low residence time reservoirs can be mostly described using inflow‐based variables. Operations at higher residence time reservoirs are more related to previous release variables or storage variables, depending on the current inflow. The ability of the models presented to capture operational dynamics of many types of reservoirs indicates their potential to be used for untrained and limited data reservoirs.}, number={9}, journal={WATER RESOURCES RESEARCH}, author={Ford, Lucas and Sankarasubramanian, A.}, year={2023}, month={Sep} }
 @article{ford_queiroz_decarolis_sankarasubramanian_2022, title={Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation}, volume={8}, ISSN={["2352-4847"]}, url={https://doi.org/10.1016/j.egyr.2022.06.017}, DOI={10.1016/j.egyr.2022.06.017}, abstractNote={Climate variability accounts for distinct seasonal differences in electricity demand and streamflow potential, which power systems rely on to assess available hydropower and to cool thermal power plants. Understanding the interactions between reservoir and power networks under varying climate conditions requires an integrated analysis of both systems. In this study, we develop Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that optimizes both systems with respect to reducing power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). Three optimization schemes of varying degrees of model integration are applied to Tennessee Valley Authority's reservoir and electricity systems for the summer and winters from 2003 to 2015. We find that co-optimization of the systems results in more efficient water allocation decisions than separate optimization. Co-optimization solutions reduce reservoir spill and allocate water for hydropower only when and where it is beneficial to the power system as compared to stand-alone water system optimization. As the penetration of solar and wind power continues to increase, power systems will be more reliant on flexible reliable generating services such as reservoir systems and co-optimization of both systems will become more essential for efficient seasonal planning and operation.}, journal={ENERGY REPORTS}, publisher={Elsevier BV}, author={Ford, Lucas and Queiroz, Anderson and DeCarolis, Joseph and Sankarasubramanian, A.}, year={2022}, month={Nov}, pages={8061–8078} }
 @article{xuan_ford_mahinthakumar_de souza filho_lall_sankarasubramanian_2020, title={GRAPS: Generalized Multi-Reservoir Analyses using probabilistic streamflow forecasts}, volume={133}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2020.104802}, DOI={10.1016/j.envsoft.2020.104802}, abstractNote={A multi-reservoir simulation-optimization model GRAPS, Generalized Multi-Reservoir Analyses using Probabilistic Streamflow Forecasts, is developed in which reservoirs and users across the basin are represented using a node-link representation. Unlike existing reservoir modeling software, GRAPS can handle probabilistic streamflow forecasts represented as ensembles for performing multi-reservoir prognostic water allocation and evaluate the reliability of forecast-based allocation with observed streamflow. GRAPS is applied to four linked reservoirs in the Jaguaribe Metropolitan Hydro-System (JMH) in Ceará, North East Brazil. Results from the historical simulation and the zero-inflow policy over the JMH system demonstrate the model's capability to support monthly water allocation and reproduce the observed monthly releases and storages. Additional analyses using streamflow forecast ensembles illustrate GRAP's abilities in developing storage-reliability curves under inflow-forecast uncertainty. Our analyses show that GRAPS is versatile and can be applied for 1) short-term operating policy studies, 2) long-term basin-wide planning evaluations, and 3) climate-information based application studies.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Xuan, Yi and Ford, Lucas and Mahinthakumar, Kumar and De Souza Filho, Assis and Lall, Upmanu and Sankarasubramanian, A.}, year={2020}, month={Nov}, pages={104802} }