@article{gaines_tulbure_perin_composto_tiwari_2024, title={Projecting Surface Water Area Under Different Climate and Development Scenarios}, volume={12}, ISSN={["2328-4277"]}, url={https://doi.org/10.1029/2024EF004625}, DOI={10.1029/2024EF004625}, abstractNote={Abstract Changes in climate and land‐use/land‐cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local‐scale hydrological models, with few studies accounting for climate and human drivers such as land‐use/land‐cover change. We used a data‐driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land‐cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land‐use/land‐cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data‐driven model and a process‐based model. We found that the scenario with the largest forest‐dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process‐based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest‐dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land‐use management policies for adaptation and water‐stress mitigation as well as strategies to prepare for future flood and drought events.}, number={7}, journal={EARTHS FUTURE}, author={Gaines, Mollie D. and Tulbure, Mirela G. and Perin, Vinicius and Composto, Rebecca and Tiwari, Varun}, year={2024}, month={Jul} }
 @article{tiwari_tulbure_caineta_gaines_perin_kamal_krupnik_aziz_islam_2024, title={Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh}, volume={351}, ISSN={["1095-8630"]}, url={https://doi.org/10.1016/j.jenvman.2023.119615}, DOI={10.1016/j.jenvman.2023.119615}, abstractNote={High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.}, journal={JOURNAL OF ENVIRONMENTAL MANAGEMENT}, author={Tiwari, Varun and Tulbure, Mirela G. and Caineta, Julio and Gaines, Mollie D. and Perin, Vinicius and Kamal, Mustafa and Krupnik, Timothy J. and Aziz, Md Abdullah and Islam, A. F. M. Tariqul}, year={2024}, month={Feb} }