An official website of the United States government
Abstract The available water for evaporation within a catchment is spatially variable. However, how the spatial variability of available water affects mean annual evaporation is not fully understood. For a specific catchment, a suitable distribution function defined for non‐negative random variables can be determined through statistical methods to represent the spatial variability of the available water when the point‐scale data are available. This article proposes that the distribution function representing the spatial variability of available water for evaporation determines the functional form of Budyko equation based on the one‐stage precipitation partitioning concept. Specifically, the available water for evaporation following a single‐parameter distribution function leads to a deterministic Budyko equation; whereas a two‐parameter distribution function of available water for evaporation leads to a single‐parameter Budyko equation. We identified the property of distribution function for symmetric Budyko equation, which suggests that precipitation partitioning and energy partitioning in the hydrological cycle follow the same functional form with respect to aridity index and humidity index, respectively. The lower bound of Budyko curve is explained as a result of probable distributions of available water for evaporation due to catchment co‐evolution.
more »
« less
This content will become publicly available on March 20, 2026
A novel near-infrared photoacoustic nanoscale contrast agent platform (Conference Presentation)
only short abstract available on SPIE website; but the video of the presentation is available
more »
« less
- Award ID(s):
- 2128821
- PAR ID:
- 10654800
- Editor(s):
- Oraevsky, Alexander A; Wang, Lihong V
- Publisher / Repository:
- SPIE
- Date Published:
- Page Range / eLocation ID:
- 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Cho, Charles H; Costa, Erika; Korca, Blerita (Ed.)Fictitious case study on sustainable business practices for use in business education. Teaching notes, templates and solutions are available on the online Instructor Resources available on the online Instructor Resources available on the book’s website at https://www.e-elgar.com/textbooks/cho-ustainability-accounting. Note that these are restricted and available to instructors only, and an account with access is required.more » « less
-
Acute respiratory distress syndrome (ARDS) is a fulminant inflammatory lung injury that develops in patients with critical illnesses, affecting 200,000 patients in the United States annually. However, a recent study suggests that most patients with ARDS are diagnosed late or missed completely and fail to receive life-saving treatments. This is primarily due to the dependency of current diagnosis criteria on chest x-ray, which is not necessarily available at the time of diagnosis. In machine learning, such an information is known as Privileged Information - information that is available at training but not at testing. However, in diagnosing ARDS, privileged information (chest x-rays) are sometimes only available for a portion of the training data. To address this issue, the Learning Using Partially Available Privileged Information (LUPAPI) paradigm is proposed. As there are multiple ways to incorporate partially available privileged information, three models built on classical SVM are described. Another complexity of diagnosing ARDS is the uncertainty in clinical interpretation of chest x-rays. To address this, the LUPAPI framework is then extended to incorporate label uncertainty, resulting in a novel and comprehensive machine learning paradigm - Learning Using Label Uncertainty and Partially Available Privileged Information (LULUPAPI). The proposed frameworks use Electronic Health Record (EHR) data as regular information, chest x-rays as partially available privileged information, and clinicians' confidence levels in ARDS diagnosis as a measure of label uncertainty. Experiments on an ARDS dataset demonstrate that both the LUPAPI and LULUPAPI models outperform SVM, with LULUPAPI performing better than LUPAPI.more » « less
-
This is a pointer to the Open Polar Radar website and flight based CReSIS SAR processed data. These transect projected radargrams were collected as part of the Center for Oldest Ice Exploration (COLDEX) Science and Technology Center (https://www.coldex.org) in the 2022/23 (CXA1) and 2023/24 (CXA2) airborne field seasons. Raw 3 TB data from both seasons is deposited at the USAP data center at https://doi.org/10.15784/601768. The set of images in this archive was designed for easy, non expert, access to radargrams, organized according to survey design. 2022-23 (CXA1) flight based HDF5/matlab format data is available here: https://data.cresis.ku.edu/data/rds/2022_Antarctica_BaslerMKB/ 2023-24 (CXA2) flight based data HDF5/matlab format is available here: https://data.cresis.ku.edu/data/rds/2023_Antarctica_BaslerMKB/ 2022-23 (CXA1) transect based (science organized) unfocused data in netCDF format is available here: https://doi.org/10.18738/T8/XPMLCC 2023-24 (CXA2) transect based (science organized) unfocused data in netCDF format is available here: https://doi.org/10.18738/T8/FV6VNT Reprojected transect based images (science organized) in standard image (png, jpg) formats will be available here: ttps://doi.org/10.18738/T8/NEF2XMmore » « less
