Dataset Information

Toward snowpack runoff decision support.

ABSTRACT: Rain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years 2006-2019. This framework considers the potential for terrestrial water input from the snowpack through decision tree classification of rain-on-snow and warm day melt events to aid in pattern recognition of prominent weather and antecedent snowpack conditions capable of producing snowpack runoff. Our work demonstrates how (1) present weather and (2) antecedent snowpack risk can be "learned" from hourly data to support eventual development of basin-specific snowpack runoff decision support systems aimed at providing real-time guidance for water resource management.

SUBMITTER: Heggli A

PROVIDER: S-EPMC9051623 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Toward snowpack runoff decision support.

Heggli Anne A Hatchett Benjamin B Schwartz Andrew A Bardsley Tim T Hand Emily E

iScience 20220412 5

Rain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years ...[more]

PMID: 35494240

Similar Datasets

Project description:ObjectiveClinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or "cutpoint," to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared it with alternative approaches in 2 use-cases: (i) preventing intensive care unit readmission and (ii) preventing inpatient falls.Materials and methodsParameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use-case, we simulated the expected NMB resulting from the model-guided decision using a range of cutpoint selection approaches, including our new value-optimizing approach. Sensitivity analyses applied alternative event rates, model discrimination, and calibration performance.ResultsThe proposed approach that considered expected downstream consequences was frequently NMB-maximizing compared with other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for intensive care (prevalence = 0.025, area under the receiver operating characteristic curve [AUC] = 0.70) and falls (prevalence = 0.036, AUC = 0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB, and was robust to model miscalibration.DiscussionOur results highlight the potential value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often the target of prediction model development research.ConclusionsThis study proposes a cutpoint selection method that may optimize clinical decision support systems toward value-based care.

Project description:PURPOSE:Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support (PODS) team that provides annotations at the alteration-level and subsequently determined if clinical decision-making was influenced. METHODS:Genomic alterations were annotated to determine actionability based on a variant's known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A web-based survey was implemented to capture the reasons why genotype-matched therapies were not pursued. RESULTS:PODS processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially, 29.7% unknown, 28.4% non-actionable. 66% of patients had at least one actionable/potentially actionable alteration. 20.6% (110/535) patients annotated enrolled on a genotype-matched trial. Trial enrolment was significantly higher for patients with actionable/potentially actionable alterations (92/333, 27.6%) than those with unknown (16/136, 11.8%) and non-actionable (2/66, 3%) alterations (p=0.00004). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment on genotype-matched trials. Clinicians cited a variety of reasons why patients with actionable alterations did not enroll on trials. CONCLUSION:Over half of alterations annotated were of unknown significance or non-actionable. Physicians were more likely to enroll a patient on a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.

Dataset Information

Toward snowpack runoff decision support.

Publications

Toward snowpack runoff decision support.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets