Natural Resource Sciences Program
NRS Optional Specializations
Applied Science Program
Entrance Requirements
Application
Process

Climate Assessment and Impacts Specialization in
Natural Resource Sciences Graduate Program

Available to both MS and PhD candidates.

The Climate Assessment and Impacts specialization provides students with unique opportunities to emphasize

  1. understanding the interactions between climate and society
  2. learning methodologies for climate assessment and impacts

Students selecting this specialization will be able to capitalize on the expertise of scientists and other students working on climate assessment, climate impacts, and problem-oriented policy research.

A related graduate degree program in Agronomy & Horticulture has a specialization in Agricultural Meteorology and selected faculty from the School of Natural Resources can advise potential students.

Mark Svoboda presenting drought talk

Selected Dissertations & Theses

Applications of Artificial Intelligence on Drought Impact Monitoring and Assessment - Beichen Zhang
  • Dissertation Defense
  • 05/07/2024
: Drought is a common natural disaster with complex characteristics and broad-reaching impacts. It has cost over 356 billion dollars in losses since 1980 in the US. However, while over a hundred drought indicators have been developed in recent decades, multi-dimensional drought impacts, such as those on socioeconomic sectors, are still understudied, particularly through quantitative approaches. This dissertation explores complex drought impacts using artificial intelligence (AI) across three research projects. The first study utilizes deep learning and natural language processing to predict drought impacts from diverse text sources like social media and news, outperforming the conventional method. It highlights the varied effects of drought over time and space, using California and Nebraska as case studies for integrating innovative data sources into drought assessments. The second study develops an explainable machine learning pipeline to investigate how different drought indicators relate to diverse drought impacts, illustrated through a California wildfire case study and comparisons of the relationships for multifaceted drought impacts in the selected states. The outcomes indicate the complexity and spatiotemporal heterogeneity of the relationships between drought indicators and multi-dimensional impacts. The third study dives into a specific category of drought impacts. It examines the relationship between extreme drought events and social unrest in India, using causal machine learning to quantitatively investigate the causal effect of drought on the increasing frequency of human protests. The outcomes show promising potential to develop further studies using causal machine learning by revealing the statistically significant average causal effect of drought on social unrest. Overall, the dissertation underscores the importance of applying advanced AI techniques in understanding and addressing the broad and complex drought impacts on both the natural environment and socioeconomic sectors for better climate adaptation and water resource management under the threats from climate change.
Assessing the skill of state-of-the-art seasonal climate prediction techniques over Ethiopia - Andualem Shiferaw
  • Dissertation Defense
  • 11/28/2023

Skillful, timely, and reliable seasonal forecasts are crucial in mitigating the adverse impacts of climate-induced risks in Ethiopia and the rest of Greater Horn of Africa region. However, access to skillful and usable forecasts is currently challenging. This study evaluated the skill of raw and bias-corrected deterministic and probabilistic summer (JJA) rainfall forecasts from the Climate Forecast System Version 2 (CFSv2) for Ethiopia, spanning lead times from 0.5 to 4.5 months. The investigation also considered the influence of increased ensemble size on forecast skill by comparing performance of CFSv2 with the North American Multi-Model Ensembles (NMME). The findings indicated that CFSv2 exhibited limited skill for operational use in seasonal rainfall forecasting over Ethiopia. In contrast, NMME displayed promise, suggesting that with some value addition efforts such as bias correction, statistical downscaling, and identification of smaller subset of best performing models, could position it as a valuable component in Ethiopia's seasonal climate forecast services.

Despite their potential usefulness, coarse resolution global models like CFSv2 fail to meet users’ need for forecasts at local to regional scales. To address this limitation, the Weather Research and Forecasting model (WRF) was explored for its potential to enhance CFSv2 forecasts through downscaling. A sensitivity study using WRF identified optimal parameterization schemes, utilizing Climate Forecast System Reanalysis (CFSR) for initial and boundary conditions. Subsequently, the WRF model, configured with the identified optimum model configuration, was employed to downscale operational CFSv2 summer season rainfall forecasts. Despite downscaling a small subset of ensemble members, the WRF model demonstrated value in refining raw CFSv2 forecasts. However, additional research is essential to further fine-tune WRF configurations, potentially minimizing biases and enhancing forecast skill. The findings of this study are expected to contribute towards improving access to skillful and usable seasonal predictions that could help decision-makers in mitigating adverse impacts of climate induced risks.

Monitoring and assessing forage production in grazed grasslands of Nebraska: Toward Adaptive Grazing - Biquan Zhao
  • Dissertation Defense
  • 11/06/2023
Grasslands are an important natural resource in Nebraska providing critical ecosystem services, for example, supporting the local cattle\beef production by forage production. To achieve the goal of developing adaptive grazing management strategies and operating adaptive grazing systems in Nebraska, it is necessary to understand the forage production patterns and dynamics. This presentation describes trends in forage production, responses of forage production to weather factors and management, and effects of forage condition on cattle grazing, through monitoring and assessing forage production on grazed grasslands in Nebraska. Quadrat-sampling data, drone-based remotely sensed data, and cattle GPS collar data are used in studies of this dissertation. Results of this dissertation are informative for shaping adaptive grazing management strategies in Nebraska, which provides knowledge and improves our understanding of forage production patterns in grazed grasslands in the face of climate change, various grassland management practices and grazing activities.
Localizing Climate Assessment Tools - Stonie Cooper
  • Dissertation Defense
  • 04/25/2023

Focus on global climate change can overlook the nuances of local weather and climate impacts. This study describes tools and methods for creating and observing weather and climatic conditions on a temporal and geographic scale that represents the environment of Nebraska. Recognition of the limited resources available for continuous application of new data and gathering of observations provides a guide for a “best practice” scientific, yet economic, model for maintaining an observational network and deriving value-added products.

Utilizing Federally maintained datasets of geographically relevant cooperative observations as a backdrop, the locally implemented and maintained weather observation network, the Nebraska Mesonet, is assessed against the official national climate records. Strategies for increasing the relevance and reliability of the Nebraska Mesonet observed parameters that show inconsistencies are discussed. Quality control techniques are tested and evaluated to provide confidence in the recorded observations, with recommendations made to mitigate and limit errant data from entering an official Nebraska Mesonet record.

Climate Analysis Maps