Sarah Alexander

Bio: Sarah Alexander is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Crop yield & Agroecology. The author has an hindex of 3, co-authored 5 publications receiving 29 citations.

Forecast-informed reservoir operations to guide hydropower and agriculture allocations in the Blue Nile basin, Ethiopia

Abstract: Predictive hydroclimate information, coupled with reservoir system models, offers the potential to mitigate climate variability risks. Prior methodologies rely on sub-seasonal, dynamic/synthetic fo...

Model Selection Based on Sectoral Application Scale for Increased Value of Hydroclimate-Prediction Information

Abstract: Advance predictions of seasonal precipitation may provide information to aid water resource management decisions in various sectors. Yet, a disconnect between the spatial scale upon which s...

Development of an interdisciplinary, multi-method approach to seasonal climate forecast communication at the local scale

Abstract: Bridging the gap between seasonal climate forecast development and science communication best practice is a critical step towards the integration of climate information into decision-making practices for enhanced community resilience to climate variability. Recent efforts in the physical sciences have focused on the development of seasonal climate forecasts, with increased emphasis on tailoring this information to user needs at the local scale. Advances in science communication have progressed understandings of how to leverage subjective decision-making processes and trust to communicate risky, probabilistic information. Yet, seasonal climate forecasts remain underutilized in local decision-making, due to challenging divides between the physical and social sciences and the lack of an approach that combines expert knowledge across disciplines. We outline an interdisciplinary, multi-method approach to communicate local-scale predictive information by advancing a co-produced “package” that pairs a highly visual bulletin with public engagement sessions, both developed with direct user-developer engagement, leveraging existing networks and novel inclusion of uncertainty through locally relevant analogies to enhance the understanding of probabilistic information. Systematic observations revealed some level of understanding among the target audience, yet identified major processes of confusion that inhibit forecast utility. Probabilistic predictions communicated in reference to “normal” years proved to be an unintelligible comparison for individuals, given preferences for certainty in interpreting risk-related information. Our approach addresses key gaps in the literature and serves as a framework for bridging the disconnect between forecast development and science communication to advance communication and integration of climate predictions for community benefit.

A Grand Prediction: Communicating and Evaluating 2018 summertime Upper Blue Nile rainfall and streamflow forecasts in preparation for Ethiopia's new dam

Abstract: When complete, the Grand Ethiopian Renaissance Dam (GERD) will be the largest hydropower dam in Africa. The GERD has become a focal point of geopolitical tensions because it will allow Ethiopia greater control over the Blue Nile River, Egypt’s main source of freshwater. To inform discussions of filling plans and responses, we created a probabilistic seasonal forecast for Upper Blue Nile rainfall and streamflow in the GERD basin. Eight statistical models and eight dynamical models were used to forecast the rainy season (June-September), which were then converted into river flow for June-December 2018. Both statistical and dynamical models predicted a high probability of average to above average rainfall as well as Upper Blue Nile flow in the GERD basin. Actual summer precipitation in 2018 was slightly below the long-term mean but well within the range considered to be “near normal.” Leveraging the increasingly online media landscape for science communication, we made the forecast publicly available through a blog and shared with regional decision-makers in advance of the 2018 rainy season. The blog attracted news coverage in the region focusing primarily on the relatively low likelihood of below-average Nile flow across the forecast ensemble. When asked for feedback on the blog, Ethiopian decision-makers and forecasters reported that flow predictions included in our blog were useful and not part of existing products. Access and comprehension were noted barriers to the use of these types of forecasts, consistent with prior research in forecast communication and dissemination. Forecasts available on such blogs can inform a shared understanding among decision-makers in the management of transboundary waters, yet effective communication and dissemination remain a challenge.

Ensemble flood forecasting: A review

Abstract: Operational medium range flood forecasting systems are increasingly moving towards the adoption of ensembles of numerical weather predictions (NWP), known as ensemble prediction systems (EPS), to drive their predictions. We review the scientific drivers of this shift towards such ‘ensemble flood forecasting’ and discuss several of the questions surrounding best practice in using EPS in flood forecasting systems. We also review the literature evidence of the ‘added value’ of flood forecasts based on EPS and point to remaining key challenges in using EPS successfully.

The Nile Water-Food-Energy Nexus under Uncertainty: Impacts of the Grand Ethiopian Renaissance Dam

Abstract: Achieving a water, food, and energy (WFE) nexus balance through policy interventions is challenging in a transboundary river basin because of the dynamic nature and intersectoral complexity...

Forecast-informed reservoir operations to guide hydropower and agriculture allocations in the Blue Nile basin, Ethiopia

Abstract: Predictive hydroclimate information, coupled with reservoir system models, offers the potential to mitigate climate variability risks. Prior methodologies rely on sub-seasonal, dynamic/synthetic fo...

Predicting Rainy Season Onset in the Ethiopian Highlands for Agricultural Planning

Abstract: The Kiremt rainy season is the foundation of agriculture in the Ethiopian Highlands and a key driver of economic development as well as the instigator of famines that have plagued the country’s history. Despite the importance of these rains, relatively little research exists on predicting the season’s onset; even less research evaluates statistical modeling approaches, in spite of their demonstrated utility for decision-making at local scales. To explore these methods, predictions are generated conditioned on three definitions of onset, at three lead times, using partial least squares (PLS) regression and random forest classification. Results illustrate moderate prediction skill and an ability to avoid false onsets, which may guide planting decisions; however, they are highly sensitive to how onset is defined, suggesting that future prediction approaches should additionally consider local agricultural definitions of onset.