Paul Block’s research while affiliated with University of Wisconsin–Madison and other places

The absence of a basin-wide apportionment agreement on using the Nile River equitably has been a long-standing source of disagreement among Nile riparian states. This study introduces a new approach that the riparian states can consider that quantifies the Nile River’s apportionment. The approach includes (1) developing a basin-wide database of indicators representative of the United Nations Watercourse Convention (UNWC) relevant factors and circumstances, (2) developing an ensemble of indicator weighting scenarios using various weighting methods, and (3) developing six water-sharing methods to obtain a range of apportionments for Egypt, Sudan, Ethiopia and the group of the White Nile Equatorial States for each weighting scenarios. The results illustrate a relatively narrow range of country-level water apportionments, even though some individual factor weights vary from 3% to 26%. Considering the entire Nile River, the water apportionment for Ethiopia ranges from 32% to 38%, Sudan and South Sudan from 25% to 33%, Egypt from 26% to 35%, and the Equatorial States from 5% to 7%. We trust that the six proposed equitable water-sharing methods may aid in fostering basin-wide negotiations toward a mutual agreement and address the dispute over water sharing.

Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results ( n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions. Significance Statement Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.

Streamflow forecasts play an important role in water resources operation and management and skillful seasonal forecasts can significantly facilitate the decision-making process. Streamflow variability is often associated with various large-scale, slowly evolving climate phenomena (e.g. ENSO), promoting the value of climate indices for streamflow forecast development, and has been investigated extensively. Separately, global climate models (GCMs), which provide climate forecasts out to 12-months globally, have been demonstrated to enhance seasonal streamflow predictability. However, neither the combination nor the interaction of these two sources of predictability has been given much attention. In this work, we propose a framework which can simultaneously account for antecedent climate indices and GCM forecasts in statistical streamflow forecasts and address how their interactions affect predictability. More specifically, we build a streamflow forecast framework combining statistical forecast models conditioned on climate indices with dynamical North American Multi-Model Ensemble (NMME) precipitation forecasts to generate streamflow forecast ensembles, and merge ensemble members with a BMA-bagging (Bayesian Model Averaging with bootstrap aggregating) approach. The framework is applied to streamflow on the Blue Nile River upstream of the Grand Ethiopian Renaissance Dam (GERD). Most NMME models tend to improve one-month-ahead GERD inflow forecasts however longer lead times prove challenging, and require specific NMME model selection. In contrast, although the value of climate indices varies with forecast lead time, their potential contribution grows at multi-month leads. The GERD inflow forecasts including NMME models and climate indices simultaneously can take advantage of both sources of predictability and prove superior across lead times as compared to including either source individually.

As Ethiopia's population grows, crop yield predictions are becoming increasingly important for national food security. Accurate, easy-to-implement, and computationally efficient forecast approaches are desirable for broad applications in emerging regimes like Ethiopia. In this study, we develop and test an analog approach for pre-season crop yield prediction conditioned on antecedent precipitation and planting time soil moisture content indices to guide cultivation decision making. Historical planting time soil moisture at four selected sites were simulated using the Coupled Routing and Excess STorage (CREST) hydrological model and classified into five levels. Likewise, a historical crop yield database for each of the five classes of planting time soil moisture were constructed using the Decision Support System for Agrotechnology Transfer (DSSAT) agricultural model. Adopting maize as a representative crop, analog models based on different indexes or predictors with various lead times were constructed and used to conduct hindcasts during 1979-2014 and real-time forecast in 2018 and 2019. Both the hindcast and real-time forecasts were then evaluated against yield observations. To verify the applicability at locations with various environments, the analog models were then applied in different Agro-ecological Zones. The analog models were shown to be accurate and easy to implement, which may incentivize adoption by local extension agents and regional agricultural agencies to inform farmers' crop choices.

Globally, the direct cost of natural disasters stands in the hundreds of billions of USD per year, at a time when water resources are under increasing stress and variability. Much of this burden rests on low- and middle-income countries that, despite their relative lack of wealth, exhibit considerable vulnerability such that losses measurably impact GDP. Within these countries, a growing middle class retains much of its wealth in property that may be increasingly exposed, while the few assets the poor may possess are often highly exposed. Vulnerability to extreme events is thus heterogeneous at both the global and subnational level. Moreover, the distribution and predictability of extreme events is also heterogeneous. Disaster managers and relief organizations are increasingly consulting operational climate information services as a way to mitigate the risks of extreme events, but appropriately targeting vulnerable communities remains a challenge. The advent of forecast-based anticipatory action has added to the suite of opportunities—and complexity—of operationalizing such services given varying prediction skill. Forecasts, including those at the subseasonal-to-seasonal (S2S) scale, may allow disaster managers to shift effort and therefore some risk from post-disaster response to pre-disaster preparedness; however, given the recent emergence of such programs, only a few, specific case studies have been evaluated. We therefore conduct a country-scale analysis pairing S2S forecast skill for monthly and seasonal lead times with flood and drought disaster risk to explore the potential for forecast-based anticipatory action programs broadly. To investigate subnational heterogeneity in risk and predictability, we also evaluate focused outcomes for the Greater Horn of Africa and Peru. Results suggest that forecast skill plays a large part in determining suitability for early action, and that skill itself varies considerably by disaster type, lead time, and location. Moreover, the physical and socioeconomic factors of risk can vary greatly between national and subnational levels, such that finer scale evaluations may considerably improve the effectiveness of early action protocols. By considering vulnerability at multiple spatial scales and forecast skill at multiple temporal scales, this analysis provides a first identification of promising locations for anticipatory action protocol development.

Probabilistic thinking underpins a wide range of scientific claims, but effectively communicating probabilistic information across audiences is challenging. In this article, we present a political–institutional approach to science that harnesses the social relationships between the people working as scientists and the public using scientific innovations. First, we show how we learned to use games and local analogies to effectively communicate probabilistic seasonal forecasts of weather and crop yields with farmers, extension workers, and water managers in Ethiopia. Second, we show how workshops—the unglamorous institutional workhorse of international development and scientific enterprises—became warmhearted events when organized around the fundamental fact of social connections between researchers and the community members and between the community members themselves. Scientists in an international scientific collaboration may not be able to become longstanding members of every community, but our approach to workshopping—and to research networks—allowed us to be engidoch (in English, guests), to tap into rich social ties to harness the humor, goodwill, and commitment that is hard to muster when scientists engage with community members as unconnected, nameless “workshop participants.”

In this qualitative study, we analyze the experiences of those living in flood-prone economically constrained communities by exploring reloca-tion, risk perceptions, and communication in the context of extreme seasonal flood disasters. Our study included semi-structured interviews with residents in three communities and unstructured interviews with local experts in Iquitos, Peru. Our results suggest that strategic communication plans and interventions for flood-prone communities should emphasize economic opportunities, rather than trying to emphasize flood risks, since the economic domain appears to be more salient for individuals living in these communities. Conversely, communication in relocated communities, should emphasize safety and overall quality of life, but also consider the economic stresses people face. Ultimately, communication and relief efforts related to addressing problems associated with disasters should start with an understanding of the experiences, perceptions, and communication practices of the communities they are assisting.

The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill, and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958–2000 with three operating schemes: basic control rules, perfect forecast-informed operations, and realistic forecast-informed operations. The realistic forecasts are issued by tailored statistical prediction models – based on lagged global and local hydroclimatic variables – predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill value and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and having the potential to reap benefits from forecast-informed operations and dams with a strong potential to benefit from forecast-informed operations but falling in regions lacking forecast accuracy. Overall, these results represent a first qualitative step toward informing site-specific hydropower studies.