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Characteristics of innovation: understanding the influence of Rogers' (2003) five innovation attributes on adoption: (a) in general; (b) for the Connecticut River Basin
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Technological advances in forecasting the Earth’s climate offer a potentially useful tool to support planning and management decisions in water resources. Previous research has found that the implementation of new ideas and practices are impeded by many challenges such as low forecast skill, institutional obstacles, and political disincentives to i...
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Citations
... This necessitates cross-sectoral efforts to develop integrated climatedriven early warning systems that leverage existing meteorological and health surveillance capacities across scale. Yet, much of the research and efforts toward improving the delivery and use of hydroclimatic information and disease surveillance capabilities in the South Asia region has at best been fragmented and sectorally focussed [66,[73][74][75][76][77]. The operationalisation of the One Health approach (which recognises the interconnectedness of human health, animal health and the environment, and advocates for integrated, holistic and transdisciplinary approaches to reduce disease impacts) is seen as a plausible vehicle for developing integrated solutions that support disease control interventions at scale [20,50,78]. ...
Background
Climate change is widely recognised to threaten human health, wellbeing and livelihoods, including through its effects on the emergence, spread and burdens of climate–and water-sensitive infectious diseases. However, the scale and mechanisms of the impacts are uncertain and it is unclear whether existing forecasting capacities will foster successful local-level adaptation planning, particularly in climate vulnerable regions in developing countries. The purpose of this scoping review was to characterise and map priority climate- and water-sensitive diseases, map existing forecasting and surveillance systems in climate and health sectors and scope out the needs and potential to develop integrated climate-driven early warning forecasting systems for long-term adaptation planning and interventions in the south Asia region.
Methods
We searched Web of Science Core Collection, Scopus and PubMed using title, abstract and keywords only for papers focussing on climate-and water-sensitive diseases and explicit mention of either forecasting or surveillance systems in south Asia. We conducted further internet search of relevant national climate adaptation plans and health policies affecting disease management. We identified 187 studies reporting on climate-sensitive diseases and information systems in the south Asia context published between 1992 and 2024.
Results
We found very few robust, evidenced-based forecasting systems for climate- and water- sensitive infectious diseases, which suggests limited operationalisation of decision-support tools that could inform actions to reduce disease burdens in the region. Many of the information systems platforms identified focussed on climate-sensitive vector-borne disease systems, with limited tools for water-sensitive diseases. This reveals an opportunity to develop tools for these neglected disease groups. Of the 34 operational platforms identified across the focal countries, only 13 (representing 38.2%) are freely available online and all were developed and implemented by the human health sector. Tools are needed for other south Asian countries (Afghanistan, Sri Lanka, Bhutan) where the risks of infectious diseases are predicted to increase substantially due to climate change, drought and shifts in human demography and use of ecosystems.
Conclusion
Altogether, the findings highlight clear opportunities to invest in the co-development and implementation of contextually relevant climate-driven early warning tools and research priorities for disease control and adaptation planning.
... The actual uptake of weather forecasts by water 30 managers varies across countries, sectors (e.g., water supply or hy-31 dropower production) and with lead time. For example, and even 32 though examples exist (e.g., Fleming et al. 2021), forecasts with a 33 lead time of over a month are still rarely used by water managers in 34 the US (Whateley et al. 2015;Turner et al. 2020) and in Europe 35 (Bruno Soares and Dessai 2016;Bruno Soares et al. 2018). This 36 is often traced back to the uncertainty and inaccuracy of these fore-37 cast products outside the tropics (Jackson-Blake et al. 2022), but 38 also to the challenges for managers to trial forecasts in low-stakes 39 situations (Whateley et al. 2015). ...
... For example, and even 32 though examples exist (e.g., Fleming et al. 2021), forecasts with a 33 lead time of over a month are still rarely used by water managers in 34 the US (Whateley et al. 2015;Turner et al. 2020) and in Europe 35 (Bruno Soares and Dessai 2016;Bruno Soares et al. 2018). This 36 is often traced back to the uncertainty and inaccuracy of these fore-37 cast products outside the tropics (Jackson-Blake et al. 2022), but 38 also to the challenges for managers to trial forecasts in low-stakes 39 situations (Whateley et al. 2015). 40 The idea that forecast inaccuracy is a key barrier to uptake by 41 water managers suggests a straightforward, or even proportional, 42 relationship between a forecast's ability to inform water manage-43 ment decisions-its value-and its predictive ability or skill. ...
A growing number of studies have investigated how forecast skill, i.e., predictive power, translates into forecast value, i.e., usefulness, 7 for improving forecast-informed decisions. The relationship between skill and value is widely understood to be complex and case-specific, 8 yet few methods enable its systematic exploration using realistic forecast errors. This paper addresses this gap by proposing a single-9 parameter linear scaling method to generate families of synthetic forecasts with the desired skill improvements on an existing hindcast 10 (a retrospective forecast of already-observed events). The method is applicable to any quantity for which a deterministic or ensemble 11 hindcast is available, and generates a set of forecasts with different skill but strictly proportional errors. This like-for-like comparison 12 preserves the autocorrelation and cross-correlations of errors, and opens the door for thorough, yet easily interpretable, explorations of the 13 relationship between skill and value of a realistic forecast. We apply this new method to seasonal precipitation hindcasts (produced by the 3 14 ECMWF-SEAS5 forecasting system) in order to explore their value for improving the management of a water supply system in the UK. 15 The application showed that although value generally increases with skill, the skill-value relationship is not necessarily linear, and it 16 strongly depends on operational preferences and hydrological conditions (wet or dry years). It also suggests that the forecast families 17 methodology can help water managers and forecast developers identify when a skill increase would be most valuable. This has the 18 potential to foster productive two-way conversations between forecast producers and users.
... For short-term operations at water utilities, the primary uncertainty arises from streamflow and short-term demand forecasts. Operational streamflow forecasts have increased application in facilitating water resources management in recent years (Golembesky et al. 2009;Gong et al. 2010;Whateley et al. 2014;Brown et al. 2015;Wang et al. 2020;Fry et al. 2020). Decision support tools incorporating uncertainty associated with streamflow and demand forecasts allow water managers to make better-informed decisions (Wang et al. 2020) while recognizing potential risks. ...
This study is motivated by multipleobjective optimization in short-term water management for a regional water utility. Although an increasing application of multi-objective evolutionary algorithm (MOEA) has been reported in the literature, we are not aware of its use for short-term water management by water utilities with diverse supply sources. This study presents an innovative practice for determining monthly resource allocation from multiple water supply sources that consider multiple objectives, including deviation from budgeted production , under or overutilization of a given portfolio of resources, and total cost of water production. This method is comprised of a simulation model, namely a production allocation model (PAM) and a MOEA. The decision variables of the MOEA optimization problem are monthly groundwater production from two groundwater wellfields. TheMOEA is used to search for Pareto optimal solutions across different objectives and the PAM uses MOEA output and considers operational constraints to determine water production from the other four supply sources in the decision horizon. Stochastic demand and supply realizations were generated to capture a wide range of uncertainties which were then sampled by a Latin Hyper Cube to make the computation tractable. A parallel computing environment was used to implement this near real-time decision support tool, providing timely guidance for water resources managers. One major difference between this study and many reported in the literature is that the MOEA was used to find Pareto solutions for each demand-supply realization rather than the entire ensemble. This setup allows water resources managers to explicitly explore Pareto solutions based on different supply and demand outlooks. The application of the innovative practice is demonstrated for a regional wholesale water supply utility, Tampa Bay Water, on the west coast of Florida in the United States. One additional advantage of MOEA-assisted planning is that it allows water managers to combine expert judgments and institutional knowledge in identifying solutions. A comparison between MOEA-assisted monthly production planning and heu-ristic planning reveals that the potential impact of short-term operations, e.g., deviation from budgeted production, is fully considered in a systematic approach. The proposed method can be applied to other regions with similar challenges in water resources management.
... The use of streamflow forecasts requires consideration of streamflow forecasting uncertainty (Zhao et al. 2011(Zhao et al. , 2012Chen et al., 2016;Turner et al. 2017) and specific application, which is beyond the scope of this paper. Nevertheless, the accuracy of streamflow forecasts is a key to its successful implementation, as water managers often hesitate to use forecasts to avoid potential risks (Whateley et al., 2015;Wang et al., 2021). The application of streamflow forecasts reported in the above-mentioned studies has provided insights that the accuracy of streamflow forecasts is critical for its practical applications. ...
Monthly streamflow forecasts have important practical applications in short-term water resources management, e.g., water allocation for different users, flooding prevention, and drought mitigation. This study focuses on developing categorical streamflow forecasts for flooding mitigation purpose, which can be used as a critical component in an early flood warning system. A Bayesian logistic regression approach is proposed to use antecedent streamflow and forecasted precipitation from General Circulation Models (GCMs) and derive the probability of streamflow greater than threshold streamflow. The logistic regression model is Hierarchical Bayesian Modeling that assumes Bernoulli distribution for monthly and Normal distribution for the parameters in the logistic function. To accommodate outliers in the modeling dataset, an additional parameter is added to the Bayesian modeling framework to make it a more robust approach. The Bayesian Logistic Regression is implemented in JAGS and posterior distributions of model parameters are estimated from Markov Chain Monte Carlo (MCM) chains. The proposed method is applied to a watershed in Hunan Province located in the middle south of China. Precipitation and streamflow data in the years 1960–2012 were used to estimate the model parameters’ posterior distributions. The model’s performance is tested for monthly streamflow data in the years 2013–2017, using one-month-ahead precipitation forecasts from GCMs. The model is superior to climatology, the reference model, in terms of the accuracy of hit rates. Potential improvement to the model is also discussed. Although the proposed method is demonstrated for the study area, it can be applied to other regions with similar applications.
... For instance, water managers are risk-averse. Even if the use of forecast information can improve outcomes on average, institutional use of forecast information can be challenged by occasional forecast errors (or events, given the low forecasted probability) that lead to poor decisions compared to status quo operations, particularly during extreme events (Rayner et al. 2005;Whateley et al. 2015). This point was underscored by Turner et al. (2017), who showed that the timing of forecast error is just as important as the average magnitude of forecast uncertainty from the perspective of forecast value. ...
... Despite publicly available hydroclimatic forecasts, for example, from NOAA Climate Prediction Center (CPC) or the International Research Institute for Climate and Society, water resource managers are not always receptive to using them (Dow et al. 2007;Lemos 2008;Bolson et al. 2013;Whateley et al. 2015;Misra et al. 2021). Among many reservations detailed in the literature, the general lack of adoption of these seasonal forecasts is partly due to the institutional inertia, lack of awareness of such data, and risk aversion from adopting new or innovative technologies. ...
We present here the analysis of 20 years of high-resolution experimental winter seasonal CLImate reForecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10km grid spacing from a global model run at T62 spectral resolution (~210km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida.
CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November-December-January (NDJ) and one-month lead for December-January-February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.
... Other research demonstrates that the risk perceptions and personal experience with risks are significant predictors of water managers' use of weather and climate forecasts (Lowrey et al. 2009;O'Connor et al. 2005). Institutional barriers (Rayner et al. 2005), forecast characteristics (Whateley et al. 2015), lack of awareness of forecasts, lack of forecast understanding, and limitations within forecasts themselves ) have been identified as reasons why stakeholders do not incorporate forecasts into decision-making. Although research has looked at these predictors and deterrents of using forecasts, limited research has examined perceptions about the attributes of flood forecasts that are likely to affect adoption of them, and scant research has examined stakeholders' flood forecast information-sharing behaviors. ...
Guided by the literature in diffusion of innovations, the technology acceptance model, and risk information sharing, this paper reports the results of a survey distributed to National Weather Service (NWS)-Memphis Weather Forecast Office (WFO) stakeholders who receive the Mississippi River Outlook product and its embedded 28-day experimental forecast. The survey examined perceptual factors that likely influence participants’ adoption of flood forecast information provided in the Outlook, and assessed Outlook recipients’ forecast-sharing behaviors and perceptions. Results revealed that the first responders perceived the Outlook product to be more useful than experts, while experts experienced less social influence to use it than first responders or the public. Although participants were generally favorable toward and intended to use the Outlook in the future, experts were significantly less likely to do so and hold a favorable attitude. The majority of participants reported sharing the Outlook with an average of 11 people, and were most likely to share either the entire Outlook verbatim or specific, verbatim sections. Implications of the Outlook’s perceived characteristics and participants’ Outlook-sharing behaviors are discussed.
... Although operating policies derived via optimization schemes such as DP and EMODPS may guarantee theoretically optimal alternatives, several factors have hindered their implementation in real-world reservoir operations (Quinn et al., 2019;Whateley et al., 2015), including the extremely risk-averse nature of decision makers (Watkins Jr and McKinney, 1997), administrative or legal constraints, and the lack of expert knowledge among related stakeholders. Alternately, decision makers have adopted various types of hedging rules on-site, such as one-point (Klemeš, 1977), two-point (Bayazit and Ünal, 1990), continuous (Hashimoto et al., 1982), and zonebased hedging (Shih and ReVelle, 1995), that conserve a portion of reservoir storage in case of future droughts (You and Cai, 2008a,b;Tu et al., 2008). ...
This study aims compares how different formulations of a reservoir operation problem with conflicting objectives affect the quality of the generated solution set. Six models were developed for comparative analysis: three using dynamic programming (DP) and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Afterward, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with zone-based hedging policy that is currently being applied in real-world reservoir operations. The solutions generated by each model were then evaluated regarding proximity to the ideal and three eminent performance indices (risk, resiliency, and vulnerability). The proposed methodology was applied to a multi-purpose reservoir located in South Korea, Boryeong Dam, which had suffered a multi-year drought recently. Consequently, the solution sets from the EMODPS model yielded closer results than those of the stochastic DP model for optimality and diversity. Although the solutions from the algorithm performed better than actual operation results under normal conditions, the actual operations executed based on the zone-based hedging rule outperformed the other two in case of droughts. Among the EMODPS models, one with the fewest parameters, the EMODPS-Gaussian model, resulted in better solutions for all cases. Finally, coupling the real-world policy with the optimally derived solutions in the case of droughts improved the frequency, duration, and magnitude of the water supplies whereas the water users experienced an improvement in scale at the expense of more recurrent failures.
... On the other hand, increased quality of water resources has the lowest frequency which can be related to the fact that it may have no explicit benefits to farmers, whereas it would be difficult to discuss it in the educational content without a clear understanding of its benefits. It is also believed that the visibility of results and benefits of an activity will affect its adoption and implementation by farmers (Whateley et al. 2015;Long et al. 2016). Also, the sub-theme of crop rotation has received the highest emphasis in the extension-educational content among all sub-themes of the theme of fertilizer, herbicide, and pesticide management. ...
Conservation agriculture (CA) is a tool of sustainable agriculture utilized in many countries. Sustainable agriculture has different dimensions among which the most important ones are environmental, social, economic, and institutional. Educational and extension programs of CA are in accordance with the principles of sustainable agriculture. Accordingly, educational and extension contents prepared for farmers should include the four dimensions of sustainability (environmental, social, economic, and institutional). The main objective of the present study was to examine the sustainability dimensions in the contents of CA training programs using an in-depth content analysis technique. Educational contents considered here included brochures, extension publications, regulations, and training slides. Based on the literature, themes and sub-themes for each of the four sustainability dimensions were extracted. Then, the frequency of each sub-theme in the CA training contents was counted. The findings showed differing emphasis on the sub-themes of sustainability dimensions in the contents of CA training programs so that the environmental and social dimensions had the highest and lowest frequencies, respectively. As the main principles of CA, no tillage/low tillage, crop rotation, and maintenance of crop residues constituted the sub-themes of the environmental dimension, and the emphasis was placed on this dimension reasonably and expectedly.
... Using inflow forecasts can improve the operation practices but the actual use of streamflow forecasts to support real-world operation problems still faces a number of obstacles (Rayner et al., 2005;Whateley et al., 2015), despite of research and institutional efforts such as the Integrated Forecast and Reservoir Management (INFORM) (Georgakakos et al., 2007) and Forecast Informed Reservoir Operations (FIRO) (Jasperse et al., 2017), and advances in forecasting skills (Whateley et al., 2015). The obstacles include insufficient forecast skills, complexities, trialability, observability of forecast adoption, the efforts needed to modify existing models to adopt forecast information, as well as institutional and political resistance (Rayner et al., 2005;Whateley et al., 2015). ...
... Using inflow forecasts can improve the operation practices but the actual use of streamflow forecasts to support real-world operation problems still faces a number of obstacles (Rayner et al., 2005;Whateley et al., 2015), despite of research and institutional efforts such as the Integrated Forecast and Reservoir Management (INFORM) (Georgakakos et al., 2007) and Forecast Informed Reservoir Operations (FIRO) (Jasperse et al., 2017), and advances in forecasting skills (Whateley et al., 2015). The obstacles include insufficient forecast skills, complexities, trialability, observability of forecast adoption, the efforts needed to modify existing models to adopt forecast information, as well as institutional and political resistance (Rayner et al., 2005;Whateley et al., 2015). ...
... Using inflow forecasts can improve the operation practices but the actual use of streamflow forecasts to support real-world operation problems still faces a number of obstacles (Rayner et al., 2005;Whateley et al., 2015), despite of research and institutional efforts such as the Integrated Forecast and Reservoir Management (INFORM) (Georgakakos et al., 2007) and Forecast Informed Reservoir Operations (FIRO) (Jasperse et al., 2017), and advances in forecasting skills (Whateley et al., 2015). The obstacles include insufficient forecast skills, complexities, trialability, observability of forecast adoption, the efforts needed to modify existing models to adopt forecast information, as well as institutional and political resistance (Rayner et al., 2005;Whateley et al., 2015). ...
The use of models and decision tools for real-world reservoir operations is limited due to the gap between the models/tools and the real-world practices, tedious amount of work in case-by-case developments, and computational difficulty of running complex numerical models. This paper presents generic diagnostic reservoir operation tools (DROT). The tools are developed based on generic properties derived from analytical optimization studies and data mining procedures. Instead of establishing a numerical model, DROT users can apply particular properties and/or procedures to diagnose a specific reservoir operation problem through providing required inputs. DROT is available online and provides auxiliary tools such as a data retrieval tool and a data visualization tool. DROT provides a software framework to include additional generic tools of models, algorithms, and functions. DROT can be used by reservoir operators, researchers, and students to obtain diagnostic information for the operation of any reservoir with a given purpose.
