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May-August global multivariate ENSO index (MEI) correlated with October-January streamflow at Algarrobal for (a) positive MEI years, (b) all MEI years, and (c) negative MEI years.
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In many semi-arid regions, multisectoral demands often stress available water
supplies. Such is the case in the Elqui River valley of northern Chile, which
draws on a limited-capacity reservoir to allocate 25 000 water rights.
Delayed infrastructure investment forces water managers to address
demand-based allocation strategies, particularly in dry...
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Statement-verification studies indicate the coexistence of intuitive conceptions and scientific concepts within learners. The underlying assumption is that the intuitive conceptions typically built in childhood never disappear, but are co-activated with scientific concepts when we face relevant situations. This is visible in increased reaction time...
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... The results are consistent with other streamflow prediction studies in the same basin, although they have different methodologies in terms of predictive performance. Likewise, they are consistent with the fact that predictive performance in the ERB tends to decrease as the prediction lead time increases [65]. In addition, the results are positive compared to those obtained historically on an official basis by the General Water Directorate of Chile in this basin, as studies indicate that no forecast performed in northern Chile has entered the "good forecast" category [43]. ...
... Regarding the most important variables for prediction, Figure 5 shows the mean importance of the variables by prediction model. prediction lead time increases [65]. In addition, the results are positive compared to those obtained historically on an official basis by the General Water Directorate of Chile in this basin, as studies indicate that no forecast performed in northern Chile has entered the "good forecast" category [43]. ...
... Indeed, El Niño has presented less predictive ability in the last decade due to the influence of other ocean climate factors, with the 500 hPa geopotential height in the Amundsen-Bellingshausen sector standing out [42,67]. Figure 6a,b presents the partial dependence plots (PDP), also known as partial dependence profiles, for both models, to show how the expected prediction value behaves as a function of some variable of interest, using the most important variable in each model [56,62,63]. The graphs highlight two important aspects for interpretation: first, it is observed that the individual ceteris paribus (CP) profiles, similar to the individual conditional expectation (ICE) plots, are parallel, which is indicative of an additive model without interaction between predictors, and facilitating extrapolation in the predictor space by making the PDP adequately represent the profile of each instance [56,65,68]. Second, the graphs show that for values between −1 and 1 SD from the predictor mean, the estimated streamflow at Río Elqui en Algarrobal presents a low linear increase, although it is clearly staggered throughout the prediction domain. ...
In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making.
... It typically combines hydrological models with ensemble Quantitative Precipitation Forecasts (QPF), resulting in probabilistic estimates of future streamflow (Cloke and Pappenberger, 2009), and the QPF are the source of information on the future water input. This method has been widely used in different works (Buizza et al., 2005;De Paiva et al., 2020;Delorit et al., 2017;Demirel et al., 2015;Hersbach, 2000;Van Dijk et al., 2013;Van Hateren et al., 2019). ...
... One can find in the literature various works that investigate monthly to seasonal streamflow forecasts at basin scales (Collischonn et al., 2005;De Paiva et al., 2020;Delorit et al., 2017;Demirel et al., 2015;Kompor et al., 2020;Mahanama et al., 2012;Peñuela et al., 2020;Tucci et al., 2003;Van Hateren et al., 2019). These works raised important conclusions at the basin level, ranging from 1000 to 1.3 M km 2 . ...
Study region: South America large basins (>5000 km 2) Study focus: This work represents a first assessment of seasonal streamflow forecasts in South America based on a continental-scale application of a large scale hydrologic-hydrodynamic model and ECMWF's seasonal forecasting system precipitation forecasts (SEAS5-SSF) with bias correction. Seasonal streamflow forecasts were evaluated against a reference model run. Forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. New hydrological insights: We observed that bias correction was essential to obtain positive skill of SEAS5-SSF over ESP, which remained a hard to beat benchmark, especially in regions with high seasonality, and highly dependent on initial conditions. SEAS5-SSF skill was found to be dependent on initialization month, basin and lead time. Rivers where the skill is higher were Amazon, Araguaia, Tocantins and Paraná.
... Recent improvements in the skill of streamflow forecasts, with lead times ranging from a few hours (Chang et al. 2020;Ossandón et al. 2021;Rasouli et al. 2012) to several months (Getirana et al. 2020;Yang et al. 2017), are generating a growing interest in testing forecasts in reservoir operation (Anghileri et al. 2016;Bertoni et al. 2021;Delaney et al. 2020;Denaro et al. 2017;Eldardiry and Hossain 2021;Giuliani et al. 2019;Nayak et al. 2018;Yang et al. 2020;Zarei et al. 2021;Zhao et al. 2012). Most of these studies reveal that the assimilation of forecasts into operational models produces an improvement in system performance, with more accurate forecasts generally yielding higher operational values (Alexander et al. 2021;Delorit et al. 2017;Doering et al. 2021;Giuliani et al. 2020). However, the accuracy of forecast models does not necessarily yield proportional marginal benefits in terms of system operations: Recent research has shown that the relationship between forecast skill and value is modulated by multiple factors, such as the operating objectives or dam design specifications (Lee et al. 2022;Ramos et al. 2013;Turner et al. 2017;Yang et al. 2021). ...
Seasonal streamflow forecasts are becoming widely used to improve water reservoir operations, especially in areas where climate teleconnections enable predictability on medium and long lead times. Most existing studies have focused on the assimilation of forecasts into operational decision models, an approach that typically banks on predeveloped forecasts to optimize water release decisions. However, this approach may overlook the potential synergies that stand in co-developing forecast and decision-making models. In other words, the opportunities that lie in coupling both forecast and operational decision models have not yet been explored. Here, we address this gap and contribute a novel approach building on the Evolutionary Multi-Objective Direct Policy Search algorithm to design forecast and decision-making models together. The proposed approach is benchmarked against operating policies not informed by any forecast, as well as by forecast-informed policies relying on predeveloped forecasts (data-driven and perfect). Numerical experiments are conducted on the Angat-Umiray water resources system, Philippines, which is operated primarily for ensuring municipal water supply to Metro Manila and irrigation supply to a large agricultural district. Our results show that the integrated design of forecast models and control policies provides a performance gain with respect to policies informed by predesigned forecasts. This result is particularly interesting because the skill of the integrated forecast models is lower than that of the predeveloped ones, thus suggesting that more accurate forecasts do not necessarily produce better water system operations. Overall, our analysis represents a step towards a deeper integration of streamflow forecast and reservoir operation models.
... Statistical, climate-driven prediction models have been used across many fields to gain insight into past and future impacts on operations and to inform policy. Models applied to the management of built and natural systems are applied broadly and produce results with varying degrees of deterministic and probabilistic skill (Delorit et al 2017, Graafland et al 2020, Theusme et al 2021, Zeng et al 2020. Models have been developed for the energy sector at a wide range of temporal, spatial, and organizational scales, and are most commonly calibrated to evaluate energy consumption with mention of climate impacts (Amato et Climate-driven, empirical statistical prediction models are developed using a variety of climate inputs, spatial scales, and regression techniques. ...
... PCR is a multiple linear regression that uses the principal components (PCs) generated through a PCA of the model inputs. Delorit et al (2017) explain that PCR is commonly applied in forecasting and hindcasting to reduce both variable dimensionality and multicollinearity, and result in a set of PCs that represent the variance in a set of predictors. First, PCA is conducted where input variables are broken down into their PCs. ...
Climate variability creates energy demand uncertainty and complicates long-term asset management and budget planning. Without understanding future energy demand trends related to intensification of climate, changes to energy consumption could result in budget escalation. Energy demand trends can inform campus infrastructure repair and modernization plans, effective energy use reduction policies, or renewable energy resource implementation decisions, all of which are targeted at mitigating energy cost escalation and variability. To make these long-term management decisions, energy managers require unbiased and accurate energy use forecasts. This research uses a statistical, model-based forecast framework, calibrated retrospectively with open-source climate data, and run in a forecast mode with CMIP5 projections of temperature for RCPs 4.5 and 8.5 to predict total daily energy consumption and costs for a campus-sized community (population: 30,000) through the end of the century. The case study of Wright-Patterson AFB is contextualized within the existing Executive Orders directing net-zero emissions and carbon-free electricity benchmarks for the federal government. The model suggests that median annual campus electric consumption, based on temperature rise alone, could increase by 4.8% with RCP4.5 and 19.3% with RCP8.5 by the end of the century, with a current carbon footprint of 547 million kg CO2e. Monthly forecasts indicate that summer month energy consumption could significantly increase within the first decade (2020-2030), and nearly all months will experience significant increases by the end of the century. Therefore, careful planning is needed to meet net-zero emissions targets with significant increases in electricity demands under current conditions. Policies and projects to reduce the carbon footprint of federal agencies need to incorporate forecasting models to understand changes in demand to appropriately size electric infrastructure.
... Cross-validation further promotes model fairness by removing the dependent variable observation being predicted. A PCR-based statistical approach is chosen because of its proven capabilities to fairly model the impact of climate on the built and natural environment (Delorit et al. 2017). ...
... Cross-validation is a bias-reduction technique. It was used to temporarily remove, or drop, the dependent variable observation for the time step, or time steps, surrounding the PCR prediction such that "perfect targets" are eliminated from consideration (Delorit et al. 2017). Since only the predicted condition value is dropped, the form used herein is referred to as drop-one crossvalidation. ...
... Understanding the intricate hydrologic and water quality processes within the catchment is essential for the successful application of hydrological models in water resource management [2][3][4][5]. Many commercial and opensource hydrologic watershed models (e.g., HSPF (Hydrological Simulation Program-Fortran), SWAT (Soil and Water Assessment Tool), WASP (Water Quality Analysis Simulation Program), etc.) have been developed in the past few decades with the rapid development of computing power and software technology [6]. ...
Sensitivity and uncertainty analyses are crucial for the quality control and application of hydrological models in water resources management. While sensitivity analysis identifies major input parameters affecting model response, uncertainty analysis evaluates the uncertainty in model predictions. In this analysis, a Hydrological Simulation Program-Fortran (HSPF) model was developed for simulating surface and sub-surface hydrology, and creek flows in the South Chickamauga Creek Watershed, TN. The HSPF model was calibrated against USGS observed data from January 2013 to December 2017 and was validated for the period January 2018 to October 2019. Parameter Sensitivity analysis using the one-at-a-time (OAT) perturbation method revealed that watershed hydrology was highly sensitive to evapotranspiration and groundwater-related parameters. Uncertainty analysis with Monte Carlo and Latin Hypercube parameter sampling demonstrated the highest uncertainty in extreme flows and least uncertainty in annual runoff predictions. Overall, model output distribution was more uniform and achieved convergence faster with Latin hypercube sampling.
... Combinations of antecedent global atmospheric phenomena and local conditions have proven skillful in the prediction of the hydroclimate and streamflow at various spatiotemporal resolutions, from global (Zimmerman et al., 2016) to regional (Delorit et al., 2017), and the subseasonal to seasonal scales (Robertson et al., 2015). Only recently have these forecasts been coupled with sectoral economic models, decision support tools, agent-based social simulators, and robust optimization frameworks as a means of translating skill to operational value (Anghileri et al., 2016;Delorit et al., 2019;Giuliani et al., 2020;Ziervogel et al., 2005). ...
Water resources are required for cooling of thermoelectric power plants and in the production of hydroelectricity. Scarcity of water resources impacts the ability to generate electricity in grids across the globe. There is extensive literature and research on the electricity‐water nexus, spanning hydrology, policy, and energy sectors. Existing research often focuses on quantifying a static relationship and rarely accounts for expectations of annual, seasonal, and subseasonal water variability in nexus research. This omission leaves an important, unanswered question in the field: how can the water footprinting framework be operationalized in the electricity‐water nexus with hydroclimatic forecasts? Building off the work by Chowdhury et al. (https://doi.org/10.1029/2020EF001814), we comment on the opportunities for climate‐informed, seasonal, or subannual assessments of the electricity‐water nexus to facilitate decision‐making.
... Hydrological models are simplified versions of real-world systems that are capable of simulating runoff and channel routing based on rainfall intensity and topography . Application of hydrological models has become an essential tool for understanding the intricate hydrologic and water quality processes within the catchment in addition to their use in watershed management practices (Bicknell et al. 2005;Roy et al. 2020;Delorit, Ortuya, and Block 2017;Turner et al. 2017). Many commercial and opensource hydrologic watershed models (e.g., HSPF, SWAT, WASP etc.) have been developed in the past few decades with the rapid development of computing power and software technology (Li et al. 2015). ...
Sensitivity analysis is an important assessment usually performed to gain a better understanding of the hydrological processes impacting model outcomes as well as to identify most dominant parameters for model optimization and calibration. In this study, a watershed model developed using the Hydrologic Simulation Program–Fortran (HSPF) for South Chickamauga Creek watershed was evaluated for parameter sensitivity. Critical HSPF model parameters, i.e., lower zone evapotranspiration (LZETP), index to infiltration capacity (INFILT), base groundwater recession (AGWRC), interflow inflow (INTFW), fraction of groundwater inflow to deep losses (DEEPFR), etc., were investigated for the sensitivity analysis. This paper presents the sensitivity analysis results using sensitivity factor, which is expressed as a ratio of the percentage change in model output (i.e., runoff, peak flow) to percentage change in model input parameter (expressed as percentage) and explains the variability of model outcomes from the selected perturbations of input parameters. Low flow conditions were mostly influenced by AGWRC, DEEPFR, LZETP, and INFILT, whereas INFILT and INTFW had most influence on the peak flows. Annual runoff volume and high flow conditions were highly sensitive to LZETP. However, AGWRC was found to be the most influential parameter in all hydrologic responses in this watershed. Overall, this study helps watershed management by providing insight into model structure and adjustment. Besides, it will work as a basis for future multisite sensitivity and uncertainty analysis.
... Trying to draw deterministic conclusions from probabilistic information was observed to be common. In practice, seasonal climate forecasts include probabilistic information, under the assumption that illustrating a potential shift towards wetter or drier conditions may better inform decision-making (Block and Rajagopalan 2007;Block et al. 2009;Delorit et al. 2017;Alexander et al. 2019). Yet, risk communication literature confirms that people want certainty and do not think in terms of probability (Freudenburg 1988). ...
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.
... If such trends continue, many regions of India will face critical levels of water scarcity during the dry season exacerbated by climate change, causing conflicts amongst sectors and regions, and affecting food supply and livelihoods [1,2,13,15]. The sustainable management of water resources across India, and the means to achieve this, is imperative going forward [16]; water availability and appropriate allocation are therefore likely to become an even more prominent issue in the near future [17,18]. ...
The Narmada river basin is a highly regulated catchment in central India, supporting a population of over 16 million people. In such extensively modified hydrological systems, the influence of anthropogenic alterations is often underrepresented or excluded entirely by large-scale hydrological models. The Global Water Availability Assessment (GWAVA) model is applied to the Upper Narmada, with all major dams, water abstractions and irrigation command areas included, which allows for the development of a holistic methodology for the assessment of water resources in the basin. The model is driven with 17 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to assess the impact of climate change on water resources in the basin for the period 2031–2060. The study finds that the hydrological regime within the basin is likely to intensify over the next half-century as a result of future climate change, causing long-term increases in monsoon season flow across the Upper Narmada. Climate is expected to have little impact on dry season flows, in comparison to water demand intensification over the same period, which may lead to increased water stress in parts of the basin.
