Water Supply Forecasts Research Articles

Operational Seasonal Water Supply and Water Level Forecasting for the Laurentian Great Lakes

AbstractSeasonal water supply forecasts are a critical component of regional water resources management planning. Across the United States, multiple modeling tools and operational protocols have ev...

Comparison and discussion of water supply and demand forecasts considering spatial resolution in the Han-river basin

Comparison and discussion of water supply and demand forecasts considering spatial resolution in the Han-river basin

Evaluating Operational Hydrodynamic Models for Real‐time Simulation of Evaporation From Large Lakes

AbstractMethods for simulating evaporative water loss from Earth's large lakes have lagged behind advances in hydrodynamic modeling. Here we explore use of oceanographic models to simulate lake evaporation from a long‐term water balance perspective. More specifically, we compare long‐term monthly simulations of latent heat flux from two configurations of a current operational hydrodynamic forecasting system (based on the Finite Volume Community Ocean Model, or FVCOM) for the Laurentian Great Lakes. We then compare these simulations to comparable simulations from a legacy conventional lake thermodynamics model, and from a recently developed statistical water balance model. We find that one of the FVCOM configurations that is currently used in operations for short‐term hydrodynamic forecast guidance is also suitable for real‐time simulation of evaporation from very large lakes. The operational versions of FVCOM should therefore be considered a readily available tool for supporting regional water supply management and, pending further research, extended water supply forecasting.

Water Resources Utilization and Protection in the Coal Mining Area of Northern China

This study investigates multiple types of water resources in the western mining area in China, their supply-demand balance by using the same dimension gray recurrence dynamic model GM (1, 1), and water content coefficients of mines to ease water supply-demand contradiction. A multi-objective programming model is proposed for optimal water resources allocation management. Optimal technical schemes for water resources allocation among different users are obtained. The optimization model improves upon the previous studies by using water demand and water supply forecast. Coordinated development for mining safety, mine water utilization, and remediation and control of water environment is achieved.

A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West

Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, flood control, recreational uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existing system; uncertainty estimates that seamlessly handle complex (heteroscedastic, non-Gaussian) prediction errors; integration of physical hydrometeorological process knowledge and domain-specific expert experience; ability to accommodate nonlinearity, model selection uncertainty and equifinality, and predictor multicollinearity and high dimensionality; and relatively easy, low-cost implementation. Some methods satisfied some of these requirements but none met all, leading us to develop a novel, interdisciplinary, and pragmatic prediction metasystem through a carefully considered synthesis of well-established, off-the-shelf components and approaches, spanning supervised and unsupervised machine learning, nonparametric statistical modeling, ensemble learning, and evolutionary optimization, focusing on maintaining but radically updating the principal components regression framework widely used for WSF. Testing this integrated multi-method prediction engine demonstrated its value for river forecasting; USDA adoption is a landmark for transitioning machine learning from research into practice in this field. Its ability to handle all the foregoing design criteria and requirements, which are not unique to WSF, suggests potential for extension to complex probabilistic prediction problems in other fields.

Moisture transport associated with large precipitation events in the Upper Colorado River Basin

Interannual streamflow variability poses numerous water supply forecasting and management challenges for mountain snowpack‐sourced basins across the western United States. Previous studies have shown that substantial proportions of annual precipitation occur during relatively few large precipitation events (LPEs) and consequently, such events tend to be significant predictors of streamflow for many basins, including the Upper Colorado River Basin (UCRB). While some primary pathways of atmospheric moisture transport, particularly atmospheric rivers, have been linked with extreme precipitation events across the western United States, this study seeks to expand on this knowledge by quantifying the relationships between moisture transport regimes, LPEs, and streamflow observed in the UCRB. A climatological analysis of integrated vapour transport, via a self‐organizing maps synoptic classification procedure, is used to identify geographic regimes of moisture transport that lead to LPEs, as observed at snow telemetry sites across the UCRB headwaters. Trajectory analyses are also calculated to further characterize LPE‐inducing atmospheric river pathways by subbasin in the UCRB. Results indicate that LPEs throughout the basin most commonly coincide with amplified trough patterns, which advect Pacific moisture into the UCRB from the southwest, through Southern California and the Four Corners region; patterns that are positively correlated to streamflow. LPEs occurring in northern portions of the UCRB exhibit a secondary corridor, featuring west to east‐oriented moisture transport, in association with zonal to meridional flow regimes. Highly amplified ridges over the region rarely lead to LPEs, as such patterns deflect or suppress moisture from the UCRB. These results suggest that climate projections of future enhanced meridional flow aloft will lead to continued streamflow variability in the UCRB.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Twenty-Seven Years of Manual Fresh Snowfall Density Measurements on Whistler Mountain, British Columbia

ABSTRACTSnow density is important information for a wide range of activities including avalanche control, marketing, building-code development, weather forecasting, and water supply forecasting. Extended recent high-quality datasets from the mountainous regions of the Pacific Northwest coastal area are rare. This paper presents a study of an unusually long and continuous (January 1990 to April 2016) manually collected dataset of fresh snowfall measurements for Whistler Mountain, British Columbia, Canada. The dataset consists of snowboard core measurements that were collected by Whistler–Blackcomb ski patrol staff twice daily for avalanche control and resort-marketing purposes. These records were collated, transcribed, quality controlled, and made computer accessible in this study. A discussion of the characteristics of the data collection site and an assessment of data reliability are presented. Two examples of the many purposes to which this high-quality dataset might be put were studied. Climatic teleconnections to winter (December–February) mean snow density were examined, which revealed a positive relationship to the quadratic form of the Pacific Decadal Oscillation pattern (i.e., PDO2). In addition, an analysis of daily snow density relationships to air mass types was performed, which suggested that higher (lower) densities are associated with maritime inflow (arctic outflow) conditions. Both of these relationships appear to be mediated by the positive correlation between snow density and air temperature.Based on the full dataset (N = 1275 individual snow density measurements) for all months with measured snowfall, annual snowfall season (November to May) mean snow densities ranged from 77 kg m−3 to 109 kg m−3 with an overall mean of 91 kg m−3, giving an overall snow-depth to water-depth ratio of 11:1.

Verification of Ensemble Water Supply Forecasts for Sierra Nevada Watersheds

This study verifies the skill and reliability of ensemble water supply forecasts issued by an innovative operational Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) at eight Sierra Nevada watersheds in the State of California. The factors potentially influencing the forecast skill and reliability are also explored. Retrospective ensemble forecasts of April–July runoff with 60 traces for these watersheds from 1985 to 2010 are generated with the HEFS driven by raw precipitation and temperature reforecasts from operational Global Ensemble Forecast System (GEFS) for the first 15 days and climatology from day 16 up to day 365. Results indicate that the forecast skill is limited when the lead time is long (over three months or before January) but increases through the forecast period. There is generally a negative bias in the most probable forecast (median forecast) for most study watersheds. When the mean forecast is investigated instead, the bias becomes mostly positive and generally smaller in magnitude. The forecasts, particularly the wet forecasts (with less than 10% exceedance probability) are reliable on the average. The low April–July flows (with higher than 90% exceedance probability) are forecast more frequently than their actual occurrence frequency, while the medium April–July flows (90% to 10% exceedance) are forecast to occur less frequently. The forecast skill and reliability tend to be sensitive to extreme conditions. Particularly, the wet extremes show more significant impact than the dry extremes. Using different forcing data, including pure climatology and Climate Forecast System version 2 (CFSv2) shows no consistent improvement in the forecast skill and reliability, neither does using a longer (than the study period 1985–2010) period of record. Overall, this study is meaningful in the context of (1) establishing a benchmark for future enhancements (i.e., newer version of HEFS, GEFS and CFSv2) to ensemble water supply forecasting systems and (2) providing critical information (on what skill and reliability to expect at a given lead time, water year type and location) to water resources managers in making uncertainty-informed decisions in maximizing the reliability of the water supply.

Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds

Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds

Climate change, water rights, and water supply: The case of irrigated agriculture in Idaho

AbstractWe conduct a hedonic analysis to estimate the response of agricultural land use to water supply information under the Prior Appropriation Doctrine by using Idaho as a case study. Our analysis includes long‐term climate (weather) trends and water supply conditions as well as seasonal water supply forecasts. A farm‐level panel data set, which accounts for the priority effects of water rights and controls for diversified crop mixes and rotation practices, is used. Our results indicate that farmers respond to the long‐term surface and ground water conditions as well as to the seasonal water supply variations. Climate change‐induced variations in climate and water supply conditions could lead to substantial damages to irrigated agriculture. We project substantial losses (up to 32%) of the average crop revenue for major agricultural areas under future climate scenarios in Idaho. Finally, farmers demonstrate significantly varied responses given their water rights priorities, which imply that the distributional impact of climate change is sensitive to institutions such as the Prior Appropriation Doctrine.

Parabolic northern-hemisphere river flow teleconnections to El Niño-Southern Oscillation and the Arctic Oscillation

It is almost universally assumed in statistical hydroclimatology that relationships between large-scale climate indices and local-scale hydrometeorological responses, though possibly nonlinear, are monotonic. However, recent work suggests that northern-hemisphere atmospheric teleconnections to El Niño-Southern Oscillation (ENSO) and the Arctic Oscillation can be parabolic. The effect has recently been explicitly confirmed in hydrologic responses, though associations are complicated by land surface characteristics and processes, and investigation of water resource implications has been limited to date. Here, we apply an Akaike Information Criterion-based polynomial selection approach to investigate annual flow volume teleconnections for 42 of the northern hemisphere’s largest ocean-reaching rivers. Though we find a rich diversity of responses, parabolic relationships are formally consistent with the data for almost half the rivers, and the optimal model for eight. These highly nonlinear water supply teleconnections could radically alter the standard conceptual model of how water resources respond to climatic variability. For example, the Sacramento river in drought-ridden California exhibits no significant monotonic ENSO teleconnection but a 0.92 probability of a quadratic relationship, reducing mean predictive error by up to 65% and suggesting greater opportunity for climate index-based water supply forecasts than previously appreciated.

An analysis of irrigated agricultural outcomes under the prior appropriation doctrine: hypotheses and applications

This article measures irrigated agricultural outcomes under the prior appropriation doctrine by developing a model of farmers’ land allocations in response to water supply change. The modelling approach considers the institutional factors of water rights and permits the inclusive determination of water and land allocations. We utilize farm-level data of irrigated agriculture in Water District #1 in Idaho to examine the predictions from our theoretical model. The water rights priority date is consolidated at the farm level and used to differentiate farmers’ responses. We test a set of hypotheses that relate to water supply and crop revenue. Our results suggest that the water rights priority has a profound impact on agricultural outcomes, indicating strong institutional effects and weak influence of market-based approaches. Farmers respond to both long-term and seasonal water supply change and variation, and they respond to seasonal water supply forecasts in varied ways depending on their water rights portfolios, thus signalling a disproportionate distribution of potential impacts of water supply change.

Western U.S. Water Supply Forecasting: A Tradition Evolves

Since 2011, the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and three major river forecasting centers of the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) have discontinued a long‐standing practice of publishing consensus water supply forecasts in western U.S. river basins.

Informing Hydrometric Network Design for Statistical Seasonal Streamflow Forecasts

Abstract A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.

The Role of Large-Scale Climate Modes in Regional Streamflow Variability and Implications for Water Supply Forecasting: A Case Study of the Canadian Columbia River Basin

The impacts of large-scale modes of climate variability on the annual cycle of terrestrial hydrometeorology in the Canadian Columbia River basin were assessed with the aim of updating our current understanding and identifying opportunities for climate-informed, early-season water supply forecasting. Composite analyses of streamflow from seven Water Survey of Canada gauging stations conditional on El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Pacific/North American pattern (PNA), Arctic Oscillation (AO), and North Pacific Gyre Oscillation (NPGO) states revealed that hydrological impacts of a climate mode could be manifested through changes in the annual runoff volume and/or changes in seasonal runoff patterns. Responses were generally non-linear. Considering ENSO and the PDO, for instance, streamflow anomalies associated with their warm phases contrast with those associated with their cool phases; however, the warm phases tend to produce more consistent streamflow responses than the cool phases. More profoundly, the PNA and AO streamflow responses appear to be highly asymmetrical—only one phase (positive PNA and negative AO) is shown to significantly affect streamflow. Some North Pacific climate indices and ENSO show reasonably consistent and strong correlations with streamflow, which suggests that further refinement of climate-informed early season water supply forecasting is possible. It is shown that further improvement of forecast skills can be attained if North Pacific climate information is included in addition to ENSO in the current generation of operational statistical water supply forecast models. RÉSUMÉ [Traduit par la rédaction] Nous avons estimé les répercussions des modes de variabilité climatique à grande échelle sur le cycle annuel de l'hydrométéorologie terrestre dans le bassin canadien du fleuve Columbia dans le but d'en actualiser notre compréhension et d'identifier des possibilités de prévisions d'apport d'eau fondées sur le climat faites en début de saison. Des analyses composites de l’écoulement fluvial faites à partir de sept stations hydrométriques de la Division des relevés hydrologiques du Canada en relation avec les états de l'oscillation australe El Niño (ENSO), de l'oscillation décennale du Pacifique (PDO), de la configuration Pacifique/Amérique du Nord (PNA), de l'oscillation arctique (AO) et de l'oscillation du gyre du Pacifique Nord (NPGO) ont révélé que les répercussions hydrologiques d'un mode climatique pouvaient se manifester comme des changements dans le volume d’écoulement annuel ou des changements dans les configurations d’écoulement saisonnier. Les réponses étaient en général non linéaires. Dans le cas de l'ENSO et de la PDO par exemple, les anomalies d’écoulement fluvial liées à leurs phases chaudes contrastent avec celles liées à leurs phases froides; les phases chaudes ont toutefois tendance à produire des réponses d’écoulement fluvial plus cohérentes que les phases froides. Plus en profondeur, les réponses de l’écoulement fluvial à la PNA et à l'AO apparaissent très asymétriques –une seule phase (la PNA positive et l'AO négative) semble modifier l’écoulement fluvial de façon appréciable. Certains indices climatiques du Pacifique Nord et l'ENSO affichent des corrélations raisonnablement régulières et fortes avec l’écoulement fluvial, ce qui donne à penser qu'il est possible d'améliorer les prévisions d'apport d'eau fondée sur le climat en début de saison. Nous montrons qu'il est possible d'améliorer l'habileté des prévisions si l'information climatique du Pacifique Nord est incluse en plus de l'ENSO dans la génération actuelle des modèles opérationnels statistiques de prévision d'apport d'eau.

Using large‐scale climatic patterns for improving long lead time streamflow forecasts for Gunnison and San Juan River Basins

AbstractIn a water‐stressed region, such as the western United States, it is essential to have long lead times for streamflow forecasts used in reservoir operations and water resources management. Current water supply forecasts provide a 3‐month to 6‐month lead time, depending on the time of year. However, there is a growing demand from stakeholders to have forecasts that run lead times of 1 year or more. In this study, a data‐driven model, the support vector machine (SVM) based on the statistical learning theory, was used to predict annual streamflow volume with a 1‐year lead time. Annual average oceanic–atmospheric indices consisting of the Pacific decadal oscillation, North Atlantic oscillation (NAO), Atlantic multidecadal oscillation, El Niño southern oscillation (ENSO), and a new sea surface temperature (SST) data set for the ‘Hondo’ region for the period of 1906–2006 were used to generate annual streamflow volumes for multiple sites in the Gunnison River Basin and San Juan River Basin, both located in the Upper Colorado River Basin. Based on the performance measures, the model showed very good forecasts, and the forecasts were in good agreement with measured streamflow volumes. Inclusion of SST information from the Hondo region improved the model's forecasting ability; in addition, the combination of NAO and Hondo region SST data resulted in the best streamflow forecasts for a 1‐year lead time. The results of the SVM model were found to be better than the feed‐forward, back propagation artificial neural network and multiple linear regression. The results from this study have the potential of providing useful information for the planning and management of water resources within these basins. Copyright © 2012 John Wiley & Sons, Ltd.

Invited Commentary: Themes and Issues from the Workshop Operational River Flow and Water Supply Forecasting

River flow and water supply forecasting are important applications of hydrologic sciences. Water management decisions and the issuance of flood warnings depend greatly upon real-time predictions of hydrologic events to come. A group of over 70 hydrologic practitioners and researchers gathered for a workshop devoted to operational hydrologic forecasting, which was held in Vancouver, British Columbia, on 67 October 2011. The goal of the workshop was to share ideas about the applicability of new operational forecasting techniques. Presentations were given on topics such as ensemble forecasting, verification of deterministic and probabilistic forecasts, and quality control of environmental data. This commentary summarizes these presentations and provides highlights from the group and open forum discussions.

Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

The distributed model intercomparison project – Phase 2: Motivation and design of the Oklahoma experiments

► We introduce the 2nd phase of the Distributed Model Intercomparison Project. ► Experiments include simulations of soil moisture and streamflow. ► Results for DMIP 2 Oklahoma experiments presented in companion paper. The Office of Hydrologic Development (OHD) of the National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) conducted the second phase of the Distributed Model Intercomparison Project (DMIP 2). After DMIP 1, the NWS recognized the need for additional science experiments to guide its research-to-operations path towards advanced hydrologic models for river and water resources forecasting. This was accentuated by the need to develop a broader spectrum of water resources forecasting products (such as soil moisture) in addition to the more traditional river, flash flood, and water supply forecasts. As it did for DMIP 1, the NWS sought the input and contributions from the hydrologic research community. DMIP 1 showed that using operational precipitation data, some distributed models could indeed perform as well as lumped models in several basins and better than lumped models for one basin. However, in general, the improvements were more limited than anticipated by the scientific community. Models combining so-called conceptual rainfall-runoff mechanisms with physically-based routing schemes achieved the best overall performance. Clear gains were achieved through calibration of model parameters, with the average performance of calibrated models being better than uncalibrated models. DMIP 1 experiments were hampered by temporally-inconsistent precipitation data and few runoff events in the verification period for some basins. Greater uncertainty in modeling small basins was noted, pointing to the need for additional tests of nested basins of various sizes. DMIP 2 experiments in the Oklahoma (OK) region were more comprehensive than in DMIP 1, and were designed to improve our understanding beyond what was learned in DMIP 1. Many more stream gauges were located, allowing for more rigorous testing of simulations at interior points. These included two new gauged interior basins that had drainage areas smaller than the smallest in DMIP 1. Soil moisture and routing experiments were added to further assess if distributed models could accurately model basin-interior processes. A longer period of higher quality precipitation data was available, and facilitated a test to note the impacts of data quality on model calibration. Moreover, the DMIP 2 calibration and verification periods contained more runoff events for analysis. Two lumped models were used to define a robust benchmark for evaluating the improvement of distributed models compared to lumped models. Fourteen groups participated in DMIP 2 using a total of sixteen models. Ten of these models were not in DMIP 1. This paper presents the motivation for DMIP 2 Oklahoma experiments, discusses the major project elements, and describes the data and models used. In addition, the paper introduces the findings, which are covered in a companion results paper ( Smith et al., this issue ). Lastly, the paper summarizes the DMIP 1 and 2 experiments with commentary from the NWS perspective. Future papers will cover the DMIP 2 experiments in the western USA mountainous basins.