scholarly journals A Framework for Advancing Streamflow and Water Allocation Forecasts in the Elqui Valley, Chile

2017 ◽  
Author(s):  
Justin Delorit ◽  
Edmundo Cristian Gonzalez Ortuya ◽  
Paul Block
Keyword(s):  
Arid Regions ◽  
Prediction Models ◽  
Statistical Prediction ◽  
Lead Times ◽  
Decision Points ◽  
Streamflow Forecasts ◽  
Water Right ◽  
Infrastructure Investments ◽  
Annual Water ◽  
Allocation Strategies

Abstract. In many semi-arid regions, agriculture, energy, municipal, and environmental 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 and annually variable snowmelt. With infrastructure investments often deferred or delayed, water managers are forced to address demand-based allocation strategies, particularly challenging in dry years. This is often realized through a reduction in the volume associated with each water right, applied across all water rights holders. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of likely future conditions upon which to set the annual water right volume and thereby guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October-January (austral growing season) streamflow at multiple lead times associated with manager and user decision points, and link predictions with a simple reservoir allocation tool.

scholarly journals Evaluation of model-based seasonal streamflow and water allocation forecasts for the Elqui Valley, Chile

2017 ◽  
Vol 21 (9) ◽  
pp. 4711-4725 ◽  
Author(s):  
Justin Delorit ◽  
Edmundo Cristian Gonzalez Ortuya ◽  
Paul Block
Keyword(s):  
Prediction Models ◽  
Water Rights ◽  
Skill Score ◽  
Statistical Prediction ◽  
Lead Times ◽  
Temperature Phase ◽  
Streamflow Forecasting ◽  
Allocation Model ◽  
Streamflow Forecasts ◽  
Water Right

Abstract. 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 years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October–January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950–2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.

scholarly journals Unfolding the relationship between seasonal forecast skill and value in hydropower production: A global analysis

2021 ◽  
Author(s):  
Donghoon Lee ◽  
Jia Yi Ng ◽  
Stefano Galelli ◽  
Paul Block
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Global Analysis ◽  
Forecast Accuracy ◽  
Forecast Skill ◽  
Statistical Prediction ◽  
Control Rules ◽  
Streamflow Forecasts ◽  
Expected Benefits ◽  
Global And Local

Abstract. 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, and realistic forecast-informed. The realistic forecasts are issued by tailored statistical prediction models—based on lagged global and local hydro-climatic 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 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 have the potential to reap benefits from forecast-informed operations, and dams with strong potential to benefit from forecast-informed operations but fall in regions lacking forecast accuracy. Overall, these results represent a first qualitative step towards informing site-specific hydropower studies.

scholarly journals Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness

2021 ◽  
Author(s):  
Colin Keating ◽  
Donghoon Lee ◽  
Juan Bazo ◽  
Paul Block
Keyword(s):  
Prediction Models ◽  
Superior Performance ◽  
Statistical Prediction ◽  
Exceedance Probability ◽  
Early Action ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Operational Practices ◽  
Statistical Prediction Model ◽  
Seasonal Streamflow

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness, however evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least squares combination) is also evaluated against current operational practices. The statistical and multi-model predictions demonstrate superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in all four historical occasions. For the Piura River, the statistical model proves superior to all other approaches, and even achieves an 86 % hit rate when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

scholarly journals Informing Hydrometric Network Design for Statistical Seasonal Streamflow Forecasts

2013 ◽  
Vol 14 (5) ◽  
pp. 1587-1604 ◽  
Author(s):  
Eric A. Rosenberg ◽  
Andrew W. Wood ◽  
Anne C. Steinemann
Keyword(s):  
Soil Moisture ◽  
Network Design ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Forecast Skill ◽  
Statistical Prediction ◽  
Moisture Sensor ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

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.

scholarly journals Leveraging multi-model season-ahead streamflow forecasts to trigger advanced flood preparedness in Peru

2021 ◽  
Vol 21 (7) ◽  
pp. 2215-2231
Author(s):  
Colin Keating ◽  
Donghoon Lee ◽  
Juan Bazo ◽  
Paul Block
Keyword(s):  
Prediction Models ◽  
Superior Performance ◽  
Statistical Prediction ◽  
Exceedance Probability ◽  
Early Action ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Operational Practices ◽  
Statistical Prediction Model ◽  
Seasonal Streamflow

Abstract. Disaster planning has historically allocated minimal effort and finances toward advanced preparedness; however, evidence supports reduced vulnerability to flood events, saving lives and money, through appropriate early actions. Among other requirements, effective early action systems necessitate the availability of high-quality forecasts to inform decision making. In this study, we evaluate the ability of statistical and physically based season-ahead prediction models to appropriately trigger flood early preparedness actions based on a 75 % or greater probability of surpassing the 80th percentile of historical seasonal streamflow for the flood-prone Marañón River and Piura River in Peru. The statistical prediction model, developed in this work, leverages the asymmetric relationship between seasonal streamflow and the ENSO phenomenon. Additionally, a multi-model (least-squares combination) is also evaluated against current operational practices. The statistical prediction demonstrates superior performance compared to the physically based model for the Marañón River by correctly triggering preparedness actions in three out of four historical occasions, while both the statistical and multi-model predictions capture all four historical events when the required threshold exceedance probability is reduced to 50 %, with only one false alarm. For the Piura River, the statistical model proves superior to all other approaches, correctly triggering 28 % more often in the hindcast period. Continued efforts should focus on applying this season-ahead prediction framework to additional flood-prone locations where early actions may be warranted and current forecast capacity is limited.

Experimental investigation of the onset of sand deposits on Hastelloy-X between 1,000°C and 1,100°C

2017 ◽  
Vol 121 (1242) ◽  
pp. 1187-1199
Author(s):  
A. Boulanger ◽  
J. Hutchinson ◽  
W.F. Ng ◽  
S.V. Ekkad ◽  
M.J. Keefe ◽  
...  
Keyword(s):  
Arid Regions ◽  
Prediction Models ◽  
Quadratic Function ◽  
Empirical Models ◽  
Turbine Engines ◽  
Near Surface ◽  
Hastelloy X ◽  
Metallic Substrates ◽  
Particle Ingestion ◽  
Health Monitor

ABSTRACTDeposit formation on turbine hardware in propulsion turbine engines can occur in many arid regions globally. Characterising crystalline deposits on metallic substrates can aid in component resilience and health monitor algorithms during particle ingestion. This study has developed two statistical empirical models for prediction from acquired experimental data for the onset of deposits. The prediction models are for crystalline particulate (Arizona Road Test Dust) deposits on a flat rectangular Hastelloy-X test coupon. Particle impingement angles varied between 20° and 80° in experimental flow temperatures of 1,000–1,100°C. Averaged deposits are methodically quantified through normalised particle deposit tallies per area and percent coverage of the surface using microscopic imaging and image processing programs. Deposit accumulation is a quadratic function of both near-surface coupon temperature and coupon angle.

scholarly journals A Comparison of Statistical Prediction Models in Household Water End-Uses

2011 ◽  
Vol 24 (4) ◽  
pp. 567-573 ◽  
Author(s):  
Sung-Min Myoung ◽  
Doo-Jin Lee ◽  
Hwa-Soo Kim ◽  
Jin-Nam Jo
Keyword(s):  
Prediction Models ◽  
Statistical Prediction ◽  
Household Water ◽  
End Uses

scholarly journals Statistical prediction models of impact damage of airport conveyor belts

2020 ◽  
Vol 51 ◽  
pp. 11-19
Author(s):  
Karol Semrád ◽  
Katarína Draganová ◽  
Peter Koščák ◽  
Jozef Čerňan
Keyword(s):  
Prediction Models ◽  
Impact Damage ◽  
Statistical Prediction ◽  
Conveyor Belts

scholarly journals Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 341 ◽  
Author(s):  
Qingwen Jin ◽  
Xiangtao Fan ◽  
Jian Liu ◽  
Zhuxin Xue ◽  
Hongdeng Jian
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Prediction Models ◽  
Weather Prediction ◽  
Back Propagation ◽  
Absolute Error ◽  
Gradient Boosting ◽  
Lead Times ◽  
Intensity Prediction ◽  
Extreme Gradient Boosting

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

scholarly journals Forecasting length-of-day using numerical weather prediction models

1988 ◽  
Vol 128 ◽  
pp. 285-286
Author(s):  
R. D. Rosen ◽  
D. A. Salstein ◽  
T. Nehrkorn ◽  
J. O. Dickey ◽  
T. M. Eubanks ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lead Times ◽  
Length Of Day ◽  
Wind Fields ◽  
Numerical Weather ◽  
Medium Range Forecast ◽  
Frequency Changes ◽  
Numerical Weather Prediction Models

A new approach to forecasting changes in length-of-day (δl.o.d) with lead times from one to ten days is examined. The approach is based on the high correlation that has been shown to exist between high frequency changes in l.o.d. and those in the atmosphere's angular momentum (M). Because forecasts of tropospheric values of M can be calculated from the zonal wind fields produced by operational numerical weather prediction models, it seems worth investigating whether these forecasts are sufficiently skillful to use to infer the evolution of δl.o.d. Here, we examine the quality of M forecasts made by the Medium Range Forecast (MRF) model of the U.S. National Meteorological Center (NMC). By comparing these forecasts against those based on a simple model of persistence, we find that skillful forecasts of M are being achieved on average by the MRF, although there has been much month-to-month variability in forecast quality. Overall, our results indicate that for prediction lead times of 1–10 days, dynamically-based forecasts of δl.o.d. represent a viable alternative to the empirical approaches currently in use.

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