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 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 Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Forecast ◽  
Continental Scale ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

scholarly journals Incorporating Antecedent Soil Moisture into Streamflow Forecasting

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 50 ◽  
Author(s):  
Abdoul Oubeidillah ◽  
Glenn Tootle ◽  
Thomas Piechota
Keyword(s):  
Soil Moisture ◽  
Snow Water Equivalent ◽  
Predictor Variable ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Antecedent Soil Moisture ◽  
Natural Resource Conservation ◽  
The North ◽  
Forecasting Methods ◽  
Natural Resource Conservation Service

This study incorporates antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin, Colorado/Wyoming (USA). The incorporation of antecedent soil moisture accounts for infiltration and can improve streamflow predictions. Current Natural Resource Conservation Service (NRCS) forecasting methods are replicated, and a comparison is drawn between current NRCS forecasts and proposed forecasting methods using antecedent soil moisture. Current predictors used by the NRCS in regression-based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods utilize antecedent soil moisture as a predictor variable in addition to the predictors noted above. A decision system was used to segregate data based on antecedent soil moisture conditions (e.g., dry, wet or normal). Principal Components Analysis and Stepwise Linear Regression were applied to generate streamflow forecasts, and numerous statistics were determined to measure forecast skill. The results show that when incorporating antecedent soil moisture, the “poor” forecasts (i.e., years in which the NRCS forecast differed greatly from the observed value) were improved, while the overall forecast skill remains unchanged. The research presented shows the need to increase the monitoring and collection of soil moisture data in mountainous western U.S. watersheds, as this parameter results in improved forecast skill.

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 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.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

Why is large sample hydrology important in hydrological forecasting? 

2021 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Marc Girons Lopez
Keyword(s):  
River Flow ◽  
Forecast Skill ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
River Systems ◽  
Local Conditions ◽  
Large Sample ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

<p>The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.</p><p>Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.</p>

scholarly journals Soil Moisture Sensor Network Design for Hydrological Applications

2020 ◽  
Author(s):  
Lu Zhuo ◽  
Qiang Dai ◽  
Binru Zhao ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Network Design ◽  
Wrf Model ◽  
Principal Component ◽  
Optimal Number ◽  
In Situ Observation ◽  
Catchment Scale ◽  
Moisture Sensor ◽  
Soil Moisture Sensors ◽  
Vital State

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence a vital state variable in the hydrological modelling. However, due to the heterogeneity of soil moisture in space most existing in-situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on the Principal Component Analysis (PCA) and Elbow curve for the determination of the optimal number of soil moisture sensors; and K-means Cluster Analysis (CA) and a selection of statistical criteria for the identification of the sensor placements. Furthermore, the long-term (10-year) soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale soil moisture (i.e., with NSE and r at 0.995 and 0.999, respectively for the areal mean estimation; and 0.973 and 0.990, respectively for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22,124 km2 catchment is needed, which in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme which are also discussed in the paper.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 10 (2) ◽  
pp. 1987-2013 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Lead Time ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Hydrologic Prediction

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic prediction skill at seasonal lead times (i.e. 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs – primarily the state of initial soil moisture and snow) and seasonal climate forecast skill (FS). In this study we quantify the contributions of IHCs and FS to seasonal hydrologic prediction skill globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the Variable Infiltration Capacity (VIC) macroscale hydrology model, one based on Ensemble Streamflow Prediction (ESP) and another based on Reverse-ESP (rESP), both for a 47 yr reforecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts obtained from each experiment with a control simulation forced with observed atmospheric forcings over the reforecast period and estimate the ratio of Root Mean Square Error (RMSE) of both experiments for each forecast initialization date and lead time. We find that in general, the contributions of IHCs are greater than the contribution of FS over the Northern (Southern) Hemisphere during the forecast period starting in October and January (April and July). Over snow dominated regions in the Northern Hemisphere the IHCs dominate the CR forecast skill for up to 6 months lead time during the forecast period starting in April. Based on our findings we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

Does the application of multiple hydrological models improve seasonal streamflow forecasting skill?

2020 ◽  
Author(s):  
Bastian Klein ◽  
Ilias Pechlivanidis ◽  
Louise Arnal ◽  
Louise Crochemore ◽  
Dennis Meissner ◽  
...  
Keyword(s):  
Central Europe ◽  
Bayesian Model Averaging ◽  
Forecast Skill ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

<p>Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.</p><p>Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn’t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF’s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.</p><p>Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.</p>

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