scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
Author(s):  
Andrew W. Wood ◽  
John C. Schaake
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Summer Period ◽  
Streamflow Forecast ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Deterministic Components ◽  
Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only. However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

scholarly journals An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting

2017 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Andrew W. Wood ◽  
Elizabeth Clark ◽  
Eric Rothwell ◽  
Martyn P. Clark ◽  
...  
Keyword(s):  
Water Supply ◽  
Pacific Northwest ◽  
Land Surface ◽  
Forecast Skill ◽  
Climate Indices ◽  
Statistical Regression ◽  
Climate Information ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Streamflow

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 IHC and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the U.S. 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 inter-compare 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 (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.

scholarly journals The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

2019 ◽  
Vol 20 (4) ◽  
pp. 731-749 ◽  
Author(s):  
Dongyue Li ◽  
Dennis P. Lettenmaier ◽  
Steven A. Margulis ◽  
Konstantinos Andreadis
Keyword(s):  
High Resolution ◽  
Sierra Nevada ◽  
Snow Accumulation ◽  
Forecast Errors ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Physically Based ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
The Impact

Abstract Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 16 (9) ◽  
pp. 3127-3137 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. de Gonçalves
Keyword(s):  
Data Assimilation ◽  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

scholarly journals Predictability of Seasonal Streamflow Forecasting Based on CSM: Case Studies of Top Three Largest Rivers in China

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 162
Author(s):  
Lyuliu Liu ◽  
Ying Wu ◽  
Peiqun Zhang ◽  
Jianqing Zhai ◽  
Li Zhang ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate Models ◽  
Climate System ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Relative Operating Characteristic ◽  
Climate System Model ◽  
Seasonal Streamflow

Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.

scholarly journals The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions

2020 ◽  
Vol 24 (7) ◽  
pp. 3851-3870
Author(s):  
Alexander Kaune ◽  
Faysal Chowdhury ◽  
Micha Werner ◽  
James Bennett
Keyword(s):  
Water Allocation ◽  
Irrigation Season ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Additional Time ◽  
Available Water ◽  
Streamflow Forecasts ◽  
Reservoir Inflow ◽  
Seasonal Streamflow ◽  
Allocation Decisions

Abstract. The area to be cropped in irrigation districts needs to be planned according to the allocated water, which in turn is a function of the available water resource. Initially conservative estimates of future (in)flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ensemble streamflow prediction – ESP (historical rainfall) – and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology as well as against allocations obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 d from the initial allocation at the start of the water year to the end of the irrigation season, including both downward and upward revisions of allocations. Results show that the inconsistency due to upward revisions in allocated water is lower when using the forecast datasets (POAMA and ESP) compared to the conservative inflow estimates (reference), which is beneficial to the planning of cropping areas by farmers. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year, especially 1.5 months before the start of the cropping season in November. This is significant as it provides farmers with additional time to make key decisions on planting. Our results show that seasonal streamflow forecasts can provide benefit in informing water allocation policies, particularly by earlier establishing final water allocations to farmers in the irrigation season. This allows them to plan better and use water allocated more efficiently.

scholarly journals Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

2020 ◽  
Vol 21 (10) ◽  
pp. 2375-2389
Author(s):  
Hector Macian-Sorribes ◽  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Manuel Pulido-Velazquez
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
River Basin Scale ◽  
Basin Scale ◽  
Streamflow Forecasts

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

Assimilation of soil moisture data for improving streamflow prediction: Is there a role for the hydrological model structure?

2020 ◽  
Author(s):  
Aruna Kumar Nayak ◽  
Basudev Biswal ◽  
Kulamulla Parambath Sudheer
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Hydrological Model ◽  
Assimilation Efficiency ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
The Past ◽  
Soil Moisture Data

<p>Soil moisture data assimilation has found increased applicability in hydrology due to easily available remotely sensed soil moisture data. Numerous studies in the past have explored the possibility of assimilating soil moisture information for improving streamflow forecasting. The general understanding is that if better soil moisture data can provide better streamflow forecast. However, to our knowledge no study has so far focused on understanding if the hydrological model itself has a role in assimilation of soil moisture data. In this regard, here we use three different conceptual hydrological models for soil moisture assimilation: (1) Dynamic Budyko (DB), (2) GR4J, and (3) PDM model. Assimilation of GLDAS observed soil moisture is carried out for four MOPEX basins using Ensemble Kalman Filter. DB model’s performance improved after soil moisture data assimilation for all the study basins. However, deterioration in performance was observed for GR4J and PDM for all the basins after the assimilation exercise. The performance of the assimilated models is evaluated in terms of Assimilation Efficiency (AE), which was found to be varying from 17.11 to 22.56%, from -20.98 to -41.29%, and from -8.4 to -38.23%, respectively, for DB, GR4J, and PDM. Overall, our results highlight the importance of the hydrological models structure in a soil moisture data assimilation exercise.</p>

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