Value of long‐term streamflow forecasts to reservoir operations for water supply in snow‐dominated river catchments

Anghileri, Daniela; Voisin, Nathalie; Castelletti, Andrea; Pianosi, Francesca; Nijssen, Bart; Lettenmaier, Dennis P.

doi:10.1002/2015wr017864

Cited by 194 publications

(167 citation statements)

References 60 publications

(114 reference statements)

Supporting

Mentioning

165

Contrasting

Order By: Relevance

“…Skilful hydrological forecasts at sub-seasonal to seasonal lead times would provide a valuable tool for improved decision making for wide range of sectors such as water resources management (Anghileri et al, 2016), hydropower operations (Hamlet et al, 2002), and agriculture (Letcher et al, 2004), particularly in times of slow onset events such as drought (Simpson et al, 2016). One of the earliest operational hydrological forecasting methods is Ensemble Streamflow Prediction (ESP).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decisionmaking in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in 10 the absence of skilful future meteorological predictions, instead using Initial Hydrological Conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1-day to 12-months initialised at the first of each month over a 50-year 15 hindcast period from 1965-2015. Results show ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialisation month, and individual catchment location and storage properties. UK-wide mean ESP skill decays exponentially as a function of lead time with mean squared error skill scores across the year of 0.839, 0.303, and 0.179 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialisation months. For lead times up to 1-20 month, ESP skill was higher than average when initialised in summer and lower in winter, whereas for longer seasonal and annual lead times skill was highest when initialised in autumn and winter months and lowest in April. ESP is most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment Baseflow Index (BFI) and ESP skill was very strong ( = 0.896 at 1-month lead time). This is in contrast to the more highly responsive catchments in the north and west which are generally not skilful 25 at seasonal lead times. Overall, this work provides a scientifically defensible justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK and creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Section: Introductionmentioning

confidence: 99%

Benchmarking Ensemble Streamflow Prediction skill in the UK

Harrigan¹,

Smith²,

Parry³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Seasonal streamflow forecasts can be a valuable source of information for water resource managers (Chiew et al, 2003;Anghileri et al, 2016), with both statistical and dynamical forecasting approaches developed and implemented internationally (Yuan et al, 2015). Sources of seasonal streamflow predictability come from initial hydrological or antecedent conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Charles

Wang

Ahmad

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (AprilSeptember) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

show abstract

“…The glaciated and snow covered sub-basins of the UIB, encompassing glaciated headwater catchments within the northern Hindu-Kush, Karakoram and western Himalayan mountain 15 ranges, dominate water generation within the Indus Basin (Alford et al, 2014). The UIB's tributaries include the Indus at Kharmong: Shigar, Shyok and Astore in the Karakoram Himalaya, the Jhelum, Chenab, Ravi and Sutlej in the western Himalaya, and the Hunza, Gilgit, Kabul, Swat and Chitral in the Hindu Kush mountains (Figure 1).…”

Section: Upper Indus Basinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

Charles

Wang

Ahmad

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the 15 BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest Upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESP) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM 20 for this location. Cross validation of the streamflow forecasts show that the BJP approach using two predictors (March flow and an ENSO climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early 25Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.Hydrol. Earth Syst. Sci. Discuss., https://doi

show abstract

Value of long‐term streamflow forecasts to reservoir operations for water supply in snow‐dominated river catchments

Cited by 194 publications

References 60 publications

Benchmarking Ensemble Streamflow Prediction skill in the UK

Benchmarking Ensemble Streamflow Prediction skill in the UK

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

Contact Info

Product

Resources

About