Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Charles, Andrew; Timbal, Bertrand; Fernandez, E.; Hendon, Harry H.

doi:10.1175/mwr-d-12-00098.1

Cited by 31 publications

(22 citation statements)

References 19 publications

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“…This approach has been explored in other studies (e.g. Manzanas et al, 2017;Charles et al, 2012), which showed that dynamic or statistical downscaling of seasonal forecasts can reduce local biases in variables such as temperature or rainfall but do not necessarily improve the overall forecast skill.…”

Section: Introductionmentioning

confidence: 99%

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Bell

Davies

Kay

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead.The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts ("hindcasts") from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ∼ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

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Section: Introductionmentioning

confidence: 99%

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Bell

Davies

Kay

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Nevertheless, based on R 2 , RMSE and pattern of generated rainfall, it can be concluded that the model can simulate precipitation within an acceptable range. The obtained results can also be compared to those of Lines et al (2006), Yang et al (2012) and Charles et al (2013), where simulation of precipitation is argumentative due to many regional driving factors but still fairly representative.…”

Section: Sdsm Calibration and Validationmentioning

confidence: 65%

Adaptation Strategies for Rice Cultivation Under Climate Change in Central Vietnam

Shrestha

2014

Climate Change Impacts and Adaptation in Water Resources and Water Use Sectors

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This study investigates the impact of climate change on winter and summer rice (Oryza sativa) yield and evaluates several adaptation measures to overcome the negative impact of climate change on rice production in the Quang Nam province of Vietnam. Future climate change scenarios for time periods in the 2020s, 2050s and 2080s were projected by downscaling the outputs of the General Circulation Model (GCM), Hadley Centre Coupled Model, version 3 (HadCM3) A2 and B2 scenarios. The AquaCrop model was used to simulate the impact of future climates on rice yield. The minimum and maximum temperature of the province is projected to increase by 0.35-1.72°C and 0.93-3.69°C respectively in future. Similarly, the annual precipitation is expected to increase by 9.75 % in the 2080s. Results show that climate change will reduce rice yield from 1.29 to 23.05 % during the winter season for both scenarios and all time periods, whereas an increase in yield by 2.07-6.66 % is expected in the summer season for the 2020s and 2050s; relative to baseline yield. The overall decrease of rice yield in the winter season can be offset, and rice yield in the summer season can be enhanced to potential levels by altering the transplanting dates and by introducing supplementary irrigation. Late transplanting of rice shows an increase of yield by 20-27 % in future. Whereas supplementary irrigation of rice in the winter season shows an increase in yield of up to 42 % in future. Increasing the fertilizer application rate enhances the yield from 0.3 to 29.8 % under future climates. Similarly, changing the number of doses of fertilizer application increased rice yield by 1.8-5.1 %, relative to the current practice of single dose application. Shifting to other heat tolerant varieties also increased the rice production. Based on the findings, changing planting dates, supplementary irrigation, proper nutrient management and adopting to new rice cultivars can be beneficial for the adaptation of rice cultivation under climate change scenarios in central Vietnam.

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“…Thus, a rule-based expert system can be transferred to a case-based reasoning system, which can deal with rules that are not in a rule set. This is somewhat like partial-rule matching algorithm [32].…”

Section: Experiments and Analysismentioning

confidence: 99%

Case-Based FCTF Reasoning System

Zhang

Li³

et al. 2015

Applied Sciences

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Case-based reasoning uses old information to infer the answer of new problems. In case-based reasoning, a reasoner firstly records the previous cases, then searches the previous case list that is similar to the current one and uses that to solve the new case. Case-based reasoning means adapting old solving solutions to new situations. This paper proposes a reasoning system based on the case-based reasoning method. To begin, we show the theoretical structure and algorithm of from coarse to fine (FCTF) reasoning system, and then demonstrate that it is possible to successfully learn and reason new information. Finally, we use our system to predict practical weather conditions based on previous ones and experiments show that the prediction accuracy increases with further learning of the FCTF reasoning system.

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Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Cited by 31 publications

References 19 publications

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Adaptation Strategies for Rice Cultivation Under Climate Change in Central Vietnam

Case-Based FCTF Reasoning System

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