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Lemos, Maria Carmen; Finan, Timothy J.; Fox, Roger W.; Nelson, Donald R.; Tucker, Joanna

doi:10.1023/a:1020785826029

Cited by 178 publications

(36 citation statements)

References 12 publications

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“…Nevertheless, several users still remain doubtful whether seasonal forecasts can be trustworthy or skilful enough to enhance decision making (Rayner et al, 2005). Lemos et al (2002) list the performance of seasonal forecasts, the misuse of seasonal forecasts by end-users and the lack of consideration of end-users' needs in the development of products as major obstacles to the widespread use of seasonal forecasting in northeast Brazil. It is therefore crucial to assess the potential of available seasonal forecasting products and communicate on the assets and shortcomings of the different approaches for the water sector (Hartmann et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Crochemore

Ramos²,

Pappenberger

2016

Hydrol. Earth Syst. Sci.

189

167

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Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

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Section: Introductionmentioning

confidence: 99%

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Crochemore

Ramos²,

Pappenberger

2016

Hydrol. Earth Syst. Sci.

189

167

View full text Add to dashboard Cite

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“…In 1992, the government used forecasts to warn local farmers of an imminent El Niño and provide them with droughttolerant seeds, substantially increasing farmers' yields compared to what was expected. Since then, the Ceará's weather forecasting agency (FUNCEME, http://www.funceme.br/) has been continuously developing SCFs to inform government sectors involved in agricultural policy-making and drought relief for subsistence farmers (Lemos et al, 2002). Another example is AgroClimate (http://agroclimate.org/) which provides a range of outlooks relevant to local producers in the southeast US, such as SCFs of temperature and rainfall, alongside shortterm rainfall forecasts, a drought outlook, hurricane forecasts, and a range of agriculture-focused tools including a planting date planner and disease advisories.…”

Section: Introductionmentioning

confidence: 99%

The land management tool: Developing a climate service in Southwest UK

Falloon

Soares

Manzanas³

et al. 2018

Climate Services

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A B S T R A C TSeasonal climate forecasts (SCFs) have significant potential to support shorter-term agricultural decisions and longer-term climate adaptation plans, but uptake in Europe has to date been low. Under the European Union funded project, European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EUPORIAS) we have developed the Land Management Tool (LMTool), a prototype seasonal climate service for land managers, working closely in collaboration with two stakeholder organizations, Clinton Devon Estates (CDE) and the National Farmers Union (NFU). LMTool was one of several prototype climate services selected for development within EUPORIAS, including those for the UK transport network, food security in Ethiopia, renewable energy production, hydroelectric energy production in Sweden, and river management in two French basins. The LMTool provides SCFs (1-3 months ahead) to farmers in the Southwest UK, alongside 14-day site specific weather forecasts during the winter months when the skill of seasonal forecasts is greatest.We describe the processes through which the LMTool was co-designed and developed with the farmers, its technical development and key features; critically examine the lessons learned and their implications for providing future climate services for land managers; and finally assess the feasibility of delivering an operational winter seasonal climate service for UK land managers.A number of key learning points from developing the prototype may benefit future work in climate services for the land management and agriculture sector; many of these points are also valid for climate services in other sectors. Prototype development strongly benefitted from; working with intermediaries to identify representative, engaged land managers; an iterative and flexible process of co-design with the farmer group; and from an interdisciplinary project team. Further work is needed to develop a better understanding of the role of forecast skill in land management decision making, the potential benefits of downscaling and how seasonal forecasts can help support land managers decision-making processes. The prototype would require considerable work to implement a robust operational forecast system, and a longer period to demonstrate the value of the services provided. Finally, the potential for such services to be applied more widely in Europe is not well understood and would require further stakeholder engagement and forecast development. Practical implicationsAs part of the EU project EUPORIAS (Buontempo and Hewitt, 2017), the UK Met Office, University of Leeds, Predictia and KNMI-in close collaboration with Clinton Devon Estates (CDE) and the National Farmers Union (NFU)-have developed the Land Management Tool (LMTool), a prototype http://dx

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“…Other areas, such as northeastern Brazil and the Amazon River Basin, also suffer from frequent 38 droughts (e.g., Lemos et al, 2002;Rowland et al, 2015). and conditions (i.e., rock types and elevation) could be critical factors for understanding the 46 nature of groundwater storage behaviour across Brazil (e.g., Zagonari, 2010).…”

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confidence: 99%