Seasonal forecasts of drought indices in African basins

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI). The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time.

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Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2359-2013 ◽

2013 ◽

Vol 17 (6) ◽

pp. 2359-2373 ◽

Cited By ~ 62

Author(s):

E. Dutra ◽

F. Di Giuseppe ◽

F. Wetterhall ◽

F. Pappenberger

Keyword(s):

Real Time ◽

Lead Time ◽

Standardized Precipitation Index ◽

Precipitation Index ◽

Seasonal Forecasting ◽

Monthly Precipitation ◽

Seasonal Forecasts ◽

Blue Nile ◽

Forecasting System ◽

The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

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Applications of Subseasonal-to-Seasonal Forecasts: Progress and Future Plans

10.5194/egusphere-egu21-15457 ◽

2021 ◽

Author(s):

Christopher White ◽

Joanne Robbins ◽

Daniela Domeisen ◽

Andrew Robertson

Keyword(s):

Real Time ◽

Disaster Preparedness ◽

Best Practice ◽

Focal Point ◽

Global Network ◽

Seasonal Forecasts ◽

Time Data ◽

Application Development ◽

User Communities ◽

New Generation

Subseasonal-to-seasonal (S2S) forecasts are bridging the gap between weather forecasts and long-range predictions. Decisions in various sectors are made in this forecast timescale, therefore there is a strong demand for this new generation of predictions. While much of the focus in recent years has been on improving forecast skill, if S2S predictions are to be used effectively, it is important that along with scientific advances, we also learn how best to develop, communicate and apply these forecasts.In this paper, we present recent progress in the applications of S2S forecasts, and provide an overview of ongoing and emerging activities and initiatives from across the wider weather and climate applications and user communities, as follows:<ul><li>To support an increased focus on applications, an additional science sub-project focused on S2S applications has been launched on the World Meteorological Organization WWRP-WCRP S2S Prediction Project: http://s2sprediction.net/. This sub-project will provide a focal point for research focused towards S2S applications by exploring the value of applications-relevant S2S forecasts and highlighting the opportunities and challenges facing their uptake.</li> <li>Also supported by the S2S Prediction Project, the ongoing Real-Time Pilot initiative http://s2sprediction.net/file/documents_reports/16Projects.pdf is making S2S forecasts available to 15 selected projects that are addressing user needs over a two year period (November 2019 through to November 2021). By making this real-time data available, the initiative is drawing on the collective experiences of the researcher and user communities from across the projects. The Real-Time Pilot will develop best practice guidelines for producing useful and useable, application-orientated forecasts and tools that can be used to guide future S2S application development. We will present an update on the initiative, including results from an initial set of questionnaires that focussed on engagement strategies and practices, supporting a review of how projects were designs, the roles and responsibilities of different project participants and the methods used to determine project success.</li> <li>To increase the uptake and use of S2S forecasts more widely across the research and user communities, we present a new initiative: a global network of researchers, modellers and practitioners focused on S2S applications, called S2Sapp.net &#8211; a community with a shared aim of exploring and promoting cross-sectoral services and applications of this new generation of predictions.</li> <li>Finally, we will provide an update on a recently-submitted applications community review paper, covering sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. Drawing from the experience of researchers and users working with S2S forecasts, we explore the value of applications-relevant S2S predictions through a series of sectoral cases where uptake is starting to occur.</li> </ul>

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Development of the Deltares global fluvial flood forecast system

10.5194/egusphere-egu2020-22344 ◽

2020 ◽

Author(s):

Matthijs den Toom ◽

Jan Verkade ◽

Albrecht Weerts ◽

Gert-Jan Schotmeijer

Keyword(s):

Real Time ◽

Flood Forecasting ◽

Pedotransfer Functions ◽

Water Runoff ◽

Multiple Model ◽

Recent Past ◽

Seasonal Forecasts ◽

Hydrological Parameters ◽

Forecasting System ◽

Near Future

Deltares operates the Global Fluvial Flood Forecasting System (GLOFFIS) is a real-time fluvial forecasting system with global coverage. At any location in the world, for both the recent past and the near future, the system produces estimates of various hydrological parameters.&#160;The continued investment in GLOFFIS is justified by various reasons. Primarily, there is an R&D rationale. Any operational system that runs in near real-time poses high requirements to the availability of input data and the runtime of the models used. This problem is augmented when applying the system to the global scale: both model domains and data volumes become significantly larger. Also, data originates from a wide variety of sources. Runtimes, however, cannot be significantly larger hence this poses additional requirements to the efficiency of models used. Solving these issues requires a considerable R&D effort. The resulting developments tend to be useful for the &#8216;local&#8217; systems we develop and maintain for our clients. An additional rationale is found in the increased demand for global forecasts &#8211; notably from a client base that is not able or willing to operate forecasting systems themselves.&#160;At its core, GLOFFIS operates a set of hydrological models that, jointly, cover the entire earth&#8217;s land. The models are forced by meteorological data &#8211; pertaining to both the recent past and the near future. The models produce estimates of various hydrological parameters such as soil moisture content, surface water runoff and streamflow rates. Future versions of GLOFFIS will include hydrodynamic models, allowing to produce estimates of water level in addition to streamflow rates. Also, future versions will include seasonal forecasts, i.e. forecasts going out several weeks if not months. In addition to real-time data, the system enables the production of long-term timeseries.&#160;In terms of the infrastructure of the system, GLOFFIS is based on the wflow framework for hydrological modelling which is embedded within a the Delft-FEWS forecast production system. Neither of these require any licensing fees and the wflow framework is available through an open source license. The Delft-FEWS system is used for many operational flood forecasting systems including those of the US National Weather Service, the English Environment Agency, the Bureau of Meteorology and many other national forecasting agencies. Wflow is a distributed modelling framework specifically designed to accommodate multiple model schematization types and data assimilation techniques. For GLOFFIS, we opted for the physically based wflow_sbm that uses kinematic wave routing for surface and subsurface flow. So-called pedotransfer functions that translate input base maps to model parameter values ensure that the models require little calibration.

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Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index

Jàmbá Journal of Disaster Risk Studies ◽

10.4102/jamba.v11i1.712 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Daniel H. Mlenga ◽

Andries J. Jordaan

Keyword(s):

Real Time ◽

Early Warning ◽

Meteorological Drought ◽

Precipitation Index ◽

Spatiotemporal Variability ◽

Drought Monitoring ◽

Drought Indices ◽

Severe Drought ◽

Standard Precipitation Index ◽

Ecological Zones

The spatiotemporal analysis of drought is of great importance to Eswatini as the country has been facing recurring droughts with negative impacts on agriculture, the environment and the economy. In 2016, the country experienced the most severe drought in over 35 years, resulting in food shortages, drying up of rivers as well as livestock deaths. The frequent occurrence of extreme drought events makes the use of drought indices essential for drought monitoring, early warning and planning. The aim of this study was to assess the applicability of the Standard Precipitation Index (SPI) for near real-time and retrospective drought monitoring in Eswatini. The 3-, 6- and 12-month SPI were computed to analyse the severity and onset of meteorological drought between 1986 and 2017. The results indicated that the climate of Eswatini exhibits geospatial and temporal variability. Droughts intensified in terms of frequency, severity and geospatial coverage, with the worst drought years being 1985–1986, 2005–2006 and 2015–2016 agricultural seasons. Moderate droughts were the most prevalent, while the frequency of severe and very severe droughts was low. Most parts of the country were vulnerable to mild and moderate agricultural droughts. Spatial analysis showed that the most severe and extreme droughts were mostly experienced in the Lowveld and Middleveld agro-ecological zones. The 3-, 6- and 12-month SPI computations conducted in January detected the onset of early season drought, thereby affirming the applicability of the index for monitoring near real-time and retrospective droughts in Eswatini. Drought monitoring using the SPI provides information for early warning, particularly in drought-prone areas, by depicting a drought before the effects are felt.

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A data based mechanistic real-time flood forecasting module for NFFS FEWS

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-7271-2012 ◽

2012 ◽

Vol 9 (6) ◽

pp. 7271-7296 ◽

Cited By ~ 1

Author(s):

D. Leedal ◽

A. H. Weerts ◽

P. J. Smith ◽

K. J. Beven

Keyword(s):

Real Time ◽

Forecast Model ◽

Open Shell ◽

Flood Forecasting ◽

Time Data ◽

Flood Alert ◽

Interaction Interface ◽

Forecast Models ◽

Forecasting System ◽

The Uk

Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.

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Development and propagation of severe thunderstorms in the Upper Danube catchment area: Towards an integrated nowcasting and forecasting system using real-time data and high-resolution simulations

Meteorology and Atmospheric Physics ◽

10.1007/s00703-008-0322-7 ◽

2008 ◽

Vol 101 (3-4) ◽

pp. 211-227 ◽

Cited By ~ 10

Author(s):

A. Tafferner ◽

C. Forster ◽

M. Hagen ◽

C. Keil ◽

T. Zinner ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Catchment Area ◽

Time Data ◽

Severe Thunderstorms ◽

Danube Catchment ◽

Real Time Data ◽

Forecasting System

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Ambulatory Assessment

European Psychologist ◽

10.1027/1016-9040.14.2.109 ◽

2009 ◽

Vol 14 (2) ◽

pp. 109-119 ◽

Cited By ~ 66

Author(s):

Ulrich W. Ebner-Priemer ◽

Timothy J. Trull

Keyword(s):

Real Time ◽

Clinical Psychology ◽

Real Life ◽

Ambulatory Assessment ◽

Computer Assisted ◽

Affective Valence ◽

Time Data ◽

Physiological Processes ◽

Context Specific ◽

Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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