scholarly journals Towards seasonal forecasting of maize yield in eastern Africa: skill in the forecast model chain as a basis for agricultural climate services

2020 ◽  
Author(s):  
Geoffrey Evans Owino Ogutu
Keyword(s):  
Forecast Model ◽  
Maize Yield ◽  
Seasonal Forecasting ◽  
Eastern Africa ◽  
Climate Services ◽  
Model Chain

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

Towards scale independent hydrological forecasting in regulated semi-arid regions

2020 ◽  
Author(s):  
Pallav Kumar Shrestha ◽  
Christof Lorenz ◽  
Husain Najafi ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
...  
Keyword(s):  
Lead Time ◽  
Arid Regions ◽  
Initial Conditions ◽  
Skill Score ◽  
Seasonal Forecasting ◽  
Forecasting Model ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Model Chain ◽  
Semi Arid

<p>Semi-arid regions are characterized by low annual precipitation that exhibit large seasonal fluctuations. While semi-arid regions cover 3.6% of the globe, 13% of world’s documented reservoirs (GRanD database) are within 100 km of semi-arid regions to fulfill water demand year-round. Reservoirs are known to increase evaporation and significantly change hydrologic regime downstream. Accurate representation of reservoirs and scale independent modeling is indispensable for reliable hydrologic forecasting systems in semi-arid regions. To address this, the mesoscale hydrological model (mHM, git.ufz.de/mhm) is augmented with a new lake/reservoir module (multiscale lake module, mLM). The objective is to measure the performance of a scalable seasonal forecasting model chain with and without reservoirs.</p><p>The experimental setup constitutes the SaWaM (http://grow-sawam.org/) project study regions encompassing seven semi-arid basins and 15 reservoirs of high significance across three continents (Sao Francisco, Jaguaribe, Piranhas in Brazil, Blue Nile, Atbara in Sudan, Karun in Iran, Chira-Catamayo in Ecuador).The calibration of mHM parameters and its initial conditions for forecsating are obtained using the spatially disaggregated ERA5 (ERA-SD, ≈ 10 km, starting 1981) climate reanalysis data. The calibrated model is forced with an ensemble of 25 realisations of ECMWF-SEAS5 seasonal hindcasts which are bias corrected and spatially disaggregated (BCSD, ≈10 km) using ERA-SD. The 2010–2016 hindcasting experiment generates hydrological forecasts with lead time of upto six months. The performance of the model chain BCSD-mHM-mLM and BCSD-mHM are evaluated using the Brier Skill Score.</p><p>Preliminary results show that incorporating reservoirs in the model improves the performance of mHM (average NSE improvement ≈ +0.1 for the period 1990–2010) and the overarching forecasting model chain. Sub-grid level lake delineation and in-/outflow calculations of mLM result in scalable reservoir states and fluxes and thus overall scalable basin hydrology. Seamless forecasts for soil moisture, streamflow, reservoir inflow and reservoir water level are achieved across scales (≈10 km to ≈1 km) showing skills to up to two months lead time. This study is the first step towards an operational hydrological seasonal forecasting system which has potential to significantly improve water management, specially in semi-arid regions.</p>

scholarly journals Verification and Bias Adjustment of ECMWF SEAS5 Seasonal Forecasts over Europe for Climate Service Applications

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 181
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Paola Marson ◽  
Christian Viel ◽  
Lucas Grigis
Keyword(s):  
Seasonal Forecasting ◽  
Climate Variables ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
Climate Services ◽  
Weather Regimes ◽  
Bias Adjustment ◽  
Medium Range ◽  
Climate Service ◽  
Forecasting System

This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities.

Modelling the Southeast African regional Climate

2021 ◽  
Author(s):  
Rasmus Benestad ◽  
Bernardino Nhantumbo ◽  
Ayabagabo Prosper ◽  
Joseph Ndakize Sebaziga ◽  
Aminadab Tuyisenge ◽  
...  
Keyword(s):  
Climate Change ◽  
Heat Waves ◽  
Regional Climate ◽  
Decision Makers ◽  
Geographical Information ◽  
Eastern Africa ◽  
Complex Data ◽  
Climate Services ◽  
South Eastern ◽  
Regional Collaboration

<p>The aim of the Flagship Pilot Study (FPS) “Modelling the Southeast African regional Climate” is to study processes and phenomena relevant to regional climate change in south-eastern Africa. The region is vulnerable to climate change due to socio-economic factors as well as its exposure to weather and climate extremes such as floods, droughts and heat waves. The FPS will foster regional collaboration on modelling and the analysis of precipitation and temperature that will be beneficial for the society in general. The FPS South-eastern Africa includes various scientists from the National Meteorological and Hydrological Services (NMHSs) and academia of South Africa, Mozambique, Zimbabwe, Malawi, Tanzania, Kenya, Rwanda, Burundi and Norway. The research will involve analysis of local observations, reanalysis, simulations from regional climate models (RCMs) and empirical-statistical downscaling (ESD) to study dependencies between large-scale conditions and local variability in the rain and temperature statistics. The expected impacts of the FPS are skills development in data analysis and modelling, and a better understanding of regional climate that is fundamental to climate services and provides guidance to decision-makers and planners. The involvement of NMHS in the project provides access to their observational networks, whose use will assist with verification of model simulations, and also increase the value of NMHSs’ work with observations and data management. Actionable information will be extracted for decision-makers, based on a synthesis of multiple sources of information which take into account the local climate, past and future trends, models’ skill, known weather/climatological phenomena, and other geographical information. Biases between the model climate and observations will be adjusted through appropriate adjustment methods such as the Quantile Mapping approach. The work will also involve capacity building on R programming language as well as other tools (e.g. CDO, python) and use R-based shiny web applications in distillation efforts and to provide a gateway to the information embedded in complex data structures.</p>

Seasonal forecasting and the predictability of the rainy and dry seasons for Peru, Tanzania and India

2020 ◽  
Author(s):  
Thomas Möller ◽  
Lydia Gates
Keyword(s):  
East Africa ◽  
Weather Conditions ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Climate Impact ◽  
Seasonal Forecasting ◽  
Mean Deviation ◽  
Local Agriculture ◽  
Forecast Models

<p>With seasonal forecast models we investigate whether it is possible to give the people in Tanzania, Peru and India time to adapt and prepare to different weather conditions. In recent years, these countries have repeatedly experienced devastating droughts or floods, such as in East Africa in November 2019.</p><p>Under the framework of the research project EPICC (East Africa Peru India Climate Capacities) supported by the BMU (Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), we aim to set up a seasonal forecast system. The goal is to make the data useful for the hydrologists at the project partner from PIK (Potsdam Institute for Climate Impact Research) for integration in a tool for adaption in local agriculture in the affected countries (India, Peru and Tanzania). In this study, we validate a number of variables of predicted anomalies in seasonal forecast models as well as of a multimodel product.</p><p>There are different methods of seasonal predictability, based on slow variations of boundary conditions, coupled ocean-atmosphere model simulations as well as the concept of ensembles, multi-model ensembles and uncertainties. The focus in this study is on the intercomparison of the single models and the multimodel in a forecast range between 1 and 6 months. In particular, we investigate three-month mean deviation from the long-term mean. It is important for the population (especially for the agriculture industry) in the focus region to know whether in a certain period (rainy season, dry season, El Nino etc.) the next 3 months will be colder, warmer, drier or even wetter compared to the long-term mean.</p><p>Due to the fact, that various seasonal forecasting models perform differently, it is the challenge, to find the best fitting seasonal forecast model for each of the affected countries.</p>

scholarly journals Should seasonal rainfall forecasts be used for flood preparedness?

2017 ◽  
Vol 21 (9) ◽  
pp. 4517-4524 ◽  
Author(s):  
Erin Coughlan de Perez ◽  
Elisabeth Stephens ◽  
Konstantinos Bischiniotis ◽  
Maarten van Aalst ◽  
Bart van den Hurk ◽  
...  
Keyword(s):  
Central Africa ◽  
Sub Saharan Africa ◽  
Seasonal Rainfall ◽  
Eastern Africa ◽  
Total Rainfall ◽  
Climate Services ◽  
Dry Spells ◽  
Flood Preparedness ◽  
Sub Saharan ◽  
Seasonal Rainfall Forecasts

Abstract. In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary indicators of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in some regions of western, central, and eastern Africa with typically wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in southern and eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of western and central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness and to avoid misleading decision-makers.

scholarly journals Uncertainties In The Effectiveness of Biological Control of Stem Borers Under Different Climate Change Scenarios In Eastern Africa

2021 ◽  
Author(s):  
Ines Gwendolyn Jendritzki ◽  
Henri E. Z. Tonnang ◽  
Paul-André Calatayud ◽  
Christian Borgemeister ◽  
Tino Johansson ◽  
...  
Keyword(s):  
Climate Change ◽  
Biological Control ◽  
Insect Pests ◽  
Maize Yield ◽  
Stem Borer ◽  
Eastern Africa ◽  
Climate Change Scenarios ◽  
Agricultural Crop ◽  
Yield Losses ◽  
Stem Borers

Abstract Climate change (CC) is expected to significantly affect biodiversity and ecosystem services. Adverse impacts from CC in the Global South are likely to be exacerbated by limited capacities to take adequate adaptation measures and existing developmental challenges. Insect pests today are already causing considerable yield losses in agricultural crop production in East Africa. Studies have shown that insects are strongly responding to CC by proliferation, shift in distribution or by altering their phenology, which is why an impact on agriculture can also be expected. Biological control (BC) has been proposed as an alternative measure to sustainably contain insect pests but few studies predict its efficacy under future CC. Using the species distribution modelling approach Maxent, we predict the current and future distribution of three important lepidopteran stem borer pests of maize in eastern Africa, i.e., Busseola fusca (Fuller, 1901), Chilo partellus (Swinhoe, 1885) and Sesamia calamistis (Hampson, 1910), and two of their parasitoids that are currently used for BC, i.e., Cotesia flavipes (Cameron, 1891) and Cotesia sesamiae (Cameron, 1906) . Based on these potential distributions and data collected during household surveys with local farmers in Kenya and Tanzania, future maize yield losses are predicted for a business-as-usual scenario and a sustainable development scenario. Accordingly, we found that BC of the three stem borer pests by C. flavipes and C. sesamiae will be less effective under more severe CC resulting in a reduced ability to curb maize yield losses caused by the stem borers. These results highlight the need to adapt BC measures to future CC to maintain its potential for environmentally-friendly pest management strategies. The findings of this research are thus of particular relevance to policy makers, extension officers and farmers in the region and will aid the adaptation of smallholder agricultural practices to current and future impacts of CC.

scholarly journals How seasonal forecast could help a decision maker: an example of climate service for water resource management

2016 ◽  
Vol 13 ◽  
pp. 51-55 ◽  
Author(s):  
Christian Viel ◽  
Anne-Lise Beaulant ◽  
Jean-Michel Soubeyroux ◽  
Jean-Pierre Céron
Keyword(s):  
Water Resource ◽  
Water Resource Management ◽  
River Flow ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Blind Test ◽  
Climate Services ◽  
Great Opportunity ◽  
Skill Scores ◽  
The Impact

Abstract. The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

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