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Micha Werner

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8 records found

Contextualising seasonal climate forecasts by integrating local knowledge on drought in Malawi

Journal article - I.N. Streefkerk, I.N. Streefkerk, Marc J.C. van den Homberg, Stephen Whitfield, Neha Mittal, Edward Pope, Micha Werner, H.C. Winsemius, H.C. Winsemius, M. Comes, M. Comes, M.W. Ertsen

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi's smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the r ...

Streamflow droughts aggravated by human activities despite management

Journal article - Anne F. Van Loon, Sally Rangecroft, None More Authors, Gemma Coxon, Micha Werner, Niko Wanders, Giuliano Di Baldassarre, Erik Tijdeman, Marianne Bosman, Jose Agustin Breña-Naranjo, G.P.W. Jewitt, G.P.W. Jewitt, G.P.W. Jewitt

Human activities both aggravate and alleviate streamflow drought. Here we show that aggravation is dominant in contrasting cases around the world analysed with a consistent methodology. Our 28 cases included different combinations of human-water interactions. We found that water ...

Communication Structures and Decision Making Cues and Criteria to Support Effective Drought Warning in Central Malawi

Journal article - Alexia Calvel, Micha Werner, Marc J.C. van den Homberg, Andrés Cabrera Flamini, I.N. Streefkerk, Neha Mittal, Stephen Whitfield, Charles Langton Vanya, Clement Boyce

Early warning systems trigger early action and enable better disaster preparedness. People-centered dissemination and communication are pivotal for the effective uptake of early warnings. Current research predominantly focuses on sudden-onset hazards, such as floods, ignoring con ...

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed

Journal article - David R. Casson, David R. Casson, Micha Werner, Micha Werner, Albrecht Weerts, Albrecht Weerts, D.P. Solomatine, D.P. Solomatine

Hydrological modelling in the Canadian sub-Arctic is hindered by sparse meteorological and snowpack data. The snow water equivalent (SWE) of the winter snowpack is a key predictor and driver of spring flow, but the use of SWE data in hydrological applications is limited due to high uncertainty. Global re-analysis datasets that provide gridded meteorological and SWE data may be well suited to improve hydrological assessment and snowpack simulation. To investigate representation of hydrological processes and SWE for application in hydropower operations, global re-analysis datasets covering 1979-2014 from the European Union FP7 eartH2Observe project are applied to global and local conceptual hydrological models. The recently developed Multi-Source Weighted-Ensemble Precipitation (MSWEP) and the WATCH Forcing Data applied to ERA-Interim data (WFDEI) are used to simulate snowpack accumulation, spring snowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded by the European Space Agency with daily coverage from 1979 to 2014 is evaluated against in situ SWE measurement over the local watershed. Results demonstrate the successful application of global datasets for streamflow prediction, snowpack accumulation and snowmelt timing in a snowmelt-driven sub-Arctic watershed. The study was unable to demonstrate statistically significant correlations (p/0.05) among the measured snowpack, global hydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peak discharge. The GlobSnow-2 product is found to under-predict late-season snowpacks over the study area and shows a premature decline of SWE prior to the true onset of the snowmelt. Of the datasets tested, the MSWEP precipitation results in annual SWE estimates that are better predictors of snowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This study demonstrates the operational and scientific utility of the global re-analysis datasets in the sub-Arctic, although knowledge gaps remain in global satellite-based datasets for snowpack representation, for example the relationship between passive-microwave-measured SWE to snowmelt runoff volume.@en

The predictability of reported drought events and impacts in the Ebro Basin using six different remote sensing data sets

Journal article - Clara Linés, Micha Werner, Micha Werner, W.G.M. Bastiaanssen, W.G.M. Bastiaanssen

The implementation of drought management plans contributes to reduce the wide range of adverse impacts caused by water shortage. A crucial element of the development of drought management plans is the selection of appropriate indicators and their associated thresholds to detect drought events and monitor the evolution. Drought indicators should be able to detect emerging drought processes that will lead to impacts with sufficient anticipation to allow measures to be undertaken effectively. However, in the selection of appropriate drought indicators, the connection to the final impacts is often disregarded. This paper explores the utility of remotely sensed data sets to detect early stages of drought at the river basin scale and determine how much time can be gained to inform operational land and water management practices. Six different remote sensing data sets with different spectral origins and measurement frequencies are considered, complemented by a group of classical in situ hydrologic indicators. Their predictive power to detect past drought events is tested in the Ebro Basin. Qualitative (binary information based on media records) and quantitative (crop yields) data of drought events and impacts spanning a period of 12 years are used as a benchmark in the analysis. Results show that early signs of drought impacts can be detected up to 6 months before impacts are reported in newspapers, with the best correlation-anticipation relationships for the standard precipitation index (SPI), the normalised difference vegetation index (NDVI) and evapotranspiration (ET). Soil moisture (SM) and land surface temperature (LST) offer also good anticipation but with weaker correlations, while gross primary production (GPP) presents moderate positive correlations only for some of the rain-fed areas. Although classical hydrological information from water levels and water flows provided better anticipation than remote sensing indicators in most of the areas, correlations were found to be weaker. The indicators show a consistent behaviour with respect to the different levels of crop yield in rain-fed areas among the analysed years, with SPI, NDVI and ET providing again the stronger correlations. Overall, the results confirm remote sensing products' ability to anticipate reported drought impacts and therefore appear as a useful source of information to support drought management decisions.@en

The potential of global reanalysis datasets in identifying flood events in Southern Africa

Journal article - Gaby J. Gründemann, Gaby J. Gründemann, Micha Werner, Micha Werner, Ted I.E. Veldkamp, Ted I.E. Veldkamp

Sufficient and accurate hydro-meteorological data are essential to manage water resources. Recently developed global reanalysis datasets have significant potential in providing these data, especially in regions such as Southern Africa that are both vulnerable and data poor. These global reanalysis datasets have, however, not yet been exhaustively validated and it is thus unclear to what extent these are able to adequately capture the climatic variability of water resources, in particular for extreme events such as floods. This article critically assesses the potential of a recently developed global Water Resources Reanalysis (WRR) dataset developed in the European Union's Seventh Framework Programme (EU-FP7) eartH2Observe (E2O) project for identifying floods, focussing on the occurrence of floods in the Limpopo River basin in Southern Africa. The discharge outputs of seven global models and ensemble mean of those models as available in the WRR dataset are analysed and compared against two benchmarks of flood events in the Limpopo River basin. The first benchmark is based on observations from the available stations, while the second is developed based on flood events that have led to damages as reported in global databases of damaging flood events. Results: show that, while the WRR dataset provides useful data for detecting the occurrence of flood events in the Limpopo River basin, variation exists amongst the global models regarding their capability to identify the magnitude of those events. The study also reveals that the models are better able to capture flood events at stations with a large upstream catchment area. Improved performance for most models is found for the 0.25° resolution global model, when compared to the lower-resolution 0.5° models, thus underlining the added value of increased-resolution global models. The skill of the global hydrological models (GHMs) in identifying the severity of flood events in poorly gauged basins such as the Limpopo can be used to estimate the impacts of those events using the benchmark of reported damaging flood events developed at the basin level, though this could be improved if further details on location and impacts are included in disaster databases. Large-scale models such as those included in the WRR dataset are used by both global and continental forecasting systems, and this study sheds light on the potential these have in providing information useful for local-scale flood risk management. In conclusion, this study offers valuable insights in the applicability of global reanalysis data for identifying impacting flood events in data-sparse regions.@en

River Bar Downscaling with Bed Sills and Bottom Groynes: A Numerical Study

Conference paper - Gamal Alahmady, Gamal Alahmady, A. Crosato, A. Crosato, Micha Werner

Numerical study on the effects of river bank stabilization

Poster - Maha Sheikh, A. Crosato, A. Crosato, V. Chavarrías, Micha Werner

Contributed

1 records found

Linking Drought Forecast Information to Smallholder Farmer's Agricultural Strategies and Local Knowledge in Southern Malawi

Master thesis - I.N. Streefkerk, H.C. Winsemius, M.W. Ertsen, M. Comes, Marc van den Homberg, Micha Werner

Most people of Malawi are dependent on rainfed agriculture for their livelihoods. This leaves them vulnerable to drought and changing rainfall patterns due to climate change. Over time, farmers have adopted local strategies and knowledge that help reducing the overall vulnerability to climate variability shocks. One other option to increase the resilience of rainfed farmers to drought, is providing forecast information on the upcoming rainfall season. Forecast information has the potential to inform farmers in their decisions surrounding agricultural strategies. However, significant challenges remain in the provision of forecast information. Often, the forecast information is not tailored to farmers, resulting in limited uptake of forecast information into their agricultural decision-making. Therefore, this study explores whether drought forecast information can be linked to existing farmers strategies and local knowledge on predicting future rainfall patterns. During a period of three months in Malawi, participatory research approaches are used to create an understanding of what requirements drought forecast information should meet to effectively inform farmers in their decision-making. Consequently, a sequential threshold model was established that relates annually monitored meteorological indicators before the rainy season, to the occurrence of dry conditions during the season. Dry conditions were expressed in the drought indicators that farmers require for their agricultural decision-making. Additionally, using interviews among stakeholders and a visualisation of the current information flow, further insights on the current drought information system were developed. Although farmers have their own strategies and timing of decision-making, this research has generalized some of the opinions and strategies to develop the ‘requirements’ which a contextualized forecast should meet. In August farmers require a prediction of the onset of the rainy season, typically starting mid-November. In addition, an update on the timing of the onset of rains is required in beginning of November. An overall indication of the ‘dryness’ of the rainy season is required in September. Here, ‘dryness’ is characterized by the number of dry spells, a composite ‘drought index’ of associated rainfall variables by the farmers. The forecast should be on a scale that is locally relevant (EPA level). This research consequently established a forecasting model, based on meteorological variables from local knowledge which can complement the forecast variables from the DCCMS. The results of forecast verification show that meteorological indicators based on local knowledge have a predictive value for forecasting drought indicators. Subsequently, skill analysis of forecasting incorporating all the above dimensions shows that the accuracy of the forecast differs per location with an increased skill to the Southern locations. In addition, it is also location dependent whether the contribution of wind, temperature or ENSO indicators gives the most predictive value. The results show that a combination of all indicators have the best predictive value. In addition, the results show that local knowledge indicators have an increased predictive value in forecasting the locally relevant critical events in comparison to the currently used ENSO-related indicators by the DCCMS. Additional research is needed to further analyse certain aspects of this research, such as research on the robustness of the model used. Research on the risk farmers are willing to take in their respective decisions could act as another requirement the forecast skill should meet. This highlights the importance of having continuous feedback from the farmers, since farmers may experience adverse impacts from wrongly informed decisions. Despite these limitations, it is argued that the inclusion of local knowledge in the current drought information system of Malawi may improve the provision of forecast information for farmers and shows that it is possible to capture local knowledge in a technical approach. The findings have relevant implications for other stakeholders, such as humanitarian and meteorological organisations, that are implementing drought-risk reduction approaches and climate services.