Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

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Effects of political instability on the volatility of Palestinian food prices
Ihle, R. ; El-Jafari, Mahmoud Khader ; Cramon-Taubadel, Stephan von - \ 2019
New Medit 18 (2019)3. - ISSN 1594-5685 - p. 59 - 76.
Food prices - MENA - Middle East - Uncertainty - War
Political instabilities and violent political conflict have in recent years risen substantially throughout the world. Especially in the Middle East and North Africa they have grown to decisive factors permanently challenging the livelihoods of millions. We assess whether and to what extent varying intensities of conflict impact economic activity in Palestine which has been subject to substantial violent political conflict for decades. In particular, we analyse the relationship between various intensity levels of political instability measured by conflict-caused fatalities and uncertainty of weekly food prices in the West Bank between 2004 and 2011 using a GARCH model. We consider four food commodities covering vegetables, fruits and animal products. Banana and milk prices are found not to show clustered volatility while onion and pear prices do. The impact of varying conflict intensities on weekly average prices appears to be modest. This might suggest that effects happen on a temporally and geographically more disaggregated scale.
Novelty detection in very high resolution urban scenes with Density Forests
Wendl, Cyril ; Marcos, Diego ; Tuia, Devis - \ 2019
In: 2019 Joint Urban Remote Sensing Event, JURSE 2019. - Institute of Electrical and Electronics Engineers Inc. (Joint Urban Remote Sensing Event (JURSE) ) - ISBN 9781728100104
Convolutional Neural Networks - Density Forest - land cover - novelty detection - Uncertainty

Uncertainty in deep learning has recently received a lot of attention. While deep neural networks have shown better accuracy than other competing methods in many benchmarks, it has been shown that they may yield wrong predictions with unreasonably high confidence. This has increased the interest in methods that help providing better confidence estimates in neural networks, some using specifically designed architectures with probabilistic building blocks, and others using a standard architecture with an additional confidence estimation step based on its output. This work proposes a confidence estimation method for Convolutional Neural Networks based on fitting a forest of randomized density estimation decision trees to the network activations before the final classification layer and compares it to other confidence estimation methods based on standard architectures. The methods are compared on a semantic labelling dataset with very high resolution satellite imagery. Our results show that methods based on intermediate network activations lead to better confidence estimates in novelty detection, i.e., in the discovery of classes that are not present in the training set.

Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose
Guillaume, Joseph H.A. ; Jakeman, John D. ; Marsili-Libelli, Stefano ; Asher, Michael ; Brunner, Philip ; Croke, B. ; Hill, Mary C. ; Jakeman, Anthony J. ; Keesman, Karel J. ; Razavi, S. ; Stigter, Johannes D. - \ 2019
Environmental Modelling & Software 119 (2019). - ISSN 1364-8152 - p. 418 - 432.
Derivative based methods - Emulation - Hessian - Identifiability - Non-uniqueness - Response surface - Uncertainty

Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes.

On the communication of statistical information about uncertainty in flood risk management
Poortvliet, P.M. ; Knotters, Martin ; Bergsma, Petra ; Verstoep, Joël ; Wijk, Jiska van - \ 2019
Safety Science 118 (2019). - ISSN 0925-7535 - p. 194 - 204.
Decision analysis - Flood risk management - Risk communication - Uncertainty

Uncertainty analysis is not typically performed in hydrological and hydraulic modelling. This is problematic because this may lead to inefficient decision making in water management. We therefore explored the role of statistical knowledge on uncertainty in decision-making processes in long term flood risk management within the context of regional water boards in the Netherlands. Research questions were: (1) in which parts of flood risk management statistical information about uncertainty is presented to professionals of district water boards, and in which forms?; (2) how is this information interpreted and used by these professionals, and how does this influence decision-making processes in district water boards?; and (3) how can communication about statistically quantified uncertainty be improved? To answer these questions we conducted interviews and surveys among professionals and board members of Dutch district water boards. Results suggest that statistical information on uncertainty is hard to interpret by professionals. The amount of statistical information on uncertainty strongly reduces during the decision making process, during which the information transforms from quantitative to qualitative. As a result the statistical information on uncertainty is not utilized to solve flood risk management decision problems. These decision problems are not formulated within statistical frameworks for decision making, and statistical information on uncertainty is not collected and presented with the purpose to be input of such frameworks. Practical recommendations for long term flood risk management are discussed.

Uncertainties of prediction accuracy in shallow landslide modeling : Sample size and raster resolution
Shirzadi, Ataollah ; Solaimani, Karim ; Roshan, Mahmood Habibnejad ; Kavian, Ataollah ; Chapi, Kamran ; Shahabi, Himan ; Keesstra, Saskia ; Ahmad, Baharin Bin ; Bui, Dieu Tien - \ 2019
Catena 178 (2019). - ISSN 0341-8162 - p. 172 - 188.
Alternating decision tree - GIS - Landslide susceptibility - Pixel and sample size - Uncertainty

Understanding landslide characteristics such as their locations, dimensions, and spatial distribution is of highly importance in landslide modeling and prediction. The main objective of this study was to assess the effect of different sample sizes and raster resolutions in landslide susceptibility modeling and prediction accuracy of shallow landslides. In this regard, the Bijar region of the Kurdistan province (Iran) was selected as a case study. Accordingly, a total of 20 landslide conditioning factors were considered with six different raster resolutions (10 m, 15 m, 20 m, 30 m, 50 m, and 100 m) and four different sample sizes (60/40%, 70/30%, 80/20%, and 90/10%) were investigated. The merit of each conditioning factors was assessed using the Information Gain Ratio (IGR) technique, whereas Alternating decision tree (ADTree), which has been rarely explored for landslide modeling, was used for building models. Performance of the models was assessed using the area under the ROC curve (AUROC), sensitivity, specificity, accuracy, kappa and RMSE criteria. The results show that with increasing the number of training pixels in the modeling process, the accuracy is increased. Findings also indicate that for the sample sizes of 60/40% (AUROC = 0.800) and 70/30% (AUROC = 0.899), the highest prediction accuracy is derived with the raster resolution of 10 m. With the raster resolution of 20 m, the highest prediction accuracy for the sample size of 80/20% (AUROC = 0.871) and 90/10% (AUROC = 0.864). These outcomes provide a guideline for future research enabling researchers to select an optimal data resolution for landslide hazard modeling.

Effects of DEM resolution on the accuracy of gully maps in loess hilly areas
Dai, Wen ; Yang, Xin ; Na, Jiaming ; Li, Jingwei ; Brus, Dick ; Xiong, Liyang ; Tang, Guoan ; Huang, Xiaoli - \ 2019
Catena 177 (2019). - ISSN 0341-8162 - p. 114 - 125.
Accuracy assessment - DEM resolution - Gully mapping - The Loess Plateau of China - Uncertainty

Gully maps are important prerequisites for the study of gully erosion and land degradation. Many digital elevation model (DEM)-based methods have been proposed to enable automated gully mapping. However, the accuracy of a gully map derived from a DEM is inevitably affected by the DEM resolution. This study investigates the effects of DEM resolution on the accuracy of gully maps. A series of DEMs with resolutions of 0.1–10 m is employed to map gully areas. The effects of DEM resolution on the error in the mapped gully area and on the position error are described by regression models. The results from two catchments in hilly areas of the Loess Plateau in China are as follows. DEMs with resolutions of 0.5–2 m are the most suitable for gully mapping. Very high-resolution DEMs increase local position errors and over-predict the extents of gullies, whereas DEMs with coarser resolutions cause the downward migration of mapped gully boundaries, resulting in the under-prediction of gully areas. However, the effects of DEM resolution on gully maps are not constant but vary in space. The spatial disparities of the resolution effects are related to the gully morphology. The resolution effects on the gully maps in V-shaped gullies are stronger than those in U-shaped gullies. The findings of this study can be used to select a suitable DEM resolution for gully mapping in loess hilly areas and contribute to understanding the characterization of gullies by using DEMs.

Modelling aggregate exposure to pesticides from dietary and crop spray sources in UK residents
Kennedy, Marc C. ; Garthwaite, David G. ; Boer, Waldo J. de; Kruisselbrink, Johannes W. - \ 2019
Environmental Science and Pollution Research 26 (2019)10. - ISSN 0944-1344 - p. 9892 - 9907.
Cumulative assessment group - Non-dietary exposure - Pesticide usage survey - Simulation model - Uncertainty

Human exposure to pesticide mixtures can occur from the diet and other sources. Realistic exposure and risk assessments should include multiple sources and compounds and include the relative hazards of the different compounds. The EU-funded Euromix project is developing new web-based tools to facilitate these calculations. A case study is presented that exemplifies their use for a population of UK residents, including exposure from crop-spraying. A UK pesticide usage survey provided information on real pesticide combinations applied to crops of wheat, potatoes, sugar beet and dessert apples. This information was combined with outputs from two alternative simulation models of spray drift to estimate dermal, oral and inhalation exposures of residents. These non-dietary exposures were combined with dietary exposure estimates using the Monte Carlo Risk Assessment software to produce a distribution of aggregated and cumulative exposures. Compounds are weighted by relative potency to generate a measure of overall risk. Uncertainty quantification was also included in the distribution of exposures. These tools are flexible to allow diverse sources of exposure and can provide important information to decision-makers and help to prioritise testing of pesticide mixtures. Including non-dietary sources changed the prioritisation of pesticide mixtures, when compared to dietary exposure alone.

Designing with pathways : A spatial design approach for adaptive and sustainable landscapes
Zandvoort, Mark ; Kooijmans, Nora ; Kirshen, Paul ; Brink, Adri van den - \ 2019
Sustainability 11 (2019)3. - ISSN 2071-1050
Adaptiveness - Climate adaptation - Decision pathways - Flood risk management - Landscape architecture - Spatial design - Uncertainty - Visualization

Despite rising attention to pathways thinking in multiple domains such as climate adaptation, energy supply planning, and flood risk management, their spatial translation is so far understudied. We set out to study how spatial design based on pathways thinking can help develop more adaptive and sustainable landscapes. Using landscape analysis, field research, and research-through-designing in a case study on climate resilience in Boston (USA), we argue for better understanding of the spatial and design consequences of pathways in general. Our results indicate that pathways can be spatially translated, demanding landscape-informed choices when sequencing different policy actions. We found that spatial designing makes the landscape consequences of pathways transparent and enables policy-makers to replace the input of policy actions with spatial interventions, select pathways according to different underlying design strategies, use the mapped pathways to initiate an iterative research-through-designing process to test and inform different designs, and spatially visualize the pathways and possible sequences of actions. We conclude that policy-makers should be cognizant about the spatial implications of pathways and offer directions to enrich applications of pathways thinking for achieving adaptive and sustainable landscapes.

Recent insights on uncertainties present in integrated catchment water quality modelling
Tscheikner-Gratl, Franz ; Bellos, Vasilis ; Schellart, Alma ; Moreno-Rodenas, Antonio ; Muthusamy, Manoranjan ; Langeveld, Jeroen ; Clemens, Francois ; Benedetti, Lorenzo ; Rico-Ramirez, Miguel Angel ; Carvalho, Rita Fernandes de; Breuer, Lutz ; Shucksmith, James ; Heuvelink, Gerard B.M. ; Tait, Simon - \ 2019
Water Research 150 (2019). - ISSN 0043-1354 - p. 368 - 379.
Complexity management - Integrated catchment modelling - Sub-models of integrated modelling - Uncertainty - Water quality

This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.

Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations
Rodríguez, A. ; Ruiz-Ramos, M. ; Palosuo, T. ; Carter, T.R. ; Fronzek, S. ; Lorite, I.J. ; Ferrise, R. ; Pirttioja, N. ; Bindi, M. ; Baranowski, P. ; Buis, S. ; Cammarano, D. ; Chen, Y. ; Dumont, B. ; Ewert, F. ; Gaiser, T. ; Hlavinka, P. ; Hoffmann, H. ; Höhn, J.G. ; Jurecka, F. ; Kersebaum, K.C. ; Krzyszczak, J. ; Lana, M. ; Mechiche-Alami, A. ; Minet, J. ; Montesino, M. ; Nendel, C. ; Porter, J.R. ; Ruget, F. ; Semenov, M.A. ; Steinmetz, Z. ; Stratonovitch, P. ; Supit, I. ; Tao, F. ; Trnka, M. ; Wit, A. de; Rötter, R.P. - \ 2019
Agricultural and Forest Meteorology 264 (2019). - ISSN 0168-1923 - p. 351 - 362.
Climate change - Decision support - Outcome confidence - Response surface - Uncertainty - Wheat adaptation

Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.

Communicating Climate Information : Traveling Through the Decision-Making Process
Dubois, Ghislain ; Stoverinck, Femke ; Amelung, Bas - \ 2018
In: Communicating Climate Change Information for Decision-Making / Serrao-Neumann, Silvia, Coudrain, Anne, Coulter, Liese, Springer (Springer Climate ) - ISBN 9783319746685 - p. 119 - 137.
Climate change - Climate services - Communication - Uncertainty - Visualization

Climate change forces society to adapt. Adaptation strategies are preferably based on the best available climate information. Climate projections, however, often inform adaptation strategies after being interpreted once or several times. This process affects the original message put forward by climate scientists when presenting the basic climate projections, in particular regarding uncertainties. The nature of this effect and its implications for decision-making are as yet poorly understood. This chapter explores the nature and consequences of (a) the communication tools used by scientists and experts and (b) changes in the communicated information as it travels through the decision-making process. It does so by analyzing observatories; the interpretative steps taken in a sample of 25 documents, pertaining to the field of public policies for climate change impact assessment and adaptation strategies. Five phases in the provisioning of climate information are distinguished: pre-existing knowledge (i.e., climate models and data), climate change projection, impact assessment, adaptation strategy, and adaptation plan. Between the phases, climate information is summarized and synthesized in order to be passed on. The results show that in the sample, information on uncertainty is underrepresented: e.g., studies focus on only one scenario and/or disregard probability distributions. In addition, visualization tools are often used ineffectively, leading to confusion and unintended interpretations. Several recommendations are presented. While climatologists need better training in communication issues, decision-makers also need training in climatology to adopt more cautious and robust adaptation strategies that account for the uncertainty inherent in climate projections.

Retirement concerns and planning of cooperative members : a study in the Dutch healthcare sector
Apostolakis, George ; Dijk, Gert van - \ 2018
Business Economics 53 (2018)4. - ISSN 0007-666X - p. 209 - 224.
Cooperatives - Healthcare - Retirement planning - Uncertainty

Retirement planning is a key component in achieving goals and fulfilling expectations. Although several socioeconomic and psychological factors associated with retirement planning have been reported in the literature, little is known about the influence that specific retirement-related issues have on retirement planning. We examine the influence of five concerns—the individual’s financial situation, living situation, care provision, health condition, and loneliness—on retirement planning. In addition, we investigate the influence of these concerns on individuals’ perceptions of their ideal post-retirement situations in terms of financial standards. Our dataset is derived from a 2010 web-based survey of the care and well-being sector in the Netherlands.

Nine lives of uncertainty in decision-making: strategies for dealing with uncertainty in environmental governance
Dewulf, A.R.P.J. ; Biesbroek, G.R. - \ 2018
Policy and Society 37 (2018). - ISSN 1449-4035 - p. 441 - 458.
Uncertainty - ambiguity - wicked problems - decisionmaking - environmental governance
Governing complex environmental issues involves intensive interaction between public and private actors. These governance processes are fraught with uncertainties about, for example, the current state of environmental affairs, the relevant set of decision alternatives, the reactions of other actors to proposed solutions or the future developments likely to affect an issue. Uncertainty comes in different shapes and sizes and different strands in the literature, which has placed emphasis either on the substance of the issue (e.g. in environmental sciences) or on the decision-making process (e.g. policy sciences). In this paper, we bring together these different strands of literature on uncertainty to present a novel analytical framework. We build on the argument that the nature of uncertainty consists of three types: epistemic uncertainty (involving the lack of knowledge about a particular system), ontological uncertainty (irreducible unpredictability due to inherently complex system behavior) and ambiguity (conflicts between fundamentally different frames about the issue at hand). Scholars have also argued the importance of differentiating between three different objects of uncertainty: substantive uncertainty (uncertainty about the content of decisions or policy issues), strategic uncertainty (uncertainty about the actions of other actors in the strategic game of decision-making) and institutional uncertainty (uncertainty about the rules of the game in decision-making). The framework is useful for analyzing and addressing the nine lives of uncertainty in decisionmaking. Better understanding of the range of uncertainties is crucial to design more robust policies and governance arrangements and to deal with wicked environmental problems.
Governing marine ecosystem restoration : the role of discourses and uncertainties
Ounanian, Kristen ; Carballo-Cárdenas, Eira ; Tatenhove, Jan P.M. van; Delaney, Alyne ; Papadopoulou, K.N. ; Smith, Christopher J. - \ 2018
Marine Policy 96 (2018). - ISSN 0308-597X - p. 136 - 144.
Governance challenges - Human intervention - Motivations - Restoration approaches - Uncertainty

Governing marine environments has evolved from dominant interests in exploitation, allocation, conservation, and protection to restoration. Compared to terrestrial and freshwater environments, restoration of and in marine ecosystems presents a new mode of intervention with both technical and governance challenges. This paper aims to enhance understanding of the important factors at play in governing marine ecosystem restoration. Discourses of marine ecosystem restoration are an important factor which shape how the restoration activity is governed, as discourses structure how actors and coalitions define problems and their approaches to solutions. The article produces a conceptual model of the discourses of marine ecosystem restoration, built on two dimensions: (1) the degree of human intervention and (2) motivations for restoration. Together, these dimensions create four broad restoration discourses: “Putting Nature First,” “Bringing Nature Back,” “Helping Nature support Humans,” and “Building with Nature.” Moreover, marine ecosystem restoration is confronted with different forms of uncertainty, such as incomplete knowledge, unpredictability, and ambiguity, which must be managed by actors participating in restoration initiatives. The article's overall contribution is the synthesis of these components, which illuminates the specific governance challenges under various circumstances.

Strategies for dealing with uncertainties in strategic environmental assessment : An analytical framework illustrated with case studies from The Netherlands
Bodde, Maartje ; Wel, Karin van der; Driessen, Peter ; Wardekker, Arjan ; Runhaar, Hens - \ 2018
Sustainability 10 (2018)7. - ISSN 2071-1050
Effectiveness - Environmental assessment - Governance - Uncertainty

Strategic environmental assessment (SEA) is a widely applied policy tool that aims to aid decision-makers in making informed, higher-quality decisions that minimize negative environmental impacts. However, different types of uncertainties complicate the ex ante assessment of environmental impacts. Literature suggests uncertainties are often not well addressed, resulting in inaccurate and even unreliable SEAs. At the same time, SEA literature offers limited guidance in how to systematically identify and deal with uncertainties. Therefore, in this paper, we present an analytical framework for characterizing and classifying different forms of uncertainty in SEA, and for identifying strategies for dealing with these uncertainties. The framework is based on literature on uncertainties in other subdomains of the environmental sciences. The framework is applied to five case studies of SEAs for spatial planning in The Netherlands in order to illustrate and critically reflect on our framework, and to bridge the gap between theory and practice. Based on these case studies we concluded the following: (1) The framework is useful for identifying uncertainties in SEA in a systematic way; (2) There is a discrepancy between how uncertainties are dealt with in theory and in practice; (3) In practice, uncertainties seem to be dealt with in a rather implicit way. The framework may help dealing with uncertainties more systematically and more proactively; (4) The most successful way of coping with uncertainties seems to be the application of multiple strategies.

Spatial resolutions in areal rainfall estimation and their impact on hydrological simulations of a lowland catchment
Terink, Wilco ; Leijnse, Hidde ; Eertwegh, Gé van den; Uijlenhoet, Remko - \ 2018
Journal of Hydrology 563 (2018). - ISSN 0022-1694 - p. 319 - 335.
Hupsel Brook catchment - Hydrological simulations - Spatial resolutions - SPHY - Uncertainty - X-band radar

Many studies suggest that high-density rain gauge networks are required to capture the rainfall heterogeneities necessary to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network will also involve high costs. Although many studies provided useful insights on the required accuracy of rainfall estimates to accurately describe the components of the hydrological cycle, most of these studies focused on streamflow simulations, large river basins or urban environments. The objective of this study is therefore to evaluate the impact of uncertainties in areal rainfall, estimated at several spatial resolutions, on hydrological simulations of a small ∼6.5 km2 rural lowland catchment. The approach followed in this study is to force a calibrated spatially-distributed hydrological model (SPHY) with rainfall retrieved from an X-band radar and various synthetic rainfall products, calculated using bootstrap samples of a varying number of radar pixels, treated as virtual rain gauge locations within the catchment. This enables us to determine the most appropriate resolution of rainfall data to accurately describe the hydrology of a small rural lowland catchment. We found that the use of one rain gauge to estimate the catchment's areal rainfall may lead to a potential error of more than six times the average hourly rainfall. This may lead to uncertainties in simulated discharge that approach 60% of the average hourly discharge. More than 40 rain gauges are required to reduce the potential error in areal rainfall estimation to values <0.1 mm h−1. The associated uncertainty in discharge simulations is 20% if 10 rain gauges are used, and 10% if 40 rain gauges are used. The simulation of soil moisture contents and evapotranspiration rates are hardly affected by the number of rain gauges used to estimate the areal rainfall, which is due to the high saturated hydraulic conductivities of the top-soil. At least 12 gauges per km2 are required to capture the spatial rainfall variation that is present in radar rainfall estimates. Analysis of an individual 18-h rainfall event revealed that the uncertainty in peak areal rainfall estimated using one rain gauge may range between −100% and 600%. The associated uncertainty in simulated discharge for this event ranges between −67 and 233%. With 25 rain gauges the uncertainty in simulated discharge is still in the range of −17 to 33%.

Geostatistical disaggregation of polygon maps of average crop yields by area-to-point kriging
Brus, D.J. ; Boogaard, H. ; Ceccarelli, T. ; Orton, T.G. ; Traore, S. ; Zhang, M. - \ 2018
European Journal of Agronomy 97 (2018). - ISSN 1161-0301 - p. 48 - 59.
Aggregated data - Uncertainty - Yield gap

Crop yield data are often available as statistics of areas, such as administrative units, generated by national agricultural surveys and censuses. This paper shows that such areal data can be used in area-to-point kriging (ATP kriging) to estimate the crop yield at the nodes of a fine grid that discretizes the study area, so that a more detailed map of the crop yield is obtained. The theory behind ATP kriging is explained, and illustrated with a one-dimensional simulation study and two real-world case studies. Vegetation, precipitation, temperature and soil data were used as potential covariates in the spatial trend part of the geostatistical model. ATP kriging requires the covariogram at point support, which can be recovered from the areal data by restricted maximum likelihood. The standard errors of the estimated variogram parameters can then be obtained by the Fisher information matrix. The average yields of only 17 administrative units in Shandong province (China) were not enough to obtain reliable estimates of the covariogram at point support. Also the ranges of the regional averages of the covariates were very narrow, so that the model must be extrapolated in the largest part of the study area. We were more confident about the covariogram parameters estimated from 45 provinces in Burkina Faso. We conclude that ATP kriging is an interesting method for disaggregation of spatially averaged crop yields. Contrary to other downscaling methods ATP kriging is founded on statistical theory, and consequently provides estimates of the precision of the disaggregated yields. Shortcomings are related to the uncertainty in the estimated covariogram parameters, as well as to the extrapolation of the model outside the range of the regional means of the covariates. Opportunities for future advancements are the use of modelled yields as covariates and the introduction of expert knowledge at different levels. For the latter a Bayesian approach to ATP kriging can be advantageous, introducing prior knowledge about the model parameters, as well as accounting for uncertainty about the model parameters.

Re-thinking socio-economic impact assessments of disasters : The 2015 flood in Rio Branco, Brazilian Amazon
Dolman, Dorien Irene ; Brown, Irving Foster ; Anderson, Liana Oighenstein ; Warner, Jeroen Frank ; Marchezini, Victor ; Santos, George Luiz Perreira - \ 2018
International Journal of Disaster Risk Reduction 31 (2018). - ISSN 2212-4209 - p. 212 - 219.
Amazon - Brazil - Floods - Socio-economic impact assessments - Uncertainty

The impact of water-related disasters has become more acute in cities of the Amazon Basin. Socio-economic impact assessments have a key role in improving sustainable mitigation projects in order to increase resilience and reduce societal vulnerability. This paper reviews the current state of loss assessments and then explores how to improve estimates for the 2015 flood affecting the city of Rio Branco, Brazil, located on the headwaters of the Amazon Basin. Prevailing models, loss assessments, and databases are not applicable in this Amazonian context due to the lack of detailed cost administration, low levels of human and financial capital, and limited insurance coverage. This paper uses uncertainty ranges of the costs of water-related disasters to provide an assessment of the total impact. Our estimate ranges from 60 to 200 million USD of losses and damage solely due to this flood event, compared to the official estimate of 98 million USD. As floods in Rio Branco are recurrent nearly annually, the cumulative losses over the years may be significantly higher. Our study illustrates the need for improving impact assessments in order to increase the knowledge on the actual costs of flood disasters and avoiding silent impoverishment. Outcomes of impact assessments can show the necessity of mitigation activities which will reduce vulnerability of societies.

Mapping abiotic stresses for rice in Africa : Drought, cold, iron toxicity, salinity and sodicity
Oort, P.A.J. van - \ 2018
Field Crops Research 219 (2018). - ISSN 0378-4290 - p. 55 - 75.
Crop maps - GIS - HWSD - ORYZA2000 - Uncertainty
Maps of abiotic stresses for rice can be useful for (1) prioritizing research and (2) identifying stress hotspots, for directing technologies and varieties to those areas where they are most needed. Large-scale maps of stresses are not available for Africa. This paper considers four abiotic stresses relevant for rice in Africa (drought, cold, iron toxicity and salinity/sodicity). Maps showing hotspots of the stresses, the countries most affected and total potentially affected area are presented. In terms of relative importance, the study identified drought as the most important stress (33% of rice area potentially affected), followed by iron toxicity (12%) and then cold (7%) and salinity/sodicity (2%). Hotspots for iron toxicity, cold and salinity are identified. For drought, local variation along the hydromorphic zone was a stronger determinant than larger-scale climatic variation, therefore mapping of drought based on climatic zones has only limited value. Uncertainties in the mappings are discussed.
Graphical Illustration of Interaction Effects in Binary Choice Models : A Note
Franken, Jason R.V. ; Pennings, Joost M.E. ; Garcia, Philip - \ 2018
Journal of Agricultural Economics 69 (2018)3. - ISSN 0021-857X - p. 852 - 858.
Asset specificity - Binary choice models - Contracts - Transaction costs economics - Uncertainty
Graphing procedures for evaluating power or interaction terms in binary logit and probit models are illustrated in an application to hog producers' decisions based on transaction cost economics' hypothesised positive effect of the interaction of uncertainty and asset specificity on contract use. Results support the hypothesis, particularly for producers that are otherwise on the cusp (near the 50/50 probability) of choosing either contract or spot transactions based on their responses for other variables. Such insights may not be drawn without use of the demonstrated graphing procedures.
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