Including climate change projections in probabilistic flood risk assessment
Ward, P.J. ; Pelt, S.C. van; Keizer, O. de; Aerts, J.C.J.H. ; Beersma, J.J. ; Hurk, B.J.J.M. van den - \ 2014
Journal of Flood Risk Management 7 (2014)2. - ISSN 1753-318X - p. 141 - 151.
klimaatverandering - overstromingen - risicoschatting - modellen - climatic change - floods - risk assessment - models - rhine basin - model - precipitation - uncertainty - simulations - decisions
This paper demonstrates a framework for producing probabilistic flood risk estimates, focusing on two sections of the Rhine River. We used an ensemble of six (bias-corrected) regional climate model (RCM) future simulations to create a 3000-year time-series through resampling. This was complemented with 12 global climate model (GCM)-based future time-series, constructed by resampling observed time-series of daily precipitation and temperature and modifying these to represent future climate conditions using an advanced delta change approach. We used the resampled time-series as input in the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV)-96 to simulate daily discharge and extreme discharge quantiles for return periods up to 3000 years. To convert extreme discharges to estimates of flood damage and risk, we coupled a simple inundation model with a damage model. We then fitted probability density functions (PDFs) for the RCM, GCM, and combined ensembles. The framework allows for the assessment of the probability distribution of flood risk under future climate scenario conditions. Because this paper represents a demonstration of a methodological framework, the absolute figures should not be used in decision making at this time.
Mapping vegetation density in a heterogeneous river floodplain ecosystem using pointable CHRIS/PROBA data
Verrelst, J. ; Romijn, J.E. ; Kooistra, L. - \ 2012
Remote Sensing 4 (2012)9. - ISSN 2072-4292 - p. 2866 - 2889.
leaf-area index - radiative-transfer model - hyperspectral brdf data - chris-proba data - flow resistance - climate-change - rhine basin - sugar-beet - forest - cover
River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (-36°) and forward (+36°) scatter direction. The -36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.
A process-based typology of hydrological drought
Loon, A.F. van; Lanen, H.A.J. van - \ 2012
Hydrology and Earth System Sciences 16 (2012). - ISSN 1027-5606 - p. 1915 - 1946.
conceptual runoff model - atmospheric circulation - streamflow drought - climate-change - winter drought - hbv model - 20th-century drought - united-states - forcing data - rhine basin
Hydrological drought events have very different causes and effects. Classifying these events into distinct types can be useful for both science and management. We propose a hydrological drought typology that is based on governing drought propagation processes derived from catchment-scale drought analysis. In this typology six hydrological drought types are distinguished, i.e. (i) classical rainfall deficit drought, (ii) rain-to-snow-season drought, (iii) wet-to-dry-season drought, (iv) cold snow season drought, (v) warm snow season drought, and (vi) composite drought. The processes underlying these drought types are the result of the interplay of temperature and precipitation at catchment scale in different seasons. As a test case, about 125 groundwater droughts and 210 discharge droughts in five contrasting headwater catchments in Europe have been classified. The most common drought type in all catchments was the classical rainfall deficit drought (almost 50% of all events), but in the selected catchments these were mostly minor events. If only the five most severe drought events of each catchment are considered, a shift towards more rain-to-snow-season droughts, warm snow season droughts, and composite droughts was found. The occurrence of hydrological drought types is determined by climate and catchment characteristics. The drought typology is transferable to other catchments, including outside Europe, because it is generic and based upon processes that occur around the world. A general framework is proposed to identify drought type occurrence in relation to climate and catchment characteristics.
Impact of climate change on low-flows in the river Meuse
Wit, M.J.M. de; Hurk, B. van den; Warmerdam, P.M.M. ; Torfs, P.J.J.F. ; Roulin, E. ; Deursen, W.P.A. van - \ 2007
Climatic Change 82 (2007)3-4. - ISSN 0165-0009 - p. 351 - 372.
hydrological model - change scenarios - rhine basin - runoff - temperature - simulations - resources
In this study observed precipitation, temperature, and discharge records from the Meuse basin for the period 1911-2003 are analysed. The primary aim is to establish which meteorological conditions generate (critical) low-flows of the Meuse. This is achieved by examining the relationships between observed seasonal precipitation and temperature anomalies, and low-flow indices. Secondly, the possible impact of climate change on the (joint) occurrence of these low-flow generating meteorological conditions is addressed. This is based on the outcomes of recently reported RCM climate simulations for Europe given a scenario with increased atmospheric greenhouse-gas concentrations. The observed record (1911-2003) hints at the importance of multi-seasonal droughts in the generation of critical low-flows of the river Meuse. The RCM simulations point to a future with wetter winters and drier summers in Northwest Europe. No increase in the likelihood of multi-seasonal droughts is simulated. However, the RCM scenario runs produce multi-seasonal precipitation and temperature anomalies that are out of the range of the observed record for the period 1911-2003. The impact of climate change on low-flows has also been simulated with a hydrological model. This simulation indicates that climate change will lead to a decrease in the average discharge of the Meuse during the low-flow season. However, the model has difficulties to simulate critical low-flow conditions of the Meuse.
Simulation of 6-hourly rainfall and temperature by two resampling schemes
Wójcik, R. ; Buishand, T.A. - \ 2003
Journal of Hydrology 273 (2003). - ISSN 0022-1694 - p. 69 - 80.
neerslag - regen - luchttemperatuur - stochastische modellen - nederland - precipitation - rain - air temperature - stochastic models - netherlands - daily precipitation - rhine basin - model - series
The joint simulation of time series of 6-hourly precipitation and temperature using nearest-neighbour resampling is studied for Maastricht, the Netherlands. Two resampling schemes are considered: (i) straightforward resampling of 6-hourly values, and (ii) resampling of daily values followed by disaggregation into 6-hourly values using the method of fragments. Second-order statistics of the simulated values are compared with those in the observed data. It appeared that straightforward resampling of 6-hourly values does not adequately preserve the slow decay of the autocorrelation functions of precipitation and temperature. As a result the standard deviations of the monthly precipitation totals and monthly average temperature are strongly underestimated. A negative bias also shows up in the quantiles of the multi-day seasonal maximum precipitation amounts. The autocorrelation coefficients and the standard deviations of the monthly values are much better reproduced if the daily values are generated first. A good correspondence between the historical and simulated distributions of the seasonal maximum precipitation amounts is also achieved with this alternative resampling scheme. (C) 2003 Elsevier Science B.V. All rights reserved.