Civil Engineering Technology

As an initial step in assessing the prospect of using global hydrological models (GHMs) for hydrological forecasting, this study investigates the skill of the GHM PCR-GLOBWB in reproducing the occurrence of past extremes in monthly discharge on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones.

1] The inundation model Delft flooding system (Delft-FLS) was applied to simulating the historical flood of 1805 in the polder Land van Maas en Waal (Netherlands). Delft-FLS is a two-dimensional (2-D) hydrodynamic model that simulates overland flow. Sensitivity analyses show a large influence of floodplain topography and hydraulic friction on the propagation of the inundation. Because of the typical topography of Dutch polders, a virtually flat floodplain bordered by high dikes, the extent of the inundated area alone cannot be used for evaluating the model performance. Therefore the inundation time and water levels within the inundated area were used to test the model capabilities. This study shows that using historic data has a potential advantage over using contemporary data. Historic data allow evaluation of a model for real flood disasters that have long return periods and, fortunately, have not occurred in modern times. Inundation of a Dutch river polder, sensitivity analysis of a physically based inundation model using historic data, Water Resour.

The International Commission for the Hydrology of the Rhine basin (CHR) has carried out a research project to assess the impact of climate change on the river flow conditions in the Rhine basin. Along a bottom-up line, different detailed hydrological models with hourly and daily time steps have been developed for representative sub-catchments of the Rhine basin. Along a topdown line, a water balance model for the entire Rhine basin has been developed, which calculates monthly discharges and which was tested on the scale of the major tributaries of the Rhine. Using this set of models, the effects of climate change on the discharge regime in different parts of the Rhine basin were calculated using the results of UKHI and XCCC GCM-experiments. All models indicate the same trends in the changes: higher winter discharge as a result of intensified snow-melt and increased winter precipitation, and lower summer discharge due to the reduced winter snow storage and an increase of evapotranspiration. When the results are considered in more detail, however, several differences show up. These can firstly be attributed to different physical characteristics of the studied areas, but different spatial and temporal scales used in the modelling and different representations of several hydrological processes (e.g., evapotranspiration, snow melt) are responsible for the differences found as well. Climate change can affect various socio-economic sectors. Higher temperatures may threaten winter tourism in the lower winter sport areas. The hydrological changes will increase flood risk during winter, whilst low flows during summer will adversely affect inland navigation, and reduce water availability for agriculture and industry. Balancing the required actions against economic cost and the existing uncertainties in the climate change scenarios, a policy of 'noregret and flexibility' in water management planning and design is recommended, where anticipatory adaptive measures in response to climate change impacts are undertaken in combination with ongoing activities.

RHINEFLOW is a GIS based water balance model that has been developed to study the changes in the water balance compartments of the river Rhine basin on a monthly time basis. The model has been designed to study the sensitivity of the Rhine discharge to a climate change. The calculated discharge has been calibrated and validated on the period 1956 to 1980. For this period the model efficiency of RHINEFLOW is between 0.74 and 0.81 both for the entire Rhine and for its tributaries. Also calculated values for variations in other compartments, e.g. snow storage and actual evapotranspiration, were in good agreement with the measured values. Since a high correlation between monthly discharge and peak discharge was found for the period 1900-1980 The RHINEFLOW model is used to assess the probability of exceedence for discharge peaks under possible future climate conditions. The probabilities of exceedence were calculated from the conditional probabilities of peak discharges for a series of 15 classes of monthly discharges. Comparison of a calculated frequency distribution of high discharge peaks with observed peaks in a test series showed that the method performs well. Scenarios for temperature changes between 0 °C and plus 4 °C and precipitation changes between plus 20% and minus 20% have been applied. Within this range flood frequencies are more sensitive for a precipitation change than for a temperature change. The present two-year return period peak flow (6500-7000 m3/s) decreases by about 6% due to a temperature rise of 4 °C; a precipitation decrease of 20% leads to 30% lower two-year peaks whilst 20% precipitation increase raises them by approximately 30%. Application of a 'Business As Usual' (BAU) and an 'Accelerated Policy' (AP) climate scenario resulted in a significant increase in probability of peak flows for the BAU scenario, while for the AP scenario no significant change could be found. Due to sampling errors, accurate estimations of recurrence times of discharge peaks/>7000 m3/s require a longer sampling time series than 90 years. For management purposes the method can be applied to estimate changes of probabilities of events with a relatively long recurrence time.

Studies on the impact of climate change and sea level rise usually take climate scenarios as their starting point. To support long‐term water management planning in the Netherlands, we carried out a study that started at the opposite end of the effect chain. In the study we refer to three aspects of water management, flood defense, drinking water supply, and protection of the Rotterdam Harbour. We examined whether, and for how long, current water management strategies will continue to be effective under different climate change scenarios. We did this by applying the concept of ‘adaptation tipping points’, and reached it if the magnitude of change is such that the current management strategy can no longer meet its objectives. Beyond the tipping points, an alternative adaptive strategy is needed. By applying this approach, the following basic questions of decision makers are answered: what are the first issues that we will face as a result of climate change and when can we expect this...

Climate scenarios are used to explore impacts of possible future climates and to assess the robustness of adaptation actions across a range of futures. Time-dependent climate scenarios are commonly used in mitigation studies. However, despite the dynamic nature of adaptation, most scenarios for local or regional decision making on climate adaptation are static 'endpoint' projections. This paper describes the development and use of transient (time-dependent) scenarios by means of a case on water management in the Netherlands. Relevant boundary conditions (sea level, precipitation and evaporation) were constructed by generating an ensemble of synthetic time-series with a rainfall generator and a transient delta change method. Climate change impacted river flows were then generated with a hydrological simulation model for the Rhine basin. The transient scenarios were applied in model simulations and game experiments. We argue that there are at least three important assets of using transient scenarios for supporting robust climate adaptation: (1) raise awareness about (a) the implications of climate variability and climate change for decision making and (b) the difficulty of finding proof of climate change in relevant variables for water management; (2) assessment of when to adapt by identifying adaptation tipping points which can then be used to explore adaptation pathways, and (3) identification of triggers for climate adaptation.

Understanding global future river flood risk is a prerequisite for the quantification of climate change impacts and planning e ective adaptation strategies 1. Existing global flood risk projections fail to integrate the combined dynamics of expected socioeconomic development and climate change. We present the first global future river flood risk projections that separate the impacts of climate change and socioeconomic development. The projections are based on an ensemble of climate model outputs 2 , socioeconomic scenarios 3 , and a state-of-the-art hydrologic river flood model combined with socioeconomic impact models 4,5. Globally, absolute damage may increase by up to a factor of 20 by the end of the century without action. Countries in Southeast Asia face a severe increase in flood risk. Although climate change contributes significantly to the increase in risk in Southeast Asia 6 , we show that it is dwarfed by the e ect of socioeconomic growth, even after normalization for gross domestic product (GDP) growth. African countries face a strong increase in risk mainly due to socioeconomic change. However, when normalized to GDP, climate change becomes by far the strongest driver. Both highand low-income countries may benefit greatly from investing in adaptation measures, for which our analysis provides a basis. Between 1980 and 2013, the global direct economic losses due to floods exceeded $1 trillion (2013 values), and more than 220,000 people lost their lives 7. Global flood damages have been increasing steeply over the past decades, so far mainly driven by steady growth in population and economic activities in flood-prone areas 8,9. Future increases in flood frequency and severity due to changes in extreme weather are expected 1,9. Such increasing trends in flood risk may have severe direct humanitarian and economic impacts and lasting long-term negative effects on economic growth 10,11. In 2015, several major international policies are being initiated or renewed that may catalyse flood risk adaptation and hence risk reduction, such as the Sustainable Development Goals, Conference of the Parties (COP) 21, and the Sendai Framework for Disaster Risk Reduction. Such efforts require global understanding of the drivers of flood risk change in the future. Past efforts to enhance this understanding have focused on the global-scale mapping of present-day flood hazard 12,13 and risk 4,5 and future changes in global flood exposure and risk 14 due to either climate change 6,15,16 or socioeconomic development 8,17. One recent study 18 combined global socioeconomic and climate change into future global flood risk projections for the first time, however, this work did not reveal regional patterns nor quantify the drivers of risk change. Furthermore, no study has so far accounted for installed and maintained flood protection standards (FPS; ref. 10).

Floods cause billions of dollars of damage each year 1 , and flood risks are expected to increase due to socioeconomic development, subsidence, and climate change 2-4. Implementing additional flood risk management measures can limit losses, protecting people and livelihoods 5. Whilst several models have been developed to assess global-scale river-flood risk 2,4,6-8 , methods for evaluating flood risk management investments globally are lacking 9. Here, we present a framework for assessing costs and benefits of structural flood protection measures in urban areas around the world. We demonstrate its use under di erent assumptions of current and future climate change and socioeconomic development. Under these assumptions, investments in dykes may be economically attractive for reducing risk in large parts of the world, but not everywhere. In some regions, economically e cient investments could reduce future flood risk below today's levels, in spite of climate change and economic growth. We also demonstrate the sensitivity of the results to di erent assumptions and parameters. The framework can be used to identify regions where river-flood protection investments should be prioritized, or where other risk-reducing strategies should be emphasized. Recently, a first generation of global river-flood risk models has been developed 2,4,6-8. A limitation is their assumption that no flood protection infrastructure is in place, leading to overestimations of risk 6. Several studies have assessed flood risk using simple assumptions of current protection standards 3,10,11. However, they did not assess costs and benefits of further investments in increasing flood protection. This information is useful for planning investments in flood risk management and adaptation 12-14. Here, we demonstrate a framework for cost-benefit analysis of flood risk reduction using the GLOFRIS 6,7 global flood risk model. First, we used GLOFRIS 6,7 to calculate current river-flood risk in urban areas, with and without protection. Assumptions for current protection standards are from FLOPROS 15 (Supplementary Fig. 1), a database of sub-national scale protection standards. Globally, modelled Expected Annual Damage (EAD) is 91% lower when estimates of current protection standards are included ($94 billion versus $1031 billion). Therefore, current protection already provides large societal benefits (Fig. 1). Since flood protection is not optimal today and risk will change over time, it may be desirable to increase protection standards in some regions. We explore three 'adaptation objectives'-that is, three approaches to developing risk reduction strategies through dykes. The 'optimize' objective prescribes protection standards that maximize Net Present Value (NPV). Since optimization studies are complex and rare in practice 13 , we also test two simpler objectives. The 'constant absolute risk' objective keeps future EAD constant in absolute terms at current levels, assuming no change in societal preferences towards absolute risk. The 'constant relative risk' objective keeps future EAD as a percentage of Gross Domestic Product (GDP) constant, reflecting a desire to keep flood risk constant as a share of the economy. Aggregated globally, modelled Benefit:Cost (B:C) ratios exceed 1 for all objectives for the scenarios shown in Table 1. The only exception is RCP6.0/SSP3, for 'constant absolute risk'. Results shown here are averaged across five global climate models (GCMs), using a 5% per year discount rate and middle-estimate investment costs (Methods). The four scenarios shown represent plausible combinations of Representative Concentration Pathways 16 (RCPs) and Shared Socioeconomic Pathways 17 (SSPs). The scenario selection is described in Supplementary Information 1. Other RCP/SSP combinations are plausible, so Supplementary Table 1 shows results for all combinations. By definition, highest NPVs are achieved under the 'optimize' objective. Global costs are lowest for 'optimize' , and highest for 'constant absolute risk'. For the latter, NPV is on average 61% lower than for 'optimize' (range: 16-138% lower), whilst for 'constant relative risk' , NPV is on average 35% lower than for 'optimize' (range: 15-69% lower). Given the high B:C ratios, even if the 'optimize' objective is not pursued, the simpler objectives are preferable to doing nothing. RCP8.5/SSP5 would entail higher investments than if more stringent international climate policies achieve lower greenhouse gas concentrations (Table 1). Sensitivity analysis was carried out using 3% and 8% per year discount rates (Supplementary Table 2); only for RCP8.5/SSP5 do we see B:C ratios under 1 (for 'constant absolute risk' and 'constant relative risk' and 8% discount rate). Protection standards required per sub-national unit to achieve the 'optimize' objective are shown in Fig. 2, with associated B:C ratios in Supplementary Fig. 2. For large parts of North America, Australia, northern Europe, and East Asia, these optimal standards could decrease future absolute EAD (in 2080) below current values (Supplementary Fig. 3). However, for most of the world, their implementation would still lead to overall increases in absolute EAD. Nevertheless, the optimal standards would lead to decreases in future EAD as a percentage of GDP, in large parts of the world (Supplementary Fig. 4). This is particularly the case for the aforementioned regions, and for South Asia, Europe, and Central

Sea level rise, especially combined with possible changes in storm surges and increased river discharge resulting from climate change, poses a major threat in low-lying river deltas. In this study we focus on a specific example of such a delta: the Netherlands. To evaluate whether the country's flood protection strategy is capable of coping with future climate conditions, an assessment of low-probability/highimpact scenarios is conducted, focusing mainly on sea level rise. We develop a plausible high-end scenario of 0.55 to 1.15 m global mean sea level rise, and 0.40 to 1.05 m rise on the coast of the Netherlands by 2100 (excluding land subsidence), and more than three times these local values by 2200. Together with projections for changes in storm surge height and peak river discharge, these scenarios depict a complex, enhanced flood risk for the Dutch delta.

This study makes a thorough global assessment of the effects of climate change on hydrological regimes and their accompanying uncertainties. Meteorological data from twelve GCMs (SRES scenarios A1B and control experiment 20C3M) are used to drive the global hydrological model PCR-GLOBWB. This reveals in which regions of the world changes in hydrology can be detected that have a high likelihood and are consistent amongst the ensemble of GCMs. New compared to existing studies is: (1) the comparison of spatial patterns of regime changes and (2) the quantification of notable consistent changes calculated relative to the GCM specific natural variability. The resulting consistency maps indicate in which regions the likelihood of hydrological change is large. Projections of different GCMs diverge widely. This underscores the need of using a multi-model ensemble. Despite discrepancies amongst models, consistent results are revealed: by 2100 the GCMs project consistent decreases in discharge for southern Europe, southern Australia, parts of Africa and southwestern South-America. Discharge decreases strongly for most African rivers, the Murray and the Danube while discharge of monsoon influenced rivers slightly increases. In the Arctic regions river discharge increases and a phase-shift towards earlier peaks is observed. Results are comparable to previous global studies, with a few exceptions. Globally we calculated an ensemble mean discharge increase of more than ten percent. This increase contradicts previously estimated decreases, which is amongst others caused by the use of smaller GCM ensembles and different reference periods.

Data from General Circulation Models (GCMs) are often used to investigate hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies, with focus on discharge variability and extremes, was tested by using bias-corrected daily climate data of the 20CM3 control experiment from a selection of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results of these runs were compared with discharge observations of the GRDC and discharges calculated from model runs based on two meteorological datasets constructed from the observation-based CRU TS2.1 and ERA-40 reanalysis. In the first dataset the CRU TS 2.1 monthly timeseries were downscaled to daily timeseries using the ERA-40 dataset (ERA6190). This dataset served as a best guess of the past climate and was used to analyze the performance of PCR-GLOBWB. The second dataset was created from the ERA-40 timeseries bias-corrected with the CRU TS 2.1 dataset using the same bias-correction method as applied to the GCM datasets (ERACLM). Through this dataset the influence of the bias-correction method was quantified. The bias-correction was limited to monthly mean values of precipitation, potential evaporation and temperature, as our focus was on the reproduction of interand intra-annual variability. After bias-correction the spread in discharge results of the GCM based runs decreased and results were similar to results of the ERA-40 based runs, especially for rivers with a strong seasonal pattern. Overall the bias-correction method resulted in a slight reduction of global runoff and the method performed less well in arid and mountainous

The representation of hydrological processes in land surface schemes (LSSs) has recently been improved. In this study, the usability of GCM runoff for river discharge modeling is evaluated by validating the mean, timing, and amplitude of the modeled annual discharge cycles against observations. River discharge was calculated for six large rivers using runoff, precipitation, and actual evaporation from the GCMs ECHAM5 and Hadley Centre Global Environmental Model version 2 (HadGEM2). Four methods were applied: 1) accumulation of GCM runoff, 2) routing of GCM runoff, 3) routing of GCM runoff combined with temporal storage of subsurface runoff, and 4) offline hydrological modeling with the global distributed hydrological model PCRaster Global Water Balance (PCR-GLOBWB) using meteorological data from the GCMs as forcing. The quality of discharge generated by all four methods is highly influenced by the quality of the GCM data. In small catchments, the methods that include runoff routing ...

As an initial step in assessing the prospect of using macro-scale hydrological models (MHMs) for hydrological forecasting, this study investigates the skill of the MHM PCR-GLOBWB in reproducing past discharge extremes on a global scale. Global terrestrial hydrology from 1958 until 2001 is simulated by forcing PCR-GLOBWB with daily meteorological data obtained by downscaling the CRU dataset to daily fields using the ERA-40 reanalysis. Simulated discharge values are compared with observed monthly streamflow records for a selection of 20 large river basins that represent all continents and a wide range of climatic zones. We assess model skill in three ways. First, the general performance of the model in reproducing hydrographs is evaluated. Second, model skill in reproducing significantly higher and lower flows than the monthly normals is assessed in terms of skill scores used for forecasts of categorical events. Third, model skill in reproducing flood and drought events is assessed by constructing binary contingency tables for floods and droughts for each basin. The results show that the model has skill in all three types of hindcasting. After bias correction the model skill in simulating hydrographs is improved considerably. For most basins it is much higher than that of the climatology. The skill in hindcasting monthly anomalies is high compared to that of an imaginary unskilled system. The model also performs better than an unskilled system in hindcasting floods and droughts, with a markedly higher skill in floods. We conclude that the prospect for using PCR-GLOBWB for monthly and seasonal hydrological forecasting is positive. Our results which we argue are representative for other similar MHMs, show that MHMs have sufficient skill for use in forecasting flow extremes.

Uncertainties in the rate and magnitude of sea-level rise (SLR) complicate decision making on coastal adaptation. Large uncertainty arises from potential ice mass-loss from Antarctica that could rapidly increase SLR in the second half of this century. The implications of SLR may be existential for a low-lying country like the Netherlands and warrant exploration of high-impact low-likelihood scenarios. To deal with uncertain SLR, the Netherlands has adopted an adaptive pathways plan. This paper analyzes the implications of storylines leading to extreme SLR for the current adaptive plan in the Netherlands, focusing on flood risk, fresh water resources, and coastline management. It further discusses implications for coastal adaptation in low-lying coastal zones considering timescales of adaptation including the decisions lifetime and lead-in time for preparation and implementation. We find that as sea levels rise faster and higher, sand nourishment volumes to maintain the Dutch coast m...