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Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study
Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and Zambezi). Furthermore, the Penman-Monteith equation has a high data demand and the equation is sensitive to input data inaccuracy. Therefore, we recommend the re-calibrated form of the Hargreaves equation which globally gave reference PET values comparable to CRU derived values for multiple climate conditions. The resulting gridded daily PET time series provide a new reference dataset that can be used for future hydrological impact assessments in further research, or more specifically, for the statistical downscaling of daily PET derived from raw GCM data.
ENSURF : multi-model sea level forecast - implementation and validation results for the IBIROOS and Western Mediterranean regions
ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast that makes use of several storm surge or circulation models and near-real time tide gauge data in the region, with the following main goals: 1. providing easy access to existing forecasts, as well as to its performance and model validation, by means of an adequate visualization tool ; 2. generation of better forecasts of sea level, including confidence intervals, by means of the Bayesian Model Average technique (BMA). The Bayesian Model Average technique generates an overall forecast probability density function (PDF) by making a weighted average of the individual forecasts PDF's; the weights represent the Bayesian likelihood that a model will give the correct forecast and are continuously updated based on the performance of the models during a recent training period. This implies the technique needs the availability of sea level data from tide gauges in near-real time. The system was implemented for the European Atlantic facade (IBIROOS region) and Western Mediterranean coast based on the MATROOS visualization tool developed by Deltares. Results of validation of the different models and BMA implementation for the main harbours are presented for these regions where this kind of activity is performed for the first time. The system is currently operational at Puertos del Estado and has proved to be useful in the detection of calibration problems in some of the circulation models, in the identification of the systematic differences between baroclinic and barotropic models for sea level forecasts and to demonstrate the feasibility of providing an overall probabilistic forecast, based on the BMA method.
Inception of a global atlas of sea levels since the Last Glacial Maximum (introduction)
Determining the rates, mechanisms, and geographic variability of relative sea-level (RSL) change following the Last Glacial Maximum (LGM) provides insight into the sensitivity of ice sheets to climate change, the response of the solid Earth and gravity field to ice-mass redistribution, and constrains statistical and physical models used to project future sea-level rise. To do so in a scientifically robust way requires standardized datasets that enable broad spatial comparisons that minimize bias. As part of a larger goal to develop a unified, spatially-comprehensive post-LGM global RSL database, in this special issue we provide a standardized global synthesis of regional RSL data that resulted from the first ‘Geographic variability of HOLocene relative SEA level (HOLSEA)’ meetings in Mt Hood, Oregon (2016) and St Lucia, South Africa (2017). The HOLSEA meetings brought together sea-level researchers to agree upon a consistent protocol to standardize, interpret, and incorporate realistic uncertainties of RSL data. This special issue provides RSL data from ten geographical regions including new databases from Atlantic Europe and the Russian Arctic and revised/expanded databases from Atlantic Canada, the British Isles, the Netherlands, the western Mediterranean, the Adriatic, Israel, Peninsular Malaysia, Southeast Asia, and the Indian Ocean. In total, the database derived from this special issue includes 5634 (5290 validated) index (n ¼ 3202) and limiting points (n ¼ 2088) that span from ~20,000 years ago to present. Progress in improving the standardization of sea-level databases has also been accompanied by advancements in statistical and analytical methods used to infer spatial patterns and rates of RSL change from geological data that have a spatially and temporally sparse distribution and geochronological and elevational uncertainties. This special issue marks the inception of a unified, spatially-comprehensive post-LGM global RSL database.
State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management
Combining metamodels with data assimilation schemes allows the incorporation of up-to-date information in metamodels, offering new opportunities for operational water resources management. We developed a data assimilation scheme for the unsaturated zone metamodel MetaSWAP using OpenDA, which is an open source data assimilation framework. A twin experiment showed the feasibility of applying an Ensemble Kalman Filter as a data assimilation method for updating metamodels. Furthermore, we assessed the accuracy of root zone soil moisture model estimates when assimilating the regional SMAP L3 Enhanced surface soil moisture product. The model accuracy is assessed using in situ soil moisture measurements collected at 12 locations in the Twente region, the Netherlands. Although the accuracy of the model estimates does not improve in terms of correlation coefficient, the accuracy does improve in terms of Root Mean Square Error and bias. Therefore, the assimilation of surface soil moisture observations has value for updating root zone soil moisture model estimates. In addition, the accuracy of the model estimates improves on both regional and local spatial scales. The increasing availability of remotely sensed soil moisture data will lead to new possibilities for integrating metamodelling and data assimilation in operational water resources management. However, we expect that significant investments in computational capacities are necessary for effective implementation in decision-making.
Uncertainty quantification of flood mitigation predictions and implications for interventions
Reduction of water levels during river floods is key in preventing damage and loss of life. Computer models are used to design ways to achieve this and assist in the decision-making process. However, the predictions of computer models are inherently uncertain, and it is currently unknown to what extent that uncertainty affects predictions of the effect of flood mitigation strategies. In this study, we quantify the uncertainty of flood mitigation interventions on the Dutch River Waal, based on 39 different sources of uncertainty and 12 intervention designs. The aim of each intervention is to reduce flood water levels. Our objective is to investigate the uncertainty of model predictions of intervention effect and to explore relationships that may aid in decision-making. We identified the relative uncertainty, defined as the ratio between the confidence interval and the expected effect, as a useful metric to compare uncertainty between different interventions. Using this metric, we show that intervention effect uncertainty behaves like a traditional backwater curve with an approximately constant relative uncertainty value. In general, we observe that uncertainty scales with effect: high flood level decreases have high uncertainty, and, conversely, small effects are accompanied by small uncertainties. However, different interventions with the same expected effect do not necessarily have the same uncertainty. For example, our results show that the large-scale but relatively ineffective intervention of floodplain smoothing by removing vegetation has much higher uncertainty compared to alternative options. Finally, we show how a level of acceptable uncertainty can be defined and how this can affect the design of interventions. In general, we conclude that the uncertainty of model predictions is not large enough to invalidate model-based intervention design, nor small enough to neglect altogether. Instead, uncertainty information is valuable in the selection of alternative interventions.
A generically parameterized model of Lake eutrophication (GPLake) that links field-, lab- and modelbased knowledge
To support lake management numerous eutrophication models have been developed. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-a relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.
The long heat wave and drought in Europe in 2018
Fosfaatroutes van boerenperceel naar sloot
Deltares en Waterschap Rijn en IJssel hebben op een veehouderij in de Achterhoek detailmetingen gedaan die inzicht geven in de bronnen en routes voor zowel stikstof als fosfaat, met als doel om op basis daarvan de meest effectieve maatregelen te identificeren voor het verminderen van de nutriëntenverliezen naar grond- en oppervlaktewater.
On the bound wave phase lag
More than three decades ago, it was noted that the ocean infragravity bound wave increasingly lags behind the forcing short-wave groups when propagating towards the shore. To date, the most recent theoretical prediction of this so-called phase lag remained a first-order approximation in terms of depth variations. Here, a new semi-analytical solution is proposed which does not rely on this approximation. Strong agreement is obtained when the new solution is compared with high-resolution laboratory data involving both bichromatic and random wave conditions. This newly proposed theoretical phase lag is then extensively compared with the former one, highlighting an increasing discrepancy between the two solutions as the relative bottom slope increases. The four influencing parameters, namely the bottom slope, the water depth, the incident short-wave peak period and the incident group period, are shown to impact, each in a specific way, the bound wave phase lag. While the latter is seen to increase with lower water depths and/or with higher short-wave peak periods, both the bottom slope and the group period can affect the phase lag in a different manner. Indeed, steeper bed slopes induce lower phase lags in shallow water but higher ones in deep water, while higher group periods induce higher phase lags for gentle slopes but lower ones for steep slopes.
Implementation strategy of integrated coastal development in national capital city Jakarta, Indonesia
National Capital Integrated Coastal Development (hereinafter called NCICD) is one of the national strategical programs in Indonesia. The main purpose of this program is to countermeasure flooding from sea side and river side. Jakarta sinks down overtime due to land subsidence, the rate of land subsidence is various spatially 2-20 cm/year and in average the rate is around 7 cm/year. The long term planning of the coastal development for Jakarta is a compulsory to be conducted. There are three staging program of NCICD; (1) Phase-A and Phase-Emergency, here in after Phase-E, are the first phase for the critical area, where the coastal dike 120 km in total constructed along the coastline and the downstream area of the river, besides that in this phase the 13 polder system are also considered to be constructed ; (2) Phase-Midterm is the coastal dike constructed shift to the sea for near shore storage purpose; (3) Phase-Optional is the offshore sea dike construction in Jakarta Bay. The NCICD Program itself has two main scenarios for the staging. First scenario is E-A-O scenario and the second scenario is E-M-O scenario. This paper discusses the consideration of the scenario option in term of technical and financial aspects. According to the implementation strategy analysis, the E-A-O scenario is the preferable option scenario for the implementation strategy of NCICD Program if the land subsidence can not be stopped. The consideration of the scenario option, because the E-A-O scenario is cheaper than E-M-O scenario. However, if the land subsidence can be stopped than scenario E-M without O will be more efficient. In order to monitor the land subsidence, the monitoring system is deployed spatially. Nowadays, the Government of Indonesia will accelerate the surface water provision to compensate the land subsidence and issue the law enforcement to regulate the ground water abstraction after water supply distributed, even though the excessive of groundwater abstraction is one of the factor in land subsidence. In addition, the water quality improvement is also conducted parallelly.