scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Vol 24 (7) ◽  
pp. 3725-3735
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Modeling ◽  
Water Cycle ◽  
Rain Gauge ◽  
Data Sets ◽  
Precipitation Data ◽  
Multiple Sources ◽  
Study Region ◽  
Global Precipitation Climatology Centre ◽  
Wide Range ◽  
The Impact

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over poorly gauged regions. This study examines the usefulness of current state-of-the-art precipitation data sets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation data sets in >200 European catchments to obtain runoff and evapotranspiration. We consider a wide range of precipitation products, which are generated via (1) the interpolation of gauge measurements (E-OBS and Global Precipitation Climatology Centre (GPCC) V.2018), (2)  data assimilation into reanalysis models (ERA-Interim, ERA5, and Climate Forecast System Reanalysis – CFSR), and (3) a combination of multiple sources (Multi-Source Weighted-Ensemble Precipitation; MSWEP V2). Evaluation is done at the daily and monthly timescales during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs; in contrast, simulated evapotranspiration is generally much less influenced in our comparatively wet study region. We also find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation data sets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the particularly strong agreement, while ERA5, GPCC V.2018, and MSWEP V2 show good performances. We further reveal climate-dependent performance variations of the considered data sets, which can be used to guide their future development. The overall best agreement is achieved when using an ensemble mean generated from all the individual products. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions, such as central Europe, there are increasing implications for evapotranspiration in drier regions.

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah Maraghi ◽  
Sungmin Oh ◽  
Rene Orth
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact

<p>Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in >200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR) and (3) combination of multiple sources (MSWEP V2). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984-2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.</p>

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah ◽  
Sungmin O ◽  
Rene Orth
Keyword(s):  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact ◽  
Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

scholarly journals Considerations of the Scale of Radiocarbon Offsets in the East Mediterranean, and Considering a Case for the Latest (Most Recent) Likely Date for the Santorini Eruption

Radiocarbon ◽  
2012 ◽  
Vol 54 (3-4) ◽  
pp. 449-474 ◽  
Author(s):  
Sturt W Manning ◽  
Bernd Kromer
Keyword(s):  
Time Horizon ◽  
Weighted Average ◽  
17Th Century ◽  
Data Sets ◽  
East Mediterranean ◽  
Average Value ◽  
Accuracy And Precision ◽  
Wide Range ◽  
The Impact ◽  
East Mediterranean Region

The debate over the dating of the Santorini (Thera) volcanic eruption has seen sustained efforts to criticize or challenge the radiocarbon dating of this time horizon. We consider some of the relevant areas of possible movement in the14C dating—and, in particular, any plausible mechanisms to support as late (most recent) a date as possible. First, we report and analyze data investigating the scale of apparent possible14C offsets (growing season related) in the Aegean-Anatolia-east Mediterranean region (excluding the southern Levant and especially pre-modern, pre-dam Egypt, which is a distinct case), and find no evidence for more than very small possible offsets from several cases. This topic is thus not an explanation for current differences in dating in the Aegean and at best provides only a few years of latitude. Second, we consider some aspects of the accuracy and precision of14C dating with respect to the Santorini case. While the existing data appear robust, we nonetheless speculate that examination of the frequency distribution of the14C data on short-lived samples from the volcanic destruction level at Akrotiri on Santorini (Thera) may indicate that the average value of the overall data sets is not necessarily the most appropriate14C age to use for dating this time horizon. We note the recent paper of Soter (2011), which suggests that in such a volcanic context some (small) age increment may be possible from diffuse CO2emissions (the effect is hypothetical at this stage and hasnotbeen observed in the field), and that "if short-lived samples from the same stratigraphic horizon yield a wide range of14C ages, the lower values may be the least altered by old CO2." In this context, it might be argued that a substantive “low” grouping of14C ages observable within the overall14C data sets on short-lived samples from the Thera volcanic destruction level centered about 3326–3328 BP is perhaps more representative of the contemporary atmospheric14C age (without any volcanic CO2contamination). This is a subjective argument (since, in statistical terms, the existing studies using the weighted average remain valid) that looks to support as late a date as reasonable from the14C data. The impact of employing this revised14C age is discussed. In general, a late 17th century BC date range is found (to remain) to be most likelyeven ifsuch a late-dating strategy is followed—a late 17th century BC date range is thus a robust finding from the14C evidence even allowing for various possible variation factors. However, the possibility of a mid-16th century BC date (within ∼1593–1530 cal BC) is increased when compared against previous analyses if the Santorini data are considered in isolation.

scholarly journals “1D+4DVAR” Assimilation of NCEP Stage-IV Radar and Gauge Hourly Precipitation Data at ECMWF

2007 ◽  
Vol 135 (7) ◽  
pp. 2506-2524 ◽  
Author(s):  
Philippe Lopez ◽  
Peter Bauer
Keyword(s):  
Stage Iv ◽  
The United States ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Surface Precipitation ◽  
Weather Radars ◽  
Potential Impact ◽  
Microwave Imager ◽  
The Impact

Abstract The one- plus four-dimensional variational data assimilation (“1D+4DVAR”) method currently run in operations at ECMWF with rain-affected radiances from the Special Sensor Microwave Imager is used to study the potential impact of assimilating NCEP stage-IV analyses of hourly accumulated surface precipitation over the U.S. mainland. These data are a combination of rain gauge measurements and observations from the high-resolution Doppler Next-Generation Weather Radars. Several 1D+4DVAR experiments have been run over a month in spring 2005. First, the quality of the precipitation forecasts in the control experiment is assessed. Then, it is shown that the impact of the assimilation of the additional rain observations on global scores of dynamical fields and temperature is rather neutral, while precipitation scores are improved for forecast ranges up to 12 h. Additional 1D+4DVAR experiments in which all moisture-affected observations are removed over the United States demonstrate that the NCEP stage-IV precipitation data on their own can clearly be beneficial to the analyses and subsequent forecasts of the moisture field. This result suggests that the potential impact of precipitation observations is overshadowed by the influence of other high-quality humidity observations, in particular, radiosondes. It also confirms that the assimilation of precipitation observations has the ability to improve the quality of moisture analyses and forecasts in data-sparse regions. Finally, the limitations inherent in the current assimilation of precipitation data, their implications for the future, and possible ways of improvement are discussed.

Towards a scale-independent fractional snow-covered area parameterization for complex terrain

2020 ◽  
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Ground Surface ◽  
Longwave Radiation ◽  
Data Sets ◽  
Depth Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range

<p>Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.</p><p>We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.</p>

scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions

2017 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Global Water Cycle ◽  
Basin Scale ◽  
Daily Scale ◽  
Spatio Temporal

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we proposed a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05° × 0.05° precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3 hourly 0.25° × 0.25° Climate Prediction Center Morphing method (CMORPH) dataset and the hourly 0.05° × 0.05° FY2-E Geostationary (GEO) Infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between precipitation rate and Tb for each 1° × 1° region. Then the CMORPH data were downscaled to 0.05° × 0.05°. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions coefficients were able to implicitly reflect the characteristics of precipitation. Qualitatively, the downscaled estimates were able to capture more details about rainfall motions and changes. Quantitatively, the time series of the downscaled estimates were more similar to the rain gauge data than the original CMORPH product at the daily scale. The downscaled estimates not only improved spatio-temporal resolutions, but also performed better (Bias: −7.35 %~10.35 %; correlation coefficient (CC): 0.48~0.60) than the CMORPH product (Bias: 20.82 %~94.19 %; CC: 0.31~0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate.

scholarly journals Doppler radar rainfall prediction and gauge data

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jesse W. Lansford ◽  
Tyson H. Walsh ◽  
T. V. Hromadka ◽  
P. Rao
Keyword(s):  
Doppler Radar ◽  
Weather Forecasting ◽  
Rain Gauge ◽  
Data Sets ◽  
Multiple Sources ◽  
Data Set ◽  
Radar Rainfall ◽  
Rainfall Prediction ◽  
Doppler Data ◽  
Precipitation Estimates

Abstract Objective The data herein represents multiple gauge sets and multiple radar sites of like-type Doppler data sets combined to produce populations of ordered pairs. Publications spanning decades yet specific to Doppler radar sites contain graphs of data pairs of Doppler radar precipitation estimates versus rain gauge precipitation readings. Data description Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 8830 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar and precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.

Impact of subjective modeling decisions on hydrological modeling

2021 ◽  
Author(s):  
Ashlin Ann Alexander ◽  
Dasika Nagesh Kumar
Keyword(s):  
Hydrological Modeling ◽  
Systematic Evaluation ◽  
Suitable Model ◽  
Model Simulations ◽  
Model Configuration ◽  
Wide Range ◽  
Model Components ◽  
Parameter Values ◽  
The Impact ◽  
Subjective Modeling

<p>Modelers often make different decisions in building hydrologic models based on their experience and modeling philosophy. Consequently, a wide range of models is developed, which differ in many aspects of conceptualization and implementation. This diversity of models has been useful to explore a myriad of scientific and applied questions, but it has also led to great confusion on choosing the appropriate model configurations in compliance with the dominant processes in the study area. Also, modeling decisions during model configuration introduce subjectivity from the modeler. To provide guidance to select the best-suited model configuration for a catchment it is required to examine and evaluate the different model representations of hydrological processes and their impact on model simulations. In this study, we show that modeling decisions during the model configuration, beyond the model choice, also impact the model results. The framework, Structure for Unifying Multiple Modeling Alternatives (SUMMA; Clark et al., 2015a, b) is used in this study to disentangle the model components which helps to have a controlled and systematic evaluation of multiple models representations. The area chosen for the study is the Malaprabha catchment in the Karnataka state of India. The impact of the choice of parameterizations and parameter values on the model simulations are shown. To improve upon the traditional model evaluation methods, hydrological signatures are made use to have a hydrologically meaningful evaluation of model simulations. This study helped to identify the suitable model configuration for the Malaprabha catchment. Multiple working hypotheses during model configuration which is possible with the help of such flexible framework like SUMMA can provide insights on the impact of subjective modeling decisions.</p>

Socio-Hydrogeology: uncovering the hidden connections within the Human-Groundwater Cycle

2021 ◽  
Author(s):  
Viviana Re ◽  
Paul Hynds ◽  
Theresa Frommen ◽  
Shrikant Limaye
Keyword(s):  
Hydrological Cycle ◽  
Water Cycle ◽  
Regional Scale ◽  
Primary Source ◽  
Development Stage ◽  
Added Value ◽  
Coupled Modelling ◽  
Management Actions ◽  
Wide Range ◽  
The Impact

<p>Socio-hydrogeology has been recently proposed as a new approach in the field of human-water research, focusing on the assessment of the reciprocity between people and groundwater. Notwithstanding some obvious similarities with socio-hydrology, there are notable, and indeed important differences; while socio-hydrology aims to investigate and understand the dynamic interactions and feedbacks between (surface)water and people, due to the more private and local nature of groundwater in many instances, socio-hydrogeology seeks to understand individuals and communities as a primary source, pathway and receptor for potable groundwater supplies, including the role of (local) knowledge, beliefs, risk perception, tradition/history, and consumption. In essence, the “socio” in socio-hydrology might be said to represent society, while its counterpart within socio-hydrogeology embodies sociology, including social, cognitive, behavioural and socio-epidemiological science. Moreover, while socio-hydrology tends towards examination of human-water interactions at relatively larges scales via coupled modelling, socio-hydrogeology is often focused at a significantly smaller scale (e.g. individual household or community supplies), and as such, employs a wide range of mixed methods, including modelling, albeit to a lesser degree. Being at its early development stage, the discipline is still being defined and formalized. Nevertheless, several researchers are currently implementing this approach worldwide.</p><p>By presenting a comparative analysis of the approaches and outcomes from several socio-hydrogeological studies undertaken across a range of socio-demographic and climatic regions including Canada, Italy, India, Ireland, Myanmar and Tunisia, this presentation will highlight the benefits and shortcomings of going beyond classical hydrogeological and hydrogeochemical investigations targeted to assess the impact of human activities on groundwater quality and quantity, and indeed, the effects of these impacts on associated individuals and communities (i.e. humans frequently represent the issue, the receptor and the solution). By shedding light on the added value of understanding the cause-effect relations between people and the hidden component of the water cycle (e.g. to jointly assess how scarce and polluted groundwater affect human/social wellbeing), socio-hydrogeology can provide evidence-based solutions to regionally bespoke problems. Similarly, otherwise neglected local or regional information can add value to scientific outcomes and contribute to foster new groundwater management actions tailored on the needs of local populations as well as on the overall achievement of long-term sustainability. Socio-hydrogeology can therefore provide new insights useful for socio-hydrological modelling, and, together, they can effectively underpin successful Integrated Water Resources Management plans at local and regional scale. Perhaps most importantly, it is hoped that by initiating discussion between practitioners of both sub-disciplines, experiences, expertise and perspectives can be shared and employed (e.g. more “technical” modelling within socio-hydrogeology, increased integration of “non-expert” knowledge within socio-hydrology) in order to bolster both areas of study, with an overarching objective of protecting the entire hydrological cycle, and the people supplied and impacted by it.</p>

scholarly journals The impact of uncertain precipitation data on insurance loss estimates using a flood catastrophe model

2014 ◽  
Vol 18 (6) ◽  
pp. 2305-2324 ◽  
Author(s):  
C. C. Sampson ◽  
T. J. Fewtrell ◽  
F. O'Loughlin ◽  
F. Pappenberger ◽  
P. B. Bates ◽  
...  
Keyword(s):  
Risk Model ◽  
Flood Hazard ◽  
Data Uncertainty ◽  
Event Generator ◽  
Data Sets ◽  
Precipitation Data ◽  
Catastrophe Risk ◽  
Catastrophe Model ◽  
The Impact ◽  
Model Structures

Abstract. Catastrophe risk models used by the insurance industry are likely subject to significant uncertainty, but due to their proprietary nature and strict licensing conditions they are not available for experimentation. In addition, even if such experiments were conducted, these would not be repeatable by other researchers because commercial confidentiality issues prevent the details of proprietary catastrophe model structures from being described in public domain documents. However, such experimentation is urgently required to improve decision making in both insurance and reinsurance markets. In this paper we therefore construct our own catastrophe risk model for flooding in Dublin, Ireland, in order to assess the impact of typical precipitation data uncertainty on loss predictions. As we consider only a city region rather than a whole territory and have access to detailed data and computing resources typically unavailable to industry modellers, our model is significantly more detailed than most commercial products. The model consists of four components, a stochastic rainfall module, a hydrological and hydraulic flood hazard module, a vulnerability module, and a financial loss module. Using these we undertake a series of simulations to test the impact of driving the stochastic event generator with four different rainfall data sets: ground gauge data, gauge-corrected rainfall radar, meteorological reanalysis data (European Centre for Medium-Range Weather Forecasts Reanalysis-Interim; ERA-Interim) and a satellite rainfall product (The Climate Prediction Center morphing method; CMORPH). Catastrophe models are unusual because they use the upper three components of the modelling chain to generate a large synthetic database of unobserved and severe loss-driving events for which estimated losses are calculated. We find the loss estimates to be more sensitive to uncertainties propagated from the driving precipitation data sets than to other uncertainties in the hazard and vulnerability modules, suggesting that the range of uncertainty within catastrophe model structures may be greater than commonly believed.

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