Estimation of Regional Parameters in a Macro Scale Hydrological Model

2001 ◽  
Vol 32 (3) ◽  
pp. 161-180 ◽  
Author(s):  
Kolbjørn Engeland ◽  
Lars Gottschalk ◽  
Lena Tallaksen
Keyword(s):  
Additional Data ◽  
Hydrological Model ◽  
Sampling Methods ◽  
Probability Distributions ◽  
Repeated Application ◽  
Landscape Characteristics ◽  
Time Period ◽  
Macro Scale ◽  
Model Element ◽  
Regular Sampling

Macro-scale hydrological modelling implies a repeated application of a model within an area using regional parameters. These parameters are based on climate and landscape characteristics, and they are used to calculate the water balance in ungauged areas. The regional parameters ought to be robust and not too dependent of the catchment and time period used for calibration. The ECOMAG model is applied for the NOPEX-region as a macro-scale hydrological model distributed on a 2×2 km2 grid. Each model element is assigned parameters according to soil and vegetation classes. A Bayesian methodology is followed. An objective function describing the fit between observed and simulated values is used to describe the likelihood of the parameters. Using Baye's theorem these likelihoods are used to update the probability distributions of the parameters using additional data, being it either an additional year of streamflow or an additional streamflow station. Two sampling methods are used, regular sampling and Metropolis-Hastings sampling. The results show that regional parameters exist according to some predefined criteria. The probability distribution of the parameters shows a decreasing variance as data from new catchments are used for updating. A few parameters do, however, not exhibit this property, and they are therefore not suitable in a regional context.

scholarly journals A framework for global river flood risk assessments

2013 ◽  
Vol 17 (5) ◽  
pp. 1871-1892 ◽  
Author(s):  
H. C. Winsemius ◽  
L. P. H. Van Beek ◽  
B. Jongman ◽  
P. J. Ward ◽  
A. Bouwman
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Hydrological Model ◽  
Flood Hazard ◽  
Probability Distributions ◽  
Flood Routing ◽  
Risk Assessments ◽  
First Case ◽  
Impact Models ◽  
Damage Estimates

Abstract. There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals The hydrological response of baseflow in fractured mountain areas

2009 ◽  
Vol 6 (2) ◽  
pp. 3359-3384 ◽  
Author(s):  
A. Millares ◽  
M. J. Polo ◽  
M. A. Losada
Keyword(s):  
Hydrological Model ◽  
Water System ◽  
Essential Element ◽  
Mediterranean Area ◽  
Mountainous Areas ◽  
Mountain Areas ◽  
Linear Behavior ◽  
Time Period ◽  
Recession Curves

Abstract. The study of baseflow in mountainous areas of basin headwaters, where the characteristics of the often fractured materials are very different to the standard issues concerning porous material applied in conventional hydrogeology, is an essential element in the characterization and quantification of water system resources. Their analysis through recession fragments provides information on the type of response of the sub-surface and subterranean systems and on the average relation between the storage and discharge of aquifers, starting from the joining of these fragments into a single curve, the Master Recession Curve (MRC). This paper presents the generation of the downward MRC over fragments selected after a preliminary analysis of the recession curves, using a hydrological model as the methodology for the identification and the characterization of quick sub-surface flows flowing through fractured materials. The hydrological calculation has identified recession fragments through surface runoff or snowmelt and those periods of intense evapotranspiration. The proposed methodology has been applied to three sub-basins belonging to a high altitude mountain basin in the Mediterranean area, with snow present every year, and their results were compared with those for the upward concatenation of the recession fragments. The results show the existence of two different responses, one quick (at the sub-surface, through the fractured material) and the other slow, with linear behavior which takes place in periods of 10 and 17 days, respectively and which is linked to the dimensions of the sub-basin. In addition, recesses belonging to the dry season have been selected in order to compare and validate the results corresponding to the study of recession fragments. The comparison, using these two methodologies, which differ in the time period selected, has allowed us to validate the results obtained for the slow flow.

Reliability of global gridded precipitation products in assessing extremes

2021 ◽  
Author(s):  
Chandra Rupa Rajulapati ◽  
Simon Michael Papalexiou ◽  
Martyn P Clark ◽  
Saman Razavi ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Extreme Precipitation ◽  
Probability Distributions ◽  
Extreme Precipitation Events ◽  
Return Levels ◽  
Time Period ◽  
Precipitation Gauge ◽  
Global Precipitation ◽  
Surface Observations ◽  
Precipitation Events

<p>Assessing extreme precipitation events is of high importance to hydrological risk assessment, decision making, and adaptation strategies. Global gridded precipitation products, constructed by combining various data sources such as precipitation gauge observations, atmospheric reanalyses and satellite estimates, can be used to estimate extreme precipitation events. Although these global precipitation products are widely used, there has been limited work to examine how well these products represent the magnitude and frequency of extreme precipitation. In this work, the five most widely used global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR and WFDEI) are compared to each other and to GHCN-daily surface observations. The spatial variability of extreme precipitation events and the discrepancy amongst datasets in predicting precipitation return levels (such as 100- and 1000-year) were evaluated for the time period 1979-2017.  The behaviour of extremes, that is the frequency and magnitude of extreme precipitation, was quantified using indices of the heaviness of the upper tail of the probability distribution. Two parameterizations of the upper tail, the power and stretched-exponential, were used to reveal the probabilistic behaviour of extremes. The analysis shows strong spatial variability in the frequency and magnitude of precipitation extremes as estimated from the upper tails of the probability distributions. This spatial variability is similar to the Köppen-Geiger climate classification. The predicted 100- and 1000-year return levels differ substantially amongst the gridded products, and the level of discrepancy varies regionally, with large differences in Africa and South America and small differences in North America and Europe. The results from this work reveal the shortcomings of global precipitation products in representing extremes. The work shows that there is no single global product that performs best for all regions and climates.</p>

scholarly journals Study of Thermal Oscillations at the Dryout Front in Half Heated Tubes

1985 ◽  
Vol 107 (4) ◽  
pp. 343-351
Author(s):  
S. S. Samra ◽  
V. K. Dhir
Keyword(s):  
Probability Distributions ◽  
Time History ◽  
Mass Conservation ◽  
Conservation Equation ◽  
Upward Movement ◽  
Temperature Oscillations ◽  
Time Period ◽  
Dimensionless Groups ◽  
Thermal Oscillations ◽  
Temperature Time

In this work thermal oscillations at the dryout front in an electrically heated composite tube of inconel and glass have been studied experimentally and analytically. The tube has an inside diameter of 17.2 mm, and a heated length of 1913 mm. The thickness of the inconel half tube is 0.89 mm. In the experiments deionized water and Freon-113 were used as the test liquids while the pressure at the exit of the tube was one atmosphere. The dryout front was established at a predetermined height from the inlet. The frequency and magnitude of the wall temperature oscillations in the vicinity of the dryout front has been obtained from the temperature-time history. The most probable time period obtained from the probability distributions has been correlated with dimensionless groups formed with mass velocity, tube diameter and the physical properties of the test liquid. Normalized probability distributions for the time period have been found to be represented by a modified gamma-distribution. The magnitude and the nature of the temperature oscillations has been predicted by solving the energy equation for the heated tube and the mass conservation equation for the liquid film left on the wall during upward movement of the dryout front. The predictions have been compared with the data.

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

scholarly journals Seismic Tomography Using Variational Inference Methods

2020 ◽  
Author(s):  
Xin Zhang ◽  
Andrew Curtis
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Seismic Tomography ◽  
Posterior Probability ◽  
Sampling Methods ◽  
Probability Distributions ◽  
Monte Carlo Sampling ◽  
Variational Inference ◽  
Posterior Probability Distribution ◽  
Inference Methods

<p><span>In a variety of geoscientific applications we require maps of subsurface properties together with the corresponding maps of uncertainties to assess their reliability. Seismic tomography is a method that is widely used to generate those maps. Since tomography is significantly nonlinear, Monte Carlo sampling methods are often used for this purpose, but they are generally computationally intractable for large data sets and high-dimensionality parameter spaces. To extend uncertainty analysis to larger systems, we introduce variational inference methods to conduct seismic tomography. In contrast to Monte Carlo sampling, variational methods solve the Bayesian inference problem as an optimization problem yet still provide fully nonlinear, probabilistic results. This is achieved by minimizing the Kullback-Leibler (KL) divergence between approximate and target probability distributions within a predefined family of probability distributions.</span></p><p><span>We introduce two variational inference methods: automatic differential variational inference (ADVI) and Stein variational gradient descent (SVGD). In ADVI a Gaussian probability distribution is assumed and optimized to approximate the posterior probability distribution. In SVGD a smooth transform is iteratively applied to an initial probability distribution to obtain an approximation to the posterior probability distribution. At each iteration the transform is determined by seeking the steepest descent direction that minimizes the KL-divergence. </span></p><p><span>We apply the two variational inference methods to 2D travel time tomography using both synthetic and real data, and compare the results to those obtained from two different Monte Carlo sampling methods: Metropolis-Hastings Markov chain Monte Carlo (MH-McMC) and reversible jump Markov chain Monte Carlo (rj-McMC). The results show that ADVI provides a biased approximation because of its Gaussian approximation, whereas SVGD produces more accurate approximations to the results of MH-McMC. In comparison rj-McMC produces smoother mean velocity models and lower standard deviations because the parameterization used in rj-McMC (Voronoi cells) imposes prior restrictions on the pixelated form of models: all pixels within each Voronoi cell have identical velocities. This suggests that the results of rj-McMC need to be interpreted in the light of the specific prior information imposed by the parameterization. Both variational methods estimate the posterior distribution at significantly lower computational cost, provided that gradients of parameters with respect to data can be calculated efficiently. We therefore expect that the methods can be applied fruitfully to many other types of geophysical inverse problems.</span></p>

scholarly journals Comparing Evaporative Sources of Terrestrial Precipitation and Their Extremes in MERRA Using Relative Entropy

2014 ◽  
Vol 15 (1) ◽  
pp. 102-116 ◽  
Author(s):  
Paul A. Dirmeyer ◽  
Jiangfeng Wei ◽  
Michael G. Bosilovich ◽  
David M. Mocko
Keyword(s):  
Relative Entropy ◽  
Moisture Transport ◽  
Probability Distributions ◽  
Back Trajectory ◽  
Two Dimensional ◽  
Monthly Data ◽  
Atmospheric Rivers ◽  
Time Period ◽  
Key Factor ◽  
Modern Era

Abstract A quasi-isentropic, back-trajectory scheme is applied to output from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979–2005. The evaporative source patterns for any location and time period are effectively two-dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50%–400% larger than at monthly time scales. Significant differences suggest that moisture transport may be a key factor in precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.

scholarly journals Assessment of evapotranspiration simulations in the Malše basin

2010 ◽  
Vol 4 (Special Issue 2) ◽  
pp. S111-S122
Author(s):  
R. Košková ◽  
S. Němečková
Keyword(s):  
Hydrological Model ◽  
Precipitation Amount ◽  
The Other ◽  
Actual Evapotranspiration ◽  
Soil Types ◽  
Distributed Hydrological Model ◽  
The Czech Republic ◽  
Landscape Characteristics ◽  
Other Hand ◽  
Spatially Distributed

The application of the distributed hydrological model brings the benefits of assessment of the spatially distributed quantities which are hard to measure in the field over a larger area, e.g. evapotranspiration. The Malše River basin has been chosen for the evaluation of evapotranspiration simulation by the distributed hydrological model, SWIM (Soil and Water Integrated Model). The primary interest in this analysis was to assess the ability of the hydrological model to simulate the actual evapotranspiration on larger scales and to evaluate its dependence on the landscape characteristics such as the vegetation cover, soil type, and average precipitation amount during the simulation. Annual actual evapotranspiration in each hydrotope was evaluated in the simulation period of 1985–1998. Because of the lack of the data observed (evapotranspiration), the model was calibrated on the discharge time series. The credibility was quantified using Nash Sutcliffe efficiency which was more than 0.7. The main trends of the simulated actual evapotranspiration were evaluated and assessed as satisfactory. The differences in the soil types did not seem significant for the evapotranspiration variation, the monthly average values among soil types differing by ± 10% except histosol. On the other hand the differences in the land-use categories strongly influenced the amount of evapotranspiration (–30; +50%). It appears that the model SWIM overestimates the actual evapotranspiration in the spring and, on the other hand, underestimates that in the autumn according to the comparison with the only data available in the entire Climate Atlas of the Czech Republic.

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