scholarly journals A hydrologic post-processor for ensemble streamflow predictions

2011 ◽  
Vol 29 ◽  
pp. 51-59 ◽  
Author(s):  
L. Zhao ◽  
Q. Duan ◽  
J. Schaake ◽  
A. Ye ◽  
J. Xia
Keyword(s):  
Probability Distributions ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Daily Streamflow ◽  
Post Process ◽  
Process Simulations ◽  
Analysis Window ◽  
Streamflow Data ◽  
Calibration And Verification ◽  
Using Data

Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are well-calibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equal-likelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions. Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.

scholarly journals Event-scale power law recession analysis: Quantifying methodological uncertainty

2016 ◽  
Author(s):  
David N. Dralle ◽  
Nathaniel J. Karst ◽  
Kyriakos Charalampous ◽  
Sally E. Thompson
Keyword(s):  
Impact Parameter ◽  
Power Law ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Event Scale ◽  
Methodological Choices ◽  
Daily Streamflow ◽  
Streamflow Data

Abstract. The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power-law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely-used power-law recession model. We show that: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness-of-fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices.

A Comparative Study on Probabaility Analysis of Annual Maximum Daily Streamflow Using Conventional and L-Moments

2021 ◽  
Author(s):  
Srinivasa Murthy D ◽  
Aruna Jyothy S ◽  
Mallikarjuna P
Keyword(s):  
Performance Evaluation ◽  
Probability Distribution ◽  
Comparative Study ◽  
Probabilistic Analysis ◽  
Probability Distributions ◽  
Annual Maximum ◽  
Daily Streamflow ◽  
L Moments ◽  
Streamflow Data ◽  
Parameter Distributions

Abstract The study aims at the probabilistic analysis of annual maximum daily streamflows at the gauging sites of Godavari upper, Godavari middle, Pranahitha, Indravathi and Godavari lower sub-basins. The daily streamflow data at Chass, Ashwi and Pachegaon of Godavari upper, Manjalegaon, Dhalegaon, Zari, GR Bridge, Purna and Yelli of Godavari middle, Gandlapet, Mancherial, Somanpally and Perur of Pranahitha, Pathagudem, Chindnar, Sonarpal, Jagdalpur and Nowrangpur of Indravathi, and, Sardaput, Injaram, Konta, Koida and Polavaram of Godavari lower sub-basins for the period varying between 1965–2011, collected from Central Water Commission (CWC), India were used in the analysis. Statistics of annual maximum daily streamflow series during the study period at the gauging sites of sub-basins indicated moderately variedand positively skewed streamflows, and flows with sharp peaks at the upstream gauging sites. Probabilistic analysis of streamflows showed that lognormal or gamma distribution with conventional moments fitted the maximum daily streamflow data at the gauging sites of Godavari sub-basins.Among 2-parameter distributions with L-moments,GPA2 followed by GAM2/LN2 fitted annual maximum daily streamflow data at most of the gauging sites.At the downstream-most gauging sites of Pranahitha, Indravathi and Godavari lower sub-basins, the data followed W2 probability distribution. Among 3-parameter distributions with L-moments, GPA3 at seven gauging sites, W3 and P3 at five gauging sites each, GLOG at four gauging sites and GEV at two gauging sites fitted the data. Based on the performance evaluation, 2 – parameter distributions using L-moments at the upstream, 3 – parameter distributions at the middle and probability distributions using conventional moments at the downstreamgauging sites performed better in the Godavari upper and middle sub-basins. Probability distributions based on conventional moments/ 3-parameter distributions using L-momentsfitted the annual maximum daily streamflow data at the gauging sites in the Pranahitha, Indravathi and Godavari lower sub-basins satisfactorily.

scholarly journals The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments

2010 ◽  
Vol 14 (11) ◽  
pp. 2193-2205 ◽  
Author(s):  
J. L. Peña-Arancibia ◽  
A. I. J. M. van Dijk ◽  
M. Mulligan ◽  
L. A. Bruijnzeel
Keyword(s):  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Global Scale ◽  
Tree Cover ◽  
Annual Rainfall ◽  
Ungauged Catchments ◽  
Ongoing Research ◽  
Daily Streamflow ◽  
Terrain Attributes ◽  
Streamflow Data

Abstract. The understanding of low flows in rivers is paramount more than ever as demand for water increases on a global scale. At the same time, limited streamflow data to investigate this phenomenon, particularly in the tropics, makes the provision of accurate estimations in ungauged areas an ongoing research need. This paper analysed the potential of climatic and terrain attributes of 167 tropical and sub-tropical unregulated catchments to predict baseflow recession rates. Daily streamflow data (m3 s–1) from the Global River Discharge Center (GRDC) and a linear reservoir model were used to obtain baseflow recession coefficients (kbf) for these catchments. Climatic attributes included annual and seasonal indicators of rainfall and potential evapotranspiration. Terrain attributes included indicators of catchment shape, morphology, land cover, soils and geology. Stepwise regression was used to identify the best predictors for baseflow recession coefficients. Mean annual rainfall (MAR) and aridity index (AI) were found to explain 49% of the spatial variation of kbf. The rest of climatic indices and the terrain indices average catchment slope (SLO) and tree cover were also good predictors, but co-correlated with MAR. Catchment elongation (CE), a measure of catchment shape, was also found to be statistically significant, although weakly correlated. An analysis of clusters of catchments of smaller size, showed that in these areas, presumably with some similarity of soils and geology due to proximity, residuals of the regression could be explained by SLO and CE. The approach used provides a potential alternative for kbf parameterisation in ungauged catchments.

Benfordʼs Law Geometry

Benford's Law ◽  
2015 ◽  
Author(s):  
Lawrence Leemis
Keyword(s):  
Empirical Data ◽  
Probability Distributions ◽  
Benford’S Law ◽  
Data Sets ◽  
Normal Distributions ◽  
Benford's Law ◽  
Using Data

This chapter switches from the traditional analysis of Benford's law using data sets to a search for probability distributions that obey Benford's law. It begins by briefly discussing the origins of Benford's law through the independent efforts of Simon Newcomb (1835–1909) and Frank Benford, Jr. (1883–1948), both of whom made their discoveries through empirical data. Although Benford's law applies to a wide variety of data sets, none of the popular parametric distributions, such as the exponential and normal distributions, agree exactly with Benford's law. The chapter thus highlights the failures of several of these well-known probability distributions in conforming to Benford's law, considers what types of probability distributions might produce data that obey Benford's law, and looks at some of the geometry associated with these probability distributions.

scholarly journals On the scaling characteristics of observed and simulated spatial soil moisture fields

2009 ◽  
Vol 16 (1) ◽  
pp. 141-150 ◽  
Author(s):  
M. Gebremichael ◽  
R. Rigon ◽  
G. Bertoldi ◽  
T. M. Over
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Spatial Correlation ◽  
Hydrologic Model ◽  
Invariance Property ◽  
Spatial Average ◽  
Hydrologic Models ◽  
Model Simulations

Abstract. By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

scholarly journals Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

2019 ◽  
Vol 23 (5) ◽  
pp. 2225-2243 ◽  
Author(s):  
Guo Yu ◽  
Daniel B. Wright ◽  
Zhihua Zhu ◽  
Cassia Smith ◽  
Kathleen D. Holman
Keyword(s):  
Soil Moisture ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Extreme Rainfall ◽  
Hydrologic Model ◽  
Flood Frequency ◽  
Agricultural Watershed ◽  
Flood Frequency Analysis ◽  
Antecedent Conditions ◽  
Hydrologic Change

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

scholarly journals Attribution of high resolution streamflow trends in Western Austria – an approach based on climate and discharge station data

2015 ◽  
Vol 19 (3) ◽  
pp. 1225-1245 ◽  
Author(s):  
C. Kormann ◽  
T. Francke ◽  
M. Renner ◽  
A. Bronstert
Keyword(s):  
Freezing Point ◽  
Snow Accumulation ◽  
Lower Altitude ◽  
Study Region ◽  
Daily Streamflow ◽  
Streamflow Data ◽  
The One ◽  
Streamflow Trend ◽  
Streamflow Trends

Abstract. The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At mid-altitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.

SAC-SMA a priori parameter differences and their impact on distributed hydrologic model simulations

2012 ◽  
Vol 420-421 ◽  
pp. 216-227 ◽  
Author(s):  
Ziya Zhang ◽  
Victor Koren ◽  
Seann Reed ◽  
Michael Smith ◽  
Yu Zhang ◽  
...  
Keyword(s):  
A Priori ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Distributed Hydrologic Model

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

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