scholarly journals From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

2015 ◽  
Vol 19 (11) ◽  
pp. 4619-4639 ◽  
Author(s):  
M. Herrnegger ◽  
H. P. Nachtnebel ◽  
K. Schulz
Keyword(s):  
Daily Rainfall ◽  
Inverse Model ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Time Step ◽  
Lumped Model ◽  
Additional Information ◽  
Mountainous Regions ◽  
Hourly Data ◽  
Areal Rainfall

Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall–runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.

scholarly journals From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

2014 ◽  
Vol 11 (12) ◽  
pp. 13259-13309 ◽  
Author(s):  
M. Herrnegger ◽  
H. P. Nachtnebel ◽  
K. Schulz
Keyword(s):  
Temporal Resolution ◽  
Numerical Experiments ◽  
Inverse Model ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Time Step ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Rainfall Estimates

Abstract. This paper presents a novel technique to calculate mean areal rainfall in a high temporal resolution of 60 min on the basis of an inverse conceptual rainfall–runoff model and runoff observations. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. The most reliable hydrological information available refers to runoff, which in the presented work is used as input for a rainfall–runoff model. Thereby a conceptual, HBV-type model is embedded in an iteration algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value that corresponds to the observation. To verify the existence, uniqueness and stability of the inverse rainfall, numerical experiments with synthetic hydrographs as inputs into the inverse model are carried out successfully. The application of the inverse model with runoff observations as driving input is performed for the Krems catchment (38.4 km2), situated in the northern Austrian Alpine foothills. Compared to station observations in the proximity of the catchment, the inverse rainfall sums and time series have a similar goodness of fit, as the independent INCA rainfall analysis of Austrian Central Institute for Meteorology and Geodynamics (ZAMG). Compared to observations, the inverse rainfall estimates show larger rainfall intensities. Numerical experiments show, that cold state conditions in the inverse model do not influence the inverse rainfall estimates, when considering an adequate spin-up time. The application of the inverse model is a feasible approach to obtain improved estimates of mean areal rainfall. These can be used to enhance interpolated rainfall fields, e.g. for the estimation of rainfall correction factors, the parameterisation of elevation dependency or the application in real-time flood forecasting systems.

scholarly journals Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 58
Author(s):  
Ahmed Naseh Ahmed Hamdan ◽  
Suhad Almuktar ◽  
Miklas Scholz
Keyword(s):  
Geographical Information Systems ◽  
Hydrological Model ◽  
Daily Rainfall ◽  
Curve Number ◽  
Geographical Information ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Time Step ◽  
River Catchment ◽  
Northern Iraq

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment.

scholarly journals Investigation of the performance of a simple rainfall disaggregation scheme using semi-distributed hydrological modelling of the Lee catchment, UK

2010 ◽  
Vol 41 (2) ◽  
pp. 134-144
Author(s):  
Marie-Laure Segond ◽  
Howard S. Wheater ◽  
Christian Onof
Keyword(s):  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Rain Gauges ◽  
Expected Performance ◽  
Hourly Data ◽  
Temporal Disaggregation ◽  
Rainfall Runoff Model ◽  
Event Based ◽  
Spatially Varying ◽  
Runoff Model

A simple and practical spatial–temporal disaggregation scheme to convert observed daily rainfall to hourly data is presented, in which the observed sub-daily temporal profile available at one gauge is applied linearly to all sites over the catchment to reproduce the spatially varying daily totals. The performance of the methodology is evaluated using an event-based, semi-distributed, nonlinear hydrological rainfall–runoff model to test the suitability of the disaggregation scheme for UK conditions for catchment sizes of 80–1,000 km2. The joint procedure is tested on the Lee catchment, UK, for five events from a 12 year period of data from 16 rain gauges and 12 flow stations. The disaggregation scheme generally performs extremely well in reproducing the simulated flow for the natural catchments, although, as expected, performance deteriorates for localized convective rainfall. However, some reduction in performance occurs when the catchments are artificially urbanised.

scholarly journals Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?

2018 ◽  
Vol 22 (10) ◽  
pp. 5259-5280 ◽  
Author(s):  
Hannes Müller-Thomy ◽  
Markus Wallner ◽  
Kristian Förster
Keyword(s):  
Time Series ◽  
Hydrological Modeling ◽  
Extreme Values ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Lower Saxony ◽  
Rainfall Characteristics ◽  
Areal Rainfall

Abstract. In this study, the influence of disaggregated rainfall products with different degrees of spatial consistence on rainfall–runoff modeling results is analyzed for three mesoscale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values, a multiplicative random cascade model is applied. The disaggregation is applied on a station by station basis without consideration of surrounding stations; hence subsequent steps are then required to implement spatial consistence. Spatial consistence is represented here by three bivariate spatial rainfall characteristics that complement each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall products as input to hydrological modeling, we hypothesize that derived runoff statistics – with emphasis on seasonal extreme values – are subject to similar differences as well. However, an impact on the extreme values' statistics of the hydrological simulations forced by different rainfall approaches cannot be detected. Several modifications of the study design using rainfall–runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall–runoff WaSiM model is applied instead of the semi-distributed HBV-IWW model can slight differences regarding the seasonal peak flows be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.

scholarly journals Evaluation of four years continuous δ<sup>13</sup>C(CO<sub>2</sub>) data using a running Keeling approach

2016 ◽  
Author(s):  
Sanam Noreen Vardag ◽  
Samuel Hammer ◽  
Ingeborg Levin
Keyword(s):  
Fossil Fuel ◽  
Co2 Concentration ◽  
High Temporal Resolution ◽  
Data Set ◽  
Additional Information ◽  
Hourly Data ◽  
The Mean ◽  
Carbon Dioxide Co2 ◽  
End Members ◽  
Different Sources

Abstract. As different carbon dioxide (CO2) emitters have different carbon isotope ratios, measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 source mix in the catchment area of an atmospheric measurement site. Often, this information is illustratively presented as mean isotopic source signature. Recently an increasing number of continuous measurements of δ13C(CO2) and CO2 have become available, opening the door to quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) continuously without introducing biases and evaluate our result using model data. We find that biases in δS are smaller than 0.2 ‰ with uncertainties of about 1.2 ‰ for hourly data. Applying the method to a four year data set of CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. Disentangling this seasonal source signature into its source components is, however, only possible if the isotopic end members of these sources, i.e., the biosphere, δbio, and the fuel mix, δF, are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25 ± 1) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements, such as Δ14C(CO2).

Advantages of calibrating a daily rainfall-runoff model to monthly streamflow data

2021 ◽  
Author(s):  
Julien Lerat ◽  
Mark Thyer ◽  
David McInerney ◽  
Dmitri Kavetski
Keyword(s):  
Daily Rainfall ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Objective Functions ◽  
Time Step ◽  
Flow Duration ◽  
Monthly Streamflow ◽  
Daily Time Step ◽  
Streamflow Data ◽  
Daily Time

&lt;p&gt;Development of robust approaches for calibrating daily rainfall-runoff models to monthly streamflow data enable modelling platforms that operate at daily time step to be applied in practical situations. Here precipitation is available at the daily scale, but observed streamflow is available only at the monthly scale (e.g. predicting inflows into large dams). This study compares the performance of the daily GR4J hydrological model when calibrated against (1) daily and (2) monthly streamflow data. The performance comparison relies on a wide range of metrics and is undertaken for 508 Australian catchments. Two evaluation periods (1975&amp;#8211;1992 and 1992&amp;#8211;2015) and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency) were tested.&lt;/p&gt;&lt;p&gt;Monthly calibration performs similar to or better than daily calibration in most sites and both periods in terms of bias and fit of the flow duration curve. This result remains the same when the flow duration curve is computed at the daily time step, which constitutes a significant finding of this study.&lt;/p&gt;&lt;p&gt;However, the performance of monthly calibration is worse than daily calibration for daily pattern metrics such as Nash-Sutcliffe efficiency in most sites and both periods. Significant improvement can be achieved if the flow-timing parameter of GR4J is regionalised, effectively reducing the number of calibrated parameters. Similar results are obtained for other pattern metrics and all objective functions.&lt;/p&gt;&lt;p&gt;These findings suggest that monthly calibration of rainfall-runoff models using daily-rainfall and monthly-streamflow data is a viable alternative to daily calibration when no daily streamflow data are available.&lt;/p&gt;

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

A Sequential Integration Method

1988 ◽  
Vol 110 (4) ◽  
pp. 382-388
Author(s):  
Liang-Wey Chang ◽  
James F. Hamilton
Keyword(s):  
Integration Method ◽  
Equations Of Motion ◽  
Slow Motion ◽  
Explicit Integration ◽  
Time Step ◽  
Lumped Model ◽  
Guaranteed Stability ◽  
Fast Motion ◽  
The Stability ◽  
Secular Terms

This paper presents a method for simulating systems with two inertially coupled motions, i.e., a slow motion and a fast motion. The equations of motion are separated into two sets of coupled nonlinear ordinary differential equations. For each time step, the two sets of equations are integrated sequentially rather than simultaneously. Explicit integration methods are used for integrating the slow motion since the stability of the integration is not a problem and the explicit methods are very convenient for nonlinear equations. For the fast motion, the equations are linear and the implicit integrations can be used with guaranteed stability. The size of time step only needs to be chosen to provide accuracy of the solution for the modes that are excited. The interaction between the two types of motion must be treated such that secular terms do not appear due to the sequential integration method. A lumped model of a flexible pendulum will be presented in this paper to illustrate the application of the method. Numerical results for both simultaneous and sequential integration are presented for comparison.

scholarly journals A regional similarity-based approach for sub-daily rainfall nonparametric generation

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Milena Guerra de Aguilar ◽  
Veber Afonso Figueiredo Costa
Keyword(s):  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Rainfall Time Series ◽  
Storm Events ◽  
Runoff Simulation ◽  
K Nearest Neighbors ◽  
Method Of Fragments ◽  
Block Maxima ◽  
Regional Similarity ◽  
Doce River

ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.

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