A method to calibrate daily rainfall-runoff models to monthly streamflow data

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&#8211;1992 and 1992&#8211;2015) and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency) were tested.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.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.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.

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Use of Remotely Sensed Actual Evapotranspiration to Improve Rainfall–Runoff Modeling in Southeast Australia

Journal of Hydrometeorology ◽

10.1175/2009jhm1061.1 ◽

2009 ◽

Vol 10 (4) ◽

pp. 969-980 ◽

Cited By ~ 64

Author(s):

Yongqiang Zhang ◽

Francis H. S. Chiew ◽

Lu Zhang ◽

Hongxia Li

Keyword(s):

Daily Rainfall ◽

Remotely Sensed ◽

Actual Evapotranspiration ◽

Rainfall Runoff ◽

Remotely Sensed Data ◽

Ungauged Catchments ◽

Area Index ◽

Southeast Australia ◽

Streamflow Data ◽

Modis Lai

Abstract This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and ERS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall–runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments.

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

Hydrology ◽

10.3390/hydrology8020058 ◽

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.

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A hydrological modelling-based approach for vulnerable area identification under changing climate scenarios

Journal of Water and Climate Change ◽

10.2166/wcc.2020.202 ◽

2020 ◽

Cited By ~ 3

Author(s):

Sonam S. Dash ◽

Dipaka R. Sena ◽

Uday Mandal ◽

Anil Kumar ◽

Gopal Kumar ◽

...

Keyword(s):

Assessment Tool ◽

Fold Increase ◽

Maximum Temperature ◽

Climate Scenarios ◽

Vulnerable Area ◽

Temperature And Precipitation ◽

Monthly Streamflow ◽

Rcp 8.5 ◽

Streamflow Data ◽

Critical Zones

Abstract The hydrologic behaviour of the Brahmani River basin (BRB) (39,633.90 km2), India was assessed for the base period (1970–1999) and future climate scenarios (2050) using the Soil and Water Assessment Tool (SWAT). Monthly streamflow data of 2000–2009 and 2010–2012 was used for calibration and validation, respectively, and performed satisfactorily with Nash-Sutcliffe Efficiency (ENS) of 0.52–0.55. The projected future climatic outcomes of the HadGEM2-ES model indicated that minimum temperature, maximum temperature, and precipitation may increase by 1.11–3.72 °C, 0.27–2.89 °C, and 16–263 mm, respectively, by 2050. The mean annual streamflow over the basin may increase by 20.86, 11.29, 4.45, and 37.94% under RCP 2.6, 4.5, 6.0, and 8.5, respectively, whereas the sediment yield is likely to increase by 23.34, 10.53, 2.45, and 27.62% under RCP 2.6, 4.5, 6.0, and 8.5, respectively, signifying RCP 8.5 to be the most adverse scenario for the BRB. Moreover, a ten-fold increase in environmental flow (defined as Q90) by the mid-century period is expected under the RCP 8.5 scenario. The vulnerable area assessment revealed that the increase in moderate and high erosion-prone regions will be more prevalent in the mid-century. The methodology developed herein could be successfully implemented for identification and prioritization of critical zones in worldwide river basins.

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Combining a neural network with deterministic chaos theory using phase space reconstruction for daily rainfall-runoff forecasting

2015 12th International Symposium on Programming and Systems (ISPS) ◽

10.1109/isps.2015.7244983 ◽

2015 ◽

Author(s):

Mohamed Chettih ◽

Khaled Chorfi ◽

Kaddour Mouattah

Keyword(s):

Neural Network ◽

Phase Space ◽

Chaos Theory ◽

Deterministic Chaos ◽

Daily Rainfall ◽

Phase Space Reconstruction ◽

Rainfall Runoff ◽

Runoff Forecasting

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Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-058.1 ◽

2012 ◽

Vol 13 (1) ◽

pp. 122-139 ◽

Cited By ~ 145

Author(s):

Jin Teng ◽

Jai Vaze ◽

Francis H. S. Chiew ◽

Biao Wang ◽

Jean-Michel Perraud

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Daily Rainfall ◽

Low Flow ◽

Hydrological Models ◽

Rainfall Runoff ◽

Runoff Modeling ◽

Change Impact ◽

The Difference ◽

Climate Series

Abstract This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall–runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall–runoff models. The variability in the climate change impact on runoff results for one rainfall–runoff model informed by 15 GCMs (an about 28%–35% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall–runoff models informed by 1 GCM (a less than 7% difference between the minimum and maximum results). The difference between the rainfall–runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall–runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate–runoff relationship in a warmer and higher CO2 environment are modeled.

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Contrasting seasonality of storm rainfall and flood runoff in the UK and some implications for rainfall-runoff methods of flood estimation

Hydrology Research ◽

10.2166/nh.2019.040 ◽

2019 ◽

Vol 50 (5) ◽

pp. 1309-1323 ◽

Cited By ~ 1

Author(s):

Jamie Ledingham ◽

David Archer ◽

Elizabeth Lewis ◽

Hayley Fowler ◽

Chris Kilsby

Keyword(s):

Soil Moisture ◽

Risk Estimation ◽

Daily Rainfall ◽

Annual Rainfall ◽

Rainfall Runoff ◽

Frequency Model ◽

Soil Moisture Storage ◽

Flood Estimation ◽

Storm Rainfall ◽

The Uk

Abstract Using data from 520 gauging stations in Britain and gridded rainfall datasets, the seasonality of storm rainfall and flood runoff is compared and mapped. Annual maximum (AMAX) daily rainfall occurs predominantly in summer, but AMAX floods occur most frequently in winter. Seasonal occurrences of annual daily rainfall and flood maxima differ by more than 50% in dry lowland catchments. The differences diminish with increasing catchment wetness, increase with rainfalls shorter than daily duration and are shown to depend primarily on catchment wetness, as illustrated by variations in mean annual rainfall. Over the whole dataset, only 34% of AMAX daily flood events are matched to daily rainfall annual maxima (and only 20% for 6-hour rainfall maxima). The discontinuity between rainfall maxima and flooding is explained by the consideration of coincident soil moisture storage. The results have serious implications for rainfall-runoff methods of flood risk estimation in the UK where estimation is based on a depth–duration–frequency model of rainfall highly biased to summer. It is concluded that inadequate treatment of the seasonality of rainfall and soil moisture seriously reduces the reliability of event-based flood estimation in Britain.

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Picturing and modeling catchments by representative hillslopes

Hydrology and Earth System Sciences ◽

10.5194/hess-21-1225-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 1225-1249 ◽

Cited By ~ 19

Author(s):

Ralf Loritz ◽

Sibylle K. Hassler ◽

Conrad Jackisch ◽

Niklas Allroggen ◽

Loes van Schaik ◽

...

Keyword(s):

Expert Knowledge ◽

Water Dynamics ◽

Rainfall Runoff ◽

Data Set ◽

Physically Based Model ◽

Optimal Representation ◽

Physically Based ◽

Physically Based Models ◽

Streamflow Data ◽

Runoff Events

Abstract. This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.

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Disaggregation of Annual to Monthly Streamflow: A Case Study of Kızılırmak Basin (Turkey)

Advances in Meteorology ◽

10.1155/2017/3582826 ◽

2017 ◽

Vol 2017 ◽

pp. 1-16

Author(s):

Shatha H. D. Al-Zakar ◽

N. Şarlak ◽

Omar M. A. Mahmood Agha

Keyword(s):

Nearest Neighbor ◽

Planning System ◽

Monthly Streamflow ◽

Spatial Disaggregation ◽

Temporal Disaggregation ◽

Streamflow Data ◽

And Storage ◽

Resources Planning ◽

Kızılırmak Basin

This study utilizes a recent nonparametric disaggregation K-nearest neighbor (KNN) model, to resample monthly flows depending on annual flows at different sites. Both temporal and spatial approaches will be followed in this model while preserving the distributional statistics of the observed data. This model assumes that a set of aggregated annual streamflows at a key station is available and desired for disaggregation to a corresponding series of streamflow at key station (temporal disaggregation) as well as at tributary stations (spatial disaggregation). The model is applied to the annual streamflow data of particular stations in the Kızılırmak Basin, which is exposed to drought periods during the years (1970–1974 and 1994-1995). The aim of this study is to find the possibilities of using the nonparametric approaches as generators of monthly flows, with emphasis on the ability to reproduce the statistics related to drought and storage analysis for the selected stations in Turkey. The results show that the spatial disaggregation approach has the ability to reproduce the historical data better than the temporal approach for the tested sites and provides a variety of generated monthly sequence flows that can then be utilized to analyze the performance of the water resources planning system.

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