A robust approach for calibrating a daily rainfall-runoff model to monthly streamflow data

2020 ◽  
Vol 591 ◽  
pp. 125129 ◽  
Author(s):  
Julien Lerat ◽  
Mark Thyer ◽  
David McInerney ◽  
Dmitri Kavetski ◽  
Fitsum Woldemeskel ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Robust Approach ◽  
Monthly Streamflow ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Impact of downscaled rainfall biases on projected runoff changes

2020 ◽  
Vol 24 (6) ◽  
pp. 2981-2997
Author(s):  
Stephen P. Charles ◽  
Francis H. S. Chiew ◽  
Nicholas J. Potter ◽  
Hongxing Zheng ◽  
Guobin Fu ◽  
...  
Keyword(s):  
Water Supply ◽  
Grid Point ◽  
Daily Rainfall ◽  
Rainfall Runoff ◽  
Rainfall Frequency ◽  
Rainfall Runoff Model ◽  
Projected Changes ◽  
Runoff Model ◽  
Regional Water

Abstract. Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile–quantile mapping (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the state of Victoria, Australia (237 629 km2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km × 10 km grid point across Victoria, the GR4J rainfall–runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection.

scholarly journals Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

2011 ◽  
Vol 12 (5) ◽  
pp. 1100-1112 ◽  
Author(s):  
J. Vaze ◽  
D. A. Post ◽  
F. H. S. Chiew ◽  
J.-M. Perraud ◽  
J. Teng ◽  
...  
Keyword(s):  
Model Simulation ◽  
Model Performance ◽  
Daily Rainfall ◽  
Grid Cell ◽  
Rainfall Series ◽  
Data Availability ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

scholarly journals Climate change model as a decision support tool for water resources management in northern Iraq: a case study of Greater Zab River

2017 ◽  
Vol 10 (1) ◽  
pp. 197-209 ◽  
Author(s):  
Y. Osman ◽  
N. Al-Ansari ◽  
M. Abdellatif
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Daily Rainfall ◽  
Decision Support Tool ◽  
Northern Region ◽  
Future Climate Change ◽  
Rainfall Runoff ◽  
Support Tool ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the 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 Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2015 ◽  
Vol 12 (6) ◽  
pp. 5389-5426 ◽  
Author(s):  
S. Almeida ◽  
N. Le Vine ◽  
N. McIntyre ◽  
T. Wagener ◽  
W. Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.

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