scholarly journals Parameter Calibrations of a Daily Rainfall-Runoff Model Using Global Optimization Methods

2002 ◽  
Vol 35 (5) ◽  
pp. 541-552 ◽  
Author(s):  
Min-Goo Kang ◽  
Seung-Woo Park ◽  
Sang-Jun Im ◽  
Hyun-Jun Kim
Keyword(s):  
Global Optimization ◽  
Daily Rainfall ◽  
Optimization Methods ◽  
Rainfall Runoff ◽  
Global Optimization Methods ◽  
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.

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

Improving representation of zero flows in probabilistic hydrological modelling of ephemeral catchments

2021 ◽  
Author(s):  
Dmitri Kavetski ◽  
David McInerney ◽  
Mark Thyer ◽  
Julien Lerat ◽  
George Kuczera
Keyword(s):  
Method Development ◽  
Performance Metrics ◽  
Daily Rainfall ◽  
Residual Error ◽  
Rainfall Runoff ◽  
Pragmatic Approach ◽  
Power Parameter ◽  
Predictive Uncertainty ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Ephemeral catchments, where streamflow is frequently zero or negligible, are common across the world yet difficult to model reliably. This paper evaluates probabilistic approaches for modelling streamflow in ephemeral catchments, with a focus on the description of predictive uncertainty using residual error models.</p><p>We compare an explicit treatment of zero flows using a censoring approach versus a simpler pragmatic approach where the lower streamflow bound of zero is applied in prediction only. Following a theoretical exposition, empirical comparisons are reported using a daily rainfall-runoff model (GR4J), four residual error schemes (based on log, log-sinh and Box-Cox (BC) transformations with power parameter L = 0.2 and 0.5), 74 Australian catchments with diverse hydroclimatology, and five performance metrics, including reliability, precision, bias and proportion of zero flow days.</p><p>The explicit approach is most beneficial in "mid-ephemeral" catchments (5-50% zero flows) where it offers substantial improvements over the pragmatic approach. The BC0.2 and BC0.5 transformations are Pareto optimal: BC0.2 achieves better characterisation of predictive uncertainty, whereas BC0.5 attains lower volumetric bias. In "low-ephemeral" catchments (<5% zero flows) the pragmatic approach is sufficient, whereas in "high-ephemeral" catchments (>50% zero flows) both approaches incur limitations and further method development is warranted. The findings provide guidance on improving probabilistic streamflow predictions in ephemeral catchments.</p>

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