scholarly journals Impact Assessment of Rainfall-Runoff Simulations on the Flow Duration Curve of the Upper Indus River—A Comparison of Data-Driven and Hydrologic Models

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 876 ◽  
Author(s):  
Ateeq-ur Rauf ◽  
Abdul Ghumman
Keyword(s):  
Impact Assessment ◽  
Data Driven ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Indus River ◽  
Hydrologic Models ◽  
Flow Duration

Comparing flow duration curve and rainfall–runoff modelling for predicting daily runoff in ungauged catchments

2015 ◽  
Vol 525 ◽  
pp. 72-86 ◽  
Author(s):  
Yongqiang Zhang ◽  
Jai Vaze ◽  
Francis H.S. Chiew ◽  
Ming Li
Keyword(s):  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Flow Duration

scholarly journals Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis

2011 ◽  
Vol 8 (2) ◽  
pp. 3961-3992 ◽  
Author(s):  
Y. Yokoo ◽  
M. Sivapalan
Keyword(s):  
Conceptual Framework ◽  
Sensitivity Analyses ◽  
Linear Filter ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Filter Model ◽  
Flow Component ◽  
Flow Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In this paper we investigate the climatic and landscape controls on the flow duration curve (FDC) with the use of a physically-based rainfall-runoff model. The FDC is a stochastic representation of within-year variability of runoff, which arises from the transformation, by the catchment, of within-year variability of precipitation that can itself be characterized by a corresponding duration curve for precipitation (PFDC). Numerical simulations are carried out with the rainfall-runoff model under a variety of combinations of climatic inputs (i.e., precipitation, potential evaporation, including their within-year variability) and landscape properties (i.e., soil type and depth). The simulations indicated that the FDC can be disaggregated into two components, with sharply differing characteristics and origins: the FDC for surface (fast) runoff (SFDC) and the FDC for subsurface (slow) runoff (SSFDC). SFDC closely tracked PFDC and can be approximated with the use of a simple, nonlinear (threshold) filter model. On the other hand, SSFDC tracked the FDC that is constructed from the regime curve (ensemble mean within-year variation of streamflow), which can be closely approximated by a linear filter model. Sensitivity analyses were carried out to understand the climate and landscape controls on each component, gaining useful physical insights into their respective shapes. In particular the results suggested that evaporation from dynamic saturated areas, especially in the dry season, can contribute to a sharp dip at the lower tail of the FDCs. Based on these results, we develop a conceptual framework for the reconstruction of FDCs in ungauged basins. This framework partitions the FDC into: (1) a fast flow component, governed by a filtered version of PFDC, (2) a slow flow component governed by the regime curve, and (3) a correction to SSFDC to capture the effects of high evapotranspiration at low flows.

scholarly journals Rainfall Runoff Modelling Based on Genetic Programming

2002 ◽  
Vol 33 (5) ◽  
pp. 331-346 ◽  
Author(s):  
Vladan Babovic ◽  
Maarten Keijzer
Keyword(s):  
Genetic Programming ◽  
Conceptual Models ◽  
Linear Time ◽  
Physical Models ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Considerable Time ◽  
Hydrologic Models ◽  
Spatially Distributed ◽  
Using Data

The runoff formation process is believed to be highly non-linear, time varying, spatially distributed, and not easily described by simple models. Considerable time and effort has been directed to model this process, and many hydrologic models have been built specifically for this purpose. All of them, however, require significant amounts of data for their respective calibration and validation. Using physical models raises issues of collecting the appropriate data with sufficient accuracy. In most cases it is difficult to collect all the data necessary for such a model. By using data driven models such as genetic programming (GP), one can attempt to model runoff on the basis of available hydrometeorological data. This work addresses use of genetic programming for creating rainfall-runoff models on the basis of data alone, as well as in combination with conceptual models (i.e taking advantage of knowledge about the problem domain).

scholarly journals Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis

2011 ◽  
Vol 15 (9) ◽  
pp. 2805-2819 ◽  
Author(s):  
Y. Yokoo ◽  
M. Sivapalan
Keyword(s):  
Conceptual Framework ◽  
Sensitivity Analyses ◽  
Linear Filter ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Filter Model ◽  
Flow Component ◽  
Flow Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. In this paper we investigate the climatic and landscape controls on the flow duration curve (FDC) with the use of a physically-based rainfall-runoff model. The FDC is a stochastic representation of the variability of runoff, which arises from the transformation, by the catchment, of within-year variability of precipitation that can itself be characterized by a corresponding duration curve for precipitation (PDC). Numerical simulations are carried out with the rainfall-runoff model under a variety of combinations of climatic inputs (i.e. precipitation, potential evaporation, including their within-year variability) and landscape properties (i.e. soil type and depth). The simulations indicated that the FDC can be disaggregated into two components, with sharply differing characteristics and origins: the FDC for surface (fast) runoff (SFDC) and the FDC for subsurface (slow) runoff (SSFDC), which included base flow in our analysis. SFDC closely tracked PDC and can be approximated with the use of a simple, nonlinear (threshold) filter model. On the other hand, SSFDC tracked the FDC that is constructed from the regime curve (i.e. mean monthly runoff), which can be closely approximated by a linear filter model. Sensitivity analyses were carried out to understand the climate and landscape controls on each component, gaining useful physical insights into their respective shapes. In particular the results suggested that evaporation from dynamic saturated areas, especially in the dry season, can contribute to a sharp dip at the lower tail of the FDCs. Based on these results, we develop a conceptual framework for the reconstruction of FDCs in ungauged basins. This framework partitions the FDC into: (1) a fast flow component, governed by a filtered version of PDC, (2) a slow flow component governed by the regime curve, and (3) a correction to SSFDC to capture the effects of high evapotranspiration (ET) at low flows.

Diagnostic Evaluation of Hydrologic Models Employing Flow Duration Curve

2019 ◽  
Vol 24 (6) ◽  
pp. 05019009 ◽  
Author(s):  
Vinod Chilkoti ◽  
Tirupati Bolisetti ◽  
Ram Balachandar
Keyword(s):  
Diagnostic Evaluation ◽  
Flow Duration Curve ◽  
Hydrologic Models ◽  
Flow Duration

Rainfall-Discharge Simulation in Bah Bolon Catchment Area by Mock Method, NRECA Method, and GR2M Method

2016 ◽  
Vol 845 ◽  
pp. 24-29 ◽  
Author(s):  
Hadiani Rintis ◽  
Suyanto ◽  
Yosephina Puspa Setyoasri
Keyword(s):  
Catchment Area ◽  
The Other ◽  
Series Data ◽  
Flow Duration Curve ◽  
Rainfall Runoff ◽  
Flow Duration ◽  
Discharge Simulation ◽  
Rainfall Runoff Model ◽  
North Sumatra ◽  
Runoff Model

Rainfall-discharge simulation is a process transformation from rainfall to discharge in a catchment area by modelling. The most popular models are Mock method and NRECA method. It is according to the handbook of irrigation that is written by government (Indonesia). GR2M (Global Rainfall-Runoff Model) is a new model that is not usual to be used in Indonesia. GR2M is a simulation model that needs less parameter than Mock and NRECA methods. This research was conducted in the Bah Bolon catchment area, Simalungun, North Sumatra. It will analyze the simulation of rainfall-discharge by three methods, Mock, NRECA, and GR2M without considering whether the watershed was wet or dry watershed. The analysis was computed the dependable discharge by flow duration curve (fdc) in a series data on each method. The parameter that compared was the dependable discharge, i.e. the discharge with probability 70% (Q70), probability 80% (Q80), and probability 90% (Q90). GR2M will compared with Mock, then compared with NRECA. The results show that the discharge simulation by GR2M methods and the discharge simulation by Mock method has correlation 0.968. The discharge simulation by GR2M method and the discharge simulation by NRECA method has correlation 0,955. It means that GR2M close to the both of them, but GR2M can used easily because it has less parameter than the other. Based on the graphic, GR2M close to the Mock method for probability more than 50%. So, if the probability is 70%, 80%, and 90%, then GR2M method close to Mock method.

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

<p>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 (1975–1992 and 1992–2015) and four objective functions (including sum-of-squared-errors of Box-Cox transformed streamflow and the Kling-Gupta efficiency) were tested.</p><p>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.</p><p>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.</p><p>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.</p>

Sign in / Sign up

Export Citation Format

Share Document