scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 12 (4) ◽  
pp. 1141-1152 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
River Flows ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero River. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The hydrological model is first calibrated by using a 1-year record of hourly meteorological data and river flows observed at the outlet of the 1294 km2 wide Secchia River basin, of which the Riarbero is a tributary. The model is then validated by performing a 100-year long simulation of synthetic river flow data, which allowed us to compare the simulated and observed flood frequency distributions at the Secchia River outlet and the internal cross river section of Cavola Bridge, where the basin area is 337 km2. Finally, another simulation of hourly river flows was performed by referring to the outlet of the Riarbero River, therefore allowing us to estimate the related flood frequency distribution. The results were validated by using estimates of peak river flow obtained by applying hydrological similarity principles and a regional method. The results show that the flood flow estimated through the application of the distributed model is consistent with the estimate provided by the regional procedure as well as the behaviors of the river banks. Conversely, the method based on hydrological similarity delivers an estimate that seems to be not as reliable. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero Torrent. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The results were checked by using estimates of the peak river flow obtained by applying a classical procedure based on hydrological similarity principles. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

scholarly journals CLASSIC: a semi-distributed rainfall-runoff modelling system

2007 ◽  
Vol 11 (1) ◽  
pp. 516-531 ◽  
Author(s):  
S. M. Crooks ◽  
P. S. Naden
Keyword(s):  
Spatial Scales ◽  
Digital Terrain Model ◽  
Flood Frequency ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Terrain Model ◽  
Flow Duration Curves ◽  
Rainfall Runoff Model ◽  
The Uk ◽  
Modelling System

Abstract. This paper describes the development of a semi-distributed conceptual rainfall–runoff model, originally formulated to simulate impacts of climate and land-use change on flood frequency. The model has component modules for soil moisture balance, drainage response and channel routing and is grid-based to allow direct incorporation of GIS- and Digital Terrain Model (DTM)-derived data sets into the initialisation of parameter values. Catchment runoff is derived from the aggregation of components of flow from the drainage module within each grid square and from total routed flow from all grid squares. Calibration is performed sequentially for the three modules using different objective functions for each stage. A key principle of the modelling system is the concept of nested calibration, which ensures that all flows simulated for points within a large catchment are spatially consistent. The modelling system is robust and has been applied successfully at different spatial scales to three large catchments in the UK, including comparison of observed and modelled flood frequency and flow duration curves, simulation of flows for uncalibrated catchments and identification of components of flow within a modelled hydrograph. The role of such a model in integrated catchment studies is outlined.

Operational prediction of river-groundwater exchange, groundwater levels and aquifer storage: The Wairau Plain Aquifer 

2021 ◽  
Author(s):  
Thomas Wöhling
Keyword(s):  
Predictive Accuracy ◽  
Management Strategies ◽  
Rainfall Runoff ◽  
Groundwater Storage ◽  
River Flows ◽  
Groundwater Levels ◽  
Aquifer Storage ◽  
Operational Forecasting ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>Groundwater is a major resource for drinking water supply and irrigation of crops in many parts of the world. Many groundwater aquifers are already fully allocated, but the demand is projected to increase further while, concurrently, climate change may cause more variability in the natural supply. This poses enormous challenges for the future management of groundwater resources and a paradigm shift from traditional, threshold-based management strategies to more flexible, adaptive management strategies.</p><p>For that purpose, operational forecasting tools are required that predict future states of groundwater aquifers under various scenarios and to predict the risk of critical states which would have adverse effects either for the environment or for water users, or both.</p><p>The mathematical description of the complex interactions particularly of shallow, unconfined river-fed aquifers typically requires the use of spatially explicit numerical models. These are, however, not suitable for operational forecasting due to lengthy run-times and extensive data requirements. This also poses strong limitations with respect to predictive uncertainty analysis – which should be an integral part of predictive management tools. Model simplification or model surrogates are the method of choice to circumvent the problem.</p><p>An operational forecasting tool is presented here to predict groundwater heads and groundwater storage in the unconfined Wairau Plain Aquifer in Marlborough, New Zealand, during flow recession times. The tool uses low-complexity “eigenmodels” to describe groundwater flow and to provide an early warning for critical groundwater storage levels to the Marlborough District Council, which manages the groundwater resource. These critical levels have been approached more frequently during the past years when the natural recession of groundwater storage in summer is exacerbated by groundwater abstraction to satisfy the irrigation water demand of the Plain’s viticulture.</p><p>The forecasting tool requires, amongst others, daily forecasts of Wairau River flows because the river is the major recharge source for the aquifer. Flow forecasts and their uncertainty are computed i) by using a master recession curve for predictions during flow recession times and ii) by a lumped rainfall-runoff model for times of aquifer storage recovery. This allows a broad evaluation of forecasting scenarios. The tool has been tested and is operational for recession times (worst-case scenario predictions; Wöhling & Burbery, 2020). The rainfall-runoff model performs reasonably well in predicting river flows and correspondingly in predicting groundwater storage recovery for historic data (hindcasting). The 30-day predictive uncertainty bounds generally cover the observations of river flows, groundwater levels and aquifer storage. The predictive accuracy of the tool largely depends on the predictive accuracy of the drivers, particularly of the areal estimates of precipitation that drives the rainfall-runoff model and the river-groundwater exchange function that describes aquifer recharge rates.</p><p> </p><p><strong>References</strong><strong> </strong></p><p>Wöhling T, Burbery L (2020). Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession. Science of the Total Environment, 747, 141220, doi: 10.1016/j.scitotenv.2020.141220.</p>

Perhitungan ketersediaan air permukaan di Indonesia berdasarkan data satelit

2018 ◽  
Vol 13 (2) ◽  
pp. 115-130 ◽  
Author(s):  
Radhika Radhika ◽  
Rendy Firmansyah ◽  
Waluyo Hatmoko
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Availability ◽  
River Flow ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Satellite Technology ◽  
Rainfall Runoff Model ◽  
The Tropics ◽  
Runoff Model

Information on water availability is vital in water resources management. Unfortunately, information on the condition of hydrological data, either river flow data, or rainfall data is very limited temporally and spatially. With the availability of satellite technology, rainfall in the tropics can be monitored and recorded for further analysis. This paper discusses the calculation of surface water availability based on rainfall data from TRMM satellite, and then Wflow, a distributed rainfall-runoff model generates monthly time runoff data from 2003 to 2015 for all river basin areas in Indonesia. It is concluded that the average surface water availability in Indonesia is 88.3 thousand m3/s or equivalent to 2.78 trillion m3/ year. This figure is lower than the study of Water Resources Research Center 2010 based on discharge at the post estimated water that produces 3.9 trillion m3/year, but very close to the study of Aquastat FAO of 2.79 trillion m3 / year. The main benefit of this satellite-based calculation is that at any location in Indonesia, potential surface water can be obtained by multiplying the area of the catchment and the runoff height.

Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions

2010 ◽  
pp. n/a-n/a ◽  
Author(s):  
Hilary McMillan ◽  
Jim Freer ◽  
Florian Pappenberger ◽  
Tobias Krueger ◽  
Martyn Clark
Keyword(s):  
Model Calibration ◽  
River Flow ◽  
Rainfall Runoff ◽  
Flow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach

2000 ◽  
Vol 4 (3) ◽  
pp. 463-482 ◽  
Author(s):  
A. M. Hashemi ◽  
M. Franchini ◽  
P. E. O’Connell
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Flood Frequency ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Frequency Curve ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Insight Into ◽  
Frequency Curves

Abstract. Regionalized and at-site flood frequency curves exhibit considerable variability in their shapes, but the factors controlling the variability (other than sampling effects) are not well understood. An application of the Monte Carlo simulation-based derived distribution approach is presented in this two-part paper to explore the influence of climate, described by simulated rainfall and evapotranspiration time series, and basin factors on the flood frequency curve (ffc). The sensitivity analysis conducted in the paper should not be interpreted as reflecting possible climate changes, but the results can provide an indication of the changes to which the flood frequency curve might be sensitive. A single site Neyman Scott point process model of rainfall, with convective and stratiform cells (Cowpertwait, 1994; 1995), has been employed to generate synthetic rainfall inputs to a rainfall runoff model. The time series of the potential evapotranspiration (ETp) demand has been represented through an AR(n) model with seasonal component, while a simplified version of the ARNO rainfall-runoff model (Todini, 1996) has been employed to simulate the continuous discharge time series. All these models have been parameterised in a realistic manner using observed data and results from previous applications, to obtain ‘reference’ parameter sets for a synthetic case study. Subsequently, perturbations to the model parameters have been made one-at-a-time and the sensitivities of the generated annual maximum rainfall and flood frequency curves (unstandardised, and standardised by the mean) have been assessed. Overall, the sensitivity analysis described in this paper suggests that the soil moisture regime, and, in particular, the probability distribution of soil moisture content at the storm arrival time, can be considered as a unifying link between the perturbations to the several parameters and their effects on the standardised and unstandardised ffcs, thus revealing the physical mechanism through which their influence is exercised. However, perturbations to the parameters of the linear routing component affect only the unstandardised ffc. In Franchini et al. (2000), the sensitivity analysis of the model parameters has been assessed through an analysis of variance (ANOVA) of the results obtained from a formal experimental design, where all the parameters are allowed to vary simultaneously, thus providing deeper insight into the interactions between the different factors. This approach allows a wider range of climatic and basin conditions to be analysed and reinforces the results presented in this paper, which provide valuable new insight into the climatic and basin factors controlling the ffc. Keywords: stochastic rainfall model; rainfall runoff model; simulation; derived distribution; flood frequency; sensitivity analysis

scholarly journals Hydrological modeling using Effective Rainfall routed by the Muskingum method (ERM)

2013 ◽  
Vol 15 (4) ◽  
pp. 1437-1455 ◽  
Author(s):  
M. Baymani-Nezhad ◽  
D. Han
Keyword(s):  
Hydrological Modeling ◽  
Effective Rainfall ◽  
Rainfall Runoff ◽  
Optimization Scheme ◽  
Calibration And Validation ◽  
Wide Range ◽  
Muskingum Method ◽  
Rainfall Runoff Model ◽  
Main Components ◽  
Runoff Model

This paper introduces a new rainfall runoff model called ERM (Effective Rainfall routed by Muskingum method), which has been developed based on the popular IHACRES model. The IHACRES model consists of two main components to transfer rainfall to effective rainfall and then to streamflow. The second component of the IHACRES model is a linear unit hydrograph which has been replaced by the classic and well-known Muskingum method in the ERM model. With the effective rainfall by the first component of the IHACRES model, the Muskingum method is used to estimate the quick flow and slow flow separately. Two different sets of input data (temperature or evapotranspiration, rainfall and observed streamflow) and genetic algorithm (GA) as an optimization scheme have been selected to compare the performance of IHACRES and ERM models in calibration and validation. By testing the models in three different catchments, it is found that the ERM model has better performance over the IHACRES model across all three catchments in both calibration and validation. Further studies are needed to apply the ERM on a wide range of catchments to find its strengths and weaknesses.

scholarly journals A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments

2011 ◽  
Vol 59 (3) ◽  
pp. 145-156 ◽  
Author(s):  
Marco Vinagre ◽  
Claudio Blanco ◽  
André Amarante Mesquita
Keyword(s):  
Frequency Distribution ◽  
Power Plants ◽  
Gain Factor ◽  
Performance Criteria ◽  
Rainfall Runoff ◽  
Process Data ◽  
Non Linear ◽  
Flow Frequency ◽  
Rainfall Runoff Model ◽  
Runoff Model

A Non-Linear Rainfall-Runoff Model with a Sigmoid Gain Factor to Simulate Flow Frequency Distribution Curves for Amazon Catchments The objective of this paper is to simulate flow frequency distribution curves for Amazon catchments with the aim of scaling power generation from small hydroelectric power plants. Thus, a simple nonlinear rainfall-runoff model was developed with sigmoid-variable gain factor due to the moisture status of the catchment, which depends on infiltration, and is considered a factor responsible for the nonlinearity of the rainfall-runoff process. Data for a catchment in the Amazon was used to calibrate and validate the model. The performance criteria adopted were the Nash-Sutcliffe coefficient (R2), the RMS, the Q95% frequencyc flow percentage error, and the mean percentage errors ranging from Q5% to Q95%.. Calibration and validation showed that the model satisfactorily simulates the flow frequency distribution curves. In order to find the shortest period of rainfall-runoff data, which is required for applying the model, a sensitivity analysis was performed whereby rainfall and runoff data was successively reduced by 1 year until a 1.5-year model application minimum period was found. This corresponds to one hydrological year plus the 6-month long "memory". This analysis evaluates field work in the ungauged sites of the region.

Sign in / Sign up

Export Citation Format

Share Document