scholarly journals Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes

2010 ◽  
Author(s):  
N. Bulygina ◽  
N. McIntyre ◽  
C. Ballard ◽  
H. Wheater
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Bayesian Conditioning ◽  
Runoff Model

scholarly journals Use of the KINFIL rainfall-runoff model on the Hukava catchment

2009 ◽  
Vol 4 (No. 1) ◽  
pp. 1-9
Author(s):  
P. Kovář ◽  
V. Kadlec
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Practical Implementation ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Event Duration ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Runoff Events ◽  
Small Catchments

The paper reports on the flood events on the forested Hukava catchment. It describes practical implementation of the KINFIL rainfall-runoff model. This model has been used for the reconstruction of the rainfall-runoff events and thus for the calibration of its parameters. The model was subsequently used to simulate the design discharges with an event duration of t<sub>d</sub> = 30, 60, and 300 min in the period of recurrence of 100 years, and during the scenario simulations of the land use change when 40% and 80% of the forest in the catchment had been cleared out and then replaced by permanent grasslands. The implementation of the KINFIL model supported by GIS proved to be a proper method for the flood runoff assessment on small catchments, during which different scenarios of the land use changes were tested.

scholarly journals A Physically Based Runoff Model Analysis of the Querétaro River Basin

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Carlos Javier Villa Alvarado ◽  
Eladio Delgadillo-Ruiz ◽  
Carlos Alberto Mastachi-Loza ◽  
Enrique González-Sosa ◽  
Ramos Salinas Norma Maricela
Keyword(s):  
Land Use ◽  
Water Balance ◽  
River Basin ◽  
Land Use Changes ◽  
Geographical Location ◽  
Physical Parameters ◽  
Rainfall Runoff ◽  
Strong Base ◽  
Physically Based ◽  
Runoff Model

Today the knowledge of physical parameters of a basin is essential to know adequately the rainfall-runoff process; it is well known that the specific characteristics of each basin such as temperature, geographical location, and elevation above sea level affect the maximum discharge and the basin time response. In this paper a physically based model has been applied, to analyze water balance by evaluating the volume rainfall-runoff using SHETRAN and hydrometric data measurements in 2003. The results have been compared with five ETp different methodologies in the Querétaro river basin in central Mexico. With these results the main effort of the authorities should be directed to better control of land-use changes and to working permanently in the analysis of the related parameters, which will have a similar behavior to changes currently being introduced and presented in observed values in this basin. This methodology can be a strong base for sustainable water management in a basin, the prognosis and effect of land-use changes, and availability of water and also can be used to determine application of known basin parameters, basically depending on land-use, land-use changes, and climatological database to determine the water balance in a basin.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2007 ◽  
Vol 11 (2) ◽  
pp. 703-710 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Climate Statistics ◽  
Runoff Model ◽  
German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

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.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2016 ◽  
Vol 20 (2) ◽  
pp. 887-901 ◽  
Author(s):  
Susana Almeida ◽  
Nataliya Le Vine ◽  
Neil McIntyre ◽  
Thorsten Wagener ◽  
Wouter 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 misinformative.

Investigating land use and land cover changes in Dublin, Ireland using Satellite Imagery: A comparative analysis

2020 ◽  
Author(s):  
Bidroha Basu ◽  
Arunima Sarkar Basu ◽  
Srikanta Sannigrahi ◽  
Francesco Pilla
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Population Growth ◽  
Satellite Imagery ◽  
Rainfall Runoff ◽  
Lulc Changes ◽  
The Past ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Over Time

&lt;p&gt;Over the past few decades, there has been over increasing pressure on land due to population growth, urbanization, agriculture expansion and industrialization. The change in land use and land cover (LULC) pattern are highly dependent on human intervention. Deforestation pattern has started due to growth of suburbs, cities, and industrial land. The alarming rate in change of LULC pattern was on a rising trend since 1990s and has been increasing over time. This study focuses on analyzing the changes in LULC pattern in Dublin, Ireland over the past two decades using remotely sensed LANDSAT satellite imagery data, and quantify the effect of LULC change in streamflow simulation in watershed at Dublin by using rainfall-runoff model. Benefit of using remotely sensed image to investigate LULC changes include availability of high-resolution spatial data at free of cost, images captured at high temporal resolution to monitor the changes in LULC during both seasonal and yearly timescale and readily availability of data. The potential classification of landforms has been done by performing both supervised as well as unsupervised classification. The results obtained from the classified images have been compared to google earth images to understand the accuracy of the image classification. The change in LULC can be characterized by changes in building density and urban/artificial area (build up areas increase due to population growth), changes in vegetation area as well as vegetation health, changes in waterbodies and barren land. Furthermore, a set of indices such as vegetation index, building index, water index and drought index were estimated, and their changes were monitored over time. Results of this analysis can be used to understand the driving factors affecting the changes in LULC and to develop mathematical models to predict future changes in landforms. Soil Water Assessment Tool (SWAT) based rainfall-runoff model were used to simulate the changes in runoff due to the LULC changes in watershed over two decades. The developed framework is highly replicable because of the used LANDSAT data and can be applied to generate essential information for conservation and management of green/forest lands, as well as changes in water availability and water stress in the assessed area.&lt;/p&gt;

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