Parameters determination of a conceptual rainfall-runoff model for a small catchment in Carpathians

Abstract Parameters determination of a conceptual rainfall-runoff model for a small catchment in Carpathians. One of the most important tasks in hydrology is to simulate and forecast hydrologic processes and variables. To achieve this, various linear and nonlinear hydrologic models were developed. One of the most commonly applied rainfall-runoff models is the Nash’s model of the Instantaneous Unit Hydrograph (IUH) (Nash, 1957) used jointly with the CN-NRCS method. Within this paper, the Nash’s model was applied to a small forested basin (Vištucký Creek, Slovakia) to reconstruct rainfall-runoff events based on the recorded precipitation. The Vištucký Creek catchment, located in the Little Carpathians, is a part of the flood protection management of regional sites in the Little Carpathians. Therefore, the object of this paper is, first, to determine the parameters of a conceptual rainfall-runoff model for the Vištucký creek catchment, second, to analyse how the selected characteristics of the model depend on the rainfall characteristics, and third, to compare obtained results with a similar study of Sikorska and Banasik (2010). The computer programme developed at the Dept. of Water Engineering (WULS-SGGW) was used to obtain the rainfall-runoff characteristics based on the Nash´s model. The derived characteristics were parameters of the Nash’s model (N, k, lag time) and rainfall-runoff characteristics (sum of total and effective precipitation, rainfall duration, runoff coefficient, time to IUH peak, value of IUH peak, goodness of fit). A relatively small effective precipitation from the rainfall events was derived. For the purpose of the analysis, a correlation between the lag time (and k parameter) and the sum of the total and effective precipitation was used. The use of the conceptual rainfall-runoff model (Nash´s model) for the small catchment in Carpathians was proved to give satisfactory results. The rainfall characteristics derived in this study are comparable to the results obtained by Spál et. al (2011), who used the same catchment in their analysis. Interestingly, our analysis indicated that there is a correlation between the rainfall duration and the lag time, what is opposite to the compared results of Sikorska and Banasik (2010).

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽

10.3390/w13091226 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1226

Author(s):

Pakorn Ditthakit ◽

Sirimon Pinthong ◽

Nureehan Salaeh ◽

Fadilah Binnui ◽

Laksanara Khwanchum ◽

...

Keyword(s):

Exchange Rate ◽

Rainfall Runoff ◽

Management Planning ◽

Runoff Variation ◽

Groundwater Exchange ◽

Monthly Runoff ◽

Rainfall Runoff Model ◽

Two Parameters ◽

Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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Using NDII pattern for a semi-distributed rainfall-runoff model in tropical nested catchments

10.5194/hess-2020-82 ◽

2020 ◽

Author(s):

Nutchanart Sriwongsitanon ◽

Wasana Jandang ◽

Thienchart Suwawong ◽

Hubert H.~G. Savenije

Keyword(s):

Spatial Distribution ◽

Root Zone ◽

Lag Time ◽

Model Parameters ◽

Model Structure ◽

Rainfall Runoff ◽

Moisture Storage ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Better Than

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 10 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, before adding to runoff of the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at P.1; 2) the results of the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better performance than URBS, but a bit lower than the calibrated FLEXL model with NSE of 0.74, 0.71, and 0.76, respectively. Subsequently, at the level of the gauged internal sub-catchments, runoff estimates of FLEX-SD were compared to observations and calibrated FLEXL model results. The results demonstrate that FLEX-SD provides more accurate runoff estimates at P.1, P.67 and P.75 stations which are located along the main Ping River, compared to those provided by the lumped calibrated FLEXL model. The results were less good at 2 tributary stations (P.20 and P.21), where calibrated FLEXL output performed better, while performance was similar at one tributary station (P.4A). Overall, FLEX-SD performed better than URBS at 5 out of 6 stations except at P.21. Subsequently, the effect of distributing moisture storage capacity was tested. Since the FLEX-SD uses the same Sumax value - the maximum moisture holding capacity of the root zone - for all sub-catchments, FLEX-SD-NDII was set-up making use of the spatial distribution of the NDII (the normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone, particularly during dry periods. The maximum moisture holding capacity in the root zone assumed to be a function of the maximum seasonal range of NDII values. The spatial distribution of this range among sub-catchments was used to calibrate the semi-distributed FLEX-SD-NDII model. The additional constraint by the NDII improved the performance of the model and the realism of the distribution. To test how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for verification.

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A non-linear rainfall-runoff model, providing for variable lag time

Journal of Hydrology ◽

10.1016/0022-1694(75)90027-x ◽

1975 ◽

Vol 25 (3-4) ◽

pp. 295-305 ◽

Cited By ~ 11

Author(s):

D.W. Reed ◽

P. Johnson ◽

J.M. Firth

Keyword(s):

Lag Time ◽

Rainfall Runoff ◽

Non Linear ◽

Rainfall Runoff Model ◽

Runoff Model

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Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model

Journal of Hydroinformatics ◽

10.2166/hydro.1999.0009 ◽

1999 ◽

Vol 1 (2) ◽

pp. 103-114 ◽

Cited By ~ 35

Author(s):

Robert J. Abrahart ◽

Linda See ◽

Pauline E. Kneale

Keyword(s):

Neural Network ◽

Genetic Algorithms ◽

Design Tool ◽

Forecasting Model ◽

Network Architectures ◽

Rainfall Runoff ◽

Small Catchment ◽

Pruning Algorithms ◽

Rainfall Runoff Model ◽

Runoff Model

Four design tool procedures are examined to create improved neural network architectures for forecasting runoff from a small catchment. Different algorithms are used to remove nodes and connections so as to produce an optimised forecasting model, thereby reducing computational expense without loss in performance. The results also highlight issues in selecting analytical methods to compare outputs from different forecasting procedures.

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Using NDII patterns to constrain semi-distributed rainfall-runoff models in tropical nested catchments

10.5194/hess-2021-598 ◽

2021 ◽

Author(s):

Nutchanart Sriwongsitanon ◽

Wasana Jandang ◽

Thienchart Suwawong ◽

Hubert H. G. Savenije

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Lag Time ◽

Model Parameters ◽

Model Structure ◽

Rainfall Runoff ◽

Annual Average ◽

Moisture Storage ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 32 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at the internal stations; 2) the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better or similar performance both during calibration and especially in validation. Subsequently, we tried to distribute the moisture storage capacity by constraining FLEX-SD on patterns of the NDII (normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone during dry periods. The maximum moisture holding capacity in the root zone is assumed to be a function of the maximum seasonal range of NDII values, and the annual average NDII values to construct 2 alternative models: FLEX-SD-NDIIMax-Min and FLEX-SD-NDIIAvg, respectively. The additional constraint on the moisture holding capacity by the NDII improved both model performance and the realism of the distribution. Distribution of Sumax using annual average NDII values was found to be well correlated with the percentage of evergreen forest in 31 sub-catchments. Spatial average NDII values were proved to be highly corresponded with the root zone soil moisture of the river basin, not only in the dry season but also in the water limited ecosystem. To check how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for validation.

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Digital Elevation Hydrological Modelling in a Small Catchment in South Australia

Hydrology Research ◽

10.2166/nh.2003.0002 ◽

2003 ◽

Vol 34 (3) ◽

pp. 161-178

Author(s):

H. Sun ◽

P. S. Cornish ◽

T. M. Daniell

Keyword(s):

Soil Moisture ◽

Spatial Data ◽

South Australia ◽

Moisture Distribution ◽

Rainfall Runoff ◽

Estimation Errors ◽

Small Catchment ◽

Digital Elevation ◽

Rainfall Runoff Model ◽

Runoff Model

A rainfall runoff model based on a digital elevation model (DEM) was applied to a small catchment in Happy Valley, South Australia to predict catchment storm runoff. The DEM was used to partition the catchment into several thousand irregular shaped elements. These elements, with an average size of 825 m2 each, form an interconnected one-dimensional flow network for runoff routing. The rainfall runoff model is a kinematic flow model which combines the solving of flow continuity equation and the Manning's equation to generate surface and subsurface runoff. This study improves on the existing rainfall runoff model in several areas. It adds spatial rainfall averaging methods to derive spatial rainfalls for catchment modelling; and it improves the catchment soil moisture representation by developing a boundary wetness index, and relates this index to antecedent catchment flow to derive spatial catchment moisture distribution. Improved runoff predictions were obtained as a result of the improvement in spatial data input and spatial soil moisture representation. The study identifies these improvements as the key areas for better runoff prediction. It demonstrates that where prediction results showed larger than expected variance, it is frequently caused by the inability to derive good spatially distributed input data rather than parameter estimation errors.

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in The Southern Basin of Thailand

10.20944/preprints202101.0605.v1 ◽

2021 ◽

Author(s):

Pakorn Ditthakit ◽

Sirimon Pinthong ◽

Nureehan Salaeh ◽

Fadilah Binnui ◽

Laksanara Khwanchum

Keyword(s):

East Coast ◽

Rainfall Runoff ◽

Management Planning ◽

Resources Management ◽

Runoff Variation ◽

Monthly Runoff ◽

Rainfall Runoff Model ◽

Two Parameters ◽

Runoff Model

Accurate monthly runoff estimation is fundamental in water resources management, planning, and development, resulting in preventing and reducing water-related problems, such as flooding and drought. This article evaluates the performance of the monthly hydrological rainfall-runoff model, GR2M model, in Thailand's southern basins. The GR2M model requires only two parameters, and no prior research has been reported on its application in this region. The 37 runoff stations, which are distributively located in three sub-watersheds of Thailand's southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. Thornthwaite method was utilized for the determination of evapotranspiration. The model's performance was conducted using three statistical indices: Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OPI). The model's calibration results for 37 runoff stations gave the average of NSE, r, and OPI of 0.637, 0.825, and 0.757, and those values for verification of 0.465, 0.750, and 0.639, respectively. It indicated a model's acceptable performance and could apply the GR2M model for determining monthly runoff variation in this region. The spatial distribution of X1 and X2 values was conducted by using IDW method. It was susceptible to the X1 value and X2 value of approximately more than 0.90 gave the higher model's performance.

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Determination of evaporation from a catchment water balance at a monthly time scale

Hydrology and Earth System Sciences ◽

10.5194/hess-1-93-1997 ◽

1997 ◽

Vol 1 (1) ◽

pp. 93-100 ◽

Cited By ~ 22

Author(s):

H. H. G. Savenije

Keyword(s):

Water Balance ◽

Time Scale ◽

Climate Models ◽

Rainfall Runoff ◽

Soil Moisture Storage ◽

Moisture Storage ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Term Storage

Abstract. A method is presented to determine total evaporation from the earth's surface at a spatial scale that is adequate for linkage with climate models. The method is based on the water balance of catchments, combined with a calibrated autoregressive rainfall-runoff model. The time scale used is in the order of decades (10 days) to months. The rainfall-runoff model makes a distinction between immediate processes (interception and short term storage) and the remaining longer-term processes. Besides the calibrated rainfall-runoff model and the time series of observed rainfall and runoff, the method requires a relation between transpiration and soil moisture storage. The method is applied to data of the Bani catchment in Mali, a sub-catchment of the Niger river basin.

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