scholarly journals Rainfall-runoff simulations in the Lukovska River Basin with the HEC-HMS model

2019 ◽  
pp. 33-60
Author(s):  
Ranka Eric ◽  
Andrijana Todorovic ◽  
Jasna Plavsic ◽  
Vesna Djukic
Keyword(s):  
River Basin ◽  
Optimal Parameter ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Hydrologic Models ◽  
Generation Mechanisms ◽  
Event Based

Hydrologic models are important for effective water resources management at a basin level. This paper describes an application of the HEC-HMS hydrologic model for simulations of flood hydrographs in the Lukovska River basin. Five flood events observed at the Mercez stream gauge were available for modelling purposes. These events are from two distinct periods and two seasons with different prevailing runoff generation mechanisms. Hence the events are assigned to either ?present? or ?past?, and ?spring? or ?summer? group. The optimal parameter sets of each group are obtained by averaging the optimal parameters for individual events within the group. To assess model transferability, its applicability for simulation of flood events which are not considered in the model calibration, a cross-validation is performed. The results indicate that model parameters vary across the events, and that parameter transfer generally leads to considerable errors in hydrograph peaks and volumes, with the exception of simulation of summer events with ?spring? parameters. Based on these results, recommendations for event-based modeling are given.

scholarly journals Integration and calibration of a conceptual rainfall-runoff model in the framework of a decision support system for river basin management

2005 ◽  
Vol 5 ◽  
pp. 31-35 ◽  
Author(s):  
J. Götzinger ◽  
A. Bárdossy
Keyword(s):  
Groundwater Recharge ◽  
River Basin ◽  
Transfer Functions ◽  
Hydrologic Model ◽  
Base Flow ◽  
Flood Routing ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Water balance models provide significant input to integrated models that are used to simulate river basin processes. However, one of the primary problems involves the coupling and simultaneous calibration of rainfall-runoff and groundwater models. This problem manifests itself through circular arguments - the hydrologic model is modified to calculate highly discretized groundwater recharge rates as input to the groundwater model which provides modeled base flow for the flood-routing module of the rainfall-runoff model. A possibility to overcome this problem using a modified version of the HBV Model is presented in this paper. Regionalisation and optimization methods lead to objective and efficient calibration despite large numbers of parameters. The representation of model parameters by transfer functions of catchment characteristics enables consistent parameter estimation. By establishing such relationships, models are calibrated for the parameters of the transfer functions instead of the model parameters themselves. Simulated annealing, using weighted Nash-Sutcliffe-coefficients of variable temporal aggregation, assists in efficient parameterisations. The simulations are compared to observed discharge and groundwater recharge modeled by the State Institute for Environmental Protection Baden-Württemberg using the model TRAIN-GWN.

Understanding the role of different geographical features in the hydrological response of humid mountainous areas through a stepwise clustering scheme

2021 ◽  
Author(s):  
Yaqian Yang ◽  
Jintao Liu
Keyword(s):  
Bulk Density ◽  
Optimal Parameter ◽  
Soil Bulk Density ◽  
Model Parameters ◽  
Hydrological Response ◽  
Runoff Generation ◽  
Explanatory Variables ◽  
Hydrological Responses ◽  
Rainfall Patterns

<p>In the mountainous basins with less anthropogenic influence, the hydrological function is mainly affected by climate and landscape, which makes it possible to measure hydrological similarity indirectly by geographical features. Due to the mechanisms of runoff generation can vary geographically, in this study, a simple stepwise clustering scheme was proposed to explore the role of geographical features at different spatial hierarchy in indicating hydrological response. Research methods mainly include (1) Stepwise regression was used to quantitatively show the correlation between 35 geographical features and 35 flow features and identify the important explanatory variables for hydrological response; (2) 64 basins were divided by stepwise clustering scheme, and the overall ability of the scheme to capture hydrological similarity was tested by comparing the optimal parameters; (3) The hydrological similarity of basin groups was measured by the leave-one cross validation of hydrological model parameters. The results showed that: (1) Rainfall features, elevation, slope and soil bulk density are the main explanatory variables. (2) The NSE of basin groups based on stepwise clustering is 0.64, reaches 80% of the optimal parameter sets (NSE=0.80). The NSE of 90% basins is greater than 0.5, 80% is greater than 0.6, and 49% is greater than 0.7. (3) In humid areas, the hydrological responses of the basins with more uniform monthly rainfall and more abundant summer rainfall are more similar, e.g., the NSE of Class 4 is 0.77. Under similar rainfall patterns, the hydrological responses of the basins with higher average altitude, greater slope, more convergent of shape and richer vegetation are more similar, e.g., the NSE of Class 3-2 is 0.72 and that of Class 1-2 is 0.70. In the case of similar rainfall patterns and landforms, the hydrological responses of the basins with smaller soil bulk density are more similar, e.g., the NSE of Class 3-2-2 is 0.80. In conclusion, the stepwise clustering enhances the interpretability of basin classification, and the effect of different geographical features on hydrological response can show the applicability of hydrological simulation in ungauged basins.</p>

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

Integrated numerical model for irrigated area water resources management

2019 ◽  
Vol 11 (4) ◽  
pp. 980-991 ◽  
Author(s):  
Aidi Huo ◽  
Xiaofan Wang ◽  
Yan Liang ◽  
Cheng Jiang ◽  
Xiaolu Zheng
Keyword(s):  
River Basin ◽  
Water Resource Management ◽  
Assessment Tool ◽  
Swat Model ◽  
Hydrologic Model ◽  
Heihe River Basin ◽  
Irrigation District ◽  
Heihe River ◽  
Model Parameters ◽  
Long Term Effects

Abstract The likelihood of future global water shortages is increasing and further development of existing operational hydrologic models is needed to maintain sustainable development of the ecological environment and human health. In order to quantitatively describe the water balance factors and transformation relations, the objective of this article is to develop a distributed hydrologic model that is capable of simulating the surface water (SW) and groundwater (GW) in irrigation areas. The model can be used as a tool for evaluating the long-term effects of water resource management. By coupling the Soil and Water Assessment Tool (SWAT) and MODFLOW models, a comprehensive hydrological model integrating SW and GW is constructed. The hydrologic response units for the SWAT model are exchanged with cells in the MODFLOW model. Taking the Heihe River Basin as the study area, 10 years of historical data are used to conduct an extensive sensitivity analysis on model parameters. The developed model is run for a 40-year prediction period. The application of the developed coupling model shows that since the construction of the Heihe reservoir, the average GW level in the study area has declined by 6.05 m. The model can accurately simulate and predict the dynamic changes in SW and GW in the downstream irrigation area of Heihe River Basin and provide a scientific basis for water management in an irrigation district.

scholarly journals Analysis of flood peak scaling in mesoscale non-nested basin

2019 ◽  
Vol 20 (2) ◽  
pp. 416-427 ◽  
Author(s):  
Jianzhu Li ◽  
Keke Zhou ◽  
Ting Zhang ◽  
Qiushuang Ma ◽  
Ping Feng
Keyword(s):  
River Basin ◽  
Change Point ◽  
Scale Invariance ◽  
Scaling Theory ◽  
Scaling Exponent ◽  
Flood Events ◽  
Scaling Exponents ◽  
Event Based ◽  
Invariance Theory ◽  
Basin Area

Abstract The study of flood scaling is an important means to solve the problem of flood prediction in ungauged and poorly gauged basins. With the impact of climate change and human activities, the mechanism and process of floods are constantly changing. However, in many areas, there are only simple scaling results that can be used to guide daily work. Taking the Daqinghe River basin as an example, a fixed flood scaling exponent determined in 1974 (before the change point of 1979) is still used all over the basin, which is apparently no longer appropriate. Therefore, in this paper, we aim to explore: (1) the scale relationship between the peak flows and the basin area under changing environments; (2) the validation of the scale invariance theory; (3) the physical relationship between the event-based scaling theory and the annual flood quantile-based scaling theory in the mesoscale non-nested and partly nested basins; and (4) the modification of the existing uniform flood scaling exponent in the study area. To achieve these objectives, eight simultaneous observed flood events in seven non-nested and partly nested mesoscale sub-basins of the Daqinghe River basin were selected to analyze the flood scaling theory. The results showed that there was a scaling relationship between the flood peaks and watershed area for the flood events, and the scale invariance theory was also supported herein. To analyze the effect of the environmental conditions on flood scaling in the Daqinghe River basin, the flood events were reconstructed after the change point (the year 1979). It was found that the flood scaling exponents of the reconstructed flood events are larger than those of the observed events after the change point. The flood scaling exponent changed with flood events, varying from 0.65 to 1.26 when considering the basin area as the independent variable, and decreasing with a minimum of 0.36 when taking the rainfall characteristics into consideration. It was also found that the mean of the event-based scaling exponents is larger than the annual flood quantile-based scaling exponents.

Ingredients of German flood events

2020 ◽  
Author(s):  
Ralf Merz ◽  
Larisa Tarasova ◽  
Stefano Basso
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Rainfall Intensity ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Regional Variability ◽  
Large Variability ◽  
Runoff Events

<p>Floods can be caused by a large variety of different processes, such as short, but intense rainfall bursts, long rainfall events, which are wetting up substantial parts of the catchment, or rain on snow cover or frozen soils. Although there is a plethora on studies analysing or modelling rainfall-runoff processes, it is still not well understood, what rainfall and runoff generation conditions are needed to generate flood runoff and how these characteristics vary between catchments. In this databased approach we decipher the ingredients of flood events occurred in 161 catchments across Germany. For each catchment rainfall-runoff events are separated from observed time series for the period 1950-2013, resulting in about 170,000 single events. A peak-over-threshold approach is used to select flood events out of these runoff events. For each event, spatially and temporally distributed rainfall and runoff generation characteristics, such as snow cover and soil moisture, as well as their interaction are derived. Then we decipher those event characteristics controlling flood event occurrence by using machine learning techniques.</p><p>On average, the most important event characteristic controlling flood occurrence in Germany is, as expected, event rainfall volume, followed by the overlap of rainfall and soil moisture and the extent of wet areas in the catchment (area with high soil moisture content). Rainfall intensity is another important characteristic. However, a large variability in its importance is noticeable between dryer catchments where short rainfall floods occur regularly and wetter catchments, where rainfall intensity might be less important for flood generation. To analyse the regional variability of flood ingredients, we cluster the catchments according to similarity in their flood controlling event characteristics and test how good the flood occurrence can be predicted from regionalised event characteristics. Finally, we analyse the regional variability of the flood ingredients in the light of climate and landscape catchment characteristics.</p>

scholarly journals Spatial Combination Modeling Framework of Saturation-Excess and Infiltration-Excess Runoff for Semihumid Watersheds

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Pengnian Huang ◽  
Zhijia Li ◽  
Cheng Yao ◽  
Qiaoling Li ◽  
Meichun Yan
Keyword(s):  
Difficult Problem ◽  
State Variables ◽  
Runoff Generation ◽  
Modeling Framework ◽  
Hydrological Simulation ◽  
Infiltration Excess ◽  
Generation Mechanisms ◽  
Event Based ◽  
Parameter Values ◽  
Saturation Excess

There exist two types of direct runoff generation mechanisms in semihumid watersheds: saturation-excess mechanism and infiltration-excess mechanism. It has always been a difficult problem for event hydrological simulation to distinguish the two types of runoff processes. Based on the concept of dominant runoff processes, combined with GIS and RS techniques, this paper proposed an event-based spatial combination modeling framework and built two spatial combination models (SCMs) accordingly. The CN parameter and topographic index, both of which are widely used in hydrological researches, are adopted by the SCM to divide the entire watershed into infiltration-excess dominated (IED) areas and saturation-excess dominated (SED) areas. Dongwan watershed was taken as an example to test the performances of infiltration-excess model, saturation-excess model, and SCM, respectively. The results of parameter optimization showed that the parameter values and state variables of SCM are much more realistic than those of infiltration-excess model and saturation-excess model. The more accurate the divisions of infiltration-excess and saturation-excess dominated areas, the more realistic the SCM parameter values. The simulation results showed that the performance of SCM was improved in both calibration and validation periods. The framework is useful for flood forecasting in semihumid watersheds.

scholarly journals Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation

2011 ◽  
Vol 11 (1) ◽  
pp. 157-170 ◽  
Author(s):  
Y. Tramblay ◽  
C. Bouvier ◽  
P.-A. Ayral ◽  
A. Marchandise
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Radar Data ◽  
Rain Gauge ◽  
Production Model ◽  
Initial Condition ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Moisture Index ◽  
Event Based

Abstract. A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2) in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.

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