Bivariate Return Period for Design Hyetograph and Relationship with T-Year Design Flood Peak

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions.

Section: Introductionmentioning

confidence: 99%

Application of Copula Functions for Rainfall Interception Modelling

Bezak

Zabret

Šraj

2018

“…Sub-index "a" stands for Ésera River and "b" stands for Isábena River The statistical analysis of each variable has been developed employing three usual distribution functions: GEV, GPD and LPIII. Equations (11)- (13).…”

Section: Distributions Parameters Estimationmentioning

confidence: 99%

Synthetic Hydrographs Generation Downstream of a River Junction Using a Copula Approach for Hydrological Risk Assessment in Large Dams

Aranda

García-Bartual

2018

Peak flows values (Q) and hydrograph volumes (V) are obtained from a selected family of historical flood events (period 1957-2017), for two neighboring mountain catchments located in the Ebro river basin, Spain: rivers Ésera and Isábena. Barasona dam is located downstream of the river junction. The peaks over threshold (POT) method is used for a univariate frequency analysis performed for both variables, Q and V, comparing several suitable distribution functions. Extreme value copulas families have been applied to model the bivariate distribution (Q, V) for each of the rivers. Several goodness-of-fit tests were used to assess the applicability of the selected copulas. A similar copula approach was carried out to model the dependence between peak flows of both rivers. Based on the above-mentioned statistical analysis, a Monte Carlo simulation of synthetic design flood hydrographs (DFH) downstream of the river junction is performed. A gamma-type theoretical pattern is assumed for partial hydrographs. The resulting synthetic hydrographs at the Barasona reservoir are finally obtained accounting for flow peak time lag, also described in statistical terms. A 50,000 hydrographs ensemble was generated, preserving statistical properties of marginal distributions as well as statistical dependence between variables. The proposed method provides an efficient and practical modeling framework for the hydrological risk assessment of the dam, improving the basis for the optimal management of such infrastructure. flood hydrographs and, in particular, the frequency analysis of maximum peaks and volumes of hydrographs for a given river or hydrological system. Among others, the works of [8-13] stand out.This type of approach is very appropriate to assess and quantify the hydrological risk of large dams, since it allows the synthetic generation of maximum hydrographs with infinite combinations of flow and volume (Q, V). The subsequent application of dam routing processes allow to translate that hydrological input into hydrological risk variables for the infrastructure, which basically depend on the maximum level reached in the reservoir during the flood event, as well as on the maximum released flow during the event [4,6,14].When the dam is located downstream of the river junction of two rivers where gauge stations exist, the problem becomes significantly more complicated since the corresponding flow peaks do not generally occur at the same time. This problem has been researched by several authors in the past [15][16][17][18][19][20].The statistical analysis of peak flow time lag is a central matter in this case of river junction. Although hydrograph volumes are essentially additive, it is obviously not the case when it comes to flow peaks. In order to generate synthetic hydrographs at the desired point, it becomes necessary to incorporate time lags in a suitable way and assume a theoretical time pattern which allows to superimpose both hydrographs in time.The present study describes and applies a method for the synthetic generation ...

“…Precipitation is one of the major factors affecting the global water cycle and water balance. Heavy precipitation will cause floods, landslides and other disasters and pose a serious threat to people's safety and national property [1][2][3]. Precipitation has significant variability in different spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model

Guo

Wang²,

Zhang

et al. 2019

Highly accurate and high-quality precipitation products that can act as substitutes for ground precipitation observations have important significance for research development in the meteorology and hydrology of river basins. In this paper, statistical analysis methods were employed to quantitatively assess the usage accuracy of three precipitation products, China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), next-generation Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), for the Jinsha River Basin, a region characterized by a large spatial scale and complex terrain. The results of statistical analysis show that the three kinds of data have relatively high accuracy on the average grid scale and the correlation coefficients are all greater than 0.8 (CMADS:0.86, IMERG:0.88 and TMPA:0.81). The performance in the average grid scale is superior than that in grid scale. (CMADS: 0.86(basin), 0.6 (grid); IMERG:0.88 (basin),0.71(grid); TMPA:0.81(basin),0.42(grid)). According to the results of hydrological applicability analysis based on SWAT model, the three kinds of data fail to obtain higher accuracy on hydrological simulation. CMADS performs best (NSE:0.55), followed by TMPA (NSE:0.50) and IMERG (NSE:0.45) in the last. On the whole, the three types of satellite precipitation data have high accuracy on statistical analysis and average accuracy on hydrological simulation in the Jinsha River Basin, which have certain hydrological application potential.