Effects of temporal resolution on hydrological model parameters and its impact on prediction of river discharge / Effets de la résolution temporelle sur les paramètres d'un modèle hydrologique et impact sur la prévision de l'écoulement en rivière

Abstract

Temporal resolution of rainfall plays an important role in determining the hydrological response of river basins. Rainfall temporal variability can be considered as one of the most critical elements when dealing with input data of rainfall—runoff models. In this paper, a typical lumped rainfall—runoff model is applied to long- and short-term runoff prediction using rainfall data sets with different temporal resolution, including daily, hourly and 10-min interval data, and the dependency of model performance on the time interval of the rainfall data is discussed. Furthermore, the effect of temporal resolution on model parameter values is analysed. As results, rainfall data with shorter temporal resolution provide better performance in short-term river discharge estimation, especially for storm discharge estimation. The most accurate results are obtained on the peak discharge and recession part of the hydrograph by using 10-min interval rainfall data. It is concluded that model parameter values are influenced not only by the temporal resolution of calculation but also by the rainfall intensity—duration relationship. This study provides useful information about determination of hydrological model parameters using data of different temporal resolutions.     

Simulating temporal variability in catchment response using a monthly rainfall–runoff model

Abstract

Temporal variability can result from shifts in climate, or from changes in the runoff response due to land- or water-use changes, and represents a potential source of uncertainty in calibrating hydrological models. Parameter values were determined using Monte Carlo parameter sampling methods for a monthly rainfall–runoff model (Pitman model) for different sub-periods on four catchments, with different types and degrees of temporal variability, in Australia and Africa. For some catchments, parameters were not dependent upon the sub-period used and fell within expected ranges given the relatively high degree of model equifinality. In other catchments, dependencies can be identified that are associated with signals contained within the sub-periods. While the Pitman model is relatively robust in the face of temporal variability, it is concluded that better simulations will always be obtained from calibration data that include signals representing the total variability in climate, land-use change and catchment responses.     

Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation

Great emphasis is being placed on the use of rainfall intensity data at short time intervals to accurately model the dynamics of modern cropping systems, runoff, erosion and pollutant transport. However, rainfall data are often readily available at more aggregated level of time scale and measurements of rainfall intensity at higher resolution are available only at limited stations. A distribution approach is a good compromise between fine-scale (e.g. sub-daily) models and coarse-scale (e.g. daily) rainfall data, because the use of rainfall intensity distribution could substantially improve hydrological models. In the distribution approach, the cumulative distribution function of rainfall intensity is employed to represent the effect of the within-day temporal variability of rainfall and a disaggregation model (i.e. a model disaggregates time series into sets of higher solution) is used to estimate distribution parameters from the daily average effective precipitation. Scaling problems in hydrologic applications often occur at both space and time dimensions and temporal scaling effects on hydrologic responses may exhibit great spatial variability. Transferring disaggregation model parameter values from one station to an arbitrary position is prone to error, thus a satisfactory alternative is to employ spatial interpolation between stations. This study investigates the spatial interpolation of the probability-based disaggregation model. Rainfall intensity observations are represented as a two-parameter lognormal distribution and methods are developed to estimate distribution parameters from either high-resolution rainfall data or coarse-scale precipitation information such as effective intensity rates. Model parameters are spatially interpolated by kriging to obtain the rainfall intensity distribution when only daily totals are available. The method was applied to 56 pluviometer stations in Western Australia. Two goodness-of-fit statistics were used to evaluate the skill—daily and quantile coefficient of efficiency between simulations and observations. Simulations based on cross-validation show that kriging performed better than other two spatial interpolation approaches (B-splines and thin-plate splines).     

Rainfall downscaling in time: theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades

Abstract

During recent decades, intensive research has focused on techniques capable of generating rainfall time series at a fine time scale that are (fully or partially) consistent with a given series at a coarser time scale. Here we theoretically investigate the consequences on the ensemble statistical behaviour caused by the structure of a simple and widely-used approach of stochastic downscaling for rainfall time series, the discrete Multiplicative Random Cascade. We show that synthetic rainfall time series generated by these cascade models correspond to a stochastic process which is non-stationary, because its temporal autocorrelation structure depends on the position in time in an undesirable manner. Then, we propose and theoretically analyse an alternative downscaling approach based on the Hurst-Kolmogorov process, which is equally simple but is stationary. Finally, we provide Monte Carlo experiments which validate our theoretical results.

Editor Z.W. Kundzewicz

Citation Lombardo, F., Volpi, E., and Koutsoyiannis, D., 2012. Rainfall downscaling in time: theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades. Hydrological Sciences Journal, 57 (6), 1052–1066.     

Temporal variability of throughfall as a function of the canopy development stage: from seasonal to intra-event scale

ABSTRACT

The interception process impacts rainfall magnitude and intensity under the canopy. In this study, the effect of plant interception on throughfall characteristics was assessed in the deciduous Caatinga vegetation, at different canopy development stages and for temporal scales ranging from seasonal to the intra-event scale. Throughfall and stemflow percentages were slightly higher at the onset of the rainy season, when leaf area density is low, with resulting lower interception losses. However, there was no statistical difference among the variables at the seasonal scale. At the intra-event scale, average and maximum throughfall intensity at different time intervals showed statistical difference between the stages of canopy development. Regardless of leaf area density and rainfall depth, vegetation is able to retain all the water up to 2 min in the beginning of each rainfall event with accumulated rainfall smaller than 0.6 mm. Furthermore, the Caatinga vegetation attenuates the rainfall intensity by 30–40%.     

Space–time modelling of catchment scale drought characteristics

Drought may affect all components of the water cycle and covers commonly a large part of the catchment area. This paper examines drought propagation at the catchment scale using spatially aggregated drought characteristics and illustrates the importance of catchment processes in modifying the drought signal in both time and space. Analysis is conducted using monthly time series covering the period 1961–1997 for the Pang catchment, UK. The time series include observed rainfall and groundwater recharge, head and discharge simulated by physically-based soil water and groundwater models. Drought events derived separately for each unit area and variable are combined to yield catchment scale drought characteristics. The study reveals relatively large differences in the spatial and temporal characteristics of drought for the different variables. Meteorological droughts cover frequently the whole catchment; and they are more numerous and last for a short time (1–2 months). In comparison, droughts in recharge and hydraulic head cover typically a smaller area and last longer (4–5 months). Hydraulic head and groundwater discharge exhibit similar drought characteristics, which can be expected in a groundwater fed catchment. Deficit volume is considered a robust measure of the severity of a drought event over the catchment area for all variables; whereas, duration is less sensitive, particular for rainfall. Spatial variability in drought characteristics for groundwater recharge, head and discharge are primarily controlled by catchment properties. It is recommended not to use drought area separately as a measure of drought severity at the catchment scale, rather it should be used in combination with other drought characteristics like duration and deficit volume.     

A class of hidden Markov models for regional average rainfall

Abstract

Basic hidden Markov models are very useful in stochastic environmental research but their ability to accommodate sufficient dependence between observations is somewhat limited. However, they can be modified in several ways to form a rich class of flexible models that are useful in many environmental applications. We consider a class of hidden Markov models that incorporate additional dependence among observations to model average regional rainfall time series. The focus of the study is on models that introduce additional dependence between the state level and the observation level of the process and also on models that incorporate dependence at observation level. Construction of the likelihood function of the models is described along with the usual second-order properties of the process. The maximum likelihood method is used to estimate the parameters of the models. Application of the proposed class of models is illustrated in an analysis of daily regional average rainfall time series from southeast and southwest England for the winter season during 1931 to 2010. Models incorporating additional dependence between the state level and the observation level of the process captured the distributional properties of the daily rainfall well, while the models that incorporate dependence at the observation level showed their ability to reproduce the autocorrelation structure. Changes in some of the regional rainfall properties during the time period are also studied.

Editor D. Koutsoyiannis     

Modelling the effects of spatial and temporal resolution of rainfall and basin model on extreme river discharge

Abstract

Important characteristics of an appropriate river basin model, intended to study the effect of climate change on basin response, are the spatial and temporal resolution of the model and the rainfall input. The effects of input and model resolution on extreme discharge of a large river basin are assessed to give some indication on appropriate resolutions. A simple stochastic rainfall model and a river basin model with uniform parameters and multiple rainfall input have been developed and applied to the River Meuse basin in northwestern Europe. The results show that the effect of model resolution on extreme river discharge is much greater than that of input resolution. The highest model resolution seems to be quite accurate in determining extreme discharge. Although the results should be interpreted with caution, they may give some indication of appropriate input and model resolutions for the determination of extreme discharge of a large river basin.     

Investigation of the model complexity required in runoff simulation at different time scales / Etude de la complexité de modélisation requise pour la simulation d'écoulement à différentes échelles temporelles

Abstract

The “optimal” model complexity is defined as the minimum watershed model structure required for realistic representation of runoff processes. This paper examines the effects of model complexity at different time scales, daily and hourly. Two watershed models with different levels of complexity were constructed and their capability to simulate runoff from a watershed was evaluated. Both models were tested on the same watershed using identical meteorological input, thereby assuring that any difference between model outputs is due only to their model structure. It is demonstrated that, at a daily time scale, a simple model gives good results. For the mountain situation, in which snowmelt is a dominant influence, the nonlinearity of the runoff processes is moderate, and therefore a simple model works well. The model produced good results over a period of 28 years of continuous simulation. However, this simpler model was inadequate when tested on an hourly time scale due to greater nonlinear effects, especially when modelling high-intensity rainfall events. Therefore, the hourly simulation benefited from the more complex model structure. These model results show that optimal watershed model complexity depends on temporal resolution, namely the simulation period and the computational time step. It was shown that certain process representations and model parameters that appeared unimportant during the long-term simulation had significant effects on the short-term extreme event model simulation.     

Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables

Abstract

The development of statistical relationships between local hydroclimates and large-scale atmospheric variables enhances the understanding of hydroclimate variability. The rainfall in the study basin (the Upper Chao Phraya River Basin, Thailand) is influenced by the Indian Ocean and tropical Pacific Ocean atmospheric circulation. Using correlation analysis and cross-validated multiple regression, the large-scale atmospheric variables, such as temperature, pressure and wind, over given regions are identified. The forecasting models using atmospheric predictors show the capability of long-lead forecasting. The modified k-nearest neighbour (k-nn) model, which is developed using the identified predictors to forecast rainfall, and evaluated by likelihood function, shows a long-lead forecast of monsoon rainfall at 7–9 months. The decreasing performance in forecasting dry-season rainfall is found for both short and long lead times. The developed model also presents better performance in forecasting pre-monsoon season rainfall in dry years compared to wet years, and vice versa for monsoon season rainfall.

Editor Z.W. Kundzewicz

Citation Singhrattna, N., Babel, M.S. and Perret, S.R., 2012. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables. Hydrological Sciences Journal, 57 (1), 26–41.     

On the potential of Bartlett Lewis rectangular pulse models for simulating rainfall in Peninsular Malaysia

Abstract

The applicability of two versions of the Bartlett Lewis rectangular pulse model, the original and the modified model, is discussed for describing the temporal and spatial variation of rainfall patterns observed at 15 raingauge stations in Peninsular Malaysia over the period 1971–2008; 17 different sets of moment combinations are fitted to these models based on the generalized method of moments approach. The common statistics included in all sets are the mean, variance, lag-1 autocorrelation and the probability of dry based on the hourly rainfall data. The analysis was carried out on hourly rainfall data from all 15 stations for all months of the year. Two stations, Petaling Jaya and Kemaman, located on the west and east coasts of the Peninsula, respectively, are considered for illustration of the results, taking the months of July and November, which correspond to the driest and wettest months, corresponding to the southwest monsoon (May–August) and northeast monsoon (November–February), respectively. The best moment combination found for the illustrative results is based on the common statistics, as well as the mean and variance based on 24-h aggregated rainfall data, the inclusion of which successfully improved the model performance; the errors were significantly reduced. It was also found that the performance of the fitted models based on the mean absolute deviate error varies according to the type of Bartlett Lewis model applied: errors are much smaller for the fitted model based on the modified model as compared to the original model. In addition, the fitted statistics: mean, lag-1 autocorrelation and probability of dry are quite well fitted for several aggregated time scales; however, the variances are underestimated in both models for all aggregated time scales, particularly in the case of the original model. The results of extreme value analysis indicate that the modified model failed to reproduce the annual hourly and daily rainfall extremes satisfactorily.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Hanaish, I.S., Ibrahim, K., and Jemain, A.A., 2013. On the potential of Bartlett Lewis rectangular pulse models for simulating rainfall in Peninsular Malaysia. Hydrological Sciences Journal, 58 (8), 1690–1703.     

The importance of choosing precipitation datasets

Precipitation data are important for hydrometeorological analyses, yet there are many ways to measure precipitation. The impact of station density analysed by the current study by comparing measurements from the Missouri Mesonet available via the Missouri Climate Center and Community Collaborative Rain, Hail, and Snow (CoCoRaHS) measurements archived at the program website. The CoCoRaHS data utilize citizen scientists to report precipitation data providing for much denser data resolution than available through the Mesonet. Although previous research has shown the reliability of CoCoRaHS data, the results here demonstrate important differences in details of the spatial and temporal distribution of annual precipitation across the state of Missouri using the two data sets. Furthermore, differences in the warm and cold season distributions are presented, some of which may be related to interannual variability such as that associated with the El Niño and Southern Oscillation. The contradictory results from two widely‐used datasets display the importance in properly choosing precipitation data that have vastly differing temporal and spatial resolutions. With significantly different yearly aggregated precipitation values, the authors stress caution in selecting 1 particular rainfall dataset as conclusions drawn could be unrepresentative of the actual values. This issue may be remediated by increased spatiotemporal coverage of precipitation data.     

Towards using pedotransfer functions for estimating infiltration parameters

ABSTRACT

Water infiltration into soils is an important component of hydrological processes. Direct measurement of infiltration is time consuming, expensive and often involves large spatial and temporal variability. The objective of this study was to develop and verify parametric pedotransfer functions (PTFs) to predict infiltration parameters. Consequently, 119 double-ring infiltration data were collected. The parameters of Philip, Kostiakov, Kostiakov-Lewis and Horton models were obtained, using the sum of squares error optimization method. Some parametric PTFs were then derived to predict the parameters of the infiltration models, using stepwise regression analysis. The results indicated a reasonable estimation of infiltration parameters by the derived PTFs. These results were more accurate when the land use of the studied area was considered. Overall results of this study suggest infiltration-based PTFs could be established as a reasonable indirect method for estimating infiltration parameters.

Editor M.C. Acreman; Associate editor N. Verhoest     

A preliminary investigation on the scaling behaviour of rainfall observed in two different climates

Abstract

The problem of transformation of rainfall data from one scale to another has been gaining considerable importance in recent years. Though the application of the concept of fractal theory, in the studies conducted thus far, nearly unanimously points at the possibility of such a transformation, the suitability of the theory to the highly variable rainfall in time and space has very often been questioned. A preliminary attempt is made herein to address this issue by investigating the existence of temporal scaling behaviour in rainfall data observed in two different climatic regions: (a) a subtropical climatic region (Leaf River basin, Mississippi, USA) and (b) an equatorial climatic region (Singapore). Rainfall data of three different resolutions, six-hourly, daily, and weekly, observed over a period of 25 years, are investigated. A mono- or simple-scaling method (box dimension method) is employed. The results achieved for the different data sets clearly indicate the existence of temporal scaling in rainfall observed in the two regions, an encouraging news on the suitability of fractal theory in understanding and modelling the rainfall process. However, the insufficiency of a single dimension to characterize the rainfall behaviour is realized, as the dimension depends on the rainfall intensity level, which, in turn, may be related to the rainfall generating mechanisms. A comparison of the box-dimension results obtained for data of different resolutions, from each of the regions, seems to indicate a possible connection between them, a prospect of tremendous practical importance. Another interesting observation is the similarity between the box dimension results obtained for rainfall data from Leaf River basin and Singapore, but this is also clearly related to the intensity level. The dependence of the dimension on the intensity threshold suggests the use of a multi-dimensional fractal approach, where the process is characterized by more than one dimension (or a dimension function) instead of one single dimension. On the basis of the present results, some potential areas for further study are identified.     

Regional scale spatial and temporal variability of soil moisture in a prairie region

Understanding the dynamics of spatial and temporal variability of soil moisture at the regional scale and daily interval, respectively, has important implications for remote sensing calibration and validation missions as well as environmental modelling applications. The spatial and temporal variability of soil moisture was investigated in an agriculturally dominated region using an in‐situ soil moisture network located in central Saskatchewan, Canada. The study site evaluated three depths (5, 20, 50 cm) through 139 days producing a high spatial and temporal resolution data set, which were analysed using statistical and geostatistical means. Processes affecting standard deviation at the 5‐cm depth were different from the 20‐cm and 50‐cm depths. Deeper soil measurements were well correlated through the field season. Further analysis demonstrated that lag time to maximum correlation between soil depths increased through the field season. Temporal autocorrelation was approximately twice as long at depth compared to surface soil moisture as measured by the e‐folding frequency. Spatial correlation was highest under wet conditions caused by uniform rainfall events with low coefficient of variation. Overall soil moisture spatial and temporal variability was explained well by rainfall events and antecedent soil moisture conditions throughout the Kenaston soil moisture network. It is expected that the results of this study will support future remote sensing calibration and validation missions, data assimilation, as well as hydrologic model parameterization for use in agricultural regions. Copyright © 2016 John Wiley & Sons, Ltd.     

Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales

This study applied sample entropy (SampEn) to rainfall and runoff time series to investigate the complexity of different temporal scales. Rainfall and runoff time series with intervals of 1, 10, 30, 90, and 365 days for the Wu-Tu upstream watershed were used. Thereafter, SampEn was computed for the five rainfall and runoff time series. The results show that for the various temporal scales, comparisons of the complexity between the rainfall and runoff time series based on the SampEn are inconsistent. Calculating the dynamic SampEn further elucidated variations of the complexity in the rainfall and runoff time series. In addition, the results show that SampEn measures of the rainfall and runoff time series are typically higher than the approximate entropy measures of the rainfall and runoff time series for a specific temporal scale. The complexity increases when the sample size increases for a specific temporal scale. Furthermore, temporal scales with low complexity and high predictability are obtained from the variations of SampEn for the rainfall and runoff time series with different temporal scales, thereby providing a reference for determining the appropriate temporal scale for rainfall and runoff time series forecasting.     

Daily precipitation variability in semiarid agricultural regions in Macedonia,Greece

Abstract

The spatio-temporal variability of daily precipitation series was investigated in a semiarid region of central Macedonia in northern Greece, Ten years of daily rainfall records for seven stations in the region constituted the data base. The spatial characteristics were examined by drawing composite correlation diagrams for the cool (October-March) season and the warm (April-September) season, and the results confirmed the regional homogeneity of the data sets. Furthermore, the temporal analysis indicated that the non-rainy days constituted the major portion of days throughout the year at all the stations. Similarly, light rainfall represented the majority of rainy days. Moreover, the annual rainfall variation showed high values in March, April and November with low values occurring in the summer and autumn. A sharp increase of rainfall between the 185th and the 195th day of the year must be taken into account when the harvest is scheduled. Harmonic and Power Spectrum analyses applied to the annual variation of rain depths using 5-day intervals revealed significant periodicities of 26, 122, 365 and 55 days. Finally the analysis of the annual variation of rain occurrences. revealed periodicities of 365 and 122 days.     

Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments

Abstract

A novel approach is presented for combining spatial and temporal detail from newly available TRMM-based data sets to derive hourly rainfall intensities at 1-km spatial resolution for hydrological modelling applications. Time series of rainfall intensities derived from 3-hourly 0.25° TRMM 3B42 data are merged with a 1-km gridded rainfall climatology based on TRMM 2B31 data to account for the sub-grid spatial distribution of rainfall intensities within coarse-scale 0.25° grid cells. The method is implemented for two dryland catchments in Tunisia and Senegal, and validated against gauge data. The outcomes of the validation show that the spatially disaggregated and intensity corrected TRMM time series more closely approximate ground-based measurements than non-corrected data. The method introduced here enables the generation of rainfall intensity time series with realistic temporal and spatial detail for dynamic modelling of runoff and infiltration processes that are especially important to water resource management in arid regions.

Editor D. Koutsoyiannis

Citation Tarnavsky, E., Mulligan, M. and Husak, G., 2012. Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments. Hydrological Sciences Journal, 57 (2), 248–264.     

Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

ABSTRACT

The spread of impervious surfaces in urban areas combined with the rise in the intensity of rainfall events as a result of climate change has led to dangerous increases in storm water flows. This paper discusses a new implementation of the fully distributed hydrological model Multi-Hydro (developed at École des Ponts ParisTech), when operating storage basins, and its ability to deal with high-resolution radar rainfall data. The peri-urban area of Massy (south of Paris, France) was selected as a case study for having six of these drainage facilities, used extensively in flood control. Two radar rainfall datasets with different spatiotemporal resolutions were used: Météo-France’s PANTHER rainfall product (C-band) and ENPC’s X-band DPSRI. The rainfall spatiotemporal variability was analysed statistically using Universal Multifractals (UM). Finally, to validate the application, the water level simulations were compared with local measurements in the Cora storage basin located next to the catchment’s single outlet.     

Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms

We developed a difference infiltrometer to measure time series of non‐steady infiltration rates during rainstorms at the point scale. The infiltrometer uses two, tipping bucket rain gages. One gage measures rainfall onto, and the other measures runoff from, a small circular plot about 0.5‐m in diameter. The small size allows the infiltration rate to be computed as the difference of the cumulative rainfall and cumulative runoff without having to route water through a large plot. Difference infiltrometers were deployed in an area burned by the 2010 Fourmile Canyon Fire near Boulder, Colorado, USA, and data were collected during the summer of 2011. The difference infiltrometer demonstrated the capability to capture different magnitudes of infiltration rates and temporal variability associated with convective (high intensity, short duration) and cyclonic (low intensity, long duration) rainstorms. Data from the difference infiltrometer were used to estimate saturated hydraulic conductivity of soil affected by the heat from a wildfire. The difference infiltrometer is portable and can be deployed in rugged, steep terrain and does not require the transport of water, as many rainfall simulators require, because it uses natural rainfall. It can be used to assess infiltration models, determine runoff coefficients, identify rainfall depth or rainfall intensity thresholds to initiate runoff, estimate parameters for infiltration models, and compare remediation treatments on disturbed landscapes. The difference infiltrometer can be linked with other types of soil monitoring equipment in long‐term studies for detecting temporal and spatial variability at multiple time scales and in nested designs where it can be linked to hillslope and basin‐scale runoff responses. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.