Rainfall disaggregation for hydrological modeling: is there a need for spatial consistence?

Abstract. In this study, the influence of disaggregated rainfall products with different degrees of spatial consistence on rainfall–runoff modeling results is analyzed for three mesoscale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values, a multiplicative random cascade model is applied. The disaggregation is applied on a station by station basis without consideration of surrounding stations; hence subsequent steps are then required to implement spatial consistence. Spatial consistence is represented here by three bivariate spatial rainfall characteristics that complement each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall products as input to hydrological modeling, we hypothesize that derived runoff statistics – with emphasis on seasonal extreme values – are subject to similar differences as well. However, an impact on the extreme values' statistics of the hydrological simulations forced by different rainfall approaches cannot be detected. Several modifications of the study design using rainfall–runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall–runoff WaSiM model is applied instead of the semi-distributed HBV-IWW model can slight differences regarding the seasonal peak flows be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.

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Rainfall disaggregation for hydrological modeling: Is there a need for spatial consistence?

10.5194/hess-2017-609 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hannes Müller ◽

Markus Wallner ◽

Kristian Förster

Keyword(s):

Time Series ◽

Hydrological Modeling ◽

Extreme Values ◽

Rainfall Data ◽

Data Sets ◽

Rainfall Time Series ◽

Rainfall Runoff ◽

Lower Saxony ◽

Rainfall Characteristics ◽

Peak Flows

Abstract. In this investigation, the influence of disaggregated rainfall data sets with different degrees of spatial consistence on rainfall runoff modeling results is analyzed for three meso-scale catchments in Lower Saxony, Germany. For the disaggregation of daily rainfall time series into hourly values a multiplicative random cascade model is applied. The disaggregation is applied on a per station basis without consideration of surrounding stations, hence subsequent steps are then required to implement spatial consistence. Spatial consistence is here represented by three bivariate spatial rainfall characteristics, complementing each other. A resampling algorithm and a parallelization approach are evaluated against the disaggregated time series without any subsequent steps. With respect to rainfall, clear differences between these three approaches can be identified regarding bivariate spatial rainfall characteristics, areal rainfall intensities and extreme values. The resampled time series lead to the best agreement with the observed ones. Using these different rainfall data sets as input to hydrological modeling, we hypothesize that derived runoff statistics are subject to similar differences as well. However, an impact on the runoff statistics summer and winter peak flows, monthly average discharge and flow duration curve of the simulated runoff time series cannot be detected. Several modifications of the investigation using rainfall runoff models with and without parameter calibration or using different rain gauge densities lead to similar results in runoff statistics. Only if the spatially highly resolved rainfall-runoff WaSiM-model is applied instead of the semi-distributed HBV-IWW-model, slight differences regarding the seasonal peak flows can be identified. Hence, the hypothesis formulated before is rejected in this case study. These findings suggest that (i) simple model structures might compensate for deficiencies in spatial representativeness through parameterization and (ii) highly resolved hydrological models benefit from improved spatial modeling of rainfall.

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Analysis of the Behavior of Daily Maximum Rainfall within the Department of Atlántico, Colombia

Water ◽

10.3390/w11122453 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2453

Author(s):

Orlando M. Viloria-Marimón ◽

Álvaro González-Álvarez ◽

Javier A. Mouthón-Bello

Keyword(s):

Time Series ◽

Daily Rainfall ◽

Information Criterion ◽

Rain Gauge ◽

Caribbean Region ◽

Daily Maximum ◽

Maximum Rainfall ◽

Rain Gauges ◽

Stationary Conditions ◽

Areal Rainfall

In the Colombian Caribbean region, there are few studies that evaluated the behavior of one of the most commonly used variables in hydrological analyses: the maximum daily rainfall (Pmax-24h). In this study, multiannual Pmax-24h time series from 19 rain gauges, located within the department of Atlántico, were analyzed to (a) determine possible increasing/decreasing trends over time, (b) identify regions with homogeneous behavior of Pmax-24h, (c) assess whether the time series are better suited under either a stationary or non-stationary frequency analysis, (d) generate isohyetal maps under stationary, non-stationary, and mixed conditions, and (e) evaluate the isohyetal maps by means of the calculation of areal rainfall (Pareal) in nine watersheds. In spite of the presence of both increasing and decreasing trends, only the Puerto Giraldo rain gauge showed a significant decreasing trend. Also, three regions (east, central, and west) with similar Pmax-24h behavior were identified. According to the Akaike information criterion test, 79% of the rain gauges showed better fit under stationary conditions. Finally, statistical analysis revealed that, under stationary conditions, the errors in the calculation of Pareal were more frequent, while the magnitude of the errors was larger under non-stationary conditions, especially in the central–south region.

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Cascade Rainfall Disaggregation Application in U.S. Central Plains

Environment and Natural Resources Research ◽

10.5539/enrr.v7n4p30 ◽

2017 ◽

Vol 7 (4) ◽

pp. 30 ◽

Cited By ~ 1

Author(s):

Jurgen D. Garbrecht ◽

Rabi Gyawali ◽

Robert W. Malone ◽

John C. Zhang

Keyword(s):

Time Series ◽

Model Performance ◽

Daily Rainfall ◽

Rainfall Data ◽

Rainfall Time Series ◽

Random Component ◽

Rainfall Characteristics ◽

Hourly Rainfall ◽

Replicated Observations

Long-term observations of daily rainfall are common and routinely available for a variety of hydrologic applications. In contrast, observations of 10 or more years of continuous hourly rainfall are rare. Yet, sub-daily rainfall data are required in rainfall-runoff models. Rainfall disaggregation can generate sub-daily time-series from available long term daily observations. Herein, the performance of Multiplicative Random Cascade (MRC) model at disaggregating daily-to-hourly rainfall was investigated. The MRC model was parameterized and validated with 15 years of continuous observed daily and hourly rainfall data at three weather stations in Oklahoma. Model performance, or degree to which the disaggregated rainfall time series replicated observations, was assessed using 46 variables of hourly rainfall characteristics, such as longest wet spell duration, average number of rainfall hours per year, and largest hourly rainfall. Findings include: a) average-type hourly rainfall characteristics were better replicated than single value characteristics such as longest, maximum, or peak hourly rainfall; b) the large number of sub-trace hourly rainfall values (<0.254 mm h-1) generated by the MRC model were not supported by observations; c) the random component of the MRC model led to a variation under 15% of the average value for most rainfall characteristics with the exceptions of the “longest wet spell duration” and “maximum hourly rainfall”; and d) the MRC model produced fewer persistent rainfall events compared to those in the observed rainfall record. The large number of generated trace rainfall values and difficulties to replicate reliably extreme rainfall characteristics, reduces the number of potential hydrologic applications that could take advantage of the MRC disaggregated hourly rainfall. Nevertheless, in most cases, the disaggregated rainfall generated by the MRC model replicated observed average-type rainfall characteristics well.

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Spatial representation of stochastically generated rainfall for derived flood frequency analysis

10.5194/egusphere-egu21-1971 ◽

2021 ◽

Author(s):

Luisa-Bianca Thiele ◽

Ross Pidoto ◽

Uwe Haberlandt

Keyword(s):

Time Series ◽

Spatial Representation ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Multiple Point ◽

Rainfall Time Series ◽

Rainfall Runoff ◽

Areal Rainfall ◽

Rainfall Runoff Model ◽

Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. The stochastic rainfall time series, which are used as input for the rainfall-runoff model, can be generated with different spatial resolution: (a) Point rainfall, which is stochastically generated rainfall at a single site. (b) Areal rainfall, which is catchment rainfall averaged over multiple sites before using the single-site stochastic rainfall model. (c) Multiple point rainfall, which is stochastically generated at multiple sites with spatial correlation before averaging to catchment rainfall. To find the most applicable spatial representation of stochastically generated rainfall for derived flood frequency analysis, simulated and observed runoff time series will be compared based on runoff statistics. The simulated runoff time series are generated utilizing the rainfall-runoff model HBV-IWW with an hourly time step. The rainfall-runoff model is driven with point, areal and multiple point stochastic rainfall time series generated by an Alternating Renewal rainfall model (ARM). In order to take into account the influence of catchment size on the results, catchments of different sizes within Germany are considered in this study. &#160;While point rainfall may be applicable for small catchments, it is expected that above a certain catchment size a more detailed spatial representation of stochastically generated rainfall is necessary. Here, it would be advantageous if the results based on areal rainfall are comparable to those of the multiple point rainfall. The stochastically generation of areal rainfall is less complex compared to the stochastically generation of multiple point rainfall and extremes at the catchment scale may also be better represented by areal rainfall. &#160;&#160;&#160;</p>

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Daily precipitation records over mainland Spain and the Balearic Islands

Natural Hazards and Earth System Science ◽

10.5194/nhess-13-2483-2013 ◽

2013 ◽

Vol 13 (10) ◽

pp. 2483-2491 ◽

Cited By ~ 12

Author(s):

C. Ramis ◽

V. Homar ◽

A. Amengual ◽

R. Romero ◽

S. Alonso

Keyword(s):

Extreme Events ◽

Extreme Values ◽

Daily Rainfall ◽

Rain Gauge ◽

Balearic Islands ◽

Precipitation Event ◽

Similar Distribution ◽

Major Interest ◽

Precipitation Records ◽

Different Characteristics

Abstract. Understanding the spatial distribution of extreme precipitations is of major interest in order to improve our knowledge of the climate of a region and its relationship with society. These analyses inevitably require the use of directly observed values to account for the actual extreme amounts rather than analyzed gridded values. A study of daily rainfall extremes observed over mainland Spain and the Balearic Islands is performed by using records from 8135 rain gauge stations from the Spanish Weather Agency (AEMET). Results show that the heaviest daily precipitations have been observed mainly on the coastal Mediterranean zone from Gibraltar to the Pyrenees. In particular, a record value of 817 mm was recorded in the Valencia region in 1987. The current map of daily records in Spain, which updates the pioneering work of the Spanish meteorologist Font, shows similar distribution of extreme events but with notably higher amounts. Generalized extreme values distributions fit the Mediterranean and Atlantic rain gauge measurements and shows the different characteristics of the extreme daily precipitations in both regions. We identify the most extreme events (above 500 mm per day) and provide a brief description of a typical meteorological situation in which these damaging events occur. An analysis of the low-level circulation patterns producing such extremes – by means of simple indices such as NAO, WeMOi and IBEI – confirms the relevance of local flows in the generation of either Mediterranean or Atlantic episodes. WeMOi, and even more IBEI, are good discriminants of the region affected by the record precipitation event.

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A multiple threshold method for fitting the generalized Pareto distribution and a simple representation of the rainfall process

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-4957-2010 ◽

2010 ◽

Vol 7 (4) ◽

pp. 4957-4994 ◽

Cited By ~ 5

Author(s):

R. Deidda

Keyword(s):

Time Series ◽

Distribution Function ◽

Pareto Distribution ◽

Generalized Pareto Distribution ◽

Daily Rainfall ◽

Rainfall Time Series ◽

Threshold Method ◽

Generalized Pareto ◽

Multiple Threshold ◽

Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

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Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

Journal of Hydrometeorology ◽

10.1175/2007jhm792.1 ◽

2007 ◽

Vol 8 (6) ◽

pp. 1204-1224 ◽

Cited By ~ 82

Author(s):

J. M. Schuurmans ◽

M. F. P. Bierkens ◽

E. J. Pebesma ◽

R. Uijlenhoet

Keyword(s):

High Resolution ◽

Hydrological Modeling ◽

Daily Rainfall ◽

Rain Gauge ◽

Added Value ◽

Rain Gauge Data ◽

Geostatistical Methods ◽

Gauge Data ◽

Event Based ◽

Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

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Modelling of monsoon rainfall for a mesoscale catchment in North-West India II: stochastic rainfall simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-10-807-2006 ◽

2006 ◽

Vol 10 (6) ◽

pp. 807-815 ◽

Cited By ~ 1

Author(s):

E. Zehe ◽

A. K. Singh ◽

A. Bárdossy

Keyword(s):

Time Series ◽

Meteorological Data ◽

Daily Rainfall ◽

Time Variability ◽

Western India ◽

Data Sets ◽

Rainfall Time Series ◽

North West ◽

Monthly Scale ◽

Circulation Patterns

Abstract. Within this study we present a robust method for generating precipitation time series for the Anas catchment in North Western India. The method employs a multivariate stochastic simulation model that is driven by a time series of objectively classified circulation patterns (CPs). In a companion study (Zehe et al., 2006) it was already shown that CPs classified from the 500 or 700 Hpa levels are suitable to explain space-time variability of precipitation in that area. The model is calibrated using observed rainfall time series for the period 1985–1992 for two different CP time series, one from the 500 Hpa level and the over from the 700 Hpa level, and 200 realizations of daily rainfall are simulated for the period 85–94. Simulations using the CPs from the 500 Hpa level as input yield a good match of the observed averages and standard deviations of daily rainfall. They show furthermore good performance at the monthly scale. When used with the 700 Hpa level CPs as inputs the model clearly underestimates the standard deviation and performs much worse at the monthly scale, especially in the validation period 93–94. The presented results give evidence that CPs from the 500 Hpa, level in combination with a multivariate stochastic model, make up a suitable tool for reducing the sparsity of precipitation data in developing regions with sparse hydro-meteorological data sets.

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A Step towards Integrating CMORPH Precipitation Estimation with Rain Gauge Measurements

Advances in Meteorology ◽

10.1155/2018/2095304 ◽

2018 ◽

Vol 2018 ◽

pp. 1-24

Author(s):

Augusto José Pereira Filho ◽

Felipe Vemado ◽

Guilherme Vemado ◽

Fábio Augusto Gomes Vieira Reis ◽

Lucilia do Carmo Giordano ◽

...

Keyword(s):

Standard Deviation ◽

Amazon Basin ◽

Daily Rainfall ◽

Rain Gauge ◽

Brazilian Coast ◽

Rainfall Estimation ◽

Peruvian Andes ◽

Positive Tendency ◽

Analysis Scheme ◽

Areal Rainfall

Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h−1) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation.

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Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

Journal of Hydrometeorology ◽

10.1175/2011jhm1340.1 ◽

2011 ◽

Vol 12 (5) ◽

pp. 1100-1112 ◽

Cited By ~ 36

Author(s):

J. Vaze ◽

D. A. Post ◽

F. H. S. Chiew ◽

J.-M. Perraud ◽

J. Teng ◽

...

Keyword(s):

Model Simulation ◽

Model Performance ◽

Daily Rainfall ◽

Grid Cell ◽

Rainfall Series ◽

Data Availability ◽

Rainfall Time Series ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

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