Resolution considerations in using radar rainfall data for flood forecasting

Kouwen, N.; Garland, Gerald G.

doi:10.1139/l89-053

Cited by 35 publications

(20 citation statements)

References 14 publications

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“…Another reason is that only four grid cells are used in a distributed representation of this basin. Such a small number of cells may not be enough to adequately capture the rainfall variability and properly represent the actual channel structure (Kouwen and Garland, 1989).…”

Section: Hydrograph Simulations At Selected Watershed Outletsmentioning

confidence: 99%

“…They range in complexity from the so-called 'physically based fully distributed' (Wigmosta et al, 1994;Abbott et al, 1986;Garrote and Bras, 1995;Julien et al, 1995) to 'semi-distributed' (Boyle et al, 2001;Obled et al, 1994;Schumann, 1993), and to conceptual lumped rainfall-runoff models applied at smaller scales (Michaud and Sorooshian, 1994;White, 1988). In terms of computational elements, they may be built on grid (Ogden and Julien, 1994;Kouwen and Garland, 1989), small sub-basins (Carpenter et al, 2001;Obled et al, 1994), triangulated irregular network (TIN) (Goodrich et al, 1991) and stream tubes (Grayson et al, 1992;Moore and Grayson, 1991). Recently, efforts have been undertaken to account for the effects of 'sub-grid' heterogeneity on hydrologic processes Beven, 1995).…”

Section: Introductionmentioning

confidence: 99%

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Hydrology laboratory research modeling system (HL-RMS) of the US national weather service

Koren

Reed

Smith

et al. 2004

Journal of Hydrology

240

237

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This study investigates an approach that combines physically-based and conceptual model features in two stages of distributed modeling: model structure development and estimation of spatially variable parameters. The approach adds more practicality to the process of model parameterization, and facilitates an easier transition from current lumped model-based operational systems to more powerful distributed systems. This combination of physically-based and conceptual model features is implemented within the Hydrology Laboratory Research Modeling System (HL-RMS). HL-RMS consists of a well-tested conceptual water balance model applied on a regular spatial grid linked to physically-based kinematic hillslope and channel routing models. Parameter estimation procedures that combine spatially distributed and 'integrated' basin-outlet properties have been developed for the water balance and routing components. High-resolution radar-based precipitation data over a large region are used in testing HL-RMS. Initial tests show that HL-RMS yields results comparable to well-calibrated lumped model simulations in several headwater basins, and it outperforms a lumped model in basins where spatial rainfall variability effects are significant. It is important to note that simulations for two nested basins (not calibrated directly, but parameters from the calibration of the parent basin were applied instead) outperformed lumped simulations even more consistently, which means that HL-RMS has the potential to improve the accuracy and resolution of river runoff forecasts. Published by Elsevier B.V.

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Section: Hydrograph Simulations At Selected Watershed Outletsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hydrology laboratory research modeling system (HL-RMS) of the US national weather service

Koren

Reed

Smith

et al. 2004

Journal of Hydrology

240

237

View full text Add to dashboard Cite

show abstract

“…This is supported by Lobligeois et al (2014), where results from modelling 181 basins in France across a range of hydroclimatic conditions with lumped and semi-distributed hydrological models showed that in almost every case the performance of lumped and semi-distributed models was very similar. Other studies have also observed that models utilizing basin-averaged rainfall show similar performances to those utilizing more detailed rainfall (Kouwen and Garland, 1989;Nicótina et al, 2008;Pessoa et al, 1993). The apparent lack of improved performance with finer spatial and temporal resolutions may also be explained by the ability to highly calibrate hydrological model outputs to field data.…”

Section: Introductionmentioning

confidence: 97%

The sensitivity of landscape evolution models to spatial and temporal rainfall resolution

Coulthard

Skinner

2016

Earth Surf. Dynam.

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Abstract. Climate is one of the main drivers for landscape evolution models (LEMs), yet its representation is often basic with values averaged over long time periods and frequently lumped to the same value for the whole basin. Clearly, this hides the heterogeneity of precipitation -but what impact does this averaging have on erosion and deposition, topography, and the final shape of LEM landscapes? This paper presents results from the first systematic investigation into how the spatial and temporal resolution of precipitation affects LEM simulations of sediment yields and patterns of erosion and deposition. This is carried out by assessing the sensitivity of the CAESAR-Lisflood LEM to different spatial and temporal precipitation resolutions -as well as how this interacts with different-size drainage basins over short and long timescales. A range of simulations were carried out, varying rainfall from 0.25 h × 5 km to 24 h × Lump resolution over three different-sized basins for 30-year durations. Results showed that there was a sensitivity to temporal and spatial resolution, with the finest leading to > 100 % increases in basin sediment yields. To look at how these interactions manifested over longer timescales, several simulations were carried out to model a 1000-year period. These showed a systematic bias towards greater erosion in uplands and deposition in valley floors with the finest spatial-and temporal-resolution data. Further tests showed that this effect was due solely to the data resolution, not orographic factors. Additional research indicated that these differences in sediment yield could be accounted for by adding a compensation factor to the model sediment transport law. However, this resulted in notable differences in the topographies generated, especially in third-order and higher streams. The implications of these findings are that uncalibrated past and present LEMs using lumped and time-averaged climate inputs may be under-predicting basin sediment yields as well as introducing spatial biases through under-predicting erosion in first-order streams but overpredicting erosion in second-and third-order streams and valley floor areas. Calibrated LEMs may give correct sediment yields, but patterns of erosion and deposition will be different and the calibration may not be correct for changing climates. This may have significant impacts on the modelled basin profile and shape from longtimescale simulations.

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“…Kouwen and Garland [1989] applied radarestimated rainfall to a rainfall-runoff model based on hydrologically similar regions derived from remotely sensed land classification. In a study conducted by Becchi et al [1994], simulated radar-rainfall maps were employed as input to a distributed hydrologic model to highlight the advantages of radar data of high spatial and temporal resolution.…”

Section: Radar-rainfall Estimationmentioning

confidence: 99%

Propagation of Radar-Rainfall Uncertainty in Runoff Predictions

Ogden¹,

Sharif²

2001

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Public Reporting burden for this collection of mfcrtnanon a «tunned u average I hour per response, including Ihenme lor reviewing instrucnoro. searching cuaing dan sources, gathering and maintaining Ihe data needed and completing and reviewing Ihe collection of inlbrmarioa Send cornment regarding this burden estimates or any orher aspect oflhiscollecnonof informal»«, including suggestions for reducing this burden, lo Washington Headquarters Services. Directorate for information Operanons and Reports. 1215 SUPPLEMENTARY NOTESThe views, opinions and/or findings contained in this Army position, policy or decision, unless so designated by ABSTRACT (Maximum 200 words)This final report dociments the results obtained through the final phase of the research study. It consists of two papers that have been submitted for publication based on the research. THe objective of this project is to quantitatively evaluate the worth of radar-rainfall estimates for physicallybased hydrologic modeling. These two papers contain valuable results. To sunnarize, our study has shown that the physical relationship between radar reflectivity and rainfall rate begins to disappear at a range of approximately 60 km from the radar site for radars with approximately 1 degree beam width. At farther ranges, radar returns are significantly affected by the horizontal and vertical gradients of cloud water. This result was verified through careful testing using a simulation methodology wherein convective rainfall is simulated using the Advanced Regional Prediction Model (ARPS), a radar simulator, and the two-dimensional hydrologic model CASC2D. Techniques to correct runoff predictions for likely errors using a Bayesian approach are suggested. The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study, a physically-based atmospheric model of convective rainfall is coupled with an active microwave radiative transfer model to simulate radar observation of thunderstorms. Radar observations of these storms are generated and used to evaluate the propagation of radar-rainfall errors through distributed hydrologic simulations. This physically-based methodology allows one to directly examine the impact of radar-rainfall estimation errors on land-surface hydrologic predictions and to avoid the limitations imposed by the use of rain gauge data. Results indicate that the geometry of the radar beam and coordinate transformations, due to radar-watershed-storm orientation, have an effect on radar-rainfall estimation and runoff prediction errors. In addition to uncertainty in the radar reflectivity vs. rainfall intensity relationship, there are significant range-dependent and orientation-related radar-rainfall estimation errors that should be Hatim Osman Sharif-University ...

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Resolution considerations in using radar rainfall data for flood forecasting

Cited by 35 publications

References 14 publications

Hydrology laboratory research modeling system (HL-RMS) of the US national weather service

Hydrology laboratory research modeling system (HL-RMS) of the US national weather service

The sensitivity of landscape evolution models to spatial and temporal rainfall resolution

Propagation of Radar-Rainfall Uncertainty in Runoff Predictions

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