Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

Zhang, Ziya; Koren, Victor; Smith, Michael; Reed, Seann; Wang, David

doi:10.1061/(asce)1084-0699(2004)9:2(103)

Cited by 42 publications

(26 citation statements)

References 15 publications

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“…An obvious approach is to divide the watershed into its natural sub-watersheds, thus preserving the watershed's natural boundaries, flow-paths and channels for realistic water routing [23][24][25][26][27][28][29]. The concepts of critical source area [30][31][32][33][34][35][36], threshold drainage area [37] and aggregated simulation area [38] have also been used to delineate sub-watersheds within semi-distributed models.…”

Section: Overview Of Schematization and Parameterization Approaches Imentioning

confidence: 99%

The Curve Number Concept as a Driver for Delineating Hydrological Response Units

Savvidou

Efstratiadis

Koussis

et al. 2018

Water

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Abstract:In this paper, a new methodology for delineating Hydrological Response Units (HRUs), based on the Curve Number (CN) concept, is presented. Initially, a semi-automatic procedure in a GIS environment is used to produce basin maps of distributed CN values as the product of the three classified layers, soil permeability, land use/land cover characteristics and drainage capacity. The map of CN values is used in the context of model parameterization, in order to identify the essential number and spatial extent of HRUs and, consequently, the number of control variables of the calibration problem. The new approach aims at reducing the subjectivity introduced by the definition of HRUs and providing parsimonious modelling schemes. In particular, the CN-based parameterization (1) allows the user to assign as many parameters as can be supported by the available hydrological information, (2) associates the model parameters with anticipated basin responses, as quantified in terms of CN classes across HRUs, and (3) reduces the effort for model calibration, simultaneously ensuring good predictive capacity. The advantages of the proposed approach are demonstrated in the hydrological simulation of the Nedontas River Basin, Greece, where parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS model. A modelling experiment with a varying number of HRUs, where the parameter estimation problem was handled through automatic optimization, showed that the parameterization with three HRUs, i.e., equal to the number of flow records, ensured the optimal performance. Similarly, tests with alternative HRU configurations confirmed that the optimal scores, both in calibration and validation, were achieved by the CN-based approach, also resulting in parameters values across the HRUs that were in agreement with their physical interpretation.

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Section: Overview Of Schematization and Parameterization Approaches Imentioning

confidence: 99%

The Curve Number Concept as a Driver for Delineating Hydrological Response Units

Savvidou

Efstratiadis

Koussis

et al. 2018

Water

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“…MBC is the simplest method in which a bias correction factor is constant over time and space [31][32][33]. The MBC factor is calculated for each time step as an average across all point-pixel pairs and is multiplied with the satellite estimates over the entire study area.…”

Section: Mean Bias Correction (Mbc)mentioning

confidence: 99%

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

2018

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Abstract:The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.

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“…Mean field bias correction is the simplest method in which a bias correction factor is constant over time and space [13][14][15] . It is suitable for an area where a dense rain gauge network is available 48 .…”

Section: Mean Field Bias Correction (Mfb)mentioning

confidence: 99%

“…After these two sources of error correction have been performed, it is generally necessary to use rain gauge observations to bias correction of initial radar rainfall estimates for increased accuracy of radar rainfall. Different corrective measures such as mean field bias correction [13][14][15] , range dependent bias correction [16][17][18] , local bias correction [19][20][21] , spatio-temporal bias correction [22][23][24] , and bias correction methods that account on the magnitude of rain rate 25,26 have been proposed to remove bias in radar rainfall estimates when compared with rain gauge data. Recently, radar-gauge merging methods have been introduced to produce more accurate rainfall fields [27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

Bias correction of radar rainfall estimates based on a geostatistical technique

Hanchoowong¹,

Weesakul²,

Chumchean

2012

ScienceAsia

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Various types of rainfall characteristic have different rainfall drop size distributions (DSDs). DSDs that are measured by radar have a fundamental influence on parameters of the Z-R relationship; using a climatological Z-R relationship to estimate radar rainfall can lead to bias in radar rainfall estimates. This paper attempts to remove the source of bias in radar rainfall estimates due to an uncertain Z-R relationship by applying a local bias adjustment factor to a region that has the same climatological rainfall characteristic. Recorded historical daily rainfall data from 188 uniformly distributed rain gauges located under the radar umbrella and its vicinity were used for describing the climatological spatial pattern of rainfall in the study area based on kriging approaches. It was found that a simple kriging technique with the isotropic Bessel-J semivariogram model was the best method to classify climatological patterns of rainfall characteristic of the study area and therefore it has been used for identifying local bias correction areas of the proposed hourly local bias (HLB) correction method. Performances of different bias correction methods with various levels of complexity were evaluated from 500 of the calibrated and cross-validated gauges of the validated data set, selected randomly. These methods include mean field bias correction (MFB), hourly mean field bias correction (HMFB), hourly range dependent mean field bias correction (HRMFB), and HLB correction. Forty-four rainfall events recorded during 2003-2005 from the S-band Pimai radar located in Nakhon-Ratchasima Province, Thailand, and 50 automatic rain gauges were used in this study. The results of this study showed that, on average, the proposed HLB method could improve accuracy of radar rainfall estimates by 16.7%, 14.3%, 2.8%, 0.4% for the calibrated gauges, and by 11.8%, 10.2%, 9.4%, 4.1% for the cross-validated gauges when compared to non-bias corrected, MFB, HMFB, and HRMFB methods, respectively.

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Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

Cited by 42 publications

References 15 publications

The Curve Number Concept as a Driver for Delineating Hydrological Response Units

The Curve Number Concept as a Driver for Delineating Hydrological Response Units

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

Bias correction of radar rainfall estimates based on a geostatistical technique

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