Water Level Retrieval Using SARAL/AltiKa Observations in the Braided Brahmaputra River, Eastern India

Dubey, Amit Kumar; Gupta, Praveen Kumar; Dutta, Subashisa; Singh, Raghvendra Pratap

doi:10.1080/01490419.2015.1008156

Cited by 31 publications

(15 citation statements)

References 21 publications

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“…The Jason Virtual station (j2_020_1) is located 115.4 Km upstream from Lokoja along the Niger river stretch, with no tributary influence and at a river cross-sectional width of 2.37 Km, while the Topex/Poseidon Virtual station (tp198_4_moy) is located 234.7 Km downstream of Onitsha, influenced by Nun and Anambra river tributaries, and at a river cross-sectional width of 0.47 Km. These findings are consistent with studies at Brahmaputra River [88], Lake Argyle [34], Lake Victoria [34,38,88], and Benue River [35], where the distance between in situ and RA virtual stations, existence of tributaries between the stations, and river width impacted the correlation between datasets. Figure 3a-d shows the annual maximum timeseries data for the four gauging stations in Nigeria for gapped and infilled datasets.…”

Section: Missing Data Infilling With Radar Altimetry and Multiple Impsupporting

confidence: 90%

“…Water 2018, 10, x 11 of 26 studies at Brahmaputra River [88], Lake Argyle [34], Lake Victoria [34,38,88], and Benue River [35], where the distance between in situ and RA virtual stations, existence of tributaries between the stations, and river width impacted the correlation between datasets. Figure 3a-d shows the annual maximum timeseries data for the four gauging stations in Nigeria for gapped and infilled datasets.…”

Section: Missing Data Infilling With Radar Altimetry and Multiple Impmentioning

confidence: 99%

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Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions

Ekeu-wei

Blackburn

Pedruco

2018

Water

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In developing regions missing data are prevalent in historical hydrological datasets, owing to financial, institutional, operational and technical challenges. If not tackled, these data shortfalls result in uncertainty in flood frequency estimates and consequently flawed catchment management interventions that could exacerbate the impacts of floods. This study presents a comparative analysis of two approaches for infilling missing data in historical annual peak river discharge timeseries required for flood frequency estimation: (i) satellite radar altimetry (RA) and (ii) multiple imputation (MI). These techniques were applied at five gauging stations along the floodprone Niger and Benue rivers within the Niger River Basin. RA and MI enabled the infilling of missing data for conditions where altimetry virtual stations were available and unavailable, respectively. The impact of these approaches on derived flood estimates was assessed, and the return period of a previously unquantified devastating flood event in Nigeria in 2012 was ascertained. This study revealed that the use of RA resulted in reduced uncertainty when compared to MI for data infilling, especially for widely gapped timeseries (>3 years). The two techniques did not differ significantly for data sets with gaps of 1–3 years, hence, both RA and MI can be used interchangeably in such situations. The use of the original in situ data with gaps resulted in higher flood estimates when compared to datasets infilled using RA and MI, and this can be attributed to extrapolation uncertainty. The 2012 flood in Nigeria was quantified as a 1-in-100-year event at the Umaisha gauging station on the Benue River and a 1-in-50-year event at Baro on the Niger River. This suggests that the higher levels of flooding likely emanated from the Kiri and Lagdo dams in Nigeria and Cameroon, respectively, as previously speculated by the media and recent studies. This study demonstrates the potential of RA and MI for providing information to support flood management in developing regions where in situ data is sparse.

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Section: Missing Data Infilling With Radar Altimetry and Multiple Impsupporting

confidence: 90%

Section: Missing Data Infilling With Radar Altimetry and Multiple Impmentioning

confidence: 99%

Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions

Ekeu-wei

Blackburn

Pedruco

2018

Water

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“…The cumulative distribution of the cycle-by-cycle difference between all the measurement pairs (inset in Figure 17) shows that 90% of the measurement pairs present a difference in height less than 25 cm. The present result is consistent with the 10-30 cm errors found by [24,25] in the Ganga-Bramapoutra basin and by [26] in the Amazon basin. Noteworthy, there is no straightforward relationship with the width of the reach.…”

Section: Discharge Estimates In the Congo Basin From Validated Saral/supporting

confidence: 82%

“…In such a context, satellite altimetry provides a complement of information of highest interest [23]. A limited amount of works has been conducted to date to assess the quality of the SARAL/AltiKa data over rivers [24][25][26]. In the present study, we use the ranges issued at 40 Hz by the ICE1 retracking algorithm and applied the corrections for propagation in the atmosphere provided by global models such as ECMWF and Global Ionosphere Maps (GIM).…”

Section: Discharge Estimates In the Congo Basin From Validated Saral/mentioning

confidence: 99%

The Benefits of the Ka-Band as Evidenced from the SARAL/AltiKa Altimetric Mission: Scientific Applications

et al. 2018

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Abstract:The India-France SARAL/AltiKa mission is the first Ka-band altimetric mission dedicated primarily to oceanography. The mission objectives were firstly the observation of the oceanic mesoscales but also global and regional sea level monitoring, including the coastal zone, data assimilation, and operational oceanography. SARAL/AltiKa proved also to be a great opportunity for inland waters applications, for observing ice sheet or icebergs, as well as for geodetic investigations. The mission ended its nominal phase after three years in orbit and began a new phase (drifting orbit) in July 2016. The objective of this paper is to highlight some of the most remarkable achievements of the SARAL/AltiKa mission in terms of scientific applications. Compared to the standard Ku-band altimetry measurements, the Ka-band provides substantial improvements in terms of spatial resolution and data accuracy. We show here that this leads to remarkable advances in terms of observation of the mesoscale and coastal ocean, waves, river water levels, ice sheets, icebergs, fine scale bathymetry features as well as for the many related applications.

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“…River stage variations over the floodplains can provide the vital information about the flood wave distribution and energy dissipation over a period of time to the downstream of the nodal locations. Water level retrieval using satellite altimetry has shown the great potential to capture the river stage fluctuations over the sites, where installation and monitoring of traditional river gauge stations are difficult such as Brahmaputra river (Birkett, 1995;Frappart et al, 2006;Calmant et al, 2008;Dubey et al, 2014Dubey et al, , 2015. Satellite altimetry applications for monitoring inland water bodies can play a vital role in enhancing our ability to monitor the water cycle from the regional scale to the global scale (Berry, 2006).…”

Section: Introductionmentioning

confidence: 98%

An improved methodology to estimate river stage and discharge using Jason-2 satellite data

Dubey

Gupta

Dutta

et al. 2015

Journal of Hydrology

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Water Level Retrieval Using SARAL/AltiKa Observations in the Braided Brahmaputra River, Eastern India

Cited by 31 publications

References 21 publications

Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions

Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions

The Benefits of the Ka-Band as Evidenced from the SARAL/AltiKa Altimetric Mission: Scientific Applications

An improved methodology to estimate river stage and discharge using Jason-2 satellite data

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