Irrawaddy dolphin Orcaella brevirostris in the Cambodian Mekong River: an initial survey

Irrawaddy dolphins Orcaella brevirostiris are found in coastal waters from the Bay of Bengal east to Palawan, Philippines and south to northern Australia. They also occur in three large tropical river systems in South-east Asia: the Mekong, Mahakam and Ayeyarwady. In March and May 1997 approximately 350 km of riverine habitat in parts of north-east Cambodia were surveyed, discussions took place with local people, and reported dry season dolphin habitat was mapped. Our objectives were to investigate the status, habitat and distribution of dolphins in north-east Cambodia and identify threats to the continued survival of dolphins in the Mekong River Basin. Nine groups of dolphins were observed in the Mekong River. A ‘best’ estimate of 40 animals were seen. Irrawaddy dolphins were generally confined to sections of the river with water levels >8–10 m during the dry season. It appears that the Mekong River dolphin population is rapidly declining. In 1997 there were probably no more than 100¨C150 dolphins left in north-east Cambodia (including southern Laos) and no more than 200 within the entire Mekong River Basin, although these numbers remain tentative. Anthropogenic mortality is high, albeit largely unintentional, and there is considerable risk that the dolphin population will become locally extinct in the Mekong River in the near future. The establishment of community-managed deep water Fish Conservation Zones with government support may represent the best opportunity for reducing dry season dolphin mortality from large-meshed gillnet entanglement. Efforts to establish protected areas for dolphins are currently underway.

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Estimation of supplementary water to paddy fields in the lower Mekong River basin during the dry season

Paddy and Water Environment ◽

10.1007/s10333-005-0015-5 ◽

2005 ◽

Vol 3 (3) ◽

pp. 177-186 ◽

Cited By ~ 1

Author(s):

Hiroaki Somura ◽

Hajime Tanji ◽

Koshi Yoshida ◽

Osamu Toda ◽

Katsuhiro Higuchi

Keyword(s):

River Basin ◽

Dry Season ◽

Paddy Fields ◽

Mekong River ◽

Mekong River Basin

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Water levels of the Mekong River Basin based on CryoSat-2 SAR data classification

10.5194/hess-2017-217 ◽

2017 ◽

Author(s):

Eva Boergens ◽

Karina Nielsen ◽

Ole B. Andersen ◽

Denise Dettmering ◽

Florian Seitz

Keyword(s):

River Basin ◽

Target Identification ◽

Unsupervised Classification ◽

Radar Data ◽

Water Levels ◽

Mekong River ◽

Mekong River Basin ◽

Synthetic Aperture Radar Data ◽

Sar Data ◽

Land Water

Abstract. In this study we use CryoSat-2 SAR (Delay-Doppler Synthetic Aperture Radar) data over the Mekong River Basin to estimate water levels. Smaller inland waters can be observed with CryoSat-2 data with a higher accuracy compared to the classical radar altimeters due to the increased along track resolution of SAR and the smaller footprint. However, even with this SAR data the estimation of water levels over smaller (width less than 500 m) is still challenging as only very few consecutive observations over the water body are present. The usage of land-water-masks for target identification tends to fail as the river becomes smaller. Therefore, we developed a classification to divide the observations into water and land observations based solely on the observations. The classification is done with an unsupervised classification algorithm, and it is based on features derived from the SAR and RIP (Range Integrated Power) waveforms. After the classification, classes representing water and land are identified. The measurements classified as water are used in a next step to estimate water levels for each crossing over the Mekong River. The resulting water levels are validated and compared to gauge data, Envisat data and CryoSat-2 water levels derived with a land-water mask. The CryoSat-2 classified water levels perform better than results based on the land-water-mask and Envisat. Especially, in the smaller upstream regions the improvements of the classification approach for CryoSat-2 are evident.

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Human-Induced Alterations to Land Use and Climate and Their Responses for Hydrology and Water Management in the Mekong River Basin

Water ◽

10.3390/w11061307 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1307 ◽

Cited By ~ 12

Author(s):

Venkataramana Sridhar ◽

Hyunwoo Kang ◽

Syed A. Ali

Keyword(s):

Climate Change ◽

Land Use ◽

River Basin ◽

Dry Season ◽

Mekong River ◽

Anthropogenic Factors ◽

Wet Season ◽

Industrial Growth ◽

Mekong River Basin ◽

Parameter Estimations

The Mekong River Basin (MRB) is one of the significant river basins in the world. For political and economic reasons, it has remained mostly in its natural condition. However, with population increases and rapid industrial growth in the Mekong region, the river has recently become a hotbed of hydropower development projects. This study evaluated these changing hydrological conditions, primarily driven by climate as well as land use and land cover change between 1992 and 2015 and into the future. A 3% increase in croplands and a 1–2% decrease in grasslands, shrublands, and forests was evident in the basin. Similarly, an increase in temperature of 1–6 °C and in precipitation of 15% was projected for 2015–2099. These natural and climate-induced changes were incorporated into two hydrological models to evaluate impacts on water budget components, particularly streamflow. Wet season flows increased by up to 10%; no significant change in dry season flows under natural conditions was evident. Anomaly in streamflows due to climate change was present in the Chiang Saen and Luang Prabang, and the remaining flow stations showed up to a 5% increase. A coefficient of variation <1 suggested no major difference in flows between the pre- and post-development of hydropower projects. The results suggested an increasing trend in streamflow without the effect of dams, while the inclusion of a few major dams resulted in decreased river streamflow of 6% to 15% possibly due to irrigation diversions and climate change. However, these estimates fall within the range of uncertainties in natural climate variability and hydrological parameter estimations. This study offers insights into the relationship between biophysical and anthropogenic factors and highlights that management of the Mekong River is critical to optimally manage increased wet season flows and decreased dry season flows and handle irrigation diversions to meet the demand for food and energy production.

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Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

Remote Sensing ◽

10.3390/rs11222684 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2684 ◽

Cited By ~ 3

Author(s):

Kim ◽

Lee ◽

Chang ◽

Bui ◽

Jayasinghe ◽

...

Keyword(s):

River Basin ◽

Ensemble Learning ◽

River Discharge ◽

Water Levels ◽

Mekong River ◽

Altimetry Data ◽

Rating Curve ◽

Mekong River Basin ◽

Radar Altimetry

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q (QELQ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of QELQ decreased by 1504/1338 m3/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

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Deriving the Reservoir Conditions for Better Water Resource Management Using Satellite-Based Earth Observations in the Lower Mekong River Basin

Remote Sensing ◽

10.3390/rs11232872 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2872 ◽

Cited By ~ 5

Author(s):

Syed A. Ali ◽

Venkataramana Sridhar

Keyword(s):

Remote Sensing ◽

River Basin ◽

Water Resource Management ◽

Large Population ◽

Dry Season ◽

Water Diversion ◽

Mekong River ◽

Landsat 8 ◽

Wet Season ◽

Mekong River Basin

The Mekong River basin supported a large population and ecosystem with abundant water and nutrient supply. However, the impoundments in the river can substantially alter the flow downstream and its timing. Using limited observations, this study demonstrated an approach to derive dam characteristics, including storage and flow rate, from remote-sensing-based data. Global Reservoir and Lake Monitor (GRLM), River-Lake Hydrology (RLH), and ICESat-GLAS, which generated altimetry from Jason series and inundation areas from Landsat 8, were used to estimate the reservoir surface area and change in storage over time. The inflow simulated by the variable infiltration capacity (VIC) model from 2008 to 2016 and the reservoir storage change were used in the mass balance equation to calculate outflows for three dams in the basin. Estimated reservoir total storage closely resembled the observed data, with a Nash-Sutcliffe efficiency and coefficient of determination more than 0.90 and 0.95, respectively. An average decrease of 55% in outflows was estimated during the wet season and an increase of up to 94% in the dry season for the Lam Pao. The estimated decrease in outflows during the wet season was 70% and 60% for Sirindhorn and Ubol Ratana, respectively, along with a 36% increase in the dry season for Sirindhorn. Basin-wide demand for evapotranspiration, about 935 mm, implicitly matched with the annual water diversion from 1000 to 2300 million m3. From the storage–discharge rating curves, minimum storage was also evident in the monsoon season (June–July), and it reached the highest in November. This study demonstrated the utility of remote sensing products to assess the impacts of dams on flows in the Mekong River basin.

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