NDRF deploys cadaver dogs for Uttarakhand rescue operation

For the first time, the NDRF is deploying cadaver dogs in Uttarakhand’s Dharali village to locate victims buried in debris after a cloudburst-triggered flash flood. At least four are dead and over 60 people feared trapped. Rescue operations are ongoing

New Delhi: The National Disaster Response Force (NDRF) is deploying cadaver detection dogs for the first time to assist in rescue and recovery efforts at Dharali village in Uttarakhand, where a devastating cloudburst triggered flash floods on Tuesday.

A pair of cadaver dogs was airlifted from Delhi to the affected site, official sources said. These specially trained dogs will help locate those feared dead and buried under slush and debris. In addition, regular sniffer dogs trained to detect survivors are also being deployed.

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Three NDRF teams—comprising 35 rescuers each—have already been mobilised from different locations in Uttarakhand, and two more teams are awaiting airlift from Dehradun airport.

The cloudburst struck Dharali, a key stop en route to Gangotri, sweeping away homes, vehicles, and trees. At least four people have died, and 60–70 others are feared trapped. Dharali is known for its hotels, restaurants, and homestays catering to pilgrims and tourists.

The Indo-Tibetan Border Police (ITBP), which has battalions stationed at nearby Matli and Mahidananda, has deployed about 100 personnel to assist with rescue efforts. Around 37 villagers—including 11 women and 4 children—were rescued and shifted to the ITBP post at Kopang, officials said.

The NDRF has trained about six cadaver dogs, primarily from the Belgian Malinois and Labrador breeds, using a special imported scent that mimics the smell of decomposing human remains.

“For years, the NDRF’s primary focus was saving lives within the golden hour of a disaster. But retrieving bodies for providing closure to families is also vital,” a senior NDRF official told PTI.

The cadaver dog unit will now bolster the NDRF’s ability to respond more comprehensively to disasters involving mass casualties.