I suppose it is late to answer, but it may turn out to be helpful for others.
LDA presents all topics as a mixture of keywords with their weights
For example, in gensim implementation it looks like this:
topic #0: 0.009*river + 0.008*lake + 0.006*island + 0.005*mountain + 0.004*area + 0.004*park + 0.004*antarctic + 0.004*south + 0.004*mountains + 0.004*dam
So you may name a topic using the keywords with the largest weights (like River+Lake).
Or you may use something like WordNet to find the most common hypernym for them.
For example, in python you can do it like this.
from nltk.corpus import wordnet as wn