A priori identifiability (i.e., identifiability under perfect data) is a ne
cessary condition for posteriori identifiability (i.e., identifiability und
er real data), and by implication a priori nonidentifiability is a sufficie
nt condition for posteriori nonidentifiability. Therefore, it is important
to prove a priori identifiability before attempting to estimate model param
eters through nonlinear regression with real noisy data. This paper investi
gates the a priori (also called classical, structural, or deterministic) id
entifiability of soil parameters using Richards's equation with perfect dis
tributed pressure data and prescribed initial and boundary conditions. The
study of a priori identifiability is made possible through the concept of l
inear independence of vectors. As expected, it is shown that the unsaturate
d soil parameters are not a priori identifiable, and thus not posteriori id
entifiable, with either zero flow pressure data or steady-state flow pressu
re data. In addition, it is shown that models with more than two parameters
are not a priori identifiable with transient pressure data. Therefore, mod
els with more than two parameters are not posteriori identifiable with real
pressure data regardless of the quantity and quality of this data. However
, it is found that two parameter models are a priori identifiable with tran
sient pressure data. Hence, the necessary condition for the posteriori iden
tifiability of two parameter models is proven.