  We present a framework for derivative-based global sensitivity analysis (GSA)
for models with high-dimensional input parameters and functional outputs. We
combine ideas from derivative-based GSA, random field representation via
Karhunen--Lo\`{e}ve expansions, and adjoint-based gradient computation to
provide a scalable computational framework for computing the proposed
derivative-based GSA measures. We illustrate the strategy for a nonlinear ODE
model of cholera epidemics and for elliptic PDEs with application examples from
geosciences and biotransport.
