We present an inverse modeling approach to reconstruct annual accumulation patterns from ground-penetrating radar (GPR) data. A coupled surface energy balance-snow model simulates surface melt and the evolution of subsurface density, temperature, and water content. The inverse problem consists of iteratively calibrating accumulation, serving as input for the model, by finding a match between modeled and observed radar travel times. The inverse method is applied to a 16km GPR transect on Nordenskioldbreen, Svalbard, yielding annual accumulation patterns for 2007-2012. Accumulation patterns with a mean of 0.75meter water equivalent (mwe)a(-1)contain substantial spatial variability, with a mean annual standard deviation of 0.17mwea(-1), and show only partial consistency from year to year. In contrast to traditional methods, accounting for melt water percolation, refreezing, and runoff facilitates accurate accumulation reconstruction in areas with substantial melt. Additionally, accounting for horizontal density variability along the transect is shown to reduce spatial variability in reconstructed accumulation, whereas incorporating irreducible water storage lowers accumulation estimates. Correlating accumulation to terrain characteristics in the dominant wind direction indicates a strong preference of snow deposition on leeward slopes, whereas weaker correlations are found with terrain curvature. Sensitivity experiments reveal a nonlinear response of the mass balance to accumulation changes. The related negative impact of small-scale accumulation variability on the mean net mass balance is quantified, yielding a negligible impact in the accumulation zone and a negative impact of -0.09mwea(-1)in the ablation area.