Formation of alluvial stratigraphy is controlled by autogenic processes that mix their imprints with allogenic forcing. In some alluvial successions, sedimentary cycles have been linked to astronomically-driven, cyclic climate changes. However, it remains challenging to define how such cyclic allogenic forcing leads to sedimentary cycles when it continuously occurs in concert with autogenic forcing. Accordingly, we evaluate the impact of cyclic and non-cyclic upstream forcing on alluvial stratigraphy through a process-based alluvial architecture model, the Karssenberg and Bridge (2008) model (KB08). The KB08 model depicts diffusion-based sediment transport, erosion and deposition within a network of channel belts and associated floodplains, with river avulsion dependent on lateral floodplain gradient, flood magnitude and frequency, and stochastic components. We find cyclic alluvial stratigraphic patterns to occur when there is cyclicity in the ratio of sediment supply over water discharge (Q_s/Q_w ratio), in the precondition that the allogenic forcing has sufficiently large amplitudes and long, but not very long, wavelengths, depending on inherent properties of the modelled basin (e.g. basin subsidence, size, and slope). Each alluvial stratigraphic cycle consists of two phases: an aggradation phase characterized by rapid sedimentation due to frequent channel shifting and a non-deposition phase characterized by channel belt stability and, depending on Q_s/Q_w amplitudes, incision. Larger Q_s/Q_w ratio amplitudes contribute to weaker downstream signal shredding by stochastic components in the model. Floodplain topographic differences are found to be compensated by autogenic dynamics at certain compensational timescales in fully autogenic runs, while the presence of allogenic forcing clearly impacts the compensational stacking patterns.

We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB-model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D-2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.