Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling�Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the �trading space for time� framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability

Prediction of streamflow at ungauged catchments requires transfer of hydrologic information (e.g., model parameters, hydrologic indices, streamflow values) from gauged (donor) to ungauged (receiver) catchments. A common metric used for the selection of ideal donor catchments is the spatial proximity between donor and receiver catchments. However, it is not clear whether information transfer among nearby catchments is suitable across a wide range of climatic and geographic regions. We examine this issue using the data from 756 catchments within the continental United States. Each catchment is considered ungauged in turn and daily streamflow is simulated through distance-based interpolation of streamflows from neighboring catchments. Results show that distinct geographic regions exist in US where transfer of streamflow values from nearby catchments is useful for retrospective prediction of daily streamflow at ungauged catchments. Specifically, the high predictability catchments (Nash-Sutcliffe efficiency NS > 0.7) are confined to the Appalachian Mountains in eastern US, the Rocky Mountains, and the Cascade Mountains in the Pacific Northwest. Low predictability catchments (NS < 0.3) are located mostly in the drier regions west of Mississippi river, which demonstrates the limited utility of gauged catchments in those regions for predicting at ungauged basins. The results suggest that high streamflow similarity among nearby catchments (and therefore, good predictability at ungauged catchments) is more likely in humid runoff-dominated regions than in dry evapotranspiration-dominated regions. We further find that higher density and/or closer distance of gauged catchments near an ungauged catchment does not necessarily guarantee good predictability at an ungauged catchment.

Study region Nyangores River watershed, headwater catchment of Mara River basin in Kenya. Study focus Climate variability and human activities are the main drivers of change of watershed hydrology. The contribution of climate variability and land use change to change in streamflow of Nyangores River, was investigated. Mann Kendall and sequential Mann Kendall tests were used to investigate the presence and breakpoint of a trend in discharge data (1965�2007) respectively. The Budyko framework was used to separate the respective contribution of drivers to change in discharge. Future response of the watershed to climate change was predicted using the runoff sensitivity equation developed. New hydrological insights for the region There was a significant increasing trend in the discharge with a breakpoint in 1977. Land use change was found to be the main driver of change in discharge accounting for 97.5% of the change. Climate variability only caused a net increase of the remaining 2.5% of the change; which was caused by counter impacts on discharge of increase in rainfall (increased discharge by 24%) and increase in potential evapotranspiration (decreased discharge by 21.5%). Climate change was predicted to cause a moderate 16% and 15% increase in streamflow in the next 20 and 50 years respectively. Change in discharge was specifically attributed to deforestation at the headwaters of the watershed