Can the Budyko framework and satellite data help improve hydrological modeling in ungauged and poorly monitored catchments? The case study of the Lurín catchment in Peru

Predictions in ungauged basins still present one of the major challenges in hydrology. In many cases, the absence of a stream gauge also implies a low density of the meteorological monitoring network in these catchments and surroundings as well as little available data on water management infrastructure and agricultural consumptions. This combination creates a circle of uncertainties and thus individual influences of relevant water balance components are difficult to disentangle and quantify. 

The original Budyko curve presents a very general model that yields, to first order, an estimate of the steady-state water balance of a catchment at the climatological scale, assuming its landscape and functioning has evolved naturally and free of anthropogenic interferences. Even at smaller time scales, the Budyko relationship allows approximating the water partitioning in the catchment, and thus helps correct erroneous assumptions[JW1]  or missing information about for instance unknown human-induced alterations. On the other hand, an increasing variety of global remote-sensing data products is becoming available providing spatial estimates of land surface properties such as for instance vegetation indexes or soil moisture. Even if the predictive power of such products in terms of absolute values remains questionable, it is possible to derive coarse spatial patterns or temporal dynamics to narrow down zones and orders of magnitude of interferences with the natural hydrological cycle such as reservoirs or irrigated lands. This study combines these two general approaches in order to improve hydrological modelling and system understanding of the semi-arid Lurín catchment in the Western Andes of Peru.