Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data

Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects floods, drought, landslides. However, they are collected frequently from scarce often insufficient network ground-based rain-gauge stations to generate continuous precipitation maps. Recently, maps derived satellite have not been sufficiently linked rain gauges satellite-derived soil moisture improve assessment distribution using spatial statistics. Kriging methods used enhance estimation precipitations. The aim this study was assess two geostatistical methods, ordinary kriging (OK) cokriging (OCK), one deterministic method (i.e., inverse distance weighting (IDW)) improved interpolation quarterly monthly precipitations in Poland near-border areas neighbouring countries (~325,000 or 800,000 km2). Quarterly during 5-year period (2010–2014) 113–116 located area were used. Additionally, years 2014–2017 over 400 spatiotemporal on (SM) Soil Moisture Ocean Salinity (SMOS) global (launched 2009) as an auxiliary variable addition OCK method. predictive performance best all quarters, indicated by coefficient determination (R2 = 0.944–0.992), less efficient 0.039–0.634) OK IDW methods. As precipitation, considerably higher than that OK, similarly with precipitation. better indicates SMOS can be valuable source and/or other multivariate various regions world.