Characterizing natural variability in complex hydrological systems using Passive Microwave based Climate Data Records: a case study for the Okavango Delta

The Okavango river system in southern Africa is known for its strong interannual variability of hydrological conditions. Here we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model using an inter-calibrated, long term, multi-sensor passive microwave satellite data record (1998-2020). We also investigate how these interannual variations relate to state-of-the-art climate reanalysis data from ERA5-Land. We analyzed both the upstream river catchment and the Okavango Delta, supported by independent data records of discharge measurements, inundated area, precipitation and vegetation dynamics observed by optical satellites. 

The results from this study show that the seasonal vegetation optical depth anomalies have a strong correspondence with MODIS Leaf Area Index over both the Delta and the Catchment. Land surface temperature anomalies derived from passive microwave observations best match those of ERA5-Land, as compared to MODIS nighttime LST. Although surface soil moisture anomalies from passive microwave observations and ERA5-Land also correlate well, an in-depth evaluation over the Delta uncovered situations where passive microwave satellites record strong fluctuations, while ERA5-Land does not.

This difference is further analyzed using information on inundated area, river discharge and precipitation. The passive microwave soil moisture signal demonstrates a response to both the inundated area and precipitation. ERA5-Land however, which by default does not account for any lateral influx from rivers, only shows a response to the precipitation information that is used as forcing. This also causes the reanalysis model to miss record low land surface temperature values as it underestimates the latent heat flux in certain years, which can have a large impact on detecting and assessing extremes.

These findings demonstrate the complexity of this hydrological system and suggest that future land surface model generations should also include lateral land surface exchange. Our study highlights the importance of maintaining and improving climate data records of soil moisture, vegetation and land surface temperature from passive microwave observations and other observation systems.