Remotely sensed ensemble of the water cycle

Published: 06-11-2020| Version 3 | DOI: 10.17632/r24rdxt73j.3
Ronnie Abolafia-Rosenzweig,
Ben Livneh


We present a new REmotely Sensed ENsemble of the water cycle (REESEN). The REESEN approach generates a large number of realizations of the remotely sensed water budget and enforces closure for each realization. The REESEN approach is applied to 24 large river basins from Oct. 2002- Dec. 2014. Three water balance closure algorithms are evaluated, ranging from simple redistribution of residuals to more complex Kalman-filtering and multiple-collocation approaches, to understand the impact of algorithm choice on the resulting water budget partitioning. Therefore, three REESEN ensembles are generated at each basin, one for each closure technique. Compared with a published climate data record, the ensemble shows strong agreement for precipitation, evapotranspiration and changes in storage (R2: 0.91-0.95), with less agreement for streamflow (R2: 0.42-0.47), which may be indicative of LSM biases in the climate data record. Water balance residual errors resulting from combinations of raw products vary significantly (p<0.001) with latitude, with a tendency for positive biases for low- and mid-latitude basins, and negative biases elsewhere. Overall, residual errors are equivalent to 15% of total precipitation when averaged across all data products and basins. Enforcing water balance closure reduces uncertainty relative to raw retrievals for 88%-100% of all timesteps. This observation-based dataset is distinct from modeled estimates and therefore has the potential to preserve important information of anthropogenic effects on the water balance. For example, the ensemble shows higher evapotranspiration and greater storage depletion than the model-based climate data record during months of heavy irrigation over the Sacramento-San Joaquin basin system. MATLAB files were created for REESEN ensembles used in Abolafia-Rosenzweig et al. (2020). There is one file for each basin system and each closure algorithm: proportional redistribution (PR), multiple collocation (MCL), constrained Kalman filter (CKF). The file descriptions are described in the README. Shapefiles used in the study (Fig. 1 of Abolafia-Rosenzweig et al., 2020) are in corresponding folders to each basin.