A spatiotemporal object-based rainfall analysis method is used to evaluate the hydrological response of two systematic satellite error sources for storm estimation in the Capivari catchment, Brazil. This method is called Spatiotemporal Contiguous Object-based Rainfall Analysis (ST-CORA) specifically evaluates the error structure of satellite-based rainfall products using a 3D pattern clustering algorithm. Errors due to location and magnitude in the Near Real-time (NRT) CMORPH product are subtracted by adjusting the shift and the intensity distribution with respect to a storm object obtained from gauge-adjusted weather radar. Synthetic scenarios of each error source are used as forcing for hourly calibrated distributed hydrological ‘wflow-sbm’ model to evaluate the main sources of systematic errors in the hydrological response. Two types of storm events in the study area are evaluated: short-lived and a long-lived storm. The results indicate that the spatiotemporal characteristics obtained by ST-CORA clearly reflect the main source of errors of the CMORPH storm detection. It is found that location is the main source of error for the short-lived storm event, while volume is the main source in the long-lived storm event. The subtraction of both errors leads to an important reduction of the simulated streamflow in the catchment. The method applied can be useful in bias correction schemes for satellite estimations especially for extreme precipitation events.

Advances in near real-time rainstorm prediction using remote sensing have offered important opportunities for effective disaster management. However, this information is subject to several sources of systematic errors that need to be corrected. Temporal and spatial characteristics of both satellite and in-situ data can be combined to enhance the quality of storm estimates. In this study, we present a spatiotemporal object-based method to bias correct two sources of systematic error in satellites: displacement and volume. The method, Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction (ST-CORAbico), uses the spatiotemporal rainfall analysis ST-CORA incorporated with a multivariate kernel density storm segmentation for describing the main storm event characteristics (duration, spatial extension, volume, maximum intensity, centroid). Displacement and volume are corrected by adjusting the spatiotemporal structure and the intensity distribution, respectively. ST-CORAbico was applied to correct the early version of the Integrated Multi-satellite Retrievals for the Global Precipitation Mission (GPM-IMERG) over the Lower Mekong basin in Thailand during the monsoon season from 2014 to 2017. The performance of ST-CORABico is compared against the Distribution Transformation (DT) and Gamma Quantile Mapping (GQM) probabilistic methods. A total of 120 storm events identified over the study area were classified into short and long-lived storms by using a k-means cluster analysis method. Examples for both storm event types describe the error reduction due to location and magnitude by ST-CORAbico. The results showed that the displacement and magnitude correction made by ST-CORAbico considerably reduced RMSE and bias of GPM-IMERG. In both storm event types, this method showed a lower impact on the spatial correlation of the storm event. In comparison with DT and GQM, ST-CORAbico showed a superior performance, outperforming both approaches. This spatiotemporal bias correction method offers a new approach to enhance the accuracy of satellite-derived information for near real-time estimation of storm events.

This study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events.

An important driver of the terrestrial hydrological cycle is atmospheric evaporative demand. Recent studies using measurements of pan evaporation have found evidence that the atmospheric evaporative demand has been declining over the second half of the 20th century. This work analyses long-term time series of pan evaporation obtained from approximately 150±30 weather stations located in Mexico with aridity indexes ranging from 0.3 to 10 for 1961-2010. The results show a consistent decline in annual pan evaporation for 1960-1990 (-3.8mmyear^-2) and for 1990-2010 (-2.6mmyear^-2) periods whereas the average change during the complete period corresponds to -3.3mmyear^-2. Statistically significant negative changes using the non-parametric Mann-Kendall test were found in 43% of the stations for the early and 27% for the recent periods, respectively. The temperature, relative humidity, radiative and aerodynamic controls attributed to the observed changes are analysed with the Noah model output from the Global Land Data Assimilation System Version 2 (GLDAS-2). Among the climatological variables extracted from GLDAS-2, it was the annual wind speed and net radiation that gave the highest statistical correlations. This work agrees with previous studies that pan evaporation rates have been in a declining trend during the second half of the 20th century though milder decline rates have been observed over the last 20years. Finally, we show that the magnitude of change in regions dominated by wind and in those dominated by radiative processes can strongly differ.