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Spatial information of climatological frequency distribution of daily precipitation is highly valuable for a wide range of applications. Accurate estimation of climatology can be made for gauged locations where quality and lengthy observations are available. For ungauged or poorly gauged locations, however, indirect estimation is needed. One approach is to use a gridded daily precipitation dataset derived from interpolating observations. However, gridded daily precipitation data can be subject to large errors when gauge density is low. In addition, most interpolation methods tend to smooth the extreme values and increase the low ones, leading to unrealistic statistical properties and therefore poor estimation of daily climatology. Another approach is to first derive climatology at gauged locations and then interpolate climatology to ungauged locations. While this approach is likely to be more robust than the first approach, low gauge density can still cause significant errors especially in areas of complex terrain. In this study, we develop a method that postprocesses spatially consistent and rich reanalysis data using accurate observations at gauged locations. At an ungauged location, daily precipitation amounts from the reanalysis are bias‐corrected using quantile‐mapping guided by frequency distributions of reanalysis data and observations at a nearby gauged location (reference location). The bias‐corrected precipitation amounts are then used to estimate the climatology for the ungauged location. This method eliminates the need for interpolation and therefore its adverse effects. Special care is taken in quantile‐mapping when extrapolating beyond the range of reanalysis data at the reference location. We evaluate the method at 50 locations in Australia, using the Bureau of Meteorology Atmospheric high‐resolution Regional Reanalysis for Australia (BARRA) and precipitation observation network across Australia. These locations are chosen to represent different climate regions in Australia and have observations to validate the postprocessed reanalysis climatology of daily precipitation. Results show that the postprocessed climatology is consistent with observations, in terms of frequency distribution, high quantiles, probabilities of wet and dry days and their transitions. [ABSTRACT FROM AUTHOR]

High‐resolution regional reanalysis datasets have the potential to provide valuable guidance to emergency management agencies, highlighting areas at risk of severe weather, including estimates of return periods of various hazardous weather phenomena. The BARRA regional reanalysis for Australia comprises a reanalysis for a broad region around Australia at moderately high spatial and temporal resolution (12 km/hourly), together with four subdomains at high resolution (1.5 km/1 h). Here, we document four applications of BARRA developed for emergency management: optimal placement of portable automatic weather stations for fire weather monitoring; climatology of low‐level wind shear conducive to cool‐season tornadogenesis; development of rainfall intensity–frequency–duration curves based on the gridded reanalysis data; and development of a climatology across Australia of parameters associated with severe thunderstorm occurrence. [ABSTRACT FROM AUTHOR]

Estimation of base flow from streamflow hydrographs has been a major challenge in hydrology for decades, leading to developments of base flow separation filters. When without tracer or groundwater data to calibrate the filters, the standard approach to apply these filters in practice involves some degrees of subjectivity in choosing the filter parameters. This paper investigates the use of signature-based calibration in implementing base flow filtering by testing seven possible hydrological signatures of base flow against modeled daily base flow produced by Li et al. (2014) for a range of synthetic catchments simulated with HydroGeoSphere. Our evaluation demonstrates that such a calibration method with few selected signatures as objectives is capable of calibrating a filter- Eckhardt filter-to yield satisfactory base flow estimates at daily, monthly and long-term time scales, outperforming the standard approach. The best performing signatures can be readily derived from streamflow time series. While their performance depends on the catchment characteristics, the catchments where the signature method performs can be distinguished using commonly-used descriptors of flow dynamics. [ABSTRACT FROM AUTHOR]

Recursive digital filters (RDFs) are widely used for estimating base flow from streamflow hydrographs, and various forms of RDFs have been developed based on different physical models. Numerical experiments have been used to objectively evaluate their performance, but they have not been sufficiently comprehensive to assess a wide range of RDFs. This paper extends these studies to understand the limitations of a generalized RDF method as a pathway for future field calibration. Two formalisms are presented to generalize most existing RDFs, allowing systematic tuning of their complexity. The RDFs with variable complexity are evaluated collectively in a synthetic setting, using modeled daily base flow produced by Li et al. (2014) from a range of synthetic catchments simulated with HydroGeoSphere. Our evaluation reveals that there are optimal RDF complexities in reproducing base flow simulations but shows that there is an inherent physical inconsistency within the RDF construction. Even under the idealized setting where true base flow data are available to calibrate the RDFs, there is persistent disagreement between true and estimated base flow over catchments with small base flow components, low saturated hydraulic conductivity of the soil and larger surface runoff. The simplest explanation is that low base flow 'signal' in the streamflow data is hard to distinguish, although more complex RDFs can improve upon the simpler Eckhardt filter at these catchments. [ABSTRACT FROM AUTHOR]