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Recent advancements in numerical weather predictions have improved forecasting performance at longer lead times. Seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to generate seasonal flow forecasts (SFFs) by combining seasonal weather forecasts and hydrological models. However, producing SFFs with good skill at a finer catchment scale remains challenging, hindering their practical application and adoption by water managers. Consequently, water management decisions in both South Korea and numerous other countries continue to rely on worst-case scenarios and the conventional ensemble streamflow prediction (ESP) method. This study investigates the potential of SFFs in South Korea at the catchment scale, examining 12 reservoir catchments of varying sizes (ranging from 59 to 6648 km 2) over the last decade (2011–2020). Seasonal weather forecast data (including precipitation, temperature and evapotranspiration) from the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) are used to drive the Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. We assess the contribution of each weather variable to the performance of flow forecasting by isolating individual variables. In addition, we quantitatively evaluate the "overall skill" of SFFs, representing the probability of outperforming the benchmark (ESP), using the continuous ranked probability skill score (CRPSS). Our results highlight that precipitation is the most important variable in determining the performance of SFFs and that temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the overall skill. Furthermore, bias-corrected SFFs have skill with respect to ESP up to 3 months ahead, this being particularly evident during abnormally dry years. To facilitate future applications in other regions, the code developed for this analysis has been made available as an open-source Python package. [ABSTRACT FROM AUTHOR]

Recent advancements in numerical weather predictions have improved their forecasting performance at longer lead times for several months. As a result, seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to link seasonal weather forecasts with Seasonal Flow Forecasts (SFFs) using diverse hydrological models. However, generating SFFs with good skill at finer scales such as catchment remain challenging, hindering their application in practice and adoption by water managers. Consequently, water management decisions, not only in South Korea but also in many other countries, continue to rely on worst-case scenarios and the conventional Ensemble Streamflow Prediction (ESP) method. This study examines the potential of SFFs in South Korea at a catchment-scale. The analysis was conducted across 12 operational reservoir catchments of various size (from 59 to 6648 km2) over a last decade (2011–2020). Seasonal weather forecasts data (precipitation, temperature and evapotranspiration) from the ECMWF (European Centre for Medium-Range Weather Forecasts, system5) is used to drive a Tank model (conceptual hydrological model) to generate the flow ensemble forecasts. The actual skill of the forecasts is quantitatively evaluated using the Continuous Ranked Probability Skill Score (CRPSS), and it is probabilistically compared with ESP, which is the most popular forecasting system. Our results highlight that precipitation is the most important variable in determining the skill of SFFs, while temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the skill of SFFs. Furthermore, bias corrected SFFs showed higher skill than ESP up to 3 months ahead, and it was particularly evident during abnormally dry years. To facilitate future applications to other regions, freely available Python packages for analysing seasonal weather and flow forecasts have been made accessible. [ABSTRACT FROM AUTHOR]

Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual-polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available. Index Terms--Bayes classifier, fuzzy logic, fuzzy sets, polarimetric radar, precipitation, radar, radar clutter, radar measurements, sea clutter.

Understanding soil moisture dynamics at the sub-kilometre scale is increasingly important especially with continuous development of hyper-resolution land-surface and hydrological models. Cosmic Ray Neutron Sensors (CRNS) are able to provide estimates of soil moisture at this elusive scale and networks of these sensors have been expanding across the world over the previous decade. However, each network currently implements its own protocol when processing raw data into soil moisture estimates. As a consequence, this lack of a harmonized global dataset can ultimately lead to limitations in the global assessment of the CRNS technology from multiple networks. Here we present crspy, an open-source python tool that is designed to facilitate the processing of raw CRNS data into soil moisture estimates in an easy and harmonized way. We outline the basic structure of this tool discussing the correction methods used as well as discussing the metadata that crspy can create about each site. Metadata can add value to global scale studies of field scale soil moisture estimates by providing additional routes to understanding catchment similarities and differences. We demonstrate that current differences in processing methodologies can lead to misinterpretations when comparing sites from different networks and having a tool to provide a harmonized dataset can help to mitigate these issues. By being open source, crspy can also serve as a development and testing tool for new understanding of the CRNS technology as well as being used as a teaching tool for the community. [ABSTRACT FROM AUTHOR]

Accurate estimation of the Freezing Level (FL) is essential in radar rainfall estimation to mitigate the bright band enhancement, to classify hydrometeors, to correct for rain-attenuation and to calibrate radar measurements. Here we present a novel and robust FL estimation algorithm that can be applied to either Vertical Profiles (VPs) or Quasi-Vertical Profiles (QVPs) built from operational polarimetric weather radar scans. The algorithm depends only on data collected by the radar itself, and it is based on the detection of strong gradients within the profiles and relies on the combination of several polarimetric variables. The VPs and QVPs of ZH showed a good similarity in the profiles (r ≈ 0.7) even though the QVPs are built from low-elevation angles. The algorithm is applied to one year of rainfall events and validated using measured FLs from radiosonde data. The results demonstrated that combining the profiles of ZH, ΡHV and the gradient of the velocity V showed the best FL estimation performance when using VPs, whereas combining the profiles of ZH, ΡHV and ZDR showed the best FL estimation performance when using QVPs. The VP computed from the gradient of the velocity showed to be extremely valuable in the FL estimation when using VPs. The errors in the FL estimation using either VPs or QVPs are within 250 m. [ABSTRACT FROM AUTHOR]

Short-term radar-based forecasts of precipitation can be achieved through the implementation ofnowcastingmodels, essentially based on the rainfall extrapolation from a series of consecutive radar scans. Recent advances in this field include the development ofhybrid models, aimed at merging the benefits of radar nowcasting and numerical weather prediction models, andprobabilistic systems, aimed at addressing the sources of uncertainty in radar rainfall forecasts by means ofensembles. This paper provides an overview of radar nowcasting methods and approaches, with an emphasis on recent developments in this field. [ABSTRACT FROM AUTHOR]

This paper deals with the assessment of hydrological predictions driven by probabilistic hybrid rainfall forecasts obtained by merging numerical weather prediction and radar-based rainfall forecasts. Deterministic and ensemble hybrid rainfall forecasts have been utilized to provide river flow predictions on a catchment study located in the UK. A novel assessment methodology has been developed and implemented for the purpose of comparing deterministic and ensemble river flow predictions. The assessment methodology provides a measure of the degree of underestimation/overestimation characterizing the forecasts in comparison with a series of normalized reference thresholds. The analysis of the results obtained by implementing this evaluation methodology shows that the overall performance of the deterministic forecasts is very similar to that of the ensemble forecasts, with a slightly larger degree of underestimation affecting the ensemble forecasts in comparison with the deterministic forecasts. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]