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Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many near-real-time applications require near-real-time precipitation estimates, and in the absence of reanalysis datasets due to their usually delayed release, precipitation forecasts could offer a potential alternative. This motivates the inter-comparison of forecast and reanalysis products conducted in this study investigating the statistical accuracy and hydrological utility of three precipitation datasets from European Centre for Medium-Range Weather Forecasts (ECMWF) [i.e. high-resolution (HRES) forecasts, Ensemble Mean (EM) forecasts, and ECMWF's 5th generation reanalysis (ERA5)] over Türkiye and Germany for 2007–2018 using ground-based observed data as truth. ERA5 has higher bias than HRES and EM in both regions while HRES has the lowest daily correlations. ERA5 (EM) shows the highest hydrological utility in Germany (Türkiye). ERA5 showed improved monthly correlations compared to forecasts; the improvement over Germany (i.e. 0.02) is better than over Türkiye (~0.01). Wetness and topographical complexity of a region affect precipitation estimation uncertainty there. [ABSTRACT FROM AUTHOR]

Spaceborne precipitation radars provide an unprecedented opportunity to study three-dimensional structure of precipitation, particularly over the open ocean where in-situ observations are rather meagre. In this study, instantaneous surface precipitation characteristics and their vertical structures during the tropical cyclones (TCs) over the North Indian Ocean (NIO) between 2014 and 2022 have been analysed using the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. Although stratiform precipitation accounts for more than 70% of total TC surface precipitation area, convective precipitation contributes about half of the total TC surface precipitation amount over the NIO. About 90% of stratiform TC precipitation area yields surface precipitation of less than 10 mm/hour. The vertical structures of stratiform and convective TC precipitation vary with surface precipitation intensity and have nearly similar characteristics over both basins of the NIO. This preliminary quantitative TC precipitation analysis would be useful for better understanding of precipitation processes during TCs over the NIO, and for further advancement in numerical models through improved parameterization schemes for TC precipitation forecasting. [ABSTRACT FROM AUTHOR]

Accurate precipitation forecasting with sufficient lead time is a prerequisite for developing a robust flood warning system (FWS), which is very challenging, particularly in developing countries like India. This study evaluates the utility of the TIGGE multimodel ensemble meteorological forecasts over the Upper Bhima River basin and investigated the hydrological utility of the TIGGE forecasts through a calibrated hydrological (VIC-RAPID) model followed by the post-processing of streamflow through Bayesian model average (BMA) approach. Results show that the quality of the meteorological forecasts of precipitation, and of the simulated streamflow, deteriorated with increasing lead time, which can be ameliorated with a suitable bias-correction technique. The BMA-based post-processing further improved the streamflow simulations, especially in case of extreme events, which highlighted its efficacy in flood forecasting. From the results of the study, it is recommended that a compound system of improved precipitation prediction, calibrated VIC-RAPID model and post-processing of streamflows in an integrated manner would facilitate a reliable FWS for operational purposes. [ABSTRACT FROM AUTHOR]

The Bermejo River, located in northern Argentina, has a flow regime controlled by precipitation. In an area characterized by its risk of flooding and land-sliding during the summer, seasonal precipitation forecast becomes a valuable tool for risk assessment and better management of hydric resources. This study focuses on identifying remote forcings of precipitation variability for the upper sub-basin of the Bermejo River Basin, and developing multiple linear regression models of areal spring precipitation (September to November), the beginning of the rainy season, considering predictors monitored on the preceding August. Positive rainfall anomalies in spring relate to higher monthly and maximum daily streamflow in the upper and lower sub-basins. Two forecast models arose as the ones with best performance when using leave-one-out-cross-validation. Predictors involved in these models (four and three predictors, respectively) emphasize the influence of the circulation in middle-low levels over the Pacific Ocean, as well as of the sea surface temperature in the El Niño region and the low-level meridional wind in tropical South America. The two models share similar performance metrics, although the model with less predictors has a better skill for the detection of normal and above-normal rainfall seasons. [ABSTRACT FROM AUTHOR]

A sequence of radar echo maps can visually show the motion and variation trends of the echo area, making it a common tool for precipitation forecasting. The spatiotemporal context reveals the correlations of variation trends among different parts within the echo area. This paper proposes a novel precipitation forecasting model, ISTC-SA-MIM (Interactive Spatiotemporal Context Learning with Self-Attention and Memory in Memory), based on the MIM. Leveraging the spatiotemporal interactions and self-attention mechanism of the ISTC-SA structure, the proposed model effectively captures both long-term and short-term spatiotemporal contexts. By memorizing the spatiotemporal context and non-stationary information, ISTC-SA-MIM can accurately predict the motion and variation trends of the echo area. Radar echo data from the Qingdao station are collected as the dataset to evaluate the commonly used spatiotemporal models and ISTC-SA-MIM. The experiments demonstrate that ISTC-SA-MIM can predict the variation trends of the echo area more accurately by learning the spatiotemporal context. [ABSTRACT FROM AUTHOR]

The accuracy of quantitative rainfall forecasts (QPFs) obtained from numerical weather prediction (NWP) models plays a crucial role in setting up a catchment streamflow forecasting system. Additionally, a suitable hydrological model is required. This study addresses input and model uncertainty in developing a five-day streamflow forecasting system in Narmada River Basin. We use deterministic and ensemble QPFs obtained from the Japan Meteorological Agency (JMA), National Centre for Medium Range Weather Forecasting (NCMRWF), and European Centre for Medium-Range Weather Forecasts (ECMWF). We use two hydrological models, the Soil and Water Assessment Tool (SWAT) and variable infiltration capacity (VIC), to generate streamflow forecasts. By comparing simulated streamflow forecasts with the observed discharge data, our results indicate that the forecast accuracy of NCMRWF is better than other forecasting products for lead times of two to five days. The streamflow generated using VIC produces better results than that obtained from the SWAT hydrological model. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

North-central Ukraine is vulnerable to temperature increases and precipitation pattern changes associated with climate change. With water management becoming increasingly important, information on current water sources and moisture recycling is critically needed. Isotope ratios of oxygen (δ18O) and hydrogen (δ2H) in precipitation are sensitive to these variables and allow comparisons across the region. The δ2H and δ18O values from collected precipitation in Kyiv and Cherkasy in 2020 and published 3H data for Kyiv from the year 2000 show an influence of the North Atlantic Oscillation (NAO) and provide information about processes affecting precipitation along the storm trajectory. The δ18O values also show a correlation with temperature, indicating that precipitation patterns may be affected by the rising temperatures in the region, as predicted by recent regional studies using Representative Concentration Pathway scenarios and the global climate model GFDL-ESM2M. When compared to backtracked storm trajectory and NAO data, clear relationships emerged between water isotope ratios, storm paths, and likely moisture recycling. Overall, δ2H, δ18O, 3H, and backtracked storm trajectory data provide more regional and local information on water vapour processes, improving climate-change-driven precipitation forecasts in Ukraine. [ABSTRACT FROM AUTHOR]

In this work, an adaptive real-time approach is presented to improve the rainfall forecasts of the weather research and forecasting (WRF) model. Heavy rainfall events are considered to make 6-, 12-, 18-, and 24-hour ahead predictions, and to define various initial conditions of the forecasting model. Comparing various forecasts and observations specifies precise forecasting time line and physical settings of the WRF model that would lead to an improved forecasting model. An adaptive real-time approach is defined using a combination of observed precipitation and WRF forecasts. Forecasted precipitation values for each event are updated by the observed precipitation at each three-hour interval. A sigmoidal curve is fitted to the observations and remaining forecasts to provide modified values. The process continues step by step, and thus an adaptive real-time forecasting model is developed to improve predictions. The methodology is applied to Tehran and Golestan watersheds in Iran, and the results indicate that the developed methodology improves WRF forecasts in terms of categorical and statistical metrics. [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecast (QPF) obtained from a numerical weather prediction model is used for streamflow forecasting. This study systematically evaluates the performance of a deterministic QPF obtained from a national agency, the National Centre for Medium Range Weather Forecasting (NCMRWF), compared with that obtained from three international agencies. For observation/reference datasets, we used satellite, raingauge, and satellite-gauge merged data products. The forecast skill is evaluated for 177 sub-basins in Ganga, Narmada, Mahanadi, Tapti, and Godavari river basins. Our results indicate that the forecast accuracy of NCMRWF is closely comparable with that of QPF obtained from a European agency. We conclude that selecting accurate observation/reference data is critical for forecast performance evaluation. We determine the preferred forecast and reference datasets in the selected river basins. Our results suggest that for estimating the parameters of a post-processor, a comparable dataset can be used for which data are available for a longer duration. [ABSTRACT FROM AUTHOR]

The Generalized Space Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR) model is often used to forecast data that have time and location components. At the current time, precipitation is difficult to predict because it has patterns and characteristics that are hard to identify. This phenomenon is referred to as a non-linear phenomenon. One model that considers non-linearity is the neural network. The GSTAR-SUR model is a linear model, so at the current time is needed non-linear model on precipitation forecasting. This current research compares the precipitation forecast results from the GSTAR-SUR model and a hybrid GSTAR-SUR-with-neural-network approach in residuals. The data used in this research are the precipitation records for four locations in West Java for the years 2005 to 2015. The precipitation data represent 10-day-long observations. Precipitation in the four locations will be modeled using two approaches, i.e., GSTAR-SUR and GSTAR-SUR-NN. In the GSTAR-SUR-NN model, the residuals of the GSTAR-SUR model will be the basis of a neural network model. In this case, the GSTAR-SUR-NN model resulted in forecast data that are closer to the observed values than those from the GSTAR-SUR model. The mean forecasting error of the GSTAR-SUR-NN model was 3.8917 mm, while that of the GSTAR-SUR model was 4.3295 mm. [ABSTRACT FROM AUTHOR]

A new multi-step, hybrid artificial intelligence-based model is proposed to forecast future precipitation anomalies using relevant historical climate data coupled with large-scale climate oscillation features derived from the most relevant synoptic-scale climate mode indices. First, NSGA (non-dominated sorting genetic algorithm), as a feature selection strategy, is incorporated to search for statistically relevant inputs from climate data (temperature and humidity), sea-surface temperatures (Niño3, Niño3.4 and Niño4) and synoptic-scale indices (SOI, PDO, IOD, EMI, SAM). Next, the SVD (singular value decomposition) algorithm is applied to decompose all selected inputs, thus capturing the most relevant oscillatory features more clearly; then, the monthly lagged data are incorporated into a random forest model to generate future precipitation anomalies. The proposed model is applied in four districts of Pakistan and benchmarked by means of a standalone kernel ridge regression (KRR) model that is integrated with NSGA-SVD (hybrid NSGA-SVD-KRR) and the NSGA-RF and NSGA-KRR baseline models. Based on its high-predictive accuracy and versatility, the new model appears to be a pertinent tool for precipitation anomaly forecasting. [ABSTRACT FROM AUTHOR]

This paper attempts to design statistical models to forecast annual precipitation in the Neuquen and Limay river basins in the Comahue region of Argentina. These forecasts are especially useful as they are used to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and the safety of the population. In this work, multiple linear regression statistical models are built to forecast mean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter (June–August), forecasting models have been developed for the beginning of March and for the beginning of June, just before the rainy season starts. The results show that the sea-surface temperatures of the Indian and Pacific oceans are good predictors for March models and explain 42.8% of the precipitation index variance. The efficiency of the models increases in June, adding more predictors related to the autumn circulation. [ABSTRACT FROM AUTHOR]

Maximal light-use efficiency (LUE), ɛmax, is a measure of the conversion efficiency of the photosynthetically active radiation absorbed by plants to net primary productivity (NPP), based on the principle that an optimal plant productive capability exists when the LUE is at its maximum. ɛmax is an important parameter for modelling regional NPP and is conventionally applied at the biome level using a constant value. In this study, we estimated type-specific ɛmax values for three dominant land cover types on the Mongolian Plateau: 0.621 g C MJ–1 (MJ is the mega Joules and 1 MJ = 106 J) for meadow steppe, 0.534 g C MJ–1 for typical steppe, and 0.520 g C MJ–1 for desert steppe. With these steppe-specific modified ɛmax values, we were able to examine changes in NPP for 2001–2015 on the plateau, as well as their likely responses to regional climate change. The use of different ɛmax values for each steppe type improved the accuracy of the Carnegie Ames Stanford Approach (CASA) Biosphere model predictions of grassland NPP by 18.8% (R2 = 0.48 to R2 = 0.57; R2 is the coefficient of determination) over the observation period. Previous studies based on a constant ɛmax (0.541 g C MJ–1) appear to underestimate ɛmax and NPP in meadow steppe, highlighting the importance of setting type-specific ɛmax values for different land cover types in the remote-sensing modelling of grassland NPP. However, more detailed maps of biome sub-classes, with species composition, would be valuable for future attempts to determine appropriate ɛmax values. The growing season (April–October) NPP on the plateau increased significantly from 2001 to 2015, with an annual increment of 4.44 g C m–2 y–1. This trend was strongly governed by the change in summer NPP across the plateau. In comparison, NPP in the spring and autumn did not influence the change in total NPP, which was likely due to their relatively small values. Summer precipitation and the related drought stress were the chief factors responsible for plateau-scale NPP changes, due to the high proportion of summer precipitation and NPP in the annual totals. This may induce important environmental features, such as dzud (a Mongolian term for a severe winter) and desertification on the plateau. [ABSTRACT FROM AUTHOR]

Changing thermodynamic conditions, such as pressure, temperature, and crude oil composition causes asphaltene precipitation and deposition in both reservoirs and surface facilities. Therefore, prediction of asphaltene phase behavior is crucial for reducing asphaltene deposition problems. To do so, mathematical thermodynamic modeling has been performed in this study. In this article, the cubic-plus-association (CPA) equation of state was utilized to model asphaltene precipitation behavior in live oil samples. CPA considers both physical and association terms of mixtures. Owing to the association nature of asphaltene molecules, this equation is able to predict the amount of asphaltene precipitation at different environmental conditions. Two equations of state including Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) were used for the physical part of the proposed CPA model. The results of the developed CPA-PR and CPA-SRK models were compared with the experimental data. It is shown that using SRK as the physical part of CPA makes the developed model more accurate. [ABSTRACT FROM AUTHOR]

For flood-prone countries subject to large-scale and seasonal flooding, precipitation forecasting is the single most important factor for improving the skill of flood forecasting for such large river basins dominated by the monsoon. Several flood forecasting agencies in South and Southeast Asia, where monsoon floods dominate (e.g. Bangladesh, Pakistan, India, Thailand and Vietnam), are currently using quantitative precipitation forecast (QPF) from numerical weather prediction (NWP) models. Although there are numerous studies reported in the literature to evaluate QPF precipitation performance, there appears to be lack of studies about the impact on the flood forecasting skill. In this study, we demonstrate tangible improvements in flood forecasting based on NWP precipitation forecast using an approach that is operationally feasible in resource-limited settings of many flood agencies. Our improvement is based on a bias correction methodology for enhancing the skill of QPF using observed and QPF climatology. The proposed approach can be applied to any type of QPF dataset such as those dynamically downscaled from regional NWP. We demonstrate clear and consistent improvement in the enhancement of flood forecasting skill at longer lead times of up to 7 days in three river basins of Ganges, Brahmaputra and Mekong by about 50% (reduction in RMSE) or 25% improvement in correlation when compared to the forecasts obtained from uncorrected QPF. Furthermore, our proposed bias correction methodology yields significantly higher skill improvement in flood forecast for global (non-downscaled) QPF than those dynamically downscaled QPFs for the macroscale hydrologic model used for forecasting stream flows. The simplicity of the QPF bias correction methodology along with the numerical efficiency can be of tremendous appeal to operational flood forecasting agencies of the developing world faced with large-scale monsoonal flooding and limited computational resources and time for disaster response. [ABSTRACT FROM AUTHOR]

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. "All-India" monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes. [ABSTRACT FROM AUTHOR]

Two methods for generating streamflow forecasts in a Sahelian watershed, the Sirba basin, were compared. The direct method used a linear relationship to relate sea-surface temperature to annual streamflow, and then disaggregated on a monthly time scale. The indirect method used a linear relationship to generate annual precipitation forecasts, a temporal disaggregation to generate daily precipitation and the SWAT (Soil and Water Assessment Tool) model to generate monthly streamflow. The accuracy of the forecasts was assessed using the coefficient of determination, the Nash-Sutcliffe coefficient and the Hit score, and their economic value was evaluated using the cost/loss ratio method. The results revealed that the indirect method was slightly more effective than the direct method. However, the direct method achieved higher economic value in the majority of cost/loss situations, allowed for predictions with longer lead times and required less information. [ABSTRACT FROM AUTHOR]

The Midwestern United States is the heartland of agriculture production, and changes in the hydro-climatology may affect both the quantity and the quality of production. Seasonal temperature and precipitation were analyzed for trends and shifts. The results indicate an increasing trend in spring temperature (6.4 °F) and summer precipitation (1.2 inches). Shifts in the variables were dominant during two periods: 1920–1930 and 1970–1990. The observed changes not only provide scientific reference for assessing the impact on water resources as a result of climate change, but also help water managers and planners in taking proactive decisions to mitigate the water stress in the region. [ABSTRACT FROM PUBLISHER]

A problem frequently met in engineering hydrology is the forecasting of hydrological variables conditional on their historical observations and the hindcasts and forecasts of a deterministic model. On the contrary, it is a common practice for climatologists to use the output of general circulation models (GCMs) for the prediction of climatic variables despite their inability to quantify the uncertainty of the predictions. Here we apply the well-established Bayesian processor of forecasts (BPF) for forecasting hydroclimatic variables using stochastic models through coupling them with GCMs. We extend the BPF to cases where long-term persistence appears, using the Hurst-Kolmogorov process (HKp, also known as fractional Gaussian noise) and we investigate its properties analytically. We apply the framework to calculate the distributions of the mean annual temperature and precipitation stochastic processes for the time period 2016–2100 in the United States of America conditional on historical observations and the respective output of GCMs. [ABSTRACT FROM PUBLISHER]

Assessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on the daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the numerical weather prediction (NWP) outputs. EDITOR M.C. Acreman ASSOCIATE EDITOR A. Viglione [ABSTRACT FROM AUTHOR]

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall-runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The article presents information on the papers in the issue representing ongoing research to understand how distribution and timing of precipitation and temperature, management practices and human perceptions of risk affect vulnerability of production systems and water and soil assets. It cites one group of papers that concentrate on research design and methods while another set examines intersections among particular cropping practices and soil organic carbon (SOC) retention or sequestration.

This paper explores the potential for real-time urban flood forecasting based on literature and the results from an online worldwide survey with 176 participants. The survey investigated the use of data in urban flood management as well as the perceived challenges in data acquisition and its principal constraints in urban flood modelling. It was originally assumed that the lack of real-time urban flood forecasting systems is related to the lack of relevant data. Contrary to this assumption, the study found that a significant number of the participants have used some kind of data and that a possible explanation for so few cases is that urban flood managers or modellers (practitioners)maynot be aware they have the means to make a pluvial flood forecast. This paper highlights that urban flood practitioners can make a flood forecast with the resources currently available. [ABSTRACT FROM AUTHOR]

This article presents a comparison between real-time discharges calculated by a flash-flood warning system and post-event flood peak estimates. The studied event occurred on 15 and 16 June 2010 at the Argens catchment located in the south of France. Real-time flood warnings were provided by the AIGA (Adaptation d’Information Géographique pour l’Alerte en Crue) warning system, which is based on a simple distributed hydrological model run at a 1-km2resolution using radar rainfall information. The timing of the warnings (updated every 15 min) was compared to the observed flood impacts. Furthermore, “consolidated” flood peaks estimated by an intensive post-event survey were used to evaluate the AIGA-estimated peak discharges. The results indicated that the AIGA warnings clearly identified the most affected areas. However, the effective lead-time of the event detection was short, especially for fast-response catchments, because the current method does not take into account any rainfall forecast. The flood peak analysis showed a relatively good correspondence between AIGA- and field-estimated peak values, although some differences were due to the rainfall underestimation by the radar and rainfall–runoff model limitations.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Javelle, P., Demargne, J., Defrance, D., Pansu, J. and Arnaud, P., 2014. Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system. Hydrological Sciences Journal, 59 (7), 1390–1402. http://dx.doi.org/10.1080/02626667.2014.923970 [ABSTRACT FROM AUTHOR]

Due to the relatively small spatial scale, as well as rapid response, of urban drainage systems, the use of quantitative rainfall forecasts for providing quantitative flow and depth predictions is a challenging task. Such predictions are important when consideration is given to urban pluvial flooding and receiving water quality, and it is worthwhile to investigate the potential for improved forecasting. In this study, three quantitative precipitation forecast methods of increasing complexity were compared and used to create quantitative forecasts of sewer flows 0–3 h ahead in the centre of a small town in the north of England. The HyRaTrac radar nowcast model was employed, as well as two different versions of the more complex STEPS model. The STEPS model was used as a deterministic nowcasting system, and was also blended with the Numerical Weather Prediction (NWP) model MM5 to investigate the potential of increasing forecast lead-times (LTs) using high-resolution NWP. Predictive LTs between 15 and 90 min gave acceptable results, but were a function of the event type. It was concluded that higher resolution rainfall estimation as well as nowcasts are needed for prediction of both local pluvial flooding and combined sewer overflow spill events.Editor D. Koutsoyiannis; Guest editor R.J. MooreCitation Schellart, A., Liguori, S., Krämer, S., Saul, A., and Rico-Ramirez, M.A., 2014. Comparing quantitative precipitation forecast methods for prediction of sewer flows in a small urban area. Hydrological Sciences Journal, 59 (7), 1418–1436. http://dx.doi.org/10.1080/02626667.2014.920505 [ABSTRACT FROM AUTHOR]

Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Smith, P.J., Panziera, L., and Beven, K.J., 2013. Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts. Hydrological Sciences Journal, 59 (7), 1343–1357. http://dx.doi.org/10.1080/02626667.2013.842647 [ABSTRACT FROM AUTHOR]

Quality is key to ensuring that the potential offered by weather radar networks is realized. To achieve optimum quality, a comprehensive radar data quality management system, designed to monitor the end-to-end radar data processing chain and evaluate product quality, is being developed at the UK Met Office. Three contrasting elements of this system are described: monitoring of key radar hardware performance indicators; generation of long-term integrations of radar products; and monitoring of radar reflectivity factor using synthesized observations from numerical weather prediction model fields. Examples of each component are presented and ways in which the different types of monitoring information have been used to both identify issues with the radar product data quality and help formulate solutions are given.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Harrison, D., Georgiou, S., Gaussiat, N., and Curtis, A., 2013. Long-term diagnostics of precipitation estimates and the development of radar hardware monitoring within a radar product data quality management system. Hydrological Sciences Journal, 59 (7), 1327–1342. http://dx.doi.org/10.1080/02626667.2013.841316 [ABSTRACT FROM AUTHOR]

Automatic raingauge data often serve as an important input to hydrological and weather warning operations. They are not only fundamental in quantitative rainfall analysis, but also act as the ground truth in warning operation and forecast validation. Quality control is required before the data can be used quantitatively due to systematic and random errors. Extremely large random errors and unreasonably small or false zero values can hamper effective monitoring of heavy rain. Yet both are difficult to detect in real-time by objective means. In an attempt to address these problems, a rainfall data quality-control scheme based on radar-raingauge co-kriging analysis was developed. The important threshold values required in the data quality control of 60-min raingauge rainfall were determined from a detailed analysis of the distributions of rainfall residuals defined as the arithmetic difference and the logarithm of the ratio between a raingauge measurement and its co-kriging estimate. The scheme has been developed and is in real-time use in Hong Kong, a coastal city of about 1100 km2area with more than 150 raingauges installed. Geographically, it is located in the subtropics and dominated by heavy convective rainfall in the summer. As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. The performance of the quality-control algorithm was assessed using selected cases and controlled tests, and was found to be satisfactory, with a high error detection rate for the two targeted types of error. Limitations and operational issues identified during a real-time trial of the quality-control scheme are also discussed.Citation Yeung, H.Y., Man, C., Chan, S.T., and Seed, A., 2014. Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis. Hydrological Sciences Journal, 59 (7), 1285–1299. http://dx.doi.org/10.1080/02626667.2013.839873 [ABSTRACT FROM AUTHOR]

Radar quantitative precipitation estimates (QPEs) were assessed using reference values established by means of a geostatistical approach. The reference values were estimated from raingauge data using the block kriging technique, and the reference meshes were selected on the basis of the kriging estimation variance. Agreement between radar QPEs and reference rain amounts was shown to increase slightly with the space–time scales. The statistical distributions of the errors were modelled conditionally with respect to several factors using the GAMLSS approach. The conditional bias of the errors presents a complex structure that depends on the space–time scales and the considered geographical sub-domains, while the standard deviation of the errors has a more homogeneous behaviour. The estimation standard deviation of the reference rainfall and the standard deviation of the errors between radar and reference rainfall were found to have the same magnitude, indicating the limitations of the available network in terms of providing accurate reference values for the spatial scales considered (5–100 km2).Editor D. Koutsoyiannis; Guest editor R.J. MooreCitation Delrieu, G., Bonnifait, L., Kirstetter, P.-E., and Boudevillain, B., 2013. Dependence of radar quantitative precipitation estimation error on the rain intensity in the Cévennes region, France. Hydrological Sciences Journal, 59 (7), 1300–1311. http://dx.doi.org/10.1080/02626667.2013.827337 [ABSTRACT FROM AUTHOR]

This paper estimates the marginal willingness to pay for climate amenity in the US using hedonic pricing and wage models. Research identifies that higher January temperatures are an amenity and households are willing to pay approximately US$5.90 ($2004) per month for a 1°F increase in the January temperature. Unlike the January temperature, higher July temperatures and precipitation are both disamenities, and households seek compensation of approximately US$5.46 ($2004) per month for a 1°F increase in July temperatures and approximately US$4.50 ($2004) per month for a 1-inch increase in precipitation. [ABSTRACT FROM PUBLISHER]

Changes in precipitation extremes are getting attention in the context of a warming climate. However, the lack of high-quality observations hinders the detection of variability in daily precipitation extremes over several regions, notably over the oceans. The outgoing longwave radiation (OLR) observed by satellites have long been used as a proxy to detect deep convection over the tropics. Here, we propose a heavy precipitation index based on daily OLR data over the global tropics. The new OLR-based heavy precipitation index is validated using a corresponding daily heavy precipitation index derived from the Tropical Rainfall Measuring Mission (TRMM) as well as the Global Precipitation Climatology Project (GPCP). We suggest that daily OLR can be used to further explore variability and trends in daily precipitation extremes over the tropics and to validate precipitation extreme indices derived from model simulations. [ABSTRACT FROM AUTHOR]

We propose a betting strategy based on Bayesian logistic regression modeling for the probability forecasting game in the framework of game-theoretic probability by Shafer and Vovk [1]. We prove some results concerning the strong law of large numbers in the probability forecasting game with side information based on our strategy. We also apply our strategy for assessing the quality of probability forecasting by the Japan Meteorological Agency. We find that our strategy beats the agency by exploiting its tendency of avoiding clear-cut forecasts. [ABSTRACT FROM PUBLISHER]

This paper presents the results of a direct comparison of sub-seasonal (60-day) forecast skill using two different land surface initializations in the Canadian Climate Centre for Modelling and Analysis (CCCma) CanCM3 coupled global climate model. The first land surface initialization uses randomized values of soil moisture whereas in the second case the model is initialized with a “best-estimate” of soil moisture derived from offline land surface model simulations. In this experiment the realistic soil moisture initialization improved temperature forecast skill during the boreal summer. Improvement was particularly evident for the wettest and driest quartiles of soil moisture initial conditions. Certain geographic regions, such as North America, showed the greatest improvement in temperature forecast skill. In contrast to temperature forecasts, there was much less skill improvement in precipitation forecasts between the two different soil moisture initializations, although there are geographic regions, such as North America, that do show increased skill. [Traduit par la rédaction] Cet article présente les résultats d'une comparaison directe de l'habileté des prévisions sous-saisonnières (60 jours) faites au moyen de deux initialisations de surface du terrain différentes dans le modèle couplé climatique global CanCM3 du Centre canadien de la modélisation et de l'analyse climatique (CCCma). La première initialisation de la surface du terrain se sert de valeurs d'humidité du sol choisies au hasard alors que dans le second cas, le modèle est initialisé à l'aide des « meilleures valeurs estimatives » d'humidité du sol dérivées de simulations modélisées hors ligne de la surface du terrain. Dans cette expérience, l'initialisation réaliste de l'humidité du sol a amélioré l'habileté des prévisions de température durant l’été boréal. L'amélioration était particulièrement évidente pour les quartiles le plus mouillé et le plus sec des conditions initiales d'humidité du sol. Certaines régions géographiques, comme l'Amérique du Nord, ont montré la plus forte amélioration dans l'habileté des prévisions de températures. Comparativement aux prévisions de température, il y avait beaucoup moins d'amélioration d'habileté dans les prévisions de précipitations entre les deux différentes initialisations d'humidité du sol, bien que certaines régions géographiques, comme l'Amérique du Nord, montrent une habileté accrue. [ABSTRACT FROM AUTHOR]

Areal rainfall averages derived from rain-gauge observations suffer from limitations not only due to sampling but also because gauges are usually distributed with a spatial bias towards populated areas and against areas with high elevation and slope. For a large river basin, however, heavy rainfall in the mountain upstream can result in severe flooding downstream. In this study, cloud-indexing and cloud model-based techniques were applied to Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) imager data based on the cloud-top brightness temperature (T B) and processed for estimating mesoscale grid rainfall. This study aims to improve and refine rainfall estimation in Malaysian monsoons based on cloud model techniques for operational pre-flood forecasting using readily available near-real-time satellite data such as the National Oceanic and Atmospheric Administration (NOAA)-AVHRR and GMS imager. Rain rates between 3 and 12 mm h−1 were assigned to cloud pixels of hourly coverage AVHRR or GMS data over the Langat Basin area for the duration of the monsoon rainfall event of 27 September to 8 October 2000 in Malaysia. The observed rainfall and quantitative precipitation forecast (QPF) showed an R 2 value of 0.9028, while the observed rainfall run-off (RR; recorded) and its simulated data had an R 2 value of 0.9263 and the QPF run-off and its simulated data had an R 2 value of 0.815. The rainfall estimate was used to simulate the flood event of the catchment. The estimated rainfall over the catchment showed similar flood area coverage to the observed flood event. [ABSTRACT FROM AUTHOR]

In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model. The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. The landfall prediction is significantly improved in 11 cases by about 37%. The intensity prediction also improves by 10–20%. Kinematic and thermodynamic structures of TCs are also better explained, as it could simulate heat and momentum exchange between sea surface and upper air. Due to better simulation of structure, intensity and track, the 24-hour accumulated rainfall intensity and distribution are also well predicted with the assimilation of satellite-derived winds. [ABSTRACT FROM AUTHOR]

Accurate weather benefit many key societal functions and activities, including agriculture, transportation, recreation, and basic human and infrastructural safety. Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. However, ensemble systems are uncalibrated and biased, and thus need to be statistically postprocessed. Bayesian model averaging (BMA) is a preferred way of doing this. Particularly for quantitative precipitation, biases and calibration errors depend critically on local terrain features. We introduce a geostatistical approach to modeling locally varying BMA parameters, as opposed to the extant method that holds parameters constant across the forecast domain. Degeneracies caused by enduring dry periods are overcome by Bayesian regularization and Laplace approximations. The new approach, called geostatistical model averaging (GMA), was applied to 48-hour-ahead forecasts of daily precipitation accumulation over the North American Pacific Northwest, using the eight-member University of Washington Mesoscale Ensemble. GMA had better aggregate and local calibration than the extant technique, and was sharper on average. [ABSTRACT FROM AUTHOR]

Rainfall estimates derived from satellite imagery and global circulation models are frequently used for vegetation monitoring in many areas of Africa because of the shortage of observed rainfall data and the sparse network of meteorological stations. At the same time, this scarce density of rain gauge stations makes the calibration and validation of the modelled data nearly impossible. In this study we propose a methodology for a rapid quality assessment of rainfall estimates that is based on the well-known relationship between rainfall and the Normalized Difference Vegetation Index (NDVI). The results clearly confirm that the NDVI can be used as an indicator of the quality of rainfall estimates at the continental/regional scale and allow a rapid detection of major over- and underestimations of the two rainfall datasets examined for the African continent. [ABSTRACT FROM AUTHOR]

Multivariate regression models that integrate topographic and geograph information are developed to predict monthly precipitation in the Daqing Mountains northern China. Five geographic and topographic factors, including longitude, latitud elevation, slope, and aspect, are taken into account in the model development. The data a acquired from a 100 m resolution DEM of the national topographic databases. Measuu precipitation data at 56 stations between 1955 and 1990 are used for model developmer and a leave-one-out cross-validation method is used for model evaluation. The mod explains most of the spatial variability in monthly precipitation, and can also quantify ti relative importance of different geographic and topographic variables. The overall MAE ar RMSE account for 10.42% and 13.64% of the measured precipitation for the entire ye~ respectively, and the relative errors of the monthly models are higher in the dry seas (October to March) than in the wet season, because precipitation in the dry season hard to model owing to little rainfall (11.77% of the annual total) and a different synopt system. The results indicate that the model's accuracy is influenced by the synoptic syste and rainfall amount. The model provides a way to link the descriptive and explanato functions of precipitation modeling, and could potentially be used in mountain climate ar hydrology research. [ABSTRACT FROM AUTHOR]

Dans le cadre des multifractals universels, il est possible de caracteriser la variabilite spatio-temporelle de la pluie sur une grande gamme d'echelle a l'aide de trois parametres invariants d'echelles. Dans cette etude, nous avons estime ces parametres multifractals sur des simulations numeriques effectuees avec le modele meso-echelle Meso-NH, developpe par Meteo-France et le Laboratoire d'Aerologie (Univ. P. Sabatier, Toulouse, France), et des images radar composites, couvrant le meme evenement pluvieux, a savoir un orage particulierement violent, dit de type Cevenol, ayant eu lieu sur la partie sud de la France du 5 au 9 Septembre 2005. La comparaison des resultats montre que les deux types de donnees presentent des domaines d'invariance d'echelle relativement similaires, et dont les proprietes sont en accord avec les modeles de precipitation spatio-temporels unifies et scalants les plus simples. Neanmoins l'evaluation de leurs exposants conduit a des valeurs parfois fortement differentes. Citation Gires, A., Tchiguirinskaia, I., Schertzer, D. & Lovejoy, S. (2011) Analyses multifractales et spatio-temporelles des precipitations du modele Meso-NH et des donnees radar. Hydrol. Sci. J. 56(3), 380-396. [ABSTRACT FROM AUTHOR]

This study has been carried out to forecast the impact of global warming on the precipitation pattern of Saudi Arabia by the end of year 2100. Simulation has been done using EdGCM model software (with available 8×10 resolution) developed at Columbia University on which there have been produced global precipitation maps for the seasonal and annual averages for the last 5 years (2096-2100). For each map, EdGCM grid values surrounding Saudi Arabia are used as input to one of the tools of Eagle point software called surface modelling (SM). SM is a new approach for downscaling global climate model results. SM software modelled out isohyets at 0.2 mm/day interval. The results indicate that the present pattern of precipitation (more in winter and less in summer) is going to change by almost equal occurrence of precipitation in all seasons for double_CO2 (2CO2) experiment. The 2CO2 experiment indicates an increase of about 16.05% over the annual average precipitation across the country. [ABSTRACT FROM AUTHOR]

The article discusses the value of weather observations and forecasts. It recalls the history of the first weather observing network, Smithsonian, which resulted to the development of various weather observers in Australia that participated in the New South Wales (NSW) Cooperative Observer network. The evolution of the weather observers has also motivated associated technology manufacturers to enhance their offerings to provide the most accurate and reliable weather observations and forecast.

The normalized antecedent precipitation index (NAPI) model by Heggen for the prediction of runoff yield is analytically derived from the water balance equation. Heggen's model has been simplified further to a rational form and its performance verified with the Soil Conservation Service Curve Number (SCS-CN) model. The simplified model has three coefficients specific to a watershed, and requires two inputs: rainfall and the derived parameter, NAPI. The characteristic behaviour of the NAPI has resonance with the curve number (CN) of the SCS model. The proposed NAPI model was applied to three watersheds in the semi-arid region of India to simulate runoff yield. The model showed improved correlation between the observed and predicted runoff data compared to the SCS-CN model. The F test and paired t test also confirmed the reliability of the model with significance levels of 0.01 and 0.001%, respectively. The proposed model could be used successfully for rainfall-runoff modelling in a watershed. Citation Ali, S., Ghosh, N. C. & Singh, R. (2010) Rainfall-runoff simulation using a normalized antecedent precipitation index. Hydrol. Sci. J. 55(2), 266-274. [ABSTRACT FROM AUTHOR]