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The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Niño to Yangtze River basin rainfall meant that the strong El Niño in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements. [ABSTRACT FROM AUTHOR]

Alpine and Mediterranean areas are undergoing a profound change in the typology and distribution of rainfall. In particular, there has been an increase in consecutive non-rainy days, and an escalation of extreme rainy events. The climatic characteristic of extreme precipitations over short-term intervals is an object of study in the watershed of Lake Maggiore, the second largest freshwater basin in Italy (located in the north-west of the country) and an important resource for tourism, fishing and commercial flower growing. The historical extreme rainfall series with high-resolution from 5 to 45 min and above: 1, 2, 3, 6, 12 and 24 h collected at different gauges located at representative sites in the watershed of Lake Maggiore, have been computed to perform regional frequency analysis of annual maxima precipitation based on the L-moments approach, and to produce growth curves for different return-period rainfall events. Because of different rainfall-generating mechanisms in the watershed of Lake Maggiore such as elevation, no single parent distribution could be found for the entire study area. This paper concerns an investigation designed to give a first view of the temporal change and evolution of annual maxima precipitation, focusing particularly on both heavy and extreme events recorded at time intervals ranging from few minutes to 24 h and also to create and develop an extreme storm precipitation database, starting from historical sub-daily precipitation series distributed over the territory. There have been two-part changes in extreme rainfall events occurrence in the last 23 years from 1987 to 2009. Little change is observed in 720 min and 24-h precipitations, but the change seen in 5, 10, 15, 20, 30, 45, 60, 120, 180 and 360 min events is significant. In fact, during the 2000s, growth curves have flattened and annual maxima have decreased. [ABSTRACT FROM AUTHOR]

The inverse distance method, one of the commonly used methods for analyzing spatial variation of rainfall, is flexible if the order of distances in the method is adjustable. By applying the genetic algorithm (GA), the optimal order of distances can be found to minimize the difference between estimated and measured precipitation data. A case study of the Feitsui reservoir watershed in Taiwan is described in the present paper. The results show that the variability of the order of distances is small when the topography of rainfall stations is uniform. Moreover, when rainfall characteristic is uniform, the horizontal distance between rainfall stations and interpolated locations is the major factor influencing the order of distances. The results also verify that the variable-order inverse distance method is more suitable than the arithmetic average method and the Thiessen Polygons method in describing the spatial variation of rainfall. The efficiency and reliability of hydrologic modeling and hence of general water resource management can be significantly improved by more accurate rainfall data interpolated by the variable-order inverse distance method. [ABSTRACT FROM AUTHOR]

This study investigates the daily, monthly, and seasonal characteristics of precipitation and associated forcing processes for their spatial-temporal variations over two geographically distinct river basins (Kaligandaki and Koshi) in Nepal, located on the southern slopes of the central Himalayas. A 34-year (1981–2015) daily precipitation data set of 49 stations between the elevation ranges of 143 to 3870 m asl was used for this investigation. The Gini coefficient, and index, degree and period of precipitation concentrations, and rainfall gradients were derived to examine precipitation distribution characteristics and causes for their variation. A rapid decrease of rainfall with elevation was observed in the Kaligandaki basin throughout the period of record, while this pattern was reversed in the Koshi basin, due to the rain shadow and orographic effects, which is pronounced in the monsoon season. Mountain effects are likely responsible for the distinct differences in daily and monthly precipitation distribution, with irregular patterns observed in the northern region of Kaligandaki basin and southern region in Koshi basin, with uniform patterns in the southern region of Kaligandaki basin and the northern region of Koshi basin. There is also a distinct variation in seasonality, which is higher (concentrated over several months) in the central northern region in Kaligandaki basin and less concentrated in the northern region of Koshi basin. But monthly precipitation characteristics in the western portion of Koshi and the southern region of Kaligandaki basins show high variability and short concentration duration. However, the months when precipitation is concentrated differed between the basins: from late July to August in Kaligandaki and June to September in Koshi basin. This is because the monsoon arrives later in the west than in the eastern part of the country. Variation in number of rainy days is higher in Koshi, but rainfall amount is greatest in Kaligandaki basin due to its proximity to the ocean and the intense effect of the monsoon. In addition to its value for rainfall-induced disaster mitigation strategies and management planning, this study will be useful for hydrological modeling in these areas in the future. [ABSTRACT FROM AUTHOR]

To support the GPM mission which is homologous to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), this study has been undertaken to evaluate the accuracy of Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TMPA) daily-accumulated precipitation products for 5 years (2008-2012) using the statistical methods and contingency table method. The analysis was performed on daily, monthly, seasonal and yearly basis. The TMPA precipitation estimates were also evaluated for each grid point i.e. 0.25° × 0.25° and for 18 rain gauge stations of the Betwa River basin, India. Results indicated that TMPA precipitation overestimates the daily and monthly precipitation in general, particularly for the middle sub-basin in the non-monsoon season. Furthermore, precision of TMPA precipitation estimates declines with the decrease of altitude at both grid and sub-basin scale. The study also revealed that TMPA precipitation estimates provide better accuracy in the upstream of the basin compared to downstream basin. Nevertheless, the detection capability of daily TMPA precipitation improves with increase in altitude for drizzle rain events. However, the detection capability decreases during non-monsoon and monsoon seasons when capturing moderate and heavy rain events, respectively. The veracity of TMPA precipitation estimates was improved during the rainy season than during the dry season at all scenarios investigated. The analyses suggest that there is a need for better precipitation estimation algorithm and extensive accuracy verification against terrestrial precipitation measurement to capture the different types of rain events more reliably over the sub-humid tropical regions of India. [ABSTRACT FROM AUTHOR]

Climate change has become an important issue for scientific community, for its numerous impacts, especially on agriculture and environment. To shed light on this phenomenon, long data-sets of meteorological data as well as temperature and rainfall extreme events are needed, since they are important climate variability indicators. In a small semi-agricultural catchment of the Italian Apennines, drained by the Centonara Stream, weather data have been monitored for 30 years (1986-2015). Here, their trend was identified with Mann-Kendall test, and the temperature and precipitation extremes were characterized by means of specific indices. Data were analyzed at event, daily, seasonal, and annual bases. In addition, to understand implications of rainfall patterns on catchment hydrological response, a total of 388 rainfall-streamflow events (recorded during 1994-2009) were analyzed. Results showed a significant increase of temperature (minimum (Tn), mean (Tm), and maximum (Tx)), both at annual and seasonal base, except for Winter, with an annual mean increase of 0.08, 0.09, and 0.13 °C year for Tn, Tm, and Tx, respectively. Annual rainfall data showed a positive trend for the number of wet days, but not in the quantity. In Winter, the rainfall amount and the number of wet days increased. The annual number of rainfall extreme events ( P > 20.2 mm and D > 18.3 h) significantly increased, especially in spring and autumn. The rainfall-streamflow relationship suggested that the flow response of the Centonara Stream depends mainly on the rainfall amount. So, since the number of extreme events ( P > 20.2 mm) is increasing in spring and autumn, an increase of events with high streamflow in these seasons may occur. [ABSTRACT FROM AUTHOR]

The article discusses the water supply shortage in India as its population continues to boom. Water pollution is just one of the many problems that India is facing, more to this is the availability of water where the city of Delhi is the forerunner of this looming water crisis. India consider it an ambitious plan to connect the majority of its river basins through a vast network of canals, diverting billions of litres from the more water-rich river basins to those with less. Although it is unclear whether the solution is effective, the logic behind such plan is to capture the surplus water that flows wastefully to the sea which resulted from the variable distribution of precipitation.