Heike Hartmann

Approximation and spatial regionalization of rainfall erosivity based on sparse data in a mountainous catchment of the Yangtze River in Central China.

In densely populated countries like China, clean water is one of the most challenging issues of prospective politics and environmental planning. Water pollution and eutrophication by excessive input of nitrogen and phosphorous from nonpoint sources is mostly linked to soil erosion from agricultural land. In order to prevent such water pollution by diffuse matter fluxes, knowledge about the extent of soil loss and the spatial distribution of hot spots of soil erosion is essential. In remote areas such as the mountainous regions of the upper and middle reaches of the Yangtze River, rainfall data are scarce. Since rainfall erosivity is one of the key factors in soil erosion modeling, e.g., expressed as R factor in the Revised Universal Soil Loss Equation model, a methodology is needed to spatially determine rainfall erosivity. Our study aims at the approximation and spatial regionalization of rainfall erosivity from sparse data in the large (3,200xa0km2) and strongly mountainous catchment of the Xiangxi River, a first order tributary to the Yangtze River close to the Three Gorges Dam. As data on rainfall were only obtainable in daily records for one climate station in the central part of the catchment and five stations in its surrounding area, we approximated rainfall erosivity as R factors using regression analysis combined with elevation bands derived from a digital elevation model. The mean annual R factor (Ra) amounts for approximately 5,222xa0MJu2009mmu2009ha−1u2009h−1u2009a−1. With increasing altitudes, Ra rises up to maximum 7,547xa0MJu2009mm ha−1u2009h−1 a−1 at an altitude of 3,078xa0m a.s.l. At the outlet of the Xiangxi catchment erosivity is at minimum with approximate Rau2009=u20091,986xa0MJu2009mmu2009ha−1u2009h−1u2009a−1. The comparison of our results with R factors from high-resolution measurements at comparable study sites close to the Xiangxi catchment shows good consistance and allows us to calculate grid-based Ra as input for a spatially high-resolution and area-specific assessment of soil erosion risk.

Trends in Extreme Precipitation Events in the Indus River Basin and Flooding in Pakistan

Abstract In the absence of a sufficiently dense network of climate stations covering all topographic regions of the Indus River basin and delivering high quality data over the last 30 years or more, daily precipitation data were obtained from the National Centers for Environmental Prediction-Department of the Enviornment (NCEP-DOE) Reanalysis 2 dataset for the period 1979 to 2011. The daily precipitation data were transformed into time series of frequency of extreme precipitation events of 1-day and 10-day durations defined in terms of 90th and 99th percentile threshold exceedances. The non-parametric Mann-Kendall trend test was applied to determine whether statistically significant changes in precipitation extremes occurred over time, in due consideration of autocorrelation in the data. Extreme precipitation showed a high spatial variability, with the highest daily and 10-day precipitation totals, and thus highest 90th and 99th percentiles, in the southeastern lowlands at the foot of the Himalayas and the lowest in the Karakorum. Significantly decreasing trends in extreme precipitation were observed in the western part of the Indus River basin; significantly increasing trends were mainly detected in the very high mountainous regions in the east (Transhimalaya and Himalayas) and in the north (Hindu Kush and Karakorum) of the Indus basin. High precipitation rates are not common in the arid climate of these high mountainous regions. Future flood management plans need to consider the increasing trends in extreme precipitation events in these areas.

Precipitation variability in the Yangtze River subbasins

Numerous dams and reservoirs in the Yangtze River basin have been constructed. Thus understanding the variability of hydroclimatological time series is important for the development of an optimal reservoir management strategy. This is crucial for life in the densely populated basin known to be highly susceptible to floods and droughts. Time series of annual precipitation totals and precipitation totals from May to September (the rainy season) for the period from 1961 to 2002 were averaged within the subbasins of the Yangtze River. The resulting time series were analyzed for similarities and differences and continuous wavelet transforms were produced to identify inherent quasi-periodicity. The western subbasins have shown a diverse pattern of periods with high and low precipitation variance. We have detected prominent periods of high precipitation variance for the central and eastern subbasins in the 1980s and 1990s, respectively.

Neural Network Based Seasonal Predictions of Lake-Effect Snowfall

In the case of the city of Buffalo (New York, United States), located on the eastern shore of Lake Erie and, therefore, strongly influenced by the lake-effect, total monthly snowfall was predicted one to six months in advance. For this, neural network (NN) techniques, specifically a multi-layer perceptron, as well as a multiple linear regression (LR) model were applied. The period of analysis comprised 28 years from January 1982 to December 2009. Input data included surface air temperature; the temperature difference between the lake surface water temperature (LSWT) and the 850 hPa air temperature; the u-component of the wind (u-wind) and the v-component of the wind (v-wind), geopotential height (GPH) over Lake Erie and the surrounding regions at the 1000, 925, 850 and 700 hPa levels as well as the surface pressure; the 500 hPa GPH over James Bay, Canada; the surface pressure over the Great Plains; and the mean water temperature and LSWT of Lake Erie, as well as the amount of ice cover. Moreover, several teleconnection indices were implemented: the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the Pacific/North American (PNA) pattern, the Southern Oscillation Index (SOI) and the Pacific Decadal Oscillation (PDO). Different lead times for the input variables were tested for their suitability. The most accurate result was obtained using the NN with an optimized one-month lead time approach (lead times varied between one and six months for the different input variables). R ésumé u2003[Traduit par la rédaction] Dans le cas de la ville de Buffalo (New York, États–Unis), située sur la rive est du lac Érié et donc fortement influencée par leffet de lac, nous avons prévu la chute de neige mensuelle totale de un à six mois à lavance. À cette fin, nous avons appliqué des techniques de réseau neuronal, plus précisément un perceptron multicouche, ainsi quun modèle de régression linéaire multiple. La période danalyse s’étendait sur 28 années, de janvier 1982 à décembre 2009. Les données dentrée consistaient en : la température de lair à la surface; la différence entre la température de leau à la surface du lac et celle de lair à 850u2005hPa; la composante u du vent (vent–u) et la composante v du vent (vent-v), la hauteur géopotentielle au-dessus du lac Érié et de la région environnante aux niveaux 1000, 925, 850 et 700u2005hPa ainsi que la pression à la surface; la hauteur géopotentielle à 500u2005hPa au-dessus de la baie James au Canada; la pression à la surface dans les Grandes Plaines; et la température moyenne de leau ainsi que la température de leau à la surface du lac Érié de même que l’étendue de la couverture de glace. De plus, nous nous sommes servis de plusieurs indices de téléconnexion : loscillation de lAtlantique Nord, loscillation de lArctique, la téléconnexion Pacifique-Amérique du Nord, lindice doscillation australe et loscillation décennale du Pacifique. Nous avons testé la convenance de différents délais de démarrage pour les variables dentrée. Le résultat le plus précis a été obtenu en utilisant le réseau neuronal avec une approche de délais de démarrage optimisé dun mois (les délais de démarrage variaient entre un et six mois pour les différentes variables dentrée).

Estimation of Actual Evapotranspiration by Complementary Theory-Based Advection-Aridity Model in the Tarim River Basin, China

AbstractIn this study, the complementary relationship between actual evapotranspiration (ETa) and potential evapotranspiration (ETp) was verified in the Tarim River basin (TRB) in northwest China. The advection–aridity (AA) model that is based on the complementary relationship (CR) was used to calculate ETa. Spatial and temporal trends in the estimated annual ETa and the factors that influenced ETa were investigated. The multiyear average ETa in the TRB for the period from 1961 to 2014 was 178.5 mm. There was an overall significant increasing trend (at a rate of 10.6 mm decade−1) in ETa from 1961 to 2014; ETa increased at a rate of 22.9 mm decade−1 from 1961 to 1996 and decreased at a rate of 33.9 mm decade−1 from 1996 to 2014. Seasonally, ETa was strongest in summer, followed by spring and autumn. The spatial distributions of the annual and seasonal ETa were mostly consistent, with higher ETa values in the northeast, northwest, and southwest of the TRB, and lower ETa values in the mostly desert lands in ...