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Highlights • The dependence between flow and hydroclimatic factors in Baden-Württemberg is modeled. • The probabilities of droughts and floods under various hydroclimatic scenarios is examined. • The links between flow variability and atmospheric circulation are explored. Abstract This study presents an investigation of probabilistic relationships between streamflow and hydroclimatic variables (including precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulation over Baden-Württemberg, Southwest Germany. First, the joint dependence between seasonal streamflow and hydroclimatic variables was established by using copulas. On the basis of the joint dependence structure, we estimated the probabilities of hydrological droughts and prolonged high-streamflow events conditioned upon two different scenarios (high and low) of hydroclimatic variables for various seasons over the study area. The results indicate that both precipitation and soil moisture are positively related to the streamflow variation in Baden-Württemberg for each season and strongly impact the likelihoods of hydrological droughts and high-streamflow events. Temperature tends to have less impact on the streamflow variation, and an inverse connection between streamflow and temperature is found in spring and summer. Then, the connections between streamflow variability and large-scale atmospheric circulation in summer and winter were explored by using composite analysis. Although the atmospheric circulation patterns vary in each season, it can be found that in summer high streamflow anomalies over Baden-Württemberg are related to a cyclonic activity over central Europe while low streamflow anomalies are linked to anticyclonic patterns over western Ireland. The high streamflow anomalies in winter are strongly impacted by the westerlies that play an important role in favoring warm and moist airstreams from the Atlantic Ocean towards the study area, while an opposite atmospheric pattern is found for the years with low streamflow anomalies. The presented probabilistic methodology could also be applied in other regions worldwide. [ABSTRACT FROM AUTHOR]

Geographic time series are usually non-stationary and contain different frequency components (e.g., seasonal variations, long-term and short-term fluctuations) which may significantly affect the overall variance structure in the original data. Based upon the monthly normalized difference vegetation index (NDVI), precipitation and temperature data for six different vegetation types in two precipitation regimes (low and high precipitation regimes) of Rhineland-Palatinate (Southwest Germany), this study aims to examine the temporal trends in the original time series of these variables and their relationships. In addition, the further objectives are to evaluate which time-scale is dominantly responsible for the trend production found in the original data and find out the certain time-scales that represent the strongest correlation between NDVI and climatic variables (i.e., precipitation and temperature). A combined approach using the discrete wavelet transform (DWT), Mann-Kendall (MK) trend test and correlation analysis was implemented to achieve these goals. The trend assessment for the original data shows that the monthly NDVI time series for all vegetation types in both precipitation regimes have upward trends, most of which are significant. The precipitation and temperature data for six vegetation types in two precipitation regimes present weak downward trends and significant increasing trends, respectively. The most important time-scales contributing the trend production in the original NDVI data are the 2-month and 8-month events. For precipitation, the most influential ones are 2-month and 4-month scales. The 4-month periodic mode predominantly affects the trends in the original temperature time series. Based on the original time series, the change in temperature is found to be the primary driver influencing the variability in vegetation greenness over this study area, while there is a negative correlation between NDVI and precipitation for all vegetation types and precipitation regimes. For the scale-dependent relationships between NDVI and precipitation, the 2-month and 8-month scales generally present the strongest negative correlation. The most significant positive correlations between NDVI and temperature are obtained at the 8- to 16-month scales for most vegetation types. The results of the present study might be beneficial for water resources management as well as agricultural and ecological development planning in Rhineland-Palatinate, and also provide a helpful reference for other regions with similar climate condition. [ABSTRACT FROM AUTHOR]

Summary MODIS snow data are appropriate for a wide range of eco-hydrological studies and applications in the fields of snow-related hazards, early warning systems and water resources management. However, the high spatio-temporal resolution of the remotely sensed data is often biased by snow misclassifications, and cloud cover frequently limits the availability of the MODIS-based snow cover information. In this study, we applied a four-step methodology that aims to optimize the accuracy of MODIS snow data. To reduce the cloud fraction, 8-day MODIS data from both the Aqua and Terra satellites were combined. Neighborhood analysis was applied as well for this purpose, and it also contributed to the retrieval of some omitted snow. Two meteorological filters were then applied to combine information from station-based measurements of minimum ground temperature, precipitation and air temperature. This procedure helped to reduce the overestimation of snow cover. To test this technique, the methodology was applied to the Rhineland-Palatinate region in southwestern Germany (approximately 20,000 km 2 ), where cloud cover is especially high during winter and surface heterogeneity is complex. The results show that mean annual cloud coverage (reference period 2002–2013) of the 8-day MODIS snow maps could be reduced using this methodology from approximately 14% to 4.5%. During the snow season, obstruction by clouds could be reduced by even a higher degree, but still remains at about 11%. Further, the overall snow overestimation declined from 11.0–11.9% (using the original Aqua–Terra data) to 1.0–1.5%. The method is able to improve the overall accuracy of the 8-day MODIS snow product from originally 78% to 89% and even to 93% during cloud free periods. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) was derived from the Normalised Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer (AVHRR) data for the years 1982-2004. The NDVI-derived LAI showed a very good agreement (correlation coefficient r up to 0.96) with MODIS LAI. To address the relation between precipitation and LAI, linear correlation analysis between gridded precipitation and the NDVI-derived LAI was conducted for several land uses and each month of the year. Based on the regression coefficients, LAI could be simulated as a function of precipitation. During validation, the simulated LAI showed a very good agreement (r≥ 0.75) with the NDVI-derived LAI. The simulated dynamic LAI was thereafter implemented in a hydrological model. For comparison, a model run with a static LAI without any inter-annual variations was also conducted. During abnormally dry conditions, the dynamic LAI was lower than the static LAI and less transpiration was therefore simulated. It is shown that a dynamic LAI contributes to a more realistic simulation approach during individual weather events but also that in the long run the simulated transpiration is much more strongly influenced by inter-annual variations in weather than by the additional vegetation dynamics in a semi-arid region. [ABSTRACT FROM AUTHOR]