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Net radiation is a key component of surface radiation balance and has a strong influence on hydrological processes via evapotranspiration. In this study, daily average net radiation (Rnmean) for all-sky conditions was investigated as a function of the estimated daily average global radiation (GRmean) during the summers of 2011 and 2012 (128 days total) in the Sugnugur Valley of Northern Mongolia. We present a simple alternative remote sensing approach that considers factors such as topography, cloud fraction, cloud optical thickness and surface albedo. First, a geometric model for the simulation of daily average global radiation (GRCS:mean) for clear-sky conditions was applied on a daily basis. It considers topographical effects, such as slope, azimuth and elevation. GRmean was then derived for all-sky conditions by coupling the averaged atmospheric products of MODIS. Finally, Rnmean was obtained as a function of the simulated GRmean using the linear regression parameters found at a permanent observation site. The results were validated with the data from a nearby temporary observation site. The root mean square errors (RMSE) were 44 and 52 Wm−2 for GRmean and 18 and 25 Wm−2 for Rnmean at the two different sites. This methodology requires few observations and offers a simple means for estimating GRmean with high spatial (30 m) and temporal (daily) resolution under any sky conditions in the absence of ground measurements. Furthermore, Rnmean can be modeled from the simulated GRmean at regional or watershed scales where ground observations exist at one site at least. [ABSTRACT FROM AUTHOR]

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]

Cloud cover and snow misclassifications are the two major limitations for the hydrological application of MODIS snow data. Ground-based meteorological data have the inherent potential to provide the means to reconstruct snow cover for regions in which MODIS snow maps are obstructed by clouds, and to reduce misclassified snow observations. In this study, a multistep method is developed to generate cloud-free MODIS daily snow cover products. The accuracy of the updated MODIS snow products is evaluated for a region in southwestern Germany in which winter cloud coverage and snow variability are typically high. First, we applied Aqua/Terra combination, temporal combination and spatial combination to reduce the cloud coverage and to retrieve the omitted snow. This procedure was not effective since cloud blockage occurs frequently during the snow season. A conditional probability interpolation was then employed to reclassify the remaining cloud cover on MODIS snow maps based on in situ snow depth observations. Finally, we implemented a set of meteorological filters to minimize the misclassified snow in MODIS snow products. The improved cloud-free MODIS daily snow maps showed an overall accuracy of about 92% during the snow season with significantly reduced snow overestimation errors and a slight increase in snow omission errors, compared to the overall accuracy of 87% and 94% for original MODIS Aqua and Terra data, respectively. This study suggests that the fusion of ground-based and satellite-based snow observations is an effective approach for generating cloud-free remote sensing snow data. [ABSTRACT FROM AUTHOR]

ABSTRACT An investigation of temporal dynamics and spatial patterns of dryness/wetness conditions over the eastern part of Northwest China (including Shaanxi, Ningxia, the eastern part of Gansu, and a part of southern Inner Mongolia) during the period 1960-2009 is presented. The analysis of this study is threefold. First, the dryness/wetness conditions over the study area were characterized using the self-calibrating Palmer Drought Severity Index ( PDSI). The spatiotemporal variability of dryness/wetness conditions in the study area was then investigated by the rotated empirical orthogonal function ( REOF) and Mann-Kendall trend test. Third, we examined the periodical oscillations of dryness/wetness conditions and the multi-scale relationships between dryness/wetness conditions and El Niño-Southern Oscillation ( ENSO). The results indicate that most parts of the study area were characterized by a dry trend in both the rainy season (May-September) and winter. Four sub-regions of dryness/wetness conditions across the study area were identified. Based on the wavelet analysis, the periodical features in the PDSI time series for each sub-region were explored. Negative relationships between the PDSI in the sub-region I (representing the central and southern parts of Shaanxi and the southeastern Gansu) and the Niño 3.4 index can be consistently detected on 2-6 year scales during the entire period 1960-2009. This suggests that the strong El Niño events generally lead to very dry conditions in the sub-region I on multi-year scales. In addition, we further investigated how the ENSO cycle modulates the strength of the East Asian summer monsoon ( EASM) in both developing and decaying phases of El Niño and La Niña events. The results of this study could be beneficial for efficient water resources management and drought assessment in the current study area and also provide a valuable reference for other areas with similar climatic characteristics. [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]

• Hydrologic processes are analyzed based on coupled land-hydro-atmosphere perspective. • Oscillations in atmospheric conditions cause seesaw variations in river runoff. • Warming leads to opposite hydrological responses depending on permafrost conditions. • Cross-correlation is introduced to detect the effect of permafrost degradation. Changes in the cryosphere caused by global warming are expected to alter the hydrological cycle, with consequences to freshwater availability for humans and ecosystems. Here, we combine data assimilation, cross-correlation analysis, simulation techniques, and the conceptual steady-state Budyko framework to examine the driving mechanisms of historical hydrological changes at annual, seasonal, and monthly scales. We focus on two southern Siberian basins with different landscape properties: the semi-arid Selenga, characterized by discontinuous, sporadic, and isolated permafrost; and the boreal Aldan, which is underlain by continuous permafrost. Our results indicate that the two basins show divergent trends in river runoff over the period 1954–2013. In Selenga, runoff exhibits a significant decreasing trend (−1.3 km3/10yrs, p≤0.05), whereas a remarkable increasing trend (4.4 km3/10yrs, p≤0.05) occurs in Aldan. Given the negligible trends in precipitation over both basins, we attribute these contrasting changes to different impacts from warming-induced permafrost degradation. The Selenga basin, which is dominated by lateral degradation (i.e., decreasing permafrost extent), suffers from severe water loss via the enhanced infiltration of water that was previously stored close to the surface. This leads to a water-deficit surface condition. In the Aldan basin, in contrast, vertical degradation prevails: the thickened active layer is still underlain by a frozen layer with low permeability that sustains water-rich surface conditions. Furthermore, summer runoff shows contrasting oscillations, with wet-dry-wet-dry and dry-wet-dry-wet state evolutions in the Selenga and Aldan basins, respectively. We attribute such variabilities to the "seesaw-like" oscillations in summer precipitation associated with the propagation of Rossby wave trains across the Eurasian continent. We also find that warming-induced permafrost degradation over the 30-year period from 1984 to 2013 has led to strong regime shifts in river runoff in both basins. Our study highlights the importance of examining the mechanisms that drive changes in water availability from an integrated land-hydrology-atmosphere system perspective. [ABSTRACT FROM AUTHOR]

Summary: The current and future situation of blue water availability, blue water withdrawals and the occurrence of water stress is investigated in seven case studies distributed over Africa, Europe and Central Asia, thus representing very different physiographic, climatic and socio-economic conditions. Four scenarios from two major projects dealing with global change are applied to a large scale water model which simulates the case-specific hydrological conditions and water uses. For the assessment of water stress, the ratio between simulated blue water withdrawals and blue water availability is determined. The results of this study clearly demonstrate that climate as well as socio-economic change will have a large impact on the future availability and use of water resources, and consequently on the occurrence of water stress. It is however evident that there are large regional differences of the current and future water stress levels. The application of a number of contrasting scenarios leads to a wide range of possible water futures in some of the investigated regions, thus introducing high uncertainties. Therefore, possible activities towards an implementation of integrated water resources management have to deal with these uncertainties. [Copyright &y& Elsevier]

Rapid warming has reduced seasonal snow cover and changed the hydrological regimes over many areas of the globe. Temperature increase cannot only stimulate snowmelt but also favors a shift of wintertime precipitation towards rainfall, especially in Western and Central Europe that has temperate and humid winters. We assume that the mid-season (January-February) instead of the end-season (March) snow is particularly sensitive to warming in the low mountain ranges of Europe, and increased rain-on-snow events are associated with the currently frequent winter floods. To validate this assumption, we simulated the snow water equivalent (SWE), rain-on-snow (ROS) events, evapotranspiration, runoff and percolation for the period 1961-2016 in Rhineland-Palatinate, southwestern Germany (< 820 m a.s.l.) using a distributed hydrological model (TRAIN). We analyzed the intra- and inter-annual variations of the simulated hydrological variables and the synchronous climate variables (air temperature and precipitation). Early spring (March) displayed the strongest warming and the January-February warming was less severe or insignificant. Precipitation amounts did not show significant trends. However, the snowpack in February experienced the most striking reductions, which is likely explained by both the highest susceptibility of snow to warming and the increased ROS occurrence in February since the early 1990s. An increment in runoff and percolation in the earlier winter is probably related to a combination of high temperatures and more rainfalls at that time. The expected changing climate towards warmer and wetter winters but drier springs will probably exacerbate the winter flooding and spring/summer drought risks in the future. [ABSTRACT FROM AUTHOR]

Droughts, such as the recent event of 2018 that hit many regions of Europe, have a large impact on environment and society. These droughts are often characterized and studied as slowly developing creeping phenomena. However, a root zone soil moisture shortage potentially affecting plant growth and agricultural production can develop rather quickly, e.g., under dry conditions that favor high evapotranspiration or in shallow root zones with a low available soil water content. In this study, we investigate which variables (climate and soil properties) exert a control on the likelihood and development time of simulated soil moisture shortage during drought across Baden-Württemberg (Southwestern Germany). First, the TRAIN model was used to simulate root zone soil moisture for a 1 km resolution grid over Baden-Württemberg. For major drought years (e.g., 2003 & 2018), soil moisture time series of each agricultural grid cell across the region were characterized according to 1) whether or not a soil moisture shortage was reached that year (here, defined as a level <30% of the available soil water content), and 2) the time it took to drop from field capacity to a soil moisture shortage. Finally, (logistic) regression analyses were applied to identify the dominant controls on the likelihood and development time of soil moisture shortage. Results show that the majority of the agricultural grid cells across the study region reach soil moisture shortage in major drought years such as 2018. The development time of soil moisture shortage varied substantially. The dominant control on the likelihood and development time of soil moisture shortage was found to be the available water content of the root zone, i.e., a higher likelihood and faster development for shallow root zones with a low available water content. The evapotranspiration- and precipitation- rate alone during the development of soil moisture shortage were only weakly related to the development time. However, these meteorological variables are of added explanatory value for the development time when considered in combination with the available water content of the soil. Overall, these results give insight in the variability and controls on the likelihood and development time of root zone soil moisture shortage during drought, which is important information for monitoring and early warning of the soil moisture drought hazard. [ABSTRACT FROM AUTHOR]

The effects of the 2018 drought were significant in Southwestern Germany. The lack of precipitation caused the soils to dry out so that soil moisture often reached minimum levels and crop yield was well below average. However, the spottiness of the scarce precipitation led to strong regional differences. In the framework of a long-term monitoring program, we investigated soil moisture development at two agricultural sites in the province of Baden-Württemberg. The two field sites, which differ in terms of soil physical properties and cultivated crops, are located in the Upper Rhine Graben near Heidelberg ("Grenzhof") and in the Kraichgau near Pforzheim ("Katharinentaler Hof"). The Katharinentaler Hof site is on a loess plateau, where the soils are deep and show a cohesive structure, while the Grenzhof site is characterised by more sandy soils, which developed on quaternary gravel from the Rhine and its tributaries. Another interesting feature is the crop irrigation at the Grenzhof site during the drought. Our measurements were carried out with TDR and FDR probes at hourly intervals and in five different depths with triple replication. Furthermore, the observations were compared with soil moisture data from a forest monitoring site in Rhineland-Palatinate ("Merzalben"), approx. 60 km away from the Grenzhof site. Therefore, we investigated how drought affects two different kinds of ecosystems. In addition to the measurements, water balance and soil moisture were simulated with three different hydrological models: TRAIN was applied for the three sites, DAISY for the agricultural sites, and LWF-BROOK90 for the forest. The measurements at Grenzhof showed that the average soil moisture in the root zone during the main growing season in summer 2018 did not drop below the values of summer 2017. Here, irrigation prevented excessive soil water stress. In 2018, the mean soil moisture at Katharinentaler Hof only fell below the lowest values of summer 2017 at greater depths (90 cm). There, field capacity of the soil is higher than at Grenzhof and the few precipitation events during summer 2018 were sufficient to counteract excessive dehydration at all soil depths. The simulation of soil moisture with the TRAIN model showed good results for the Grenzhof site over the period 1st April to 27th September 2018. The natural development of the drought at Grenzhof without anthropogenic control was demonstrated by a scenario with the TRAIN model, in which the irrigation events were not taken into account. According to this, soil moisture would already have dropped to wilting point by the end of June 2018, which would have caused extreme drought stress for the plants. This clearly shows that agricultural irrigation can have a strong influence on soil moisture in soils with comparatively low field capacity during drought and thus should be considered in hydrological models. [ABSTRACT FROM AUTHOR]

The recharge of groundwater resources, which is an important source for drinking water supply in southwestern Germany, is affected by droughts. In order to manage groundwater resources sustainably, knowledge about groundwater recharge, especially under drought conditions, is indispensable. We investigate and compare groundwater recharge and groundwater levels during droughts in southwestern Germany with regard to their quantification, propagation and persistence. In particular, we investigate how groundwater recharge patterns change during droughts and how this affects groundwater levels. In addition, we investigate regional similarities of groundwater recharge patterns. Therefore, we compare measured groundwater levels with the model results of groundwater recharge of the spatial version of the hydrological model TRAIN. Groundwater droughts are detected with the help of a groundwater drought indicator and evaluated with regard to their spatial distribution. The incipient (modelled) groundwater recharge is displayed with heat map diagrams, which show the temporal anomalies of the incipient time. The dependency between groundwater recharge and groundwater drought is carried out by cross correlation analysis. We were able to show that the onset of the autumnal groundwater recharge in drought years is distinctly delayed. Another interesting finding is the spatial organization, where the model results show the strongest characteristic for the Upper Rhine Valley. According to our investigations, not only the time of onset of groundwater recharge in autumn, but also the extent of groundwater recharge in the following winter influences the propagation and persistence of a groundwater drought. Our findings will enable better decision-making processes for a sustainable water and aquifer management within the context of climate change. [ABSTRACT FROM AUTHOR]

There has been an accelerated and over-proportional rate of warming in the dry mid-latitudes and boreal forests of Southern Siberia. Associated changes affect permafrost distribution, the occurrence of wild fires and hydrological dynamics. Given that the Boreal ecotone occupies roughly 20 million km² of the global land surface, it can be assumed that these changes are of major interest with regard to the Earth's freshwater resources. However, since long-term hydro-meteorological time series are often not available, the impact of climate and environmental change on flow dynamics and water availability can only be roughly estimated. To gain insight into these challenging questions, we try to quantify the discharges and their temporal developments from two large-scale river basins. In a first step, we determine associated variabilities and trends of the major influencing factors, such as air temperature, precipitation and evapotranspiration. The selected catchments represent the variety of the climatic, landscape and permafrost patterns of that region: (1) Selenga River is the largest sub-basin of Lake Baikal, with a catchment size of about 440,000 km². The basin covers the transition belt between the dry mid-latitudes and the Boreal Zone and is mostly underlain by discontinuous permafrost. (2) Aldan River is a tributary of the Lena River. It drains an area of approx. 750,000 km², which is completely attributable to the Boreal ecotone and the continuous permafrost zone. Our analysis is based on long-term historical (1946-2015) hydro-meteorological time series, large-scale gridded data sets, as well as modeling simulations. The data undergoes extensive testing to detect anomalies, trends and multiple dependencies. The strong increase in air temperature during last 60 years may result in permafrost degradation and a decrease in seasonal frost occurrence, which is evident from the decreasing intra-annual variability of discharge in both catchments. However, the changes in river discharge show different directions: while the mean discharge of Selenga has decreased significantly, the mean discharge of Aldan shows a significant increase over the long-term period (based on the Mann Kendall test at a 95% confidence level). There also exist substantial seasonal shifts in the flow dynamics over both areas. These drastic and highly significant changes can be partly (but not fully) explained by precipitation and air temperature variabilities and trends. It is therefore likely that changes in landscape patterns, such as permafrost degradation and large-scale forest fires, are of great significance for the hydrological changes. Moreover, increasing water withdrawals due to a growing population, accelerated urbanization and a highly dynamic mining sector in the Selenga basin are factors that contribute to decreasing streamflow trends. To further clarify these relationships, our investigations will be extended to neighboring catchments, and our simulation studies will include an ensemble of hydrological models. [ABSTRACT FROM AUTHOR]

The accurate spatial information of snow cover is useful for understanding the impact of global warming, and it is of high significance for hydrological disaster prediction, water resources management, and climate change research. The Normalized Difference Snow Index (NDSI) based approach has been used extensively around the world for mapping snow, and they displayed high accuracy in open areas. However, capturing snow cover in forests remains problematic due to the obstruction effects of the forest canopy, which causes the snow cover area to be seriously underestimated. In this paper, we present a new algorithm based on machine learning (ML) technology to improve the accuracy of binary snow cover (BSC) mapping in forests, using the remotely sensed surface reflectance and ground truth data. A time-lapse photography network with a two-hour resolution was established in the eastern Qilian Mountains in northwestern China to obtain the ground truth data both in forests and open areas. We trained Random Forests (RF) with the 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data from bands 1–7 to generate BSC results (RF-BSC). Then we evaluated RF-BSC and the NDSI-derived BSC maps with three different NDSI thresholds (i.e., 0.10, 0.29, and 0.40) against ground-truth data. The results indicate that the proposed algorithm has a high performance in forest BSC mapping in this area, compared to the NDSI-threshold approach. The RF-BSC can retrieve 67% of all real forest snow pixels, while the NDSI-based BSC can only detect 8–14%. We also find that the performance of the algorithm seems to be sensitive to changes in solar illumination conditions and forest coverage. This study suggests that machine learning with the fusion of optical remote sensing and ground-based observations is an effective approach for improving the accuracy of forest snow cover mapping at regional scales. • Forest snow dynamics were obtained by time-lapse photography. • On this basis, high-accuracy forest snow mapping algorithms were developed. • ML with satellite and ground data outperforms NDSI approach in forest snow mapping. • MODIS NDSI using band7/band4 improves forest snow detection than band6/band4. • Influences of cloud, topography and forest coverage on algorithms were analyzed. [ABSTRACT FROM AUTHOR]

While it is well recognized that the frequency and intensity of flood events are increasing worldwide, the environmental, economic, and societal consequences of remobilization and distribution of pollutants during flood events are not widely recognized. Loss of life, damage to infrastructure, and monetary cleanup costs associated with floods are important direct effects. However, there is a lack of attention towards the indirect effects of pollutants that are remobilized and redistributed during such catastrophic flood events, particularly considering the known toxic effects of substances present in flood-prone areas. The global examination of floods caused by a range of extreme events (e.g., heavy rainfall, tsunamis, extra- and tropical storms) and subsequent distribution of sediment-bound pollutants are needed to improve interdisciplinary investigations. Such examinations will aid in the remediation and management action plans necessary to tackle issues of environmental pollution from flooding. River basin-wide and coastal lowland action plans need to balance the opposing goals of flood retention, catchment conservation, and economical use of water. [Display omitted] • Flood-induced mobilization of contaminated sediment poses significant risk. • Scientific, economic, and political consequences of flood events in a global context. • Risks and tools to investigate effects of contaminant remobilization during floods. [ABSTRACT FROM AUTHOR]

• An observation-based natural and human-impacted catchment comparison method is proposed. • Human influence are quantified based on gridded socio-economic and land use data. • Climatic and anthropogenic influences on drought propagation are investigated. • Climate change accelerates the response of hydrological drought to meteorological drought. • Human activities delay the propagation from meteorological to hydrological drought. It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management. [ABSTRACT FROM AUTHOR]

High topographic heterogeneity and complex mechanisms between the atmosphere and the ground create unique hydro-climatic processes over mountainous regions. Based on in situ observations, we present the spatial variability of ground surface temperature (GST) in the Khentii Mountains of northern Mongolia, which is situated at the southern fringe of the Eurasian permafrost zone. Changes in the hydrothermal regime of the active layer were investigated in association with changing climate and wildfire effects. The results reveal that the GST tends to increase continuously since 2011 in both thawing and freezing seasons, and varies significantly within a short horizontal distance, particularly during the thawing season. Extreme weather events, such as drought and heavy snowfall, amplify the increase in the ground temperature and deepen the seasonal thawing depth. The fire-induced loss in organic layer resulted in a greater heat penetration deeper into the ground and unbalanced the moisture distribution. Overall, the thawing depth is greater by >1.7 m under severely burned forest, compared to unburned forest. Given that about 30% of the boreal forest was affected by wildfire in the study area, the ground thermal regime changed considerably. The findings suggest that the combination of regional temperature rise and more frequent extreme weather and wildfire events in the region triggers permafrost degradation and alters the hydrothermal regime in the future. [ABSTRACT FROM AUTHOR]

Livestock production is a socioeconomic linchpin in Mongolia and is affected by large-scale livestock die-offs. Colloquially known as dzuds, these die-offs are driven by anomalous climatic events, including extreme cold temperatures, extended snow cover duration (SCD) and drought. As average temperatures across Mongolia have increased at roughly twice the global rate, we hypothesized that increasing cold season surface melt including soil freeze/thaw (FT), snowmelt, and icing events associated with regional warming have become increasingly important drivers of dzud events as they can reduce pasture productivity and inhibit access to grazing. Here, we use daily brightness temperature (Tb) observations to identify anomalous surface melt and icing events across Mongolia from 2003–2016 and their contribution to dzuds relative to other climatic drivers, including winter temperatures, SCD, and drought. We find a positive relationship between surface melt and icing events and livestock mortality during the fall in southern Mongolia and during the spring in the central and western regions. Further, anomalous seasonal surface melt and icing events explain 17–34% of the total variance in annual livestock mortality, with cold temperatures as the leading contributor of dzuds (20–37%). Summer drought showed the greatest explanatory power (43%) but overall had less statistically significant relationships relative to winter temperatures. Our results indicate that surface melt and icing events will become an increasingly important driver of dzuds as annual temperatures and livestock populations are projected to increase in Mongolia. [ABSTRACT FROM AUTHOR]

Knowledge of the duration and distribution of seasonal snow cover is important for understanding the hydrologic regime in mountainous regions within semi-arid climates. In the headwater of the semi-arid Sugnugur catchment (in the Khentii Mountains, northern Mongolia), a spatial analysis of seasonal snow cover duration (SCD) was performed on a 30 m spatial resolution by integrating the spatial resolution of Landsat-7, Landsat-8, and Sentinel-2A images with the daily temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) snow products (2000–2017). Validation was achieved using in situ time series measurements from winter field campaigns and distributed surface temperature loggers. We found a mean increase of SCD with altitude at approximately +6 days/100 m. However, we found no altitude-dependent changes in snow depth during field campaigns. The southern exposed valley slopes are either snow free or covered by intermittent snow throughout the winter months due to high sublimation rates and prevailing wind. The estimated mean SCD ranges from 124 days in the lower parts of the catchment to 226 days on the mountain peaks, with a mean underestimation of 12–13 days. Snow onset and melt dates exhibited large inter-annual variability, but no significant trend in the seasonal SCD was evident. This method can be applied to high-resolution snow mapping in similar mountainous regions. [ABSTRACT FROM AUTHOR]

The drought of 2018 caused a wide range of impacts on environment and economy. This contribution explores how different drought impacts were linked to the occurrence of extreme values of hydrometeorological indices. Focusing on Germany, this study is based on drought impact data collected from text reports that are archived in the European Drought Impact report Inventory (EDII) and on standardized meteorological drought indices, modelled water balance and soil moisture, and observations of streamflow and groundwater. Our hypotheses included that impacts in particular sectors depended on the season (e.g. agricultural impacts), the combination of drought with heatwaves (e.g. impacts on river ecology), or other particular drought characteristics such as the prolonged duration of low flows (e.g. impacts on riverborne transportation). We therefore expected a particular temporal sequence of impact occurrences in relation to the drought evolution. Based on the currently available data there is little support of a temporal sequencing of impacts, but there are indications for drought characteristic specific impact profiles. Compared to recent previous drought events, impacts in different sectors appear to have co-occurred to a higher degree in 2018. However, results may still change as reporting and data collection are ongoing. Effects on groundwater levels and forests, for example, may lag behind by up to a year and many reports on impact-related statistics are only published with delay. Nevertheless, the combined data analysis of impacts and indices suggests that there may be potential for impact-specific drought monitoring of the multi-sectorial impacts that contributed to the event's high visibility. [ABSTRACT FROM AUTHOR]

Knowledge of the snow cover dynamics in mountains of the semi-arid regions on Earth is essential for an improved understanding of the hydrological regime and for human water supply in a water scarce environment. In the semi-arid Sugnugur headwater catchment (in the Khentii Mountains, northern Mongolia), a spatial analysis of seasonal snow cover duration (SCD) was achieved on a 30 m grid by integrating the spatial resolution of Landsat-7, Landsat-8 and Sentinel-2A images with the daily temporal resolution of MODIS snow products (2000-2017). Validation was achieved using in situ measurements from winter field campaigns and spatially distributed surface temperature recordings. We found a mean increase of SCD with altitude at approximately +6 days/100m. However, no altitude-dependent changes in snow depth could be observed during the field campaigns. The southern exposed valley slopes are either snow free or covered by intermittent snow throughout the winter months due to high sublimation rates and prevailing wind. The estimated mean SCD ranges from 124 in the lower parts of the catchment to 226 days on the mountain peaks, with a mean underestimation of 12-13 days. Snow onset and melt dates exhibited large inter-annual variability, but no significant trend in the seasonal SCD was evident. Our method supports the better quantification of snow melt rates and thus water availability in this semi-arid environment. It can be applied to high-resolution snow mapping in similar mountainous regions. [ABSTRACT FROM AUTHOR]