Soil erosion and sediment transport under climate change for Mera River, in Italian Alps of Valchiavenna

Erosion is a main form of soil degradation, with severe consequences on slope stability and productivity, and erosion studies are required to predict possible variations of such phenomena, also under climate change scenarios. Here we estimated distributed soil erosion within Valchiavenna valley in the Rhaetian Alps, drained by Mera river, and covering Italy, and Switzerland. We used a Dynamic-RUSLE (D-RUSLE) model, which provides spatially distributed estimates of soil erosion explicitly considering snow dynamic (accumulation/melting) and snow cover, and vegetation seasonality. The model was tuned here during 2010-2019, and validation was pursued using river turbidity data, used to assess riverine sediment transport. The model parameter R-factor for rainfall erosivity was estimated using a hydrological model Poli-Hydro, properly set up in the study area. C-factor for land cover was assessed against land cover maps, with seasonally variable Normalized Difference Vegetation Index from satellite images, to account for variable vegetation stage, and large leaf cover in summer. The K-factor related to erosion susceptibility was evaluated through soil texture and organic content. LS-factor depending on slope was assessed using a DTM. Poli-Hydro and D-RUSLE models were then used to project forward potential soil erosion under climate change scenarios until 2100. Climate series (temperature, precipitation) were generated using 4 shared socio-economic pathways (SSPs) of the Sixth Assessment Report of the IPCC, with 3 global circulation models, properly downscaled locally. We analysed expected soil erosion during 2051-2060, and 2091-2100. We found increase of potential soil erosion, with exception of the EC-Earth model for the SSP2.6. Erosion would especially increase in winter, in response to smaller snow accumulation, and larger liquid rainfall share thereby, and decrease in summer, as due to decreased precipitation. Our results suggest the need for adaptation strategies to counteract increasing soil loss in the future, and may highlight most critical areas of intervention.

The influence of dusts on radiation and temperature over the eastern Asia with a regional climate model

In order to investigate the climate effects of dusts, a regional climate model (RegCM 4.6) with the dust scheme was used to simulate the direct radiative forcing and air temperature response at 2 m near surface of dusts over the eastern Asia. Two sets of experiments were conducted, one with and one without dust aerosols. The experiment covered the main dust occurrence months from March to May for 8 years (2011-2018), and the simulation results were evaluated against ground station, reanalysis and satellite data. The model captured the spatiotemporal distribution of dust AOD and mass loading over the eastern Asia. However, it tended to underestimate the dust AOD and mass loading over the downwind of the dust source region and the Taklimakan Desert, and overestimate them over the north Xinjiang. The direct net radiative forcing including shortwave and longwave was up to -20 W·m^-2 at the surface and -10 W·m^-2 at the TOA over the dust source region due to the dominant negative shortwave forcing. The only exception of positive forcing at the TOA was observed along the western boundaries of the Tibetan Plateau due to the semi-persistent ice and snow cover. The dusts tended to warm the atmosphere more than 18 W·m^-2 and cool the surface locally up to -0.7 °C. Among the 5 sub-areas, the largest averaged regional direct radiative forcing induced by dusts appeared over the central Inner Mongolia in May with the value of -3.0 ± 2.1, -12.2 ± 4.1 and 9.2 ± 4.4 W·m^-2 at the TOA, surface and in the atmosphere, respectively. The results indicated that the model simulation for dusts should be further improved and the dust effects should be included in the estimates of climate change over the eastern Asia.

Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems.

Impact of climate change on hydrology components using CORDEX South Asia climate model in Wunna, Bharathpuzha, and Mahanadi, India

Detecting the probable impact of climate change responses on hydrological components is most important for understanding such changes on water resources. The impact of climate change on virtual parameters of water was assessed through hydrological modeling of the Wunna, Mahanadi (Middle), and Bharathpuzha watersheds. In this article, future hydrological component responses under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios were considered for investigating the runoff, sediment, and water storage components. RegCM4 CSIRO-Mk3.6.0 CORDEX South Asia of RCM model was used which is specially downscaled for the Asian region by IITM-India. Delta change method was adopted to remove bias correction in RCM data. Hydrological simulation for current and future periods was performed by GIS interfaced Soil Water and Assessment Tool (SWAT) model. The surface runoff of Wunna and Bharathpuzha watersheds and the yield of sediment are expected to increase further under RCP8.5 than RCP4.5 and in contrast to Mahanadi watershed. Both blue water storage (BW) and green water storage (GWS) of Wunna watershed are expected to decline under RCP4.5, and rise under RCP8.5 scenario. Both BW and GWS of Bharathpuzha are expected to increase in the future except in western region under RCP4.5 scenario. BW of Mahanadi is expected to increase in the future. However, GWS will decrease in some of the sub-basins. The model-generated results will be helpful for future water resources planning and development.

A Sensitivity Study of High-Resolution Climate Simulations for Greece

In the present study, the ability of the Advanced Weather Research and Forecasting numerical model (WRF-ARW) to perform climate regionalization studies in the topographically complex region of Greece, was examined in order to explore the possibility of a more reliable selection of physical schemes for the simulation of historical and future high resolution (5 km) climate model experiments to investigate the impact of climate change. This work is directly linked to a previous study investigating the performance of seven different model setups for one year, from which the need was derived for further examination of four different simulations to investigate the model sensitivity on the representation of surface variables statistics during a 5-year period. The results have been compared with observational data for maximum and minimum air temperature and daily precipitation through statistical analysis. Clear similarities were found in precipitation patterns among simulations and observations, yielding smoothly its inter-annual variability, especially during the wettest months and summer periods, with the lowest positive percentage BIAS calculated at about 19% for the selected combination of physics parameterizations (PP3). Regarding the maximum and minimum temperature, statistical analysis showed a high correlation above 0.9, and negative bias around 1−1.5 °C, and positive bias near 2 °C, respectively.

Assessment of Changes in Annual Maximum Precipitations in the Iberian Peninsula under Climate Change

Climate model projections can be used to assess the future expected behavior of extreme precipitation due to climate change. In Europe, the EURO-CORDEX project provides precipitation projections in the future under various representative concentration pathways (RCP), through regionalized outputs of Global Climate Models (GCM) by a set of Regional Climate Models (RCM). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX project are analyzed in the Iberian Peninsula and the Balearic Islands. Precipitation quantiles for a set of exceedance probabilities are estimated by using the Generalized Extreme Value (GEV) distribution function fitted by the L-moment method. Precipitation quantiles expected in the future period are compared with the precipitation quantiles in the control period, for each climate model. An approach based on Monte Carlo simulations is developed to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period to identify statistically significant changes. The higher the significance threshold, the fewer cells with changes are identified. Consequently, a set of maps are obtained for various thresholds to assist the decision making process in subsequent climate change studies.

A 5 km Resolution Regional Climate Simulation for Central Europe: Performance in High Mountain Areas and Seasonal, Regional and Elevation-Dependent Variations

Mountain regions with complex orography are a particular challenge for regional climate simulations. High spatial resolution is required to account for the high spatial variability in meteorological conditions. This study presents a very high-resolution regional climate simulation (5 km) using the Weather Research and Forecasting Model (WRF) for the central part of Europe including the Alps. Global boundaries are dynamically downscaled for the historical period 1980–2009 (ERA-Interim and MPI-ESM), and for the near future period 2020–2049 (MPI-ESM, scenario RCP4.5). Model results are compared to gridded observation datasets and to data from a dense meteorological station network in the Berchtesgaden Alps (Germany). Averaged for the Alps, the mean bias in temperature is about −0.3 °C, whereas precipitation is overestimated by +14% to +19%. R 2 values for hourly, daily and monthly temperature range between 0.71 and 0.99. Temporal precipitation dynamics are well reproduced at daily and monthly scales (R 2 between 0.36 and 0.85), but are not well captured at hourly scale. The spatial patterns, seasonal distributions, and elevation-dependencies of the climate change signals are investigated. Mean warming in Central Europe exhibits a temperature increase between 0.44 °C and 1.59 °C and is strongest in winter and spring. An elevation-dependent warming is found for different specific regions and seasons, but is absent in others. Annual precipitation changes between −4% and +25% in Central Europe. The change signals for humidity, wind speed, and incoming short-wave radiation are small, but they show distinct spatial and elevation-dependent patterns. On large-scale spatial and temporal averages, the presented 5 km RCM setup has in general similar biases as EURO-CORDEX simulations, but it shows very good model performance at the regional and local scale for daily meteorology, and, apart from wind-speed and precipitation, even for hourly values.

Geographic isolation and climatic variability contribute to genetic differentiation in fragmented populations of the long-lived subalpine conifer Pinus cembra L. in the western Alps

Background

Genetic processes shape the modern-day distribution of genetic variation within and between populations and can provide important insights into the underlying mechanisms of evolution. The resulting genetic variation is often unequally partitioned within species’ distribution range and especially large differences can manifest at the range limit, where population fragmentation and isolation play a crucial role in species survival. Despite several molecular studies investigating the genetic diversity and differentiation of European Alpine mountain forests, the climatic and demographic constrains which influence the genetic processes are often unknown. Here, we apply non-coding microsatellite markers to evaluate the sporadic peripheral and continuous populations of cembra pine (Pinus cembra L.), a long-lived conifer species that inhabits the subalpine treeline ecotone in the western Alps to investigate how the genetic processes contribute to the modern-day spatial distribution. Moreover, we corroborate our findings with paleoecological records, micro and macro-remains, to infer the species’ possible glacial refugia and expansion scenarios.

Results

Four genetically distinct groups were identified, with Bayesian and F_ST based approaches, across the range of the species, situated in the northern, inner and south-western Alps. We found that genetic differentiation is substantially higher in marginal populations than at the center of the range, and marginal stands are characterized by geographic and genetic isolation due to spatial segregation and restricted gene flow. Moreover, multiple matrix regression approaches revealed effects of climatic heterogeneity in species’ spatial genetic pattern. Also, population stability tests indicated that all populations had experienced a severe historical bottleneck, no heterozygosity excess was detected, suggesting that more recently population sizes have remained relatively stable.

Conclusions

Our study demonstrated that cembra pine might have survived in multiple glacial refugia and subsequently recolonized the Alps by different routes. Modern-day marginal populations, at the edge of the species’ range, could maintain stable sizes over long periods without inbreeding depression and preserve high amounts of genetic variation. Moreover, our analyses indicate that climatic variability has played a major role in shaping differentiation, in addition to past historical events such as migration and demographic changes.

Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain

This review paper explores the field of mesoscale to microscale modeling over complex terrain as it traverses multiple so-called gray zones. In an attempt to bridge the gap between previous large-scale and small-scale modeling efforts, atmospheric simulations are being run at an unprecedented range of resolutions. The gray zone is the range of grid resolutions where particular features are neither subgrid nor fully resolved, but rather are partially resolved. The definition of a gray zone depends strongly on the feature being represented and its relationship to the model resolution. This paper explores three gray zones relevant to simulations over complex terrain: turbulence, convection, and topography. Taken together, these may be referred to as the gray continuum. The focus is on horizontal grid resolutions from ∼10 km to ∼10 m. In each case, the challenges are presented together with recent progress in the literature. A common theme is to address cross-scale interaction and scale-awareness in parameterization schemes. How numerical models are designed to cross these gray zones is critical to complex terrain applications in numerical weather prediction, wind resource forecasting, and regional climate modeling, among others.

The Impact of Reservoirs on Runoff Under Climate Change: A Case of Nierji Reservoir in China

Reservoirs play an important role in responding to natural disasters (such as flood and drought) by controlling the runoff. With the climate changing, the frequency and intensity of flood and drought are likely to increase. Thus, the impact of reservoirs on runoff under climate change needs to be studied to cope with future floods and drought. In this study, the Nierji Reservoir located on the Nenjiang River Basin was chosen to explore the impact. The Nenjiany River Basin is the vital water source in the water resources transfer project in Northeast China. Climate change in Nenjiang River Basin was analyzed using the 1980 to 2013 climate observations. The results show that the temperature of the basin significantly (p < 0.05) increased while the precipitation significantly (p < 0.05) decreased. Based on the result, nine kinds of different climate scenarios were set up. For different climate scenarios, the Hydroinformatic Modeling System (HIMS) model and the HIMS model with the reservoir calculation module were used to simulate runoff during the no reservoir operation period (1980–2000) and reservoir operation period (2007–2013), respectively. The impact of reservoirs on runoff under climate change is studied. Results show that the Nierji Reservoir can effectively relieve the impact of climate change on downstream runoff. When temperature increases or precipitation decreases, there are larger differences in runoff over the non-flood period, especially during periods of no reservoir operation. Reservoir operation under climate change can provide reliability in drought protection.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis

Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio-temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan-European data sets and a set that combines eight very high-resolution station-based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post-processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small-scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate-mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

21st century climate change in the European Alps--a review

Reliable estimates of future climate change in the Alps are relevant for large parts of the European society. At the same time, the complex Alpine region poses considerable challenges to climate models, which translate to uncertainties in the climate projections. Against this background, the present study reviews the state-of-knowledge about 21st century climate change in the Alps based on existing literature and additional analyses. In particular, it explicitly considers the reliability and uncertainty of climate projections. Results show that besides Alpine temperatures, also precipitation, global radiation, relative humidity, and closely related impacts like floods, droughts, snow cover, and natural hazards will be affected by global warming. Under the A1B emission scenario, about 0.25 °C warming per decade until the mid of the 21st century and accelerated 0.36 °C warming per decade in the second half of the century is expected. Warming will probably be associated with changes in the seasonality of precipitation, global radiation, and relative humidity, and more intense precipitation extremes and flooding potential in the colder part of the year. The conditions of currently record breaking warm or hot winter or summer seasons, respectively, may become normal at the end of the 21st century, and there is indication for droughts to become more severe in the future. Snow cover is expected to drastically decrease below 1500-2000 m and natural hazards related to glacier and permafrost retreat are expected to become more frequent. Such changes in climatic parameters and related quantities will have considerable impact on ecosystems and society and will challenge their adaptive capabilities.

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Abstract. Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB). This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe. Different climate change scenarios were downscaled for the time period 1960–2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks.

How uncertain are climate model projections of water availability indicators across the Middle East?

The projection of robust regional climate changes over the next 50 years presents a considerable challenge for the current generation of climate models. Water cycle changes are particularly difficult to model in this area because major uncertainties exist in the representation of processes such as large-scale and convective rainfall and their feedback with surface conditions. We present climate model projections and uncertainties in water availability indicators (precipitation, run-off and drought index) for the 1961-1990 and 2021-2050 periods. Ensembles from two global climate models (GCMs) and one regional climate model (RCM) are used to examine different elements of uncertainty. Although all three ensembles capture the general distribution of observed annual precipitation across the Middle East, the RCM is consistently wetter than observations, especially over the mountainous areas. All future projections show decreasing precipitation (ensemble median between -5 and -25%) in coastal Turkey and parts of Lebanon, Syria and Israel and consistent run-off and drought index changes. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) GCM ensemble exhibits drying across the north of the region, whereas the Met Office Hadley Centre work Quantifying Uncertainties in Model ProjectionsAtmospheric (QUMP-A) GCM and RCM ensembles show slight drying in the north and significant wetting in the south. RCM projections also show greater sensitivity (both wetter and drier) and a wider uncertainty range than QUMP-A. The nature of these uncertainties suggests that both large-scale circulation patterns, which influence region-wide drying/wetting patterns, and regional-scale processes, which affect localized water availability, are important sources of uncertainty in these projections. To reduce large uncertainties in water availability projections, it is suggested that efforts would be well placed to focus on the understanding and modelling of both large-scale processes and their teleconnections with Middle East climate and localized processes involved in orographic precipitation.

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

Linked models to assess the impacts of climate change on nitrogen in a Norwegian river basin and FJORD system

Dynamically downscaled data from two Atmosphere-Ocean General Circulation Models (AOGCMs), ECHAM4 from the Max-Planck Institute (MPI), Germany and HadAm3H from the Hadley Centre (HAD), UK, driven with two scenarios of greenhouse gas emissions (IS92a and A2, respectively) were used to make climate change projections. These projections were then used to drive four effect models linked to assess the effects on hydrology, and nitrogen (N) concentrations and fluxes, in the Bjerkreim river basin (685-km(2)) and its coastal fjord, southwestern Norway. The four effect models were the hydrological model HBV, the water quality models MAGIC, INCA-N and the NIVA FJORD model. The downscaled climate scenarios project a general temperature increase in the study region of approximately 1 degrees C by 2030-2049 (MPI IS92a) and approximately 3 degrees C by 2071-2100 (HAD A2). Both scenarios imply increased winter precipitation, whereas the projections of summer and autumn precipitation are quite different, with the MPI scenario projecting a slight increase and the HAD scenario a significant decrease. As a response to increased winter temperature, the HBV model simulates a dramatic reduction of snow accumulation in the upper parts of the catchment, which in turn lead to higher runoff during winter and lower runoff during snowmelt in the spring. With the HAD scenario, runoff in summer and early autumn is substantially reduced as a result of reduced precipitation, increased temperatures and thereby increased evapotranspiration. The water quality models, MAGIC and INCA-N project no major changes in nitrate (NO(3)(-)) concentrations and fluxes within the MPI scenario, but a significant increase in concentrations and a 40-50% increase in fluxes in the HAD scenario. As a consequence, the acidification of the river could increase, thus offsetting ongoing recovery from acidification due to reductions in acid deposition. Additionally, the increased N loading may stimulate growth of N-limited benthic algae and macrophytes along the river channels and lead to undesirable eutrophication effects in the estuarine area. Simulations made by the FJORD model and the HAD scenario indicate that primary production in the estuary might increase up to 15-20%, based on the climate-induced changes in river flow and nitrate concentrations alone.