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Attenuation data from microwave links can be used to derive important near-ground precipitation estimates which complements observations from rain gauges and weather radars. Wetness on the cover of the microwave link antennas can, however, lead to additional attenuation, resulting in an overestimation of rainfall rates. To investigate the impact of this wet antenna attenuation (WAA) effect, a versatile microwave field experiment was built. It is based on a two-port vector network analyzer and equipped with six dual-polarized commercial microwave link antennas at different frequencies between 18 and 40 GHz. To a measurement frequency range of 150 MHz, range gating enables simultaneous measurement of antenna reflection coefficient and total path attenuation. An optical camera is integrated for visual inspection of the antenna covers. Appropriate housing and active temperature control makes the device weather-proof and enables all-season operation. Several supplementary meteorological instruments at the installation site provide information about the actual weather conditions. They can be used as the ground truth for evaluation. The presented instrument is a unique setup to study the WAA effect, by simultaneously providing measurements of the total path attenuation together with reliable WAA estimations, directly derived from measurements of the antenna reflection coefficient. Its data will give insights into WAA magnitude and temporal evolution in relation to meteorological variables. This will help developing improved WAA estimation algorithms, which will result in improved rainfall estimation from microwave links. [ABSTRACT FROM AUTHOR]

Lateral terrestrial water flow is usually not considered in regional climate modeling. This study focuses on the impact of increased hydrological model complexity for the description of the land‐atmosphere interactions in a complex terrain region. For this purpose, we apply the Weather Research and Forecasting (WRF) model with its hydrological modeling extension package WRF‐Hydro for the case of the Heihe River Basin in convection permitting atmospheric resolution (3 km). The Heihe River Basin (143,200 km2) is an arid‐semiarid inland river basin in Northwestern China. By comparing model simulations results with and without coupling for the period 2008–2010, the effect of lateral terrestrial water flow on land‐atmosphere interactions is evaluated with a joint atmospheric‐terrestrial water budget analysis, a regional precipitation recycling analysis and a fully three‐dimensional atmospheric moisture tracing method (evaporation tagging). The coupled modeling system WRF‐Hydro simulates near‐surface temperature and precipitation variability similar to the WRF model and demonstrates, in addition, its ability to reproduce daily streamflow. In the fully coupled mode, as a consequence of lateral terrestrial water flow description, the redistribution of infiltration excess in the mountainous area produces higher soil moisture content in the root zone, increases the terrestrial water storage and evapotranspiration, and decreases the total runoff. The resulting wetting and cooling in the near surface affects the regional climate by changing the regional water vapor transports and water vapor content, while, in turn, inducing precipitation differences. Overall, the fully coupled modeling increases the recycling rate, indicating that lateral terrestrial water flow influences regional climate in our study area. Key Points: Fully coupled atmospheric‐hydrological modeling is performed in convection permitting mode for a complex terrain regionThe redistribution of infiltration excess increases soil moisture and ET in mountainous areaLateral terrestrial water flow modifies the atmospheric moisture transport and increases regional precipitation recycling [ABSTRACT FROM AUTHOR]

Atmospheric models such as the Weather Research and Forecasting (WRF) model provide a tool to evaluate the behavior of regional hydrological cycle components, including precipitation, evapotranspiration, soil water storage, and runoff. Recent model developments have focused on coupled atmospheric‐hydrological modeling systems, such as WRF‐Hydro, in order to account for subsurface, overland, and river flow and potentially improve the representation of land‐atmosphere interactions. The aim of this study is to investigate the contribution of lateral terrestrial water flow to the regional hydrological cycle, with the help of a joint soil‐vegetation‐atmospheric water tagging procedure newly developed in the so‐called WRF‐tag and WRF‐Hydro‐tag models. An application of both models for the high precipitation event on 15 August 2008 in the German and Austrian parts of the upper Danube river basin (94,100 km2) is presented. The precipitation that fell in the basin during this event is considered as a water source, is tagged, and subsequently tracked for a 40‐month period until December 2011. At the end of the study period, in both simulations, approximately 57% of the tagged water has run off, while 41% has evaporated back to the atmosphere, including 2% that has recycled in the upper Danube river basin as precipitation. In WRF‐Hydro‐tag, the surface evaporation of tagged water is slightly enhanced by surface flow infiltration and slightly reduced by subsurface lateral water flow in areas with low topography gradients. This affects the source precipitation recycling only in a negligible amount. Key Points: A joint soil‐vegetation‐atmospheric water tagging procedure is incorporated into the WRF and WRF‐Hydro modelsLateral terrestrial water flow slightly modifies the partitioning of precipitation event water among terrestrial‐atmospheric compartmentsThe procedure can be adapted to track water from any water compartment in WRF‐Hydro, which widens the range of future application studies [ABSTRACT FROM AUTHOR]

Changes in extreme precipitation due to climate change often require the application of methods to bias correct simulated atmospheric fields, including extremes. Most existing bias correction techniques (i) only focus on the bias in the mean value or on the extreme values separately, and (ii) exclude zero values from analysis, even though their presence is significant in daily precipitation. We developed a copula-based bias correction scheme that is suitable for zero-inflated daily precipitation data to correct the bias in mean as well as in extreme precipitation at any specific statistical quantile. In considering the whole of Germany as a test bed, the proposed scheme is found to work well across the entire study area, including the German Alpine regions. The joint distribution between observed and regional climate model (RCM)-derived precipitation is developed through copulas. In particular, the joint distribution is modified to make it discrete at zero in order to account for zero values. The benefit of considering zero precipitation values is revealed through the improved performance of bias correction both in the mean and extreme values. Second, the quantile that best captures the bias (whether in the mean or any extreme value) is determined for a specific location and varies spatially and seasonally. This relaxation in selecting the location-specific optimal quantile renders the proposed methodology spatially transferable. By acknowledging possible changes in extreme precipitation due to climate change, the proposed scheme is expected to be suitable for climate change impact assessments for extreme events worldwide. [ABSTRACT FROM AUTHOR]

Recent developments in hydrometeorological modeling aim toward a more sophisticated treatment of terrestrial hydrologic processes. The objective of this study is to investigate the role of lateral terrestrial water flows on the regional atmospheric and terrestrial water cycle in a humid region of Central Europe. This is accomplished by evaluating model results from both the standard Weather Research and Forecasting (WRF) model and the hydrologically enhanced version WRF‐Hydro, which allows for a more comprehensive process description of the interdependencies between water and energy fluxes at the land‐atmosphere interface. To account for internal model variability, an ensemble for each model variant is generated for a 3‐month study period in the summer of 2005. The regional water cycle in the simulation domain is investigated by evaluating the ensemble results with a joint atmospheric‐terrestrial water budget analysis. We focus on six differently sized river catchments located in Southern Bavaria (Germany) and the southerly adjacent Eastern Alps, where simulation results are compared to observations of precipitation, near‐surface temperatures, and streamflow. Most prominently, WRF‐Hydro increases (near‐) surface runoff and decreases percolation, resulting in a reduced total runoff amount. Accordingly, soil moisture storage and evapotranspiration are increased. Domain‐averaged precipitation differences between WRF and WRF‐Hydro are essentially related to differences in atmospheric moisture inflow and outflow, which is the signature of internal model variability and is potentially enhanced in the WRF‐Hydro ensemble. Driven only at the boundaries of the outermost domain, the fully coupled model system shows good performance in the reproduction of observed streamflow. Key Points: Fully coupled atmosphere‐terrestrial hydrology simulations are able to reproduce observed river runoff with a reasonable performanceModel results from the WRF model and the hydrologically enhanced version WRF‐Hydro are compared and evaluated in terms of added valueThe regional water cycle in the simulation domain is investigated by a joint atmospheric‐terrestrial water budget analysis [ABSTRACT FROM AUTHOR]

A novel stochastic downscaling approach to simulate ensembles of daily precipitation fields using the Gaussian copula is presented. In contrast to many other statistical downscaling techniques, this approach uses spatial correlation (correlograms) to derive the transfer function between predictors and predictands for a parsimonious model structure. Daily regional climate model (RCM) simulations for a region in Central Europe in two different spatial resolutions (7 and 42 km) served as a training set to derive the statistics necessary to simulate fine scale precipitation values. The model was calibrated with RCM simulations for the year 1971, and the evaluation was performed for the period 1972–2000 to emulate the typical problem of limited availability of fine scale data. A comprehensive evaluation of the downscaling approach comprising the spatial correlations and statistical distributions of the simulated precipitation fields and several further performance measures was performed. The distribution of simulated precipitation is in close agreement with values simulated from a distribution function that was fitted to the complete evaluation period. Average Brier skill scores of 0.5 indicate a good performance of reproducing the daily dynamical simulations for most regions. A comparison with precipitation fields interpolated with inverse distance weighting revealed an average added skill of 42% for different precipitation thresholds; 87% of the dry days and 71% of the wet days were simulated correctly. An advantage of the proposed method over deterministic downscaling techniques is that ensembles of predictand fields are generated. Thus, the uncertainty that is inherent to downscaling can be estimated. The method has the potential to be used in other downscaling applications to generate ensembles of spatially correlated predictands based on other predictors. As copulas treat the dependence structure separately from the marginal distributions of the predictors and predictands, it is possible to simulate meteorological variables from any desired distribution function. [ABSTRACT FROM AUTHOR]

A 26-year simulation (1980-2005) was performed with the Weather Research and Forecast (WRF) model over the Volta Basin in West Africa. This was to investigate the ability of a climate version of WRF to reproduce present day temperature and precipitation over the Volta Basin. The ERA-Interim reanalysis and one realization of the ECHAM6 global circulation model (GCM) data were dynamically downscaled using two nested domains within the WRF model. The outer domain had a horizontal resolution of 50 km and covered the whole of West Africa while the inner domain had a horizontal resolution of 10 km. It was observed that biases in the respective forcing data were carried over to the RCM, but also the RCM itself contributed to the mean bias of the model. Also, the biases in the 50-km domain were transferred unchanged, especially in the case of temperature, to the 10-km domain, but, for precipitation, the higher-resolution simulations increased the mean bias in some cases. While in general, WRF underestimated temperature in both the outer (mean biases of −1.6 and −2.3 K for ERA-Interim and ECHAM6, respectively) and the inner (mean biases of −0.9 K for the reanalysis and −1.8 K for the GCM) domains, WRF slightly underestimated precipitation in the coarser domain but overestimated precipitation in the finer domain over the Volta Basin. The performance of the GCM, in general, is good, particularly for temperature with mean bias of −0.7 K over the outer domain. However, for precipitation, the added value of the RCM cannot be overlooked, especially over the whole West African region on the annual time scale (mean biases of −3% for WRF and −8% for ECHAM6). Over the whole Volta Basin and the Soudano-Sahel for the month of April and spring (MAM) rainfall, respectively, mean bias close to 0% was simulated. Biases in the interannual variability in both temperature and precipitation over the basin were smaller in the WRF than the ECHAM6. High spatial pattern correlations between 0.7 and 0.8 were achieved for the autumn precipitation and low spatial correlation in the range of 0.0 and 0.2 for the winter season precipitation over the whole basin and all the three belts over the basin. [ABSTRACT FROM AUTHOR]

Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April-October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%-20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%-0.7%. The reproduction of observed daily discharge with Nash-Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science. [ABSTRACT FROM AUTHOR]

An increase in the spatial resolution of regional climate model simulations improves the representation of land surface characteristics andmay allow the explicit calculation of important physical processes such as convection. The present study investigates further potential benefits with respect to precipitation, based on a small ensemble of high-resolution simulations with WRF with grid spacings up to 1 km. The skill of each experiment is evaluated regarding the temporal and spatial performance of the simulation of precipitation of one year over both a mountainous region in southwestern Germany and a mainly flat region in northern Germany. This study allows us to differentiate between the impact of grid spacing, topography, and convection parameterization. Furthermore, the performance of a state-of-the-art convection parameterization scheme in the gray zone of convection is evaluated against an explicit calculation of convection only. Our evaluation demonstrates the following: high-resolution simulations (5 and 1 km) are generally able to represent the diurnal cycle, structure, and intensity distribution of precipitation, when compared to observational datasets such as radar data and interpolated station data. The influence of the improved representation of the topography at higher resolution (1 km) becomes apparent in complex terrain, where the localization of precipitation maxima ismore accurate, although these maxima are overestimated. In flat areas, differences in spatial evaluations arise between simulationswith parameterized and explicitly calculated convection, whereas smaller grid spacings (1 km vs 5km) show hardly any impact on precipitation results. [ABSTRACT FROM AUTHOR]

It is commonly understood that the observed decline in precipitation in southwestern Australia during the twentieth century is caused by anthropogenic factors. Candidates therefore are changes to large-scale atmospheric circulations due to global warming, extensive deforestation, and anthropogenic aerosol emissions-all of which are effective on different spatial and temporal scales. This contribution focuses on the role of rapidly rising aerosol emissions from anthropogenic sources in southwestern Australia around 1970. An analysis of historical long-term rainfall data of the Bureau of Meteorology shows that southwestern Australia as a whole experienced a gradual decline in precipitation over the twentieth century. However, on smaller scales and for the particular example of the Perth catchment area, a sudden drop in precipitation around 1970 is apparent. Modeling experiments at a convection-resolving resolution of 3.3 km using the Weather Research and Forecasting (WRF) Model version 3.6.1 with the aerosol-aware Thompson-Eidhammer microphysics scheme are conducted for the period 1970-74. A comparison of four runs with different prescribed aerosol emissions and without aerosol effects demonstrates that tripling the pre-1960s atmospheric CCN and IN concentrations can suppress precipitation by 2%-9%, depending on the area and the season. This suggests that a combination of all three processes is required to account for the gradual decline in rainfall seen for greater southwestern Australia and for the sudden drop observed in areas along the west coast in the 1970s: changing atmospheric circulations, deforestation, and anthropogenic aerosol emissions. [ABSTRACT FROM AUTHOR]

Data from five different RCMs run in two experiments from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are applied together with the Water Flow and Balance Simulation Model (WaSiM) to assess the future availability of water in the upper Jordan River. Simulation results for 1976-2000 show that the modeling system was able to reasonably reproduce the observed discharge rates in the partially karstic complex terrain without bias correction of the precipitation input. For the future climate in the area, the applied CORDEX models indicate an increasing annual mean temperature for 2031-60 by 1.8 K above the 1971-2000 mean and by 2.6 K for 2071-2100. The simulated ensemble mean precipitation is predicted to decrease by 16.3% in the first period and 22.1% at the end of the century. In relation to the mean for 1976-2000, the discharge of the upper Jordan River is simulated to decrease by 7.4% until 2060 and by 17.5% until 2100, together with a reduction of high river flow years. [ABSTRACT FROM AUTHOR]

ABSTRACT Regional precipitation recycling is the measure of the contribution of local evaporation E to local precipitation. This study provides a set of two methods developed in the Weather Research and Forecasting WRF model system for investigating regional precipitation recycling mechanisms: (1) tracking of tagged atmospheric water species originating from evaporation in a source region, ie E-tagging, and (2) three-dimensional budgets of total and tagged atmospheric water species. These methods are used to quantify the effect of return flow and nonwell vertical mixing neglected in the computation of the bulk precipitation recycling ratio. The developed algorithms are applied to a WRF simulation of the West African Monsoon 2003. The simulated region is characterized by vertical wind shear condition, i.e., southwesterlies in the low levels and easterlies in the mid-levels, which favors return flow and nonwell vertical mixing. Regional precipitation recycling is investigated in 100 × 100 and 1000 × 1000 km2 areas. A prerequisite condition for evaporated water to contribute to the precipitation process in both areas is that it is lifted to the mid-levels where hydrometeors are produced. In the 100 × 100 (1000 × 1000) km2 area the bulk precipitation recycling ratio is 0.9 (7.3) %. Our budget analysis reveals that return flow and nonwell vertically mixed outflow increase this value by about +0.2 (2.9) and +0.2 (1.6) %, respectively, thus strengthening the well-known scale-dependency of regional precipitation recycling. [ABSTRACT FROM AUTHOR]

The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget-based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation-runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets. [ABSTRACT FROM AUTHOR]

An analysis of the effects of SST representation on precipitation in long-term continuous simulations was carried out for the Mediterranean peninsula of Calabria, Italy, which is characterized by complex coastlines and orography. A parameterization analysis was performed to find an optimal model configuration, using a method where SST fields are directly ingested from NCEP datasets into the Weather Research and Forecasting (WRF) Model lower boundary condition files. The results of the optimal configuration were used for a comparison with recorded precipitation patterns for a very wet period (from November 2008 to January 2009), adopting several interpolation options available in the WRF Preprocessing System. An additional comparison was made against a uniform variation of the original SST fields by ε = ±0.5 K. It was found that the interpolation options mainly affect near-coastline SSTs, where methods requiring fewer source data points have several advantages. Effects of SST representation on precipitation, accumulated over the whole 3-month period, are generally lower than ±2%, but a specific class of events (synoptic situations) with strong differences in precipitation patterns was identified. These events are connected to pressure systems moving from the African coast to the north and approaching the Sicilian and Calabrian coastlines. Two of these events, which occurred on 27-29 December 2008 and 9 January 2009, were analyzed in detail, highlighting that small variations of SST values induce slight shifts in the paths of the weather fronts. These slight shifts are important enough to determine whether or not wet air masses can reach the mountain ranges close to the coast, where rainfall intensity is enhanced by orographic effects. [ABSTRACT FROM AUTHOR]

The impact of climate change on the future water availability of the upper Jordan River (UJR) and its tributaries Dan, Snir, and Hermon located in the eastern Mediterranean is evaluated by a highly resolved distributed approach with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5) run at 18.6- and 6.2-km resolution offline coupled with the Water Flow and Balance Simulation Model (WaSiM). The MM5 was driven with NCEP reanalysis for 1971-2000 and with Hadley Centre Coupled Model, version 3 (HadCM3), GCM forcings for 1971-2099. Because only one regional-global climate model combination was applied, the results may not give the full range of possible future projections. To describe the Dan spring behavior, the hydrological model was extended by a bypass approach to allow the fast discharge components of the Snir to enter the Dan catchment. Simulation results for the period 1976-2000 reveal that the coupled system was able to reproduce the observed discharge rates in the partially karstic complex terrain to a reasonable extent with the high-resolution 6.2-km meteorological input only. The performed future climate simulations show steadily rising temperatures with 2.2 K above the 1976-2000 mean for the period 2031-60 and 3.5 K for the period 2070-99. Precipitation trends are insignificant until the middle of the century, although a decrease of approximately 12% is simulated. For the end of the century, a reduction in rainfall ranging between 10% and 35% can be expected. Discharge in the UJR is simulated to decrease by 12% until 2060 and by 26% until 2099, both related to the 1976-2000 mean. The discharge decrease is associated with a lower number of high river flow years. [ABSTRACT FROM AUTHOR]

Abstract: Near-surface water vapor and precipitation are central hydrometeorological observables which are still difficult to quantify accurately above the point scale. Both play an important role in modeling and remote sensing of the hydrologic cycle. We present details on the development of a new microwave transmission experiment that is capable of providing line integrated estimates of both humidity and precipitation near the surface. The system is located at a hydrometeorological test site (TERENO-prealpine) in Southern Germany. Path length is kept short at 660m to minimize the likelihood of different precipitation types and intensities along the path. It uses a monostatic configuration with a combined transmitter/receiver unit and a 70cm trihedral reflector. The transmitter/receiver unit simultaneously operates at 22.235GHz and 34.8GHz with a pulse repetition rate of 25kHz and alternating horizontal and vertical polarization, which enable the analysis of the impact of the changing drop size distribution on the rain rate retrieval. Due to the coherence and the high phase stability of the system, it allows for a sensitive observation of the propagation phase delay. Thereof, time series of line integrated refractivity can be determined. This proxy is then post-processed to absolute humidity and compared to station observations. We present the design of the system and show an analysis of selected periods for both, precipitation and humidity observations. The theoretically expected dependence of attenuation and differential attenuation on the DSD was reproduced with experimental data. A decreased performance was observed when using a fixed A–R power law. Humidity data derived from the phase delay measurement showed good agreement with in situ measurements. [Copyright &y& Elsevier]

Detection of precipitation based on the received signal level of commercial microwave links has been increasingly used in the mountain areas where meteorological radars have limited ranges, and placing rain gauges is impossible due to terrain morphology. In this paper, focused time-delay neural networks were trained to detect the appearance of precipitation based on the received signal level. The detailed testing of the trained artificial neural networks was done with the data obtained on the same link, which were not used for model development. The results show that the proposed method based on neural networks can be used for accurate precipitation detection in significantly shorter time comparing to the previous methods. [ABSTRACT FROM AUTHOR]

A set of downscaled climate change data from transient experiments with regional climate models has been used to access the future climate change signal in the area of the Figeh spring system in Syria and its potential effects on future water availability. The data ensemble at a spatial resolution of 0.25° has been investigated for the period 1961-90 for present-day climate and the periods 2021-50 and 2070-99 for future climate. The focus is on changes to annual, seasonal, and monthly surface air temperature and precipitation. For the first time, the Figeh spring discharge has been assessed with a hydrological runoff model based on an artificial neural network (ANN) approach. The ANN model was formulated and validated for the years 1987-2007, applying daily meteorological driving data. The investigations show that water supply from the spring might face serious problems under changed climate conditions. An expected, a precipitation decrease of about −11% in winter and −8% in spring, together with increased temperatures of up to +1.6°C and a significant decrease in snow mass, can substantially limit the water recharge potential already in the near future until 2050. In the period 2070-99, the annual precipitation amount is simulated to decrease by −22% and the annual mean temperature to increase by +4°C, relative to the 1961-90 mean. The ensemble mean of the relative change in mean discharge reveals a decrease during the peak flow from March to May, with values up to −20% in 2021-50 and almost −50% in the period 2069-98, both related to the 1961-90 mean. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]

The Volta Basin in West Africa is a region sensitive to water shortage. Future climate conditions therefore may put additional stress on the competition for the scarce water resources between industry, agriculture, and households. For an investigation of the sensitivity of the hydrological regime to global climate change in the data-sparse and poorly gauged region of the Volta Basin, joint regional climate-hydrology simulations were performed. MM5 was used as a regional climate model to downscale two time slices of a global ECHAM4 simulation to a resolution of 9 km. These regional climate simulations were used to drive a physically based, distributed hydrological model at 1 km resolution. The performance of the model components and the joint model system was evaluated for different historical periods. Results show that discharge in the Volta Basin reacts highly sensitively to precipitation differences. The pronounced rainfall decrease at the beginning of the rainy season is not transferred to discharge changes. During the rainy season most of the surplus rainfall evaporates due to a strong increase in evaporation as a consequence of higher near-surface air temperatures. The average change signal in precipitation, as well as surface and subsurface hydrology variables, lies in most variables within the range of inter-annual variability, but regionally stronger signals are also observed. [ABSTRACT FROM AUTHOR]

Summary: A number of future water availability scenarios in the Crati River Basin (Southern Italy) are made by applying the outputs of three Regional Climate Models (RCMs) RegCM, HIRHAM and COSMO-CLM to the newly developed Intermediate Space Time Resolution Hydrological Model (In-STRHyM). In-STRHyM is a fully distributed hydrological model detailed enough to describe the hydrological processes of several small-medium sized Mediterranean basins. It has a relatively simple structure and is suitable for long period simulations to be undertaken within acceptable time frames. The analysis was performed using two time slices (1961–1990 and 2070–2099) with the SRES A2 (HAD3AM) and A1B (ECHAM5/MPI-OM) scenarios. Observed biases in simulated precipitation and temperature fields during the control period (1961–1990) were corrected before using meteorological outputs from each RCM as input for In-STRHyM. A sensitivity analysis is used to help verify this procedure. While hydrological model simulations are based on different greenhouse gas emission scenarios, GCMs, RCMs and bias correction parameters and hence show noticeable differences, they all agree on a general reduction in future water resource availability. An increase in the average annual temperature between +3.5°C and +3.9°C and a decrease between −9% and −21% in the cumulative annual precipitation are projected, leading to a drastic reduction in snow accumulation of between −82% and −92%. Evapotranspiration is expected to increase in the wintertime and decrease in summertime, with the water stress period increasing on average by 15days. Mean annual reductions are predicted for root zone soil moisture (between −12.8±1.9% and −20.7±1.9%, with reductions reaching −37.7±2.4% during summer), groundwater storage (−6.5±1.4% and −11.6±1.6%), surface runoff (−25.4±6.0% and −41.2±5.0%) and a significant increase in runoff variability. [Copyright &y& Elsevier]

Abstract: Rainfed farming systems in sub-Saharan Africa are suffering from low productivity. Prolonged dry spells and droughts often lead to significant crop losses, a situation that is expected to be exacerbated by climate change. In this study, the impact of climate change on attainable yields of maize and groundnut, as major alimentary crops in sub-Saharan Africa, is evaluated at five stations in Cameroon under rainfed conditions. It is focussed on the contribution of future climate change in terms of the direct fertilisation effect of the expected alteration and the indirect effects of the expected temperature and precipitation change. As improved agricultural management practices in rainfed systems are crucial to increase agricultural productivity, the impact of the planting date is analysed in detail. For this purpose, a fuzzy logic-based algorithm is developed to estimate the agriculturally relevant onset of the rainy season (ORS) and, thus, the optimal planting date. This algorithm is then connected to the physically based crop model CropSyst, hereinafter referred to as optimal planting date following crop modelling system. A Monte Carlo approach is used to optimise the ORS algorithm in terms of maximising the mean annual crop yields (1979–2003). The optimal planting date following crop modelling system is applied to past and future periods, mainly for two reasons: (i) to derive optimal fuzzy rules and increase mean attainable crop yields; and (ii) to reliably estimate the impact of climate change to crop productivity with (‘optimal planting date scenario’) and without planting date adaptations (‘traditional planting date scenario’). It is shown that the fuzzy rules derived for assessing the optimal planting dates may allow for significantly increased crop yields compared to the existing planting rules in Cameroon under current climatic conditions, especially for the drier northern regions. A change in the climatic conditions due to global warming will reduce the growing cycle and, thus, the crop yields. However, the positive effect of fertilisation is likely to outweigh the negative effects of precipitation and temperature change for the 2020s and partly for the 2080s. When additionally considering planting date adaptations, groundnut yield is expected to increase for the 2020s and the 2080s, with maximum yield surpluses of about 30% for the 2020s compared to the extended baseline period. For maize, crop yield is likely to increase (decrease) for the 2020s (2080s) by approximately 15%. For the driest stations analysed, the negative impacts of temperature and precipitation change could be mitigated significantly by planting date adaptations. [Copyright &y& Elsevier]

Precipitation is one of the crucial variables within the hydrological system, and accordingly one of the main drivers for terrestrial hydrological processes. The quality of hydrological applications such as climate prediction, water resource management, and flood forecasting, depends on the correct reproduction of its spatiotemporal distribution as well as the estimation of uncertainties introduced by the usage of observational data. In order to address these challenges, we apply Random Mixing to stochastically simulate precipitation fields. We generate precipitation fields as a linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized in such a way that the observations and the spatial structure of the precipitation observations are reproduced. Our main innovation is the ability to simulate precipitation field ensembles of any size, where each ensemble member is in concordance with the underlying observations. As observations we use path-averaged rain rates, derived from attenuation data provided by a country-wide network of Commercial Microwave Links (CMLs). The difficulty when using path-averaged rain rates is that CMLs give non-linear constrains for the precipitation field in contrast to point information which give linear constrains. Therefore, based on the CML observations, we sample sets of observation points located along the CML paths. These sets of observation points are used to calculate the spatial dependence structure of the precipitation fields. Consequently, the generation of the ensemble is based on various sets of sampled observation points. The spread of such an ensemble allows for an uncertainty estimation of the generated precipitation fields and reflects the uncertainty of rainfall variability along the CML paths. For the demonstration of this strategy we use CML observations over the area of Germany. We show that the reconstructed precipitation fields reproduce the observed spatial precipitation pattern in a comparable good quality as the RADOLAN-RW data set provided by the German Weather Service. Further, we discuss the influence of the observation network topology on the uncertainty estimation, as illustrated by the uneven spread of CML observations over Germany. [ABSTRACT FROM AUTHOR]