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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]

Abstract: This study describes the implementation of a one way coupled high resolution numerical weather and river runoff forecasting system within the Perl Object Environment (POE) framework and presents its application and performance analysis for the Alpine catchment of the Ammer River located in southern Germany. The simulation system employs the hydrological water balance model WaSiM-ETH run at 100 m × 100 m grid resolution one way coupled with the numerical weather prediction model (NWP) MM5 driven at 3.5 km grid cell resolution. The state and event driven forecasting system implements the input data download, input data provision via SOAP based WEB service and the run of the hydrology model with observed and with predicted NWP meteorology fields. It applies a lagged ensemble prediction system (EPS) taking into account combination of recent and previous NWP forecasts. The simulation system has been setup and designed for flood forecasting in the alpine environment. It is run operationally as well as in extended time slice experiments for all episodes with highest observed runoff in the period 01.10.2005–30.09.2010. The system application demonstrates the great potential of the POE based system in networking, distributed computing as well in the setup of various experiments. The river runoff simulation results show high correlation with observed runoff when driven with precipitation interpolated from station observations. The performance of the forecast shows limitations resulting from deficient timing and amount of the predicted rainfall in the complex mountainous area. Forecast skills were improved after application of a lagged ensemble prediction system. [Copyright &y& Elsevier]

Abstract: Scientifically sound decisions in sustainable water management are usually based on hydrological modeling which can only be accomplished by meteorological driving information. Especially in regions with weak infrastructure this task is hampered by limited hydro-meteorological information in sufficient spatial and temporal resolution. We investigated three approaches to provide required meteorological fields driving the distributed hydrological model: the results of the mesoscale meteorological model MM5 which are available near real time, the TRMM product 3B42 available with approximately one month delay, and station data available with a delay of one year or more. The study site is the White Volta catchment in the semi-arid environment of West Africa. The results for 2004 show that the meteorological model is able to provide meteorological input data for near real time water balance estimations. In this study the TRMM product does not improve the simulation results. Besides missing important meteorological data, also gridded information on land surface properties (albedo, LAI, etc.) is usually difficult to obtain, albeit it is an essential input for distributed hydrological models. This information is commonly taken from “static” tables depending on the land use. Satellite remote sensing provides worldwide spatially detailed information on land surface properties which is particularly suitable for large regions in remote settings. Therefore the MODIS products for albedo and LAI were processed to annual time series including the identification and replacement of low quality observations by interpolation. The impact using MODIS data on the spatial distribution of water balance variables occurs mainly on local scale. The hydrological simulations using MODIS LAI and albedo values result in higher annual evapotranspiration and lower total discharge sums for 2004. Altogether it is concluded that hydrological decision support systems in regions with weak infrastructure can benefit significantly from the integration of atmospheric modeling and satellite-derived land surface data. [Copyright &y& Elsevier]

Summary: Inverse stochastic modelling and direct moment propagation is applied for deriving effective land surface parameters of a Soil–Vegetation–Atmosphere-Transfer (SVAT) model. Special attention is given to the proper representation of latent heat fluxes at grid scale when an underlying subgrid-scale heterogeneity is assumed. Derived effective land surface and vegetation parameters yield the same total latent heat fluxes on the grid scale as the averaged heat fluxes on the subgrid-scale. Upscaling relations are derived that relate mean and variance (first and second moment) of subgrid-scale heterogeneity to a corresponding effective parameter at grid-scale. Explicit upscaling relations are exemplary derived for (a) land surface property parameters roughness length, albedo, emissivity and (b) vegetation parameters wilting point soil moisture, field capacity, leaf area index, vegetation fraction, and minimal and maximal stomata resistance. It is shown that the value of effective parameters depends on the sign of the first and second derivatives of aggregated fluxes with respect to the parameter. It is demonstrated that the Second-Order-First-Moment (SOFM) Method yields results that are comparable to those obtained with inverse stochastic Monte Carlo simulations. Effective parameters were found to be independent of meteorological forcing conditions and initial conditions for a 7-day time period. [Copyright &y& Elsevier]

Abstract: Sustainable decisions in water resources management require scientifically sound information on water availability. Central support in hydrological decision making arises from hydrological modelling which in turn depends on meteorological input. This work intends to show how integration of atmospheric science and hydrology, particularly the joint modelling of atmospheric and terrestrial water cycle, allows to provide decision support for two fundamental problems in sustainable water management: (1) the impact of global climate change on water availability, and (2) the near-real time estimation of recent resources and fluxes. The performance of joint atmospheric-hydrological simulations and its application for decision support is demonstrated for the Volta Basin of West Africa. First, the impact of global warming on water availability in the Volta Basin is assessed by joint regional climate-hydrology simulations. Time slices of ECHAM4 global climate scenario IS92a are dynamically downscaled to a resolution of 9×9km2 with the mesoscale meteorological model MM5 and the terrestrial water balance is subsequently calculated by the distributed hydrological model WaSiM. Results indicate a slight increase in total annual precipitation by 5%, but also a significant decrease of precipitation in April (in total 20%, for specific regions up to 70%), at the beginning of the rainy season. Terrestrial water balance variables react highly nonlinear to changes in precipitation and temperature. Second, the performance of joint atmospheric-hydrological simulations as a tool for near-real-time, model based water monitoring system for the White Volta subcatchment is assessed. Operational available GFS (Global Forecasting System) global analyses are dynamically downscaled to 9×9km2 resolution and meteorological fields subsequently applied for calculating the terrestrial water balance. Albeit a bias in precipitation simulations can be observed when comparing to point measurements, modelled river discharge agrees well with observations in 2004. It is concluded that particularly in areas with weak infrastructures, where few meteorological observations are available (or only in long delay), atmospheric models can provide reasonable meteorological information for hydrological modelling and therefore facilitate decision support. [Copyright &y& Elsevier]

Abstract: Sustainable decisions in hydrological risk management require detailed information on the probability density function (pdf) of the model output. Only then probabilities for the failure of a specific management option or the exceedance of critical thresholds (e.g. of pollutants) can be derived. A new approach of uncertainty propagation in hydrological equations is developed that directly propagates the probability density functions of uncertain model input parameters into the corresponding probability density functions of model output. The basics of the methodology are presented and central applications to different disciplines in hydrology are shown. This work focuses on the following basic hydrological equations: (1) pumping test analysis (Theis-equation, propagation of uncertainties in recharge and transmissivity), (2) 1-dim groundwater contaminant transport equation (Gauss-equation, propagation of uncertainties in decay constant and dispersivity), (3) evapotranspiration estimation (Penman–Monteith-equation, propagation of uncertainty in roughness length). The direct propagation of probability densities is restricted to functions that are monotonically increasing or decreasing or that can be separated in corresponding monotonic branches so that inverse functions can be derived. In case no analytic solutions for inverse functions could be derived, semi-analytical approximations were used. It is shown that the results of direct probability density function propagation are in perfect agreement with results obtained from corresponding Monte Carlo derived frequency distributions. Direct pdf propagation, however, has the advantage that is yields exact solutions for the resulting hydrological pdfs rather than approximating discontinuous frequency distributions. It is additionally shown that the type of the resulting pdf depends on the specific values (order of magnitude, respectively) of the standard deviation of the input pdf. The dependency of skewness and kurtosis of the propagated pdf on the coefficient of variation of input parameter uncertainty is detected to be non-monotonic with distinctive maxima. [Copyright &y& Elsevier]

Abstract: A popular method for the determination of catchment zones is the construction by particle tracking. A new approach is presented, allowing to quantify the uncertainty of particle paths and catchment zones by the direct propagation of uncertainties of aquifer parameters into the uncertainty of the location of contaminating particles. This is achieved by applying the conditional First-Order Second Moment (FOSM) method to both the discretised groundwater flow and the particle tracking equations. Beside of the mean particle tracks, the standard deviations (σ x , σ y) of the location (x, y) of a particle within the 2-dimensional flow field are directly calculated. This allows calculating probabilities for which particles can be found within their ‘uncertainty cloud’. By starting particles at all gridpoints of the groundwater model this method is applied to the delineation of stochastic well head protection zones. The performance of the proposed new method is shown for the determination of capture zones for an aquifer in Gambach (State of Hessia, Germany). It is demonstrated that the method yields comparable results to a corresponding conditional Monte Carlo approach. Detailed statistical analysis of the conditional Monte Carlo simulations showed the following additional central results: [(1)] Conditioning with piezometric head information in the Monte Carlo approach yielded a high correlation between the transmissivity values and the recharge values. Subsequent regression analysis for the Monte Carlo results revealed that the given head measurements together with the random recharge values determined the transmissivity values used to describe the aquifer. The derived explicit linear relationships allow to calculate the transmissivity values directly from the random recharge values without inverse modelling for this study. [(2)] A covariance analysis of estimated parameters was performed. Derived confidence ellipses showed narrow confidence bounds for estimated recharge and transmissivity values. [(3)] While frequency distributions for velocities in the conditional Monte Carlo approach follow a log-normal distribution, this is not the case for piezometric heads. Here, Weibull-, Rayleigh-, FRatio and Gamma-Distribution perform more satisfactorily, in particular in the reproduction of the observed skewness of the output distributions. As the proposed FOSM-method is based on a Taylor Series approximation, its application is restricted to moderate cases of model input parameters uncertainty. [Copyright &y& Elsevier]

Abstract: When global climate scenarios are dynamically downscaled to catchment scale or when improvements of short term flood forecasts are discussed, the basic question arises: how reliable do meteorological models reproduce meteorological fields? Can they be used for driving hydrological models? Apart from this, meteorological modelling may facilitate hydrological modelling efforts when no or little meteorological station data is available. Against this background we performed coupled high resolution meteorological–hydrological simulations for the alpine and orographic complex catchment of the river Mangfall (State of Bavaria, Germany). The area of the catchment is around 1100km2. Within this task we coupled (in 1-way manner) the mesoscale meteorological model MM5 with the distributed hydrological model WaSiM. The hydrological model was calibrated at 18 gauges using interpolated meteorological station data and applied in 150×150m2 horizontal resolution. Global reanalyses were dynamically downscaled with MM5 from 100×100km2 resolution to 2×2km2 using four nests. The quality of the meteorological model was analysed by comparison to upper air and surface observations. A height dependent bias between station-based and MM5-based simulation was observed: the MM5-based interpolation of precipitation yielded 21% less total yearly precipitation in the catchment compared to the station-based interpolation (1446 vs. 1791mm). Even though not all details of observed runoff were met by the coupled meteorological–hydrological simulations, it is demonstrated that in general observed runoff was reproduced reasonable for the period investigated (1997). Difficulties to describe observed runoff during snow-accumulation and snow-melt processes were observed in both the ‘station-based’ and coupled simulations. [Copyright &y& Elsevier]

An approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comparing these with hydrological simulations, soil moisture profiles from the soil surface until a soil depth of 30 cm (assumed average P-band penetration depth) are estimated. Here, the hydrological model HYDRUS-1D, as a representative of any soil hydrological model, is employed to simulate an ensemble of realistic soil moisture profiles, which are used for a multi-layer soil scattering model to obtain forward modeled soil scattering components. Compared to the decomposed SAR-based soil scattering components, the most appropriate soil moisture profile from the ensemble is estimated. The approach is able to provide physically (hydraulic) more meaningful soil moisture profile shapes than currently existing profile estimation approaches, like polynomial fitting to few measurements at discrete soil depths. Results are presented across eight in situ measuring stations in the U.S. within six test sites of NASA's Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission between 2013 and 2015. In-depth analyzes and validations with in situ measured soil moisture information demonstrate the feasibility of the proposed approach. Overall, estimated soil moisture profiles at the different sites match the varying local climate, vegetation cover, and soil conditions. Coefficients of determination between estimated and in situ measured soil moisture values vary between 0.48 and 0.92, while unbiased errors range from 1.4 vol% to 3.7 vol%, and Fréchet distances (analyzing the similarity of profile shapes) vary between 0.1 and 0.2 [−]. • Combination of P-band SAR remote sensing with soil hydrological modeling. • Extraction of soil scattering from total SAR signal by hybrid decomposition method. • Soil scattering simulations combining a hydrological with an electromagnetic model. • Estimation of vertically continuous soil moisture profiles for different landcovers. [ABSTRACT FROM AUTHOR]

• Assessment of the performance of WRF-Solar model in 3 AWS in Ghana. • WRF-Solar model performs better in clear skies, uncertainties arise under cloudy conditions. • Poor performance of WRF-Solar under high aerosol levels. • Notable Biases in CFC Predictions from the WRF-Solar model. • WRF-Solar outperforms in predicting 72-hour GHI at Kologo under clear skies. Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model's capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region. [ABSTRACT FROM AUTHOR]

Estimates of global horizontal irradiance (GHI) from reanalysis and satellite-based data are the most important information for the design and monitoring of PV systems in Africa, but their quality is unknown due to the lack of in situ measurements. In this study, we evaluate the performance of hourly GHI from state-of-the-art reanalysis and satellite-based products (ERA5, MERRA-2, CAMS, and SARAH-2) with 37 quality-controlled in situ measurements from novel meteorological networks established in Burkina Faso and Ghana under different weather conditions for the year 2020. The effects of clouds and aerosols are also considered in the analysis by using common performance measures for the main quality attributes and a new overall performance value for the joint assessment. The results show that satellite data performs better than reanalysis data under different atmospheric conditions. Nevertheless, both data sources exhibit significant bias of more than 150 W/m2 in terms of RMSE under cloudy skies compared to clear skies. The new measure of overall performance clearly shows that the hourly GHI derived from CAMS and SARAH-2 could serve as viable alternative data for assessing solar energy in the different climatic zones of West Africa. [ABSTRACT FROM AUTHOR]

• Remarkable differences of soil texture in southern Africa are found in global soil data. • Land-atmosphere interactions are investigated by using coupled WRF-Hydro with four global soil data. • Soil texture changes lead to differences in temperature up to 1.7°C and sensible heat flux up to 25 W/m2 during austral summer. • Soil data discrepancies demonstrate physical feedback on atmospheric processes. • Consistent consideration of soil data uncertainties is needed in modeling applications. Numerical weather- and climate prediction models rely on soil data to accurately model land surface processes. However, as soil data are produced using soil profiles and maps with multiple sources of uncertainty, wide discrepancies prevail in global soil datasets. Comparison of four commonly used soil datasets in Earth system climate models, i.e., Food and Agriculture Organization soil data, Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids, yields widespread differences in southern Africa. This study investigates the simulated land-atmosphere interactions in southern Africa in the context of the uncertainties from applying different global soil datasets. We conducted ensemble simulations using the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) incorporated with each of the global soil datasets mentioned above. Model simulations were performed at 4-km convection-permitting scale from January 2015 to June 2016. By quantifying model's internal variability and comparing the modeling results, results show that the simulated temperature, soil moisture, and surface energy fluxes are largely impacted by soil texture differences. For instance, changes in soil texture and associated hydrophysical parameters result in large differences in air temperature up to 1.7°C and surface heat flux up to 25 W/m2, and disparities in averaged surface soil moisture differ up to 0.1 m3/m3 in austral summer months. Differences in soil texture characteristics also regulate local climatic conditions differently in the wet and dry seasons as well as in different climatic regions. Furthermore, the thermodynamic differences in surface energy fluxes caused by soil texture demonstrate physical feedback perspective on atmospheric processes, resulting in distinct changes in planetary boundary layer height. This study demonstrates the non-negligible impact of soil data on land surface-atmosphere coupled modeling and highlights the need for consistent consideration of modeling uncertainties from soil data in modeling applications. [ABSTRACT FROM AUTHOR]

Highlights • Coupled GEOtop-PEST interface allows automatic parameter estimation. • PCA-based analysis indicates insignificant cross-correlations between the parameters. • GEOtop parameters lay in the solution space of the inverse problem. • Post-calibration parameters uncertainty is highly reduced. • Added value of turbulent fluxes in inverse modelling are found highly beneficial. Abstract The interactions of hydrological variables in the terrestrial hydrological cycle are complex. To better predict the variables, distributed and physically based models are used as they account for the complexity of interactions. In this study, we addressed the joint simulation of water- and energy fluxes and the potential benefit of flux measurements in the parameter estimation process. For this purpose, we applied the hydrological model GEOtop to a prealpine catchment in southern Germany (River Rott, 55 km2) over two recent summer episodes, as a test case. Due to its complexity, the model is computationally demanding and only a limited number of forward runs can be afforded in inverse modelling and parameter estimation. We applied the gradient-based nonlinear Gauss-Marquardt-Levenberg (GML) parameter estimation method and linked the GEOtop model to the Parameter ESTimation tool (PEST). Using this developed GEOtop-PEST interface, we particularly investigated the value added by including turbulent flux data in the parameter estimation process, and analyse the impact of the additional flux data on the uncertainty bounds of the parameters. To better understand the interplay of the model parameters and to identify the dominating parameters in the calibration process, we also conducted a Principal Component Analysis (PCA). We were able to identify a set of model parameters that reproduced both observed streamflow and turbulent heat fluxes reasonably well. The majority of the estimated parameters were highly sensitive to the considered variables. We showed that the confidence bounds of estimated parameters are narrowed significantly when considering not only streamflow observations but also turbulent flux measurements in the calibration process. In this manner, correlations between estimated parameters could also be reduced. [ABSTRACT FROM AUTHOR]

Abstract In the future the Sudanian savanna – one of West Africa's high-potential "bread baskets" – will likely face shorter rainy seasons with more extreme rains and droughts. That could have serious impacts on the vegetation and its carbon dioxide (CO 2) exchange with potentially increasing CO 2 emissions accelerating climate warming. Understanding how the CO 2 fluxes in this area respond to environmental variables, in particular rain events, is essential, but available data are scarce. In this study, we monitored net ecosystem exchange (NEE) of CO 2 , rainfall and other environmental parameters during four years at three savannas. Savannas were characterized by different vegetation due to different land use: i) woody and nearly pristine, ii) mixture of cropland and grassland and iii) intensive grazing. The impact of rain events on CO 2 exchange for these contrasting ecosystems were analyzed for single rain events (short-term) and on a yearly time scale (long-term) using three eddy covariance towers. We found that the woody pristine savanna site was a prominent sink of CO 2 (−864 to −1299 g CO 2 m−2 y−1) while the degraded sites were net CO 2 sources (118 to 605 g CO 2 m−2 y−1) with a complicated relation with annual rainfall amounts. The NEE responses to single rain events revealed that daytime rain systematically decreased the sink strengths at all sites, which might be associated with decreased light availability. At the degraded sites, additional factors increasing CO 2 losses were rain duration and dry spell length. The observed patterns of immediate CO 2 flux responses to rainfall at differently used savannas indicate strong internal feedbacks between vegetation and land use changes and raise the question whether the CO 2 sink strengths might be overestimated with possible implications for global CO 2 budgets. Sustainable adaptation strategies need to be developed for West Africa. Graphical abstract Unlabelled Image Highlights • West Africa experiences land use and climatic changes affecting carbon cycling. • With eddy covariance towers we measured CO 2 fluxes of three contrasting savannas. • Natural and degraded savannas' CO 2 flux responses to rain were opposite. • Changing rain regimes may affect CO 2 flux balances through changes in cloud cover. [ABSTRACT FROM AUTHOR]

• The spatiotemporal continuity of droughts was explored using a three-dimensional clustering algorithm. • Droughts can migrate hundreds of kilometers from the source sites in the agro-pastoral ecotone of northern China. • The more severe and longer duration of meteorological drought, the more likely it is to trigger agricultural drought. Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought to agricultural drought to explore the simultaneous spatiotemporal evolution of droughts based on a 3-dimensional clustering algorithm. We first evaluate the improvement of the downscaled high-resolution outputs from the Weather Research and Forecasting (WRF) model to reanalysis data in detecting wet-dry variations in the agro-pastoral ecotone of northern China (APENC). The WRF simulation results well characterize the evolution of droughts with a high correlation coefficient of 0.82 (p <0.05), which implies that the WRF downscaling model provides reliable hydrometeorological results for assessing and analyzing long-term wet-dry variations. Subsequently, we identify and track the spatiotemporal evolution of each individual drought event in the APENC for the first time. Based on the standardized precipitation index calculated from the WRF outputs, a total of 185 drought clusters were identified during 2000–2017, with 28 drought events lasting at least 3 months duration occurring in the APENC. The characteristics of the different drought events vary considerably. Droughts in the APENC have a larger percentage of propagation to the northeast and northwest and migrated hundreds of kilometers from the source sites. The results of the propagation of meteorological droughts to agricultural droughts indicate that 53.75% of the meteorological drought events progressed further to agricultural droughts. Agricultural droughts exhibit spatial variations consistent with meteorological droughts. Agricultural droughts have 1.69 months onset lag time, 2.12 months termination lag time, and overall longer duration than meteorological droughts in the APENC. Our findings provide valuable information for the mechanism and prediction of drought propagation. [ABSTRACT FROM AUTHOR]

Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients (n) on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two-dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth (< 1 cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning's n on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events. [Display omitted] • Framework to estimate Manning roughness by artificial rainfall experiments. • Robust roughness coefficients for shallow water runoff for multiple vegetation covers. • Improving hydrodynamic flash flood simulations. [ABSTRACT FROM AUTHOR]

The regional drought predictability is strongly influenced by teleconnections factors. Among them, El Niño-Southern Oscillation (ENSO) is important to drought occurrence. However, it is still not fully understood how the probability of regional droughts corresponding to different ENSO regimes varies with time lags. In this study, the event-based coincidence analysis (ECA) is employed to statistically derive the relationships regarding time information and magnitude between conventional ENSO (c-ENSO)/ENSO Modoki (ENSO_M) episodes and drought events. The standardized precipitation evapotranspiration index (SPEI) based on station-records is used to determine meteorological droughts. Our study focuses on the largest freshwater lake of China, Poyang Lake basin (PLB), as severe droughts have occurred there frequently. Results indicate that the summer-autumn continuous drought dominates the severe droughts, with a frequent onset in August/September, and a cessation in October/November. The likelihood of drought occurrence is highest during cool phases of c-ENSO (CEN), followed by warm ENSO_M (WEM), warm c-ENSO (WEN), and cool ENSO_M (CEM). The influence of WEN/WEM on droughts is statistically significant mostly for a time lag of 10 months, whereas CEN/CEM shows no lagged influence. Furthermore, the variability of the 850 hPa wind anomalies during different ENSO episodes at various time lags is corresponding to the lagged influence of ENSO on droughts. The development and persistence of droughts is resulting from strengthened northeasterly wind anomalies over the PLB. Our findings demonstrate that the ENSO anomalies are early warning signals of droughts and hence can be beneficial for increasing the drought predictivity • A novel statistical framework to quantify the probability of droughts responding to ENSO with varied time lags is developed. • The influence of WEN/WEM on the droughts in the PLB is statistically significant mostly for a time lag of 10 months. • The strengthened northeasterly wind anomalies over the PLB likely results in the development of ENSO-induced droughts. [ABSTRACT FROM AUTHOR]

Summary: Recent studies identified the Mediterranean as a region particularly vulnerable to climate change. Since a large fraction of the region’s water originates from karst aquifers, information about their future water availability is important for sustainable water management. This study presents an ensemble of 50 model chains considering five different realisations of the A1B ECHAM5 and HadCM3 climate projections, two different averaging methods to transfer the climate variables to the system scale and five different hydrological models that represent reasonable conceptualizations of the karst system. The ensemble is applied to Faria spring, a large Eastern Mediterranean karst spring in the West Bank. We show that for the near future (2021–2051) variability resulting from the different climate change projections and five different models is too large to draw conclusions on any significant change. In the remote future (2068–2098), variability decreases and our simulations suggest a decrease of water availability of −15% to −30%. We also assess the impact of recent pumping activities by running our hydrological models with recently measured data. There is a strong indication that the spring, which dried out in 2007, would have still yielded significant amounts of water if groundwater extractions had been limited. This calls for a better management of groundwater abstractions to meet future water needs in this drought-prone Mediterranean region. [Copyright &y& Elsevier]

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]

Abstract: Savannahs are highly dynamic ecosystems but many of their properties and the related balances of energy, carbon, nitrogen, and water are still poorly understood. A particular scientific issue is the quantification of trace gases emitted from the soil of savannah ecosystems and their interaction with regional and global climate and air chemistry. Therefore it is important to develop and evaluate land-surface models that on the one hand represent vegetation and soil dynamics and on the other hand provide energy and water fluxes in a temporal resolution suitable for the application in climate/air chemistry models. In this paper, we present a consistent coupling between a common land-surface model (OSU) and a widely used biogeochemical model (DNDC) that is a first step for a full coupling of climate/air chemistry and biogeochemical processes. For consistency reasons, both models are linked to a general physiologically based plant model to provide the physical boundary conditions as well as the carbon and nitrogen in- and output variables. Evaluation is carried out with measurements of soil temperature, latent heat flux, soil water content, and soil emission data from two vegetation periods collected at a natural grassland site in Bontioli Nature Reserve, Burkina Faso (Africa). The results demonstrate that simulations of biogeochemical processes based on soil environmental conditions, calculated either with the land-surface model or with the unchanged biogeochemical model, do not differ significantly from each other. The OSU model simulates more realistic day-to-day variation of soil temperature as DNDC but the sensitivity of the biogeochemical simulation to this variation is small. In contrast, the sensitivity to differences in soil water content is high, but simulation results of both models are very similar on the daily scale and hardly depend on spatial soil resolution. [Copyright &y& Elsevier]

• Bias correction (BC) methods can better represent the spatial patterns of the climate and derived agricultural impacts. • Uncertainties from BC can be twice as high compared to the uncertainties from the GCM-RCM combinations. • Climate change impacts studies (CCIS) without BC or relying on any single BC approach should be interpreted with great caution. • In CCIS, a BC ensemble is suggested for generating climate projections following a super-ensemble probabilistic assessment. Many open questions and unresolved issues surround the topic of bias correction (BC) in climate change impact studies (CCIS). One question relates to the contribution of downscaling of climate change scenarios on the uncertainties in results obtained using impact models for agriculture. In particular, for large area or regional agricultural impact assessments, the question of bias correction is of high relevance. Relatively few studies exist looking at the quantification of the impacts of BC methods in general circulation model (GCM) and regional climate model (RCM) data on results of such impact studies. Here, we quantify the impact of different BC methods on temperature (T) and precipitation (P) from different CORDEX GCM-RCM combinations, and how the debiased T&P signal may propagate through agricultural impact models. Specifically, we estimate the impact of BC on (i) an empirical P- and fuzzy rule-based algorithm to estimate the crop planting date, and (ii) a mechanistic T&P-based approach to quantify the crop suitability index (CSI) for the main staple crops in West Africa (i.e. groundnut, maize, pearl millet, sorghum). Both approaches serve as a proxy for more complex process-based ecophysiological crop models. Depending on the choice of the BC method, the uncertainties in the assessment of the CSI can be more than twice as high compared to the uncertainties from the GCM-RCM model selection. Comparing the estimated CSI values with observed harvest area, it is found that BC generally improves the performance for models with low hit rates (< 30 –35%), but decreases the performance for models with relatively high hit rates (> 35 %). Such consequences can also be expected for process-based crop models, which are developed to operate on field-scale but are driven by coarser scale RCMs. It is concluded that such agriculturally oriented climate impact models as investigated here should be interpreted with great caution if applied without BC or relying on a single BC approach only. Rather, we suggest to include different BC approaches in the generation of climate projections for CCIS and quantify their uncertainties following a super-ensemble probabilistic assessment. [ABSTRACT FROM AUTHOR]

Intense precipitation events frequently occur in the Hanjiang River Basin (HRB) in China, causing major economic losses and affecting human security. Accurate precipitation simulations are sensitive to the choice of physical schemes in numerical weather prediction models. Therefore, in this study the performance of the different combinations of physical schemes in the Weather Research and Forecasting (WRF) model was investigated for the HRB. Five intense precipitation events in summer during the period 2008–2011 were selected. For simulating each intense precipitation event, an 18-member multi-physics ensemble was designed by using three cumulus (CU) schemes and four microphysics (MP) schemes. The temporal dynamics and spatial patterns of the simulated precipitation were evaluated systematically by a comparison to the China precipitation analysis reference data. A quantitative analysis using the Euclidean distance metric to synthesize the evaluation results was performed. The performed uncertainty analysis suggests that the skill of precipitation simulation is more sensitive to the choice of the CU scheme and less sensitive to the choice of the MP scheme. Our analysis shows that a suitable WRF physical configuration consists of Kain-Fritsch and WRF Single-Moment 6-class, which reaches a performance similar to that of the ensemble mean method. To our knowledge, this multi-physics ensemble study is the first effort to comprehensively evaluate the applicability of the WRF model to intense precipitation simulations over the HRB. Therefore, this study potentially contributes to the improvement of climate simulations for central China. • Cumulus schemes have primary impact on intense precipitation simulation while microphysics schemes have secondary impact. • A suitable WRF physical configuration is the combinations of the KF and WSM6 schemes. • This study potentially contributes to the improvement of climate simulations for central China. [ABSTRACT FROM AUTHOR]

• The hydrologic impact of existing and alternative reservoir network layouts was investigated. • Few large reservoirs regulate the streamflow more significantly than many small ones with similar inflow. • Locating reservoirs somewhere with similar inflow is unlikely to change the streamflow condition. • Assigning reservoirs higher inflow without changing their location can increase their functionality. Reservoirs have been continuously built in the Poyang Lake Basin (PLB) for the local economy. With pending removal and reconstruction of aging reservoirs and planning of new reservoirs in this area, it is advisable to evaluate the likely impact of the existing and alternative reservoir network layouts on the hydrologic regime for more effective water resources planning and management. In this study, reservoirs of multiple sizes in the PLB were integrated into a coupled land surface-hydrologic model (CLHMS) fully coupled with a reservoir scheme. The extended model was then validated and applied to investigate the impact of existing reservoirs. Seven hypothetical scenarios were additionally set up by removing all the reservoirs, replacing smaller reservoirs with few larger ones, replacing larger reservoirs with many smaller ones, moving all reservoirs downstream or upstream, increasing or decreasing the mean inflow of reservoirs, respectively. In all, eight simulations were conducted and analyzed using the Indictors of Hydrologic Alterations (IHA) for the period of 1981–1999. Results indicate that the existing reservoirs in the PLB are able to effectively displace the excessive streamflow in the wet season to the dry season and mitigate the hydrological extremes. By increasing the mean inflow of reservoirs, the effectiveness of reservoirs in downstream flow regulation can be further enhanced. Similarly, replacing many small reservoirs with a few large ones with a same total capacity can lead to more resiliency against the downstream flow variability and extremes, only if the total mean inflow of the new large reservoirs is not considerably lower than that of the small ones. Relocating reservoirs to a different place with similar mean inflow, however, has a relatively minor and probably case-dependent impact on the hydrologic regime. On the other hand, the ecological analysis on the IHA parameters indicates that the layout preferable for the effective flow regulation may not necessarily be ecologically preferable. In general, this study comprehensively evaluates the hydrological impact of reservoir network layouts, and can provide implications for stakeholders to reach a trade-off between reservoir capacity distribution, locations and mean inflow not only in the PLB but also in different regions worldwide. [ABSTRACT FROM AUTHOR]

• A high-density reservoir network is integrated into a distributed hydrologic model. • Despite the mitigation by reservoirs, climate impacts call on adaptation measures. • The framework can provide implications for regional water resources management. The Poyang Lake Basin (PLB) is expected to see more precipitation and higher temperature by 2050. Due to climate change, it is advisable to identify the vulnerable area and to investigate if the local reservoir network of hundreds of larger reservoirs and thousands of smaller reservoirs can effectively mitigate the climate-induced hydrologic variation. Therefore, the local high-density reservoir network is integrated into a distributed hydrologic model (HMS) through a fully coupled reservoir module. We then apply the extended HMS in a one-way coupled mode to the bias corrected Coordinated Regional Downscaling Experiment in East Asia (CORDEX-EA) regional climate simulations under two Representative Concentration Pathways – RCP4.5 and RCP8.5. Results indicate that climate change poses new challenges to water resources management for the PLB, with a likely decrease of water availability in the dry season but an increase of water availability, flood magnitude and flood frequency in the flood season till 2049. The local reservoir network can mitigate climate impacts by displacing excessive water resources in the flood season to the dry season and mitigating floods across the basin. Despite that, much of the southern basin is likely to see a higher risk of water shortage in the dry season, while most of the northern basin is expected to see a higher flood risk. The approach and findings are expected to improve the understanding of the role of reservoirs in the context of climate change, and provide implications for water resources management and climate change adaptation strategies on a regional scale. [ABSTRACT FROM AUTHOR]