The low-level jet (LLJ) over the Bodélé depression in northern Chad is a newly identified feature. Strong LLJ events are responsible for the emission of large quantities of mineral dust from the depression, the world's largest single dust source, and its subsequent transport to West Africa, the tropical Atlantic, and beyond. Accurate simulation of this key dust-generating atmospheric feature is, therefore, an important requirement for dust models. The objectives of the present study are (i) to evaluate the ability of regional climate models (RCMs) and global analyses/reanalyses to represent this feature, and (ii) to determine the driving mechanisms of the LLJ and its strong diurnal cycle. Observational data obtained during the Bodélé Dust Experiment (BoDEx 2005) are utilized for comparison. When suitably configured, the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5) RCM can represent very accurately many of the key features of the jet including the structure, diurnal cycle, and day-to-day variability. Surface winds are also well reproduced, including the peak winds, which activate dust emission. Model fidelity is, however, strongly dependent on the boundary layer parameterization scheme, surface roughness, and vertical resolution in the lowest layers. A model horizontal resolution of a few tens of kilometers is sufficient to resolve most of the key features of the LLJ, while in global analyses/reanalyses many features of the LLJ are not adequately represented. Idealized RCM simulations indicate that under strong synoptic forcing the surrounding orography of the Tibesti and Ennedi Mountains acts to focus the LLJ onto the Bodélé and to accelerate the jet by ϳ40%. From the RCM experiments it is diagnosed that the pronounced diurnal cycle of the Bodélé LLJ is largely a result of varying eddy viscosity, with elevated heating/cooling over the Tibesti Mountains to the north as a second-order contribution.

Abstract. Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Autocalibration are performed for the SWAT model. The NashSutcliffe Efficiency (NSE) and Coefficient of Determination (R 2 ) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R 2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R 2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R 2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

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Optimal designs of stormwater systems rely very much on the rainfall Intensity-Duration-Frequency (IDF) curves. As climate has shown significant changes in rainfall characteristics in many regions, the adequacy of the existing IDF curves is called for particularly when the rainfall are much more intense. For data sparse sites/regions, developing IDF curves for the future climate is even challenging. The current practice for such regions is, for example, to 'borrow' or 'interpolate' data from regions of climatologically similar characteristics. A novel (3-step) Downscaling-Comparison-Derivation (DCD) approach was presented in the earlier study to derive IDF curves for present climate using the extracted Dynamically Downscaled data an ungauged site, Darmaga Station in Java Island, Indonesia and the approach works extremely well. In this study, a well validated (3-step) DCD approach was applied to develop present-day IDF curves at stations with short or no rainfall record. This paper presents a new approach in which data are extracted from a high spatial resolution Regional Climate Model (RCM; 30 Â 30 km over the study domain) driven by Reanalysis data. A site in Java, Indonesia, is selected to demonstrate the application of this approach. Extremes from projected rainfall (6-hourly results; ERA40 Reanalysis) are first used to derive IDF curves for three sites (meteorological stations) where IDF curves exist; biases observed resulting from these sites are captured and serve as very useful information in the derivation of present-day IDF curves for sites with short or no rainfall record. The final product of the present-day climate-derived IDF curves fall within a specific range, +38% to +45%. This range allows designers to decide on a value within the lower and upper bounds, normally subjected to engineering, economic, social and environmental concerns. Deriving future IDF curves for Stations with existing IDF curves and ungauged sites with simulation data from RCM driven by global climate model (GCM ECHAM5) (6-hourly results; A2 emission scenario) have also been presented. The proposed approach can be extended to other emission scenarios so that a bandwidth of uncertainties can be assessed to create appropriate and effective adaptation strategies/ measures to address climate change and its impacts.

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Precipitation is an important climate variable to investigate extreme events (e.g. drought and flood) as well as to develop robust strategies for water resources planning and management. Lack of adequate and robust information on precipitation poses great difficulties in understanding the observed climate as well as to validate climate model outputs. To overcome this limitation gridded precipitation data sets have been constructed to supplement the lack of in situ data. This study compares five popular gridded precipitation data sets to evaluate their performance in terms of drought and wetness over Vietnam. These five gridded data sets include: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Climate Precipitation Center (CPC), (3) Climate Research Unit (CRU), (4) Global Precipitation Climatology Center (GPCC) and (5) University of Delaware (UDEL). The recently developed gridded precipitation observational data ‘VnGP’ from Vietnam is used as the reference data set to assess the performance of these five gridded precipitation products. The Standardized Precipitation Index (SPI) is used to quantify drought and wetness. GPCC and APHRODITE performed reasonably well in reproducing spatial and temporal precipitation patterns. GPCC performs consistently better than APHRODITE in all the statistical tests. Except for UDEL, other gridded data sets able to exhibit the characteristics of drought/wetness (e.g. the percentage of drought events and severity) during strong El Nino Southern Oscillation (ENSO) events. However, higher uncertainty exists to quantify drought inter‐arrival time in most of the data sets. Furthermore, trend analysis was performed to evaluate the comparative performance of gridded data sets to quantify drought (wet) spells at annual and seasonal time scales. Although the gauge‐based and hybrid satellite–gauge merged products use partly ground truth data, the different interpolation techniques and merging algorithms may contribute to large uncertainties.

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