Search

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made. [ABSTRACT FROM AUTHOR]

Summary The multisensor Quantitative Precipitation Estimates (MQPEs) created by the US National Weather Service (NWS) are subject to a non-stationary bias. This paper quantifies the impacts of climatological adjustment of MQPEs alone, as well as the compound impacts of adjustment and model calibration, on the accuracy of simulated flood peak magnitude and that in detecting flood events. Our investigation is based on 19 watersheds in the mid-Atlantic region of US, which are grouped into small (<500 km 2 ) and large (>500 km 2 ) watersheds. NWS archival MQPEs over 1997–2013 for this region are adjusted to match concurrent gauge-based monthly precipitation accumulations. Then raw and adjusted MQPEs serve as inputs to the NWS distributed hydrologic model-threshold frequency framework (DHM-TF). Two experiments via DHM-TF are performed. The first one examines the impacts of adjustment alone through uncalibrated model simulations, whereas the second one focuses on the compound effects of adjustment and calibration on the detection of flood events. Uncalibrated model simulations show broad underestimation of flood peaks for small watersheds and overestimation those for large watersheds. Prior to calibration, adjustment alone tends to reduce the magnitude of simulated flood peaks for small and large basins alike, with 95% of all watersheds experienced decline over 2004–2013. A consequence is that a majority of small watersheds experience no improvement, or deterioration in bias (0% of basins experiencing improvement). By contrast, most (73%) of larger ones exhibit improved bias. Outcomes of the detection experiment show that the role of adjustment is not diminished by calibration for small watersheds, with only 25% of which exhibiting reduced bias after adjustment with calibrated parameters. Furthermore, it is shown that calibration is relatively effective in reducing false alarms (e.g., false alarm rate is down from 0.28 to 0.19 after calibration for small watersheds with calibrated parameters); but its impacts on detection rate are mixed. As an example, the detection rate of 2-Y events in fact declines for small watersheds after calibration is performed (from 0.4 to 0.28, and from 0.28 to 0.19 with raw and adjusted MQPE, respectively). These mixed outcomes underscore the complex interplays between errors in MQPEs, conditional bias in the reference gauge-based analysis, and structural deficiencies of the hydrologic model. [ABSTRACT FROM AUTHOR]

Summary: Hillslope hydrological response to precipitation is extremely complex and poorly modeled. One possible approach for reducing the complexity of hillslope response and its mathematical parameterization is to look for macroscale hydrological behavior. Hillslope threshold response to storm precipitation is one such macroscale behavior observed at field sites across the globe. Nevertheless, the relative controls on the precipitation–discharge threshold poorly known. This paper presents a combined model development, calibration and testing experiment study to investigate the primary controls on the observed precipitation–discharge threshold relationship. We focus on the dominant hydrological processes revealed in part one of this two-part paper and with our new numerical model, replicate the threshold response seen in the discharge record and other hydrometric and tracer data available at the site. We then present a series of virtual experiments designed to probe the controls on the threshold response. We show that the threshold behavior is due to a combination of environmental (storm spacing and potential evapotranspiration) and geologic (bedrock permeability and bedrock topography) factors. The predicted precipitation–discharge threshold subsumes the complexity of plot-scale soil water response. We then demonstrate its use for prediction of whole-catchment storm discharge at other first order catchments at Maimai and the HJ Andrews Experimental Forest in Oregon. [ABSTRACT FROM AUTHOR]

Summary: The Mekong River Basin in South East Asia has experienced in recent decades large inter-annual variations between large floods and severe droughts. The general perception is that climate variability has increased in the region. In this paper our aim was to examine the role of El Niño-Southern Oscillation (ENSO) on these recent climate variations. To achieve our aim, we analysed the influence of ENSO on precipitation (over period 1981–2005) and discharge (1910–2008) by using spatial GIS analyses and statistical methods, such as linear correlations, spectral analysis and stochastic regression models. We found that the Mekong’s hydrology was significantly influenced by ENSO, particularly in the decay year of ENSO events. The influence was strongest in the southern parts of the basin. The precipitation and discharge decreased (increased) during El Niño (La Niña) and the annual flood period was shorter (longer) during El Niño (La Niña). We found also decadal variations in the ENSO-discharge relationship. Our findings indicate high potential for developing prediction methods for ENSO induced hydrological anomalies. These findings may significantly improve the possibility of preparing for extreme hydrological events, either drought or exceptional floods, and thus potentially improve water resources management in the basin. [ABSTRACT FROM AUTHOR]