Using regional regression within index flood procedures and an empirical Bayesian estimator

Studies have illustrated the performance of at-site and regional flood quantile estimators. For realistic generalized extreme value (GEV) distributions and short records, a simple index-flood quantile estimator performs better than two-parameter (2P) GEV quantile estimators with probability weighted moment (PWM) estimation using a regional shape parameter and at-site mean and L-coefficient of variation (L-CV), and full three-parameter at-site GEV/PWM quantile estimators. However, as regional heterogeneity or record lengths increase, the 2P-estimator quickly dominates. This paper generalizes the index flood procedure by employing regression with physiographic information to refine a normalized T-year flood estimator. A linear empirical Bayes estimator uses the normalized quantile regression estimator to define a prior distribution which is employed with the normalized 2P-quantile estimator. Monte Carlo simulations indicate that this empirical Bayes estimator does essentially as well as or better than the simpler normalized quantile regression estimator at sites with short records, and performs as well as or better than the 2P-estimator at sites with longer records or smaller L-CV.     

An analysis of change in alpine annual maximum discharges: implications for the selection of design discharges

The paper presents an analysis of 17 long annual maximum series (AMS) of flood flows for Swiss Alpine basins, aimed at checking the presence of changes in the frequency regime of annual maxima. We apply Pettitt's change point test, the nonparametric sign test and Sen's test on trends. We also apply a parametric goodness‐of‐fit test for assessing the suitability of distributions estimated on the basis of annual maxima collected up to a certain year for describing the frequency regime of later observations. For a number of series the tests yield consistent indications for significant changes in the frequency regime of annual maxima and increasing trends in the intensity of annual maximum discharges. In most cases, these changes cannot be explained by anthropogenic causes only (e.g. streamflow regulation, construction of dams). Instead, we observe a statistically significant relationship between the year of change and the elevation of the catchment outlet. This evidence is consistent with the findings of recent studies that explain increasing discharges in alpine catchments with an increase in the temperature controlling the portion of mountain catchments above the freezing point. Finally, we analyse the differences in return periods (RPs) estimated for a given flood flow on the basis of recent and past observations. For a large number of the study AMS, we observe that, on average, the 100‐year flood for past observations corresponds to a RP of approximately 10 to 30 years on the basis of more recent observation. From a complementary perspective, we also notice that estimated RP‐year flood (i.e. flood quantile (FQ) associated with RP) increases on average by approximately 20% for the study area, irrespectively of the RP. Practical implications of the observed changes are illustrated and discussed in the paper. Copyright © 2011 John Wiley & Sons, Ltd.     

Impact of climate change on 24‐h design rainfall depth estimation in Qiantang River Basin,East China

The frequency and magnitude of extreme meteorological or hydrological events such as floods and droughts in China have been influenced by global climate change. The water problem due to increasing frequency and magnitude of extreme events in the humid areas has gained great attention in recent years. However, the main challenge in the evaluation of climate change impact on extreme events is that large uncertainty could exist. Therefore, this paper first aims to model possible impacts of climate change on regional extreme precipitation (indicated by 24‐h design rainfall depth) at seven rainfall gauge stations in the Qiantang River Basin, East China. The Long Ashton Research Station‐Weather Generator is adopted to downscale the global projections obtained from general circulation models (GCMs) to regional climate data at site scale. The weather generator is also checked for its performance through three approaches, namely Kolmogorov–Smirnov test, comparison of L‐moment statistics and 24‐h design rainfall depths. Future 24‐h design rainfall depths at seven stations are estimated using Pearson Type III distribution and L‐moment approach. Second, uncertainty caused by three GCMs under various greenhouse gas emission scenarios for the future periods 2020s (2011–2030), 2055s (2046–2065) and 2090s (2080–2099) is investigated. The final results show that 24‐h design rainfall depth increases in most stations under the three GCMs and emission scenarios. However, there are large uncertainties involved in the estimations of 24‐h design rainfall depths at seven stations because of GCM, emission scenario and other uncertainty sources. At Hangzhou Station, a relative change of ?16% to 113% can be observed in 100y design rainfall depths. Copyright © 2012 John Wiley & Sons, Ltd.     

Derivation of a curve number and kinematic-wave based flood frequency distribution

Abstract

The physically-based flood frequency models use readily available rainfall data and catchment characteristics to derive the flood frequency distribution. In the present study, a new physically-based flood frequency distribution has been developed. This model uses bivariate exponential distribution for rainfall intensity and duration, and the Soil Conservation Service-Curve Number (SCS-CN) method for deriving the probability density function (pdf) of effective rainfall. The effective rainfall-runoff model is based on kinematic-wave theory. The results of application of this derived model to three Indian basins indicate that the model is a useful alternative for estimating flood flow quantiles at ungauged sites.     

Flood frequency analysis of Turkey using L‐moments method

The index flood procedure coupled with the L‐moments method is applied to the annual flood peaks data taken at all stream‐gauging stations in Turkey having at least 15‐year‐long records. First, screening of the data is done based on the discordancy measure (D_i) in terms of the L‐moments. Homogeneity of the total geographical area of Turkey is tested using the L‐moments based heterogeneity measure, H, computed on 500 simulations generated using the four parameter Kappa distribution. The L‐moments analysis of the recorded annual flood peaks data at 543 gauged sites indicates that Turkey as a whole is hydrologically heterogeneous, and 45 of 543 gauged sites are discordant which are discarded from further analyses. The catchment areas of these 543 sites vary from 9·9 to 75121 km² and their mean annual peak floods vary from 1·72 to 3739·5 m³ s^?1. The probability distributions used in the analyses, whose parameters are computed by the L‐moments method are the general extreme values (GEV), generalized logistic (GLO), generalized normal (GNO), Pearson type III (PE3), generalized Pareto (GPA), and five‐parameter Wakeby (WAK). Based on the L‐moment ratio diagrams and the |Z_dist|‐statistic criteria, the GEV distribution is identified as the robust distribution for the study area (498 gauged sites). Hence, for estimation of flood magnitudes of various return periods in Turkey, a regional flood frequency relationship is developed using the GEV distribution. Next, the quantiles computed at all of 543 gauged sites by the GEV and the Wakeby distributions are compared with the observed values of the same probability based on two criteria, mean absolute relative error and determination coefficient. Results of these comparisons indicate that both distributions of GEV and Wakeby, whose parameters are computed by the L‐moments method, are adequate in predicting quantile estimates. Copyright © 2011 John Wiley & Sons, Ltd.     

Process identification at a slow‐moving landslide in the Vorarlberg Alps

A fine‐grained slope that exhibits slow movement rates was investigated to understand how geohydrological processes contribute to a consecutive development of mass movements in the Vorarlberg Alps, Austria. For that purpose intensive hydrometeorological, hydrogeological and geotechnical observations as well as surveying of surface movement rates were conducted during 1998–2001. Subsurface water dynamics at the creeping slope turned out to be dominated by a three‐dimensional pressure system. The pressure reaction is triggered by fast infiltration of surface water and subsequent lateral water flow in the south‐western part of the hillslope. The related pressure signal was shown to propagate further downhill, causing fast reactions of the piezometric head at 5·5 m depth on a daily time scale. The observed pressure reactions might belong to a temporary hillslope water body that extends further downhill. The related buoyancy forces could be one of the driving forces for the mass movement. A physically based hydrological model was adopted to model simultaneously surface and subsurface water dynamics including evapotranspiration and runoff production. It was possible to reproduce surface runoff and observed pressure reactions in principle. However, as soil hydraulic functions were only estimated on pedotransfer functions, a quantitative comparison between observed and simulated subsurface dynamics is not feasible. Nevertheless, the results suggest that it is possible to reconstruct important spatial structures based on sparse observations in the field which allow reasonable simulations with a physically based hydrological model. Copyright © 2004 John Wiley & Sons, Ltd.     

Natural entropy production in an inflationary model for a polarized vacuum

Though entropy production is forbidden in standard FRW Cosmology, Berman and Som presented a simple inflationary model where entropy production by bulk viscosity, during standard inflation without ad hoc pressure terms can be accommodated with Robertson–Walker’s metric, so the requirement that the early Universe be anisotropic is not essential in order to have entropy growth during inflationary phase, as we show. Entropy also grows due to shear viscosity, for the anisotropic case. The intrinsically inflationary metric that we propose can be thought of as defining a polarized vacuum, and leads directly to the desired effects without the need of introducing extra pressure terms.     

Rainfall,runoff and shallow groundwater response in a mixed-use,agro-forested watershed of the northeast,USA

Stream and shallow groundwater responses to rainfall are characterized by high spatial variability, but hydrologic response variability across small, agro-forested sub-catchments remains poorly understood. Conceivably, improved understanding in this regard will result in agricultural practices that more effectively limit nutrient runoff, erosion, and pollutant transport. Terrestrial hydrologic response approaches can provide valuable information on stream-aquifer connectivity in these mixed-use watersheds. A study was implemented, including eight stream and co-located shallow groundwater monitoring sites, in a small sub-catchment of the Chesapeake Bay watershed in the Northeast, USA to advance this ongoing need. During the study period, 100 precipitation-receiving days (i.e., 24-hour periods, midnight to midnight) were observed. On average, the groundwater table responded more to precipitation than stream stage (level change of 0.03 vs. 0.01 m and rainfall-normalized level change estimate of 3.81 vs. 3.37). Median stream stage responses, groundwater table responses, and response ratios were significantly different between sub-catchments (n = 8; p < 0.001). Study area average precipitation thresholds for runoff and shallow groundwater flow were 2.8 and 0.6 cm, respectively. Individual sub-catchment thresholds ranged from 0.5 to 2.8 cm for runoff and 0.2 to 1.3 cm for shallow groundwater flow. Normalized response lag times between the stream and shallow groundwater ranged from −0.50 to 3.90 s·cm⁻¹, indicating that stormflow in one stream section was regulated by groundwater flow during the period of study. The observed differences in hydrologic responses to precipitation advance future modelling efforts by providing examples of how terrestrial groundwater response methods can be used to investigate sub-catchment spatial variability in stream-aquifer gradients with co-located shallow groundwater and stream stage data. Additionally, results demonstrate asynchronous stream and shallow groundwater responses on precipitation-receiving days, which may hold important implications for modelling hydrologic and biogeochemical fate and transport processes in small, agro-forested catchments.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin,Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero,en Tanzanie

Abstract

Modelling of the rainfall–runoff transformation process and routing of river flows in the Kilombero River basin and its five sub-catchments within the Rufiji River basin in Tanzania was undertaken using three system (black-box) models—a simple linear model, a linear perturbation model and a linear varying gain factor model—in their linear transfer function forms. A lumped conceptual model—the soil moisture accounting and routing model—was also applied to the sub-catchments and the basin. The HEC-HMS model, which is a distributed model, was applied only to the entire Kilombero River basin. River discharge, rainfall and potential evaporation data were used as inputs to the appropriate models and it was observed that sometimes the system models performed better than complex hydrological models, especially in large catchments, illustrating the usefulness of using simple black-box models in datascarce situations.