Satellite remote sensing and conceptual cloud modeling for convective rainfall simulation

Motivated by the need for rainfall prediction models in data scarce areas, we adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data. The data is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) satellite. Model inputs are TIR cloud top temperatures at 15-min intervals and observations of pressure, temperature, and dew point temperatures from ground-based stations at 30-min intervals. The sensitivity of the parsimonious cloud model to its parameters is evaluated by a regional sensitivity analysis (RSA) which suggests that model performance is sensitive to few parameters. The model was calibrated and tested for four convective events that were observed during the wet season in the source basin of the Upper Blue Nile River. The difference between the simulated and the observed depth of the selected rain events varies between 0.2 and 1.8 mm with a root mean square error of smaller than 0.5 mm for each event. It is shown that the updraft velocity characteristic can provide relevant information for rainfall forecasting. The simulation results suggest the effectiveness of the model approach as evaluated by selected performance measures. The various characteristics of the rainfall events as simulated generally match to observed counter parts when ground-based and remote sensing observations are combined.     

降雨条件下边坡体积含水率变化规律研究

持续降雨是边坡发生失稳破坏的主要诱因之一,基于饱和—非饱和渗流理论,对梅州市大埔县某边坡的渗流场进行模拟,研究在不同降雨工况下该边坡土体体积含水率的时空变化规律。研究结果表明:相同条件下,降雨强度越大(降雨历时越长),边坡表层土体体积含水率变化越大;降雨强度60 mm/d历时1 d的暴雨对边坡表层土体体积含水率的增幅作用存在着一定的滞后性,其余工况未表现出滞后现象;降雨强度为120mm/d和300 mm/d的两种工况各研究点任意时段体积含水率较为接近;当降雨强度达到60 mm/d以上时,边坡内部体积含水率空间变化主要受降雨历时影响,降雨历时越长,降雨入渗深度和体积含水率变化越大。     

Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment,Terengganu, Malaysia

ABSTRACT

This paper presents a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger     

A comparative study for water temperature modelling in a small basin,the Fourchue River,Quebec, Canada

Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models.

EDITOR M.C. Acreman

ASSOCIATE EDITOR R. Hirsch     

Rainfall stochastic modeling for runoff forecasting

Rainfall fields estimation over a catchment area is an important stage in many hydrological applications. In this context, weather radars have several advantages because a single-site can scan a vast area with very high temporal and spatial resolution. The construction of weather radar systems with dual polarization capability allowed progress on radar rainfall estimation and its hydro-meteorological applications. For these applications of radar data it is necessary to remove the ground clutter contamination with an algorithm based on the backscattering signal variance of the differential reflectivity. The calibration of the GDSTM model (Gaussian Displacements Spatial-Temporal Model), a cluster stochastic generation model in continuous space and time, is herewith presented. In this model, storms arrive in a Poisson process in time with cells occurring in each storm that cluster in space and time. The model is calibrated, using data collected by the weather radar Polar 55C located in Rome, inside a square area of 132 × 132 km², with the radar at the centre. The GDSTM is fitted to sequences of radar images with a time interval between the PPIs scans of 5 min. A generalized method of moment procedure is used for parameter estimation. For the validation of the ability of the model to reproduce internal structure of rain event, a geo-morphological rainfall-runoff model, based on width function (WFIUH), was calibrated using simulated and observed data. Several rainfall fields are generated with the stochastic model and later they are used as input of the WFIUH model so that the forecast discharges can be compared to the observed ones.     

Improving flood forecasting using an input correction method in urban models in poorly gauged areas

ABSTRACT

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.     

Regional scale rainfall–runoff modeling using VARX–MGARCH approach

Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance.     

Weather and seasonal climate prediction for flood planning in the Yangtze River Basin

This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.     

A two steps disaggregation method for highly seasonal monthly rainfall

 The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization of these statistical properties is an important next step for the application of this technique to regions in where daily data is not available.     

Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges; (2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations’ parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which non-parametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall. An erratum to this article can be found at     

Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network

Statistical methods have been widely used to build different streamflow prediction models; however, lacking of physical mechanism prevents precise streamflow prediction in alpine regions dominated by rainfall, snow and glacier. To improve precision, a new hybrid model (HBNN) integrating HBV hydrological model, Bayesian neural network (BNN) and uncertainty analysis is proposed. In this approach, the HBV is mainly used to generate initial snow-melt and glacier-melt runoffs that are regarded as new inputs of BNN for precision improvement. To examine model reliability, a hybrid deterministic model called HLSSVM incorporating the HBV model and least-square support vector machine is also developed and compared with HBNN in a typical region, the Yarkant River basin in Central Asia. The findings suggest that the HBNN model is a robust streamflow prediction model for alpine regions and capable of combining strengths of both the BNN statistical model and the HBV hydrological model, providing not only more precise streamflow prediction but also more reasonable uncertainty intervals than competitors particularly at high flows. It can be used in predicting streamflow for similar regions worldwide.     

V: Daily catchment rainfall estimated from meteosat

Meteosat data for 1986 to 1988 have been used to estimate the daily rainfall over catchments of tributaries of the river Senegal in Mail and Guinea. The technique uses the methodology of the TAMSAT group of the University of Reading, which involves the selection of an appropriate cloud top temperature threshold to determine whether the cloud is producing rain and the rainfall is estimated from the period during which storm clouds remain over a site. After calibration against all available raingauges in the catchments, the daily rainfall estimates derived by this technique were used as inputs to rainfall-runoff models. The results indicate that the streamflow models, which had themselves been calibrated using raingauge data, performed as well or better when the satellite derived estimates were used as inputs than when gauge data were used. An economical, automated operational system is described.     

Gridded climate data products are an alternative to instrumental measurements as inputs to rainfall–runoff models

Rainfall–runoff models are widely used to predict flows using observed (instrumental) time series of air temperature and precipitation as inputs. Poor model performance is often associated with difficulties in estimating catchment‐scale meteorological variables from point observations. Readily available gridded climate products are an underutilized source of temperature and precipitation time series for rainfall–runoff modelling, which may overcome some of the performance issues associated with poor‐quality instrumental data in small headwater monitoring catchments. Here we compare the performance of instrumental measured and E‐OBS gridded temperature and precipitation time series as inputs in the rainfall–runoff models “PERSiST” and “HBV” for flow prediction in six small Swedish catchments. For both models and most catchments, the gridded data produced statistically better simulations than did those obtained using instrumental measurements. Despite the high correspondence between instrumental and gridded temperature, both temperature and precipitation were responsible for the difference. We conclude that (a) gridded climate products such as the E‐OBS dataset could be more widely used as alternative input to rainfall–runoff models, even when instrumental measurements are available, and (b) the processing applied to gridded climate products appears to provide a more realistic approximation of small catchment‐scale temperature and precipitation patterns needed for flow simulations. Further research on this issue is needed and encouraged.     

Storage of water on vegetation under simulated rainfall of varying intensity

Little is understood about how storage of water on forest canopies varies during rainfall, even though storage changes intensity of throughfall and thus affects a variety of hydrological processes. In this study, laboratory rainfall simulation experiments using varying intensities yielded a better understanding of dynamics of rainfall storage on woody vegetation. Branches of eight species generally retained more water at higher rainfall intensities than at lower intensities, but incremental storage gains decreased as rainfall intensity increased. Leaf area was the best predictor of storage, especially for broadleaved species. Stored water ranged from 0.05 to 1.1 mm effective depth on leaves, depending on species and rainfall intensity. Storage was generally about 0.2 mm greater at rainfall intensity 420 mm h⁻¹ than at 20 mm h⁻¹. Needle-leaved branches generally retained more water per leaf area than did branches from broadleaved species, but branches that stored most at lower rainfall intensities tended to accumulate less additional storage at higher intensities. A simple nonlinear model was capable of predicting both magnitude (good model performance) and temporal scale (fair model performance) of storage responses to varying rainfall intensities. We hypothesize a conceptual mechanical model of canopy storage during rainfall that includes the concepts of static and dynamic storage to account for intensity-driven changes in storage. Scaling up observations to the canopy scale using LAI resulted in an estimate of canopy storage that generally agrees with estimates by traditional methods.     

Portable rainfall simulator for plot-scale investigation of rainfall-runoff,and transport of sediment and pollutants

A low-cost, simple to use portable rainfall simulator is developed for use over a 5 m^2 plot. The simulator is easy to transport and assemble in the field, thereby allowing for necessary experimental replicates to be done. It provides rainfall intensities of between 20 and 100 mm/h by changing the number and type of silicon nozzles used. The Christiansen coefficient of uniformities obtained in the field are appropriate and vary from 79 to 94% for rainfall intensities ranging from 30 to 70 mm/h. In addition, the median volumetric drop diameters measured for rainfall intensities of 30, 50, and 70 mm/h are in the lower range of that of natural rainfall and equal to 1.10 ± 0.08,1.69 ± 0.21, and 1.66 ± 0.20 mm, respectively. The velocities of the raindrops with diameters less than 1.2 mm reached terminal velocities, while raindrops less than 2.0 mm achieved velocities reasonably close to the terminal velocity of natural rainfall. Furthermore,the average time-specific kinetic energy(KET) for rainfall intensities of 30, 50, and 70 mm/h are 257.7,760.1, and 1645.2 J/m^2/h, respectively accounting for about 78.0 and 86.5% of the KET of natural rainfall for50 and 70 mm/h rainfall intensity, respectively. The applicability of the portable rainfall simulator for herbicide transport study is investigated using two herbicides(atrazine and metolachlor); herbicide losses in runoff and sediment samples are in the ranges reported in the literature. As a percentage of the amount of herbicide applied, 5.29% of atrazine and 2.15% of metolachlor are lost due to combined water and sediment runoff. The results show that the portable rainfall simulator can be effectively used in studying processes such as pesticide runoff, infiltration mechanisms, and sediment generation and transport at a field plot scale with an emphasis on how surface characteristics such as slope and soil properties affect these processes.     

Influence of rainfall spatial variability on flood prediction

This paper deals with the sensitivity of distributed hydrological models to different patterns that account for the spatial distribution of rainfall: spatially averaged rainfall or rainfall field. The rainfall data come from a dense network of recording rain gauges that cover approximately 2000 km² around Mexico City. The reference rain sample accounts for the 50 most significant events, whose mean duration is about 10 h and maximal point depth 170 mm. Three models were tested using different runoff production models: storm-runoff coefficient, complete or partial interception. These models were then applied to four fictitious homogeneous basins, whose sizes range from 20 to 1500 km². For each test, the sensitivity of the model is expressed as the relative differences between the empirical distribution of the peak flows (and runoff volumes), calculated according to the two patterns of rainfall input: uniform or non-uniform. Differences in flows range from 10 to 80%, depending on the type of runoff production model used, the size of the basin and the return period of the event. The differences are generally moderate for extreme events. In the local context, this means that uniform design rainfall combining point rainfall distribution and the probabilistic concept of the areal reduction factor could be sufficient to estimate major flood probability. Differences are more significant for more frequent events. This can generate problems in calibrating the hydrological model when spatial rainfall localization is not taken into account: a bias in the estimation of parameters makes their physical interpretation difficult and leads to overestimation of extreme flows.     

Estimation of flow enhancement in the Kinneret watershed due to cloud seeding

A comparison is carried out between historical records of the flow measured in Kinneret watershed during and prior to the time of cloud seeding for rainfall enhancement. Precipitation series for the control area of the meteorological experimentation serve as a reference for the comparison. The fluctuations of the flow, which would have occurred unless the effect of the seeding, are estimated by a linear regression on the precipitation as the control. The regression parameters are calibrated separately for the unseeded and for the seeded time series. The model with the parameters calibrated for the unseeded series is applied on the rainfall recorded during the seeded time, and vice versa. The difference between the measured and the computed data is attributed to the effect of cloud seeding. Similar comparisons are carried out with respect to rainfall series recorded at the target area and at the edge of the enhanced area.The results indicate that the flow from the affected sector of the watershed has been enhanced, with respect to the control, by 31×10⁶ m ³/year, at a significance level of 31. This enhancement is 5% of the volume which is generated in that area. The rates found with respect to the rainfall at the edge are higher than those found with respect to the control, while those with respect to the rainfall at the center of the target area are lower.     

Dissecting the effect of rainfall variability on the statistical structure of peak flows

This study examines the role of rainfall variability on the spatial scaling structure of peak flows using the Whitewater River basin in Kansas as an illustration. Specifically, we investigate the effect of rainfall on the scatter, the scale break and the power law (peak flows vs. upstream areas) regression exponent. We illustrate why considering individual hydrographs at the outlet of a basin can lead to misleading interpretations of the effects of rainfall variability. We begin with the simple scenario of a basin receiving spatially uniform rainfall of varying intensities and durations and subsequently investigate the role of storm advection velocity, storm variability characterized by variance, spatial correlation and intermittency. Finally, we use a realistic space–time rainfall field obtained from a popular rainfall model that combines the aforementioned features. For each of these scenarios, we employ a recent formulation of flow velocity for a network of channels, assume idealized conditions of runoff generation and flow dynamics and calculate peak flow scaling exponents, which are then compared to the scaling exponent of the width function maxima. Our results show that the peak flow scaling exponent is always larger than the width function scaling exponent. The simulation scenarios are used to identify the smaller scale basins, whose response is dominated by the rainfall variability and the larger scale basins, which are driven by rainfall volume, river network aggregation and flow dynamics. The rainfall variability has a greater impact on peak flows at smaller scales. The effect of rainfall variability is reduced for larger scale basins as the river network aggregates and smoothes out the storm variability. The results obtained from simple scenarios are used to make rigorous interpretations of the peak flow scaling structure that is obtained from rainfall generated with the space–time rainfall model and realistic rainfall fields derived from NEXRAD radar data.     

Estimation of flow enhancement in the Kinneret watershed due to cloud seeding

A comparison is carried out between historical records of the flow measured in Kinneret watershed during and prior to the time of cloud seeding for rainfall enhancement. Precipitation series for the control area of the meteorological experimentation serve as a reference for the comparison. The fluctuations of the flow, which would have occurred unless the effect of the seeding, are estimated by a linear regression on the precipitation as the control. The regression parameters are calibrated separately for the unseeded and for the seeded time series. The model with the parameters calibrated for the unseeded series is applied on the rainfall recorded during the seeded time, and vice versa. The difference between the measured and the computed data is attributed to the effect of cloud seeding. Similar comparisons are carried out with respect to rainfall series recorded at the target area and at the edge of the enhanced area.The results indicate that the flow from the affected sector of the watershed has been enhanced, with respect to the control, by 31×10⁶ m ³/year, at a significance level of 31. This enhancement is 5% of the volume which is generated in that area. The rates found with respect to the rainfall at the edge are higher than those found with respect to the control, while those with respect to the rainfall at the center of the target area are lower.