Space–time forecasting of groundwater level using a hybrid soft computing model

Forecasting of space–time groundwater level is important for sparsely monitored regions. Time series analysis using soft computing tools is powerful in temporal data analysis. Classical geostatistical methods provide the best estimates of spatial data. In the present work a hybrid framework for space–time groundwater level forecasting is proposed by combining a soft computing tool and a geostatistical model. Three time series forecasting models: artificial neural network, least square support vector machine and genetic programming (GP), are individually combined with the geostatistical ordinary kriging model. The experimental variogram thus obtained fits a linear combination of a nugget effect model and a power model. The efficacy of the space–time models was decided on both visual interpretation (spatial maps) and calculated error statistics. It was found that the GP–kriging space–time model gave the most satisfactory results in terms of average absolute relative error, root mean square error, normalized mean bias error and normalized root mean square error.     

Spatio-temporal exploratory analysis of urban surface temperature field in Shanghai, China

The integrated application of remote sensing, geographic information system and quantitative analytical modeling can provide scientific and effective methods for monitoring and studying urban heat island, based on land surface temperature (LST) retrieved from thermal infrared channel data of sensors. In this paper, LST is retrieved from Landsat TM6 and ETM + 6 data of Shanghai central city in 1989, 1997, 2000 and 2002, by using the mono-window algorithm. Based on the data, global and local spatial autocorrelation analysis, and geostatistical methods are adopted to quantitatively describe the characteristics of spatial heterogeneity and temporal evolution of land surface thermal landscape at different scales and periods in Shanghai central city, by utilizing exploratory spatial data analysis. Results show that LST field in Shanghai central city tends to fragmentize and complicate with the development of Shanghai, and its global spatial difference becomes greater gradually. The spatial variance pattern of the change of LST field from 1997 to 2002 indicates that the dynamic change of LST presents a tendency of increase in circularity. LST declines distinctly in the districts of Puxi and Pudong near and inside the inner ring road, while it rises obviously outside the central city and near the out ring road. The extrema of temporal change in LST field have a characteristic of spatial clustering. Besides, as the city of Shanghai expands in a circular pattern as a whole, the directional difference of dynamic change of urban surface thermal landscape exists but is not very obvious.     

Intercomparison across scales between remotely-sensed land surface temperature and representative equilibrium temperature from a distributed energy water balance model

Abstract

Quantifying the reliability of distributed hydrological models is an important task in hydrology to understand their ability to estimate energy and water fluxes at the agricultural district scale as well the basin scale for water resources management in drought monitoring and flood forecasting. In this context, the paper presents an intercomparison of simulated representative equilibrium temperature (RET) derived from a distributed energy water balance model and remotely-sensed land surface temperature (LST) at spatial scales from the agricultural field to the river basin. The main objective of the study is to evaluate the use of LST retrieved from operational remote sensing data at different spatial and temporal resolutions for the internal validation of a distributed hydrological model to control its mass balance accuracy as a complementary method to traditional calibration with discharge measurements at control river cross-sections. Modelled and observed LST from different radiometric sensors located on the ground surface, on an aeroplane and a satellite are compared for a maize field in Landriano (Italy), the agricultural district of Barrax (Spain) and the Upper Po River basin (Italy). A good ability of the model in reproducing the observed LST values in terms of mean bias error, root mean square error, relative error and Nash-Sutcliffe index is shown.

Editor Z.W. Kundzewicz; Associate editor D. Gerten     

Multivariate modeling of droughts using copulas and meta-heuristic methods

This study investigated the utility of two meta-heuristic algorithms to estimate parameters of copula models and for derivation of drought severity–duration–frequency (S–D–F) curves. Drought is a natural event, which has huge impact on both the society and the natural environment. Drought events are mainly characterized by their severity, duration and intensity. The study adopts standardized precipitation index for drought characterization, and copula method for multivariate risk analysis of droughts. For accurate estimation of copula model parameters, two meta-heuristic methods namely genetic algorithm and particle swarm optimization are applied. The proposed methodology is applied to a case study in Trans Pecos, an arid region in Texas, USA. First, drought severity and duration are separately modeled by various probability distribution functions and then the best fitted models are selected for copula modeling. For modeling the joint dependence of drought variables, different classes of copulas, namely, extreme value copulas, Plackett and Student’s t copulas are employed and their performance is evaluated using standard performance measures. It is found that for the study region, the Gumbel–Hougaard copula is the best fitted copula model as compared to the others and is used for the development of drought S–D–F curves. Results of the study suggest that the meta-heuristic methods have greater utility in copula-based multivariate risk assessment of droughts.     

Exploring the spatial and temporal relationships between mosquito population dynamics and dengue outbreaks based on climatic factors

Identifying the impact of climatic factors on mosquito population dynamics is of great importance for dengue outbreak control. The purpose of this study is to develop an approach to predict spatial/temporal mosquito reproduction and disease outbreaks. The prediction of a dengue outbreak is only possible if the temporal relationship between mosquito replication and the weather is known. At present, this is unclear and needs to be examined. Moreover, because the development of mosquito density is a dynamic process in the course of time, it should be observed as closely as possible, in this study in a 1-day timeframe. This paper makes a thorough study of the situation in southern Taiwan and analyzes a large amount of data from 1999 to 2004 related to dengue cases and larval density. We first use the method, k-means, to conduct data clustering and derive representative larvae replication patterns. Then, we propose mathematical models to approximate the development of larval density, describe the expansion of mosquito activity areas, and construct a surveillance system to raise alerts based on real-time input of weather data and larval indices. Analysis of historic data reveals some new information on the spatial and temporal relationships between larval density and dengue outbreaks. In Taiwan, if the weather becomes or remains warm and humid for 6?days after a bout of rain, there can be a sharp increase in the larval mosquito population. About 7?days after the Breteau index begins to rise, larval density reaches its climax; and, about 12?days after the climax of larval density, cases of dengue may be reported. The system is tested using subsequent data from 2005 to 2009 and shows satisfactory accuracy. Numerous data support these findings, and this new knowledge is thus validated and can be used to assist public health professionals to take effective dengue control measures.     

Tectonic reconstruction of batholith formation based on the spatiotemporal distribution of Cretaceous–Paleogene granitic rocks in southwestern Japan

The spatiotemporal distribution of Cretaceous–Paleogene granitic rocks in southwestern Japan is investigated to understand the origin of the granitic batholith belt and to reconstruct the tectonic setting of emplacement. New U–Pb zircon ages for 92 samples collected from a region measuring 50 km (E–W) by 200 km (N–S) reveals a stepwise northward younging of granitic rocks aged between 95 and 30 Ma with an age‐data gap between 60 and 48 Ma. Based on the spatiotemporal distribution of granite ages, we examine two plausible models to explain the pattern of magmatic activity: (i) subduction of a segmented spreading ridge and subsequent slab melting (ridge‐subduction model), and (ii) subduction with a temporally variable subduction angle and corresponding spatial distribution of normal arc magmatism (subduction angle model). We optimize the model parameters to fit the observed magmatism in time and space, and compare the best‐fit models. As to ridge subduction model, the best‐fit solution indicates that the spreading ridge started to subduct at approximately 100 Ma, and involved a 45‐km‐wide section of the ridge segment, a subduction obliquity of 30°, and a slow migration velocity (～1.6 cm/y) of the ridge. These values are within the ranges of velocities observed for present‐day ridge subduction at the Chile trench. On the other hand, the best‐fit solution of subduction angle model indicates that the subduction angle decreases stepwise from 37° at 95 Ma, 32° at 87 Ma, 22° at 72 Ma, to 20° at 65 Ma, shifting magmatic region towards the continental side. These results and comparison, together with constraints on the geometry of the tectonic setting provided by previous studies, suggest that the ridge subduction model better explains the limited duration of magmatism, although both models broadly fit the data and cannot be ruled out.     

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.     

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.     

Bayesian autoregressive spatiotemporal model of PM<Subscript>10</Subscript> concentrations across Peninsular Malaysia

Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM₁₀ are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM₁₀ levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM₁₀ concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM₁₀ in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM₁₀ concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM₁₀ forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R² and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.     

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.     

Impact of log-transmissivity variogram structure on groundwater flow and transport predictions

We analyze the impact of the choice of the variogram model adopted to characterize the spatial variability of natural log-transmissivity on the evaluation of leading (statistical) moments of hydraulic heads and contaminant travel times and trajectories within mildly (randomly) heterogeneous two-dimensional porous systems. The study is motivated by the fact that in several practical situations the differences between various variogram types and a typical noisy sample variogram are small enough to suggest that one would often have a hard time deciding which of the tested models provides the best fit. Likewise, choosing amongst a set of seemingly likely variogram models estimated by means of geostatistical inverse models of flow equations can be difficult due to lack of sensitivity of available model discrimination criteria. We tackle the problem within the framework of numerical Monte Carlo simulations for mean uniform and radial flow scenarios. The effect of three commonly used isotropic variogram models, i.e., Gaussian, Exponential and Spherical, is analyzed. Our analysis clearly shows that (ensemble) mean values of the quantities of interest are not considerably influenced by the variogram shape for the range of parameters examined. Contrariwise, prediction variances of the quantities examined are significantly affected by the choice of the variogram model of the log-transmissivity field. The spatial distribution of the largest/lowest values of the relative differences observed amongst the tested models depends on a combination of variogram shape and parameters and relative distance from internal sources and the outer domain boundary. Our findings suggest the need of developing robust techniques to discriminate amongst a set of seemingly equally likely alternative variogram models in order to provide reliable uncertainty estimates of state variables.     

Hydroclimatic teleconnection between global sea surface temperature and rainfall over India at subdivisional monthly scale

It is well established that sea surface temperature (SST) plays a significant role in the hydrologic cycle in which precipitation is the most important part. In this study, the influence of SST on Indian subdivisional monthly rainfall is investigated. Both spatial and temporal influences are investigated. The most influencing regions of sea surface are identified for different subdivisions and for different overlapping seasons in the year. The relative importance of SST, land surface temperature (LST) and ocean–land temperature contrast (OLTC) and their variation from subdivision to subdivision and from season to season are also studied. It is observed that LST does not show much similarity with rainfall series, but, in general, OLTC shows relatively higher influence in the pre‐monsoon and early monsoon periods, whereas SST plays a more important role in late‐ and post‐monsoon periods. The influence of OLTC is seen to be mostly confined to the Indian Ocean region, whereas the effect of SST indicates the climatic teleconnection between Indian regional rainfall and climate indices in Pacific and Atlantic Oceans. Copyright © 2006 John Wiley & Sons, Ltd.     

A spatio-temporal Poisson hurdle point process to model wildfires

Wildfires have been studied in many ways, for instance as a spatial point pattern or through modeling the size of fires or the relative risk of big fires. Lately a large variety of complex statistical models can be fitted routinely to complex data sets, in particular wildfires, as a result of widely accessible high-level statistical software, such as R. The objective in this paper is to model the occurrence of big wildfires (greater than a given extension of hectares) using an adapted two-part econometric model, specifically a hurdle model. The methodology used in this paper is useful to determine those factors that help any fire to become a big wildfire. Our proposal and methodology can be routinely used to contribute to the management of big wildfires.     

Topographic,pedologic and climatic interactions influencing streamflow generation at multiple catchment scales

Dominant flow pathways (DFPs) in mesoscale watersheds are poorly characterized and understood. Here, we make use of a conservative tracer (Gran alkalinity) and detailed information about climatic conditions and physical properties to examine how temporally and spatially variable factors interact to determine DFPs in 12 catchments draining areas from 3.4 to 1829.5 km² (Cairngorms, Scotland). After end‐member mixing was applied to discriminate between near surface and deep groundwater flow pathways, variation partitioning, canonical redundancy analyses and regression models were used to resolve: (i) What is the temporal variability of DFPs in each catchment?; (ii) How do DFPs change across spatial scales and what factors control the differences in hydrological responses?; and (iii) Can a conceptual model be developed to explain the spatiotemporal variability of DFPs as a function of climatic, topographic and soil characteristics? Overall, catchment characteristics were only useful to explain the temporal variability of DFPs but not their spatial variation across scale. The temporal variability of DFPs was influenced most by prevailing hydroclimatic conditions and secondarily soil drainability. The predictability of active DFPs was better in catchments with soils supporting fast runoff generation on the basis of factors such as the cumulative precipitation from the seven previous days, mean daily air temperature and the fractional area covered by Rankers. The best regression model R² was 0.54, thus suggesting that the catchments’ internal complexity was not fully captured by the factors included in the analysis. Nevertheless, this study highlights the utility of combining tracer studies with digital landscape analysis and multivariate statistical techniques to gain insights into the temporal (climatic) and spatial (topographic and pedologic) controls on DFPs. Copyright © 2011 John Wiley & Sons, Ltd.     

Can satellite land surface temperature data be used similarly to river discharge measurements for distributed hydrological model calibration?

Abstract

A new methodology is proposed for the calibration of distributed hydrological models at the basin scale by constraining an internal model variable using satellite data of land surface temperature (LST). The model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature that governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is compared to operational satellite LST, while calibrating soil hydraulic parameters and vegetation variables differently in each pixel, minimizing the errors. This procedure is compared to the traditional calibration using only discharge measurements. The distributed energy water balance model, Flash-flood Event-based Spatially-distributed rainfall–runoff Transformation – Energy Water Balance model (FEST-EWB), is used to test this approach. This methodology is applied to the Upper Yangtze River basin (China) using MODIS LST retrieved from satellite data in the framework of the NRSCC-ESA DRAGON-2 Programme. The calibration procedure based on LST seems to outperform the calibration based on discharge, with lower relative error and higher Nash-Sutcliffe efficiency index on cumulated volume.

Editor D. Koutsoyiannis; Associate editor C. Perrin     

Multimoment convecting flux tube model of the polar wind system with return current and microprocesses

Multimoment fluid simulation frameworks, which effectively account for anomalous transport due to microprocesses, combine best features of small-scale kinetic and global-scale MHD models. The most practical models of this type, 1D flux tube models, have been successfully used for realistic simulations of space plasmas including polar wind and magnetosphere–ionosphere coupling processes characterized by a wide range of temporal and spatial scales. Our earlier flux tube models with field-aligned current and microprocesses have been formulated for spatially stationary flux tubes. However, horizontal convection due to electric fields is an important aspect of the high-latitude ionosphere–polar wind system and typical time scales of the polar wind upflow are comparable to the transit time across the polar cap. To take into account this important feature we have added flux tube convection to our earlier model. Using typical convecting flux tube that starts outside auroral oval, then enters and leaves downward current region, it has been shown that anomalous transport effects due to current-driven microinstabilities significantly alter dynamics of several plasma moments and should be taken into account for an accurate interpretation and prediction of the observed data. Future applications of our new model have also been discussed.     

Water level observations from unmanned aerial vehicles for improving estimates of surface water–groundwater interaction

Integrated hydrological models are usually calibrated against observations of river discharge and piezometric head in groundwater aquifers. Calibration of such models against spatially distributed observations of river water level can potentially improve their reliability and predictive skill. However, traditional river gauging stations are normally spaced too far apart to capture spatial patterns in the water surface, whereas spaceborne observations have limited spatial and temporal resolution. Unmanned aerial vehicles can retrieve river water level measurements, providing (a) high spatial resolution; (b) spatially continuous profiles along or across the water body, and (c) flexible timing of sampling. A semisynthetic study was conducted to analyse the value of the new unmanned aerial vehicle‐borne datatype for improving hydrological models, in particular estimates of groundwater–surface water (GW–SW) interaction. Mølleåen River (Denmark) and its catchment were simulated using an integrated hydrological model (MIKE 11–MIKE SHE). Calibration against distributed surface water levels using the Differential Evolution Adaptive Metropolis algorithm demonstrated a significant improvement in estimating spatial patterns and time series of GW–SW interaction. After water level calibration, the sharpness of the estimates of GW–SW time series improves by ~50% and root mean square error decreases by ~75% compared with those of a model calibrated against discharge only.     

Detection of the urban heat island in Beijing using HJ-1B satellite imagery

Satellite images are used extensively in studying the urban heat island (UHI) phenomenon. We evaluated the suitability of thermal infrared (TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing. Two modified algorithms for retrieving the land surface temperature (LST) from HJ-1B data were tested. The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output. The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data. Of the two algorithms, the mono-window algorithm performed better but further tests are necessary. With more frequent coverage than TM and higher spatial resolution than MODIS, the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.