Rain‐gauge network evaluation and augmentation using geostatistics

Rain‐gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain‐gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei‐Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy A_p under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain‐gauges of the existing network, identify the base network, and relocate non‐base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non‐base gauges provide little contribution to A_p and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two‐thirds of the total rain‐gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei‐Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non‐base gauges which need to be relocated. Copyright © 2007 John Wiley & Sons, Ltd.     

Temporal and spatial variability in the Guadalquivir estuary: a challenge for real-time telemetry

Meteorological, hydrological, and hydrodynamic data for 3 years (2008–2010) have been used to document and explain the temporal and spatial variability of the physical–biogeochemical interactions in the Guadalquivir River Estuary. A real-time, remote monitoring network has been deployed along the course of the river between its mouth and Seville to study a broad range of temporal scales (semidiurnal, diurnal, fortnightly, and seasonal). This network consists of eight hydrological monitoring stations capable of measuring temperature, conductivity, dissolved oxygen, turbidity, and chlorophyll fluorescence at four depths. In addition, six stations have been deployed to study hydrodynamics, obtaining 20-cell water column current profiles, and there is a meteorological station at the river mouth providing data for understanding atmospheric interactions. Completing this data-gathering network, there are several moorings (tide gauges, current/wave sensors, and a thermistor chain) deployed in the estuary and river mouth. Various sources of physical forcing, such as wind, tide-associated currents, and river discharge, are responsible for the particular temporal and spatial patterns of turbidity and salinity found in the estuary. These variables force the distribution of biogeochemical variables, such as dissolved oxygen and chlorophyll fluorescence. In particular, episodes of elevated turbidity (when suspended particle matter concentration >3,000 mg/l) have been detected by the network, together with episodes of declining values of salinity and dissolved oxygen. All these patterns are related to river discharge and tidal dynamics (spring/neap and high/low tide).     

Multi-scale analysis of meteorological drought risks based on a Bayesian interpolation approach in Huai River basin,China

A scheme for meteorological drought analysis at various temporal and spatial scales based on a spatial Bayesian interpolation of drought severity derived from Standardized Precipitation Index (SPI) values at observed stations is presented and applied to the Huai River basin of China in this paper, using monthly precipitation record from 1961 to 2006 in 30 meteorological stations across the basin. After dividing the study area into regular grids, drought condition in gauged sites are classified into extreme, severe, moderate and non drought according to SPIs at month, seasonal and annual time scales respectively while that in ungauged grids are explained as risks of various drought severities instead of single state by a Bayesian interpolation. Subsequently, temporal and spatial patterns of drought risks are investigated statistically. Main conclusions of the research are as follows: (1) drought at seasonal scale was more threatening than the other two time scales with a larger number of observed drought events and more notable variation; (2) results of the Mann–Kendall test revealed an upward trend of drought risk in April and September; (3) there were larger risks of extreme and severe drought in southern and northwestern parts of the basin while the northeastern areas tended to face larger risks of moderate drought. The case study in Huai River basin suggests that the proposed approach is a viable and flexible tool for monitoring meteorological drought at multiple scales with a more specific insight into drought characteristics at each severity level.     

Temporal and spatial rainfall analysis across a humid tropical catchment

Temporal and spatial rainfall patterns were analysed to describe the distribution of daily rainfall across a medium‐sized (379km²) tropical catchment. Investigations were carried out to assess whether a climatological variogram model was appropriate for mapping rainfall taking into consideration the changing rainfall characteristics through the wet season. Exploratory, frequency and moving average analyses of 30 years' daily precipitation data were used to describe the reliability and structure of the rainfall regime. Four phases in the wet season were distinguished, with the peak period (mid‐August to mid‐September) representing the wettest period. A low‐cost rain gauge network of 36 plastic gauges with overflow reservoirs was installed and monitored to obtain spatially distributed rainfall data. Geostatistical techniques were used to develop global and wet season phase climatological variograms. The unscaled climatological variograms were cross‐validated and compared using a range of rainfall events. Ordinary Kriging was used as the interpolation method. The global climatological variogram performed better, and was used to optimize the number and location of rain gauges in the network. The research showed that although distinct wet season phases could be established based on the temporal analysis of daily rainfall characteristics, the interpolation of daily rainfall across a medium‐sized catchment based on spatial analysis was better served by using the global rather than the wet season phase climatological variogram model. Copyright © 2001 John Wiley & Sons, Ltd.     

IV: EPSAT-NIGER study of rainfall over the Sahel at small time steps using a dense network of recording raingauges

The high density, static memory raingauge network of the EPSAT-NIGER experiment was designed with the aim of: (1) studying the rainfall spatial variability in the Sahel, as may be seen from ground networks of varying density, and (2) providing reference values for the calibration of a C band radar system. A first subset of 37 raingauges was installed in 1988 and the remaining 43 in 1989, thus providing a network of 80 stations, spread over a 100 × 100 km square area. The data analysis is based on the indentification of the structural function for each rainfall event. This permits classification of the events into three main categories with respect to their spatial organization. Furthermore the differences between the shower body and the trail are important and it is shown that the analysis of the spatial organization at the event scale may not be applicable to the calibration of high temporal resolution radar data. Estimation of the areal rainfall over two reference areas is also carried out.     

Rainfall network design using kriging and entropy

The spatial distribution of rainfall is related to meteorological and topographical factors. An understanding of the weather and topography is required to select the locations of the rain gauge stations in the catchment to obtain the optimum information. In theory, a well‐designed rainfall network can accurately represent and provide the needed information of rainfall in the catchment. However, the available rainfall data are rarely adequate in the mountainous area of Taiwan. In order to provide enough rainfall data to assure the success of water projects, the rainfall network based on the existing rain gauge stations has to be redesigned. A method composed of kriging and entropy that can determine the optimum number and spatial distribution of rain gauge stations in catchments is proposed. Kriging as an interpolator, which performs linear averaging to reconstruct the rainfall over the catchment on the basis of the observed rainfall, is used to compute the spatial variations of rainfall. Thus, the rainfall data at the locations of the candidate rain gauge stations can be reconstructed. The information entropy reveals the rainfall information of the each rain gauge station in the catchment. By calculating the joint entropy and the transmitted information, the candidate rain gauge stations are prioritized. In addition, the saturation of rainfall information can be used to add or remove the rain gauge stations. Thus, the optimum spatial distribution and the minimum number of rain gauge stations in the network can be determined. The catchment of the Shimen Reservoir in Taiwan is used to illustrate the method. The result shows that only seven rain gauge stations are needed to provide the necessary information. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal design of rain gauge network in the Middle Yarra River catchment,Australia

Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A well‐designed rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging‐based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.     

Evaluating the spatiotemporal variations of water budget across China over 1951–2006 using IBIS model

The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run‐off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run‐off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run‐off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run‐off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run‐off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run‐off, and the Zhemin hydrological region showed a significant increasing trend. Copyright © 2009 John Wiley & Sons, Ltd.     

A simple method to combine snow height and meteorological observations to estimate winter precipitation at sub-daily resolution

ABSTRACT

Measuring winter solid and liquid precipitation with high temporal resolution in remote or higher elevation regions is a challenging task because of undercatch and power supply issues. However, the number of micro-meteorological stations and ultrasonic height sensors in mountain regions is steadily increasing. To gain more benefit from such stations, a new simple approach for EStimating SOlid and LIquid Precipitation (ESOLIP) is presented. The method consists of three main steps: (1) definition of precipitation events using micro-meteorological data, (2) quantification of solid and liquid precipitation using wet-bulb temperature and filtered snow height and (3) calculation of fresh snow density. ESOLIP performance was validated using data from a heated rain gauge, snow pillow and daily manual observations both for single precipitation events and over three winter seasons. Results proved ESOLIP as an effective approach for precipitation quantification, where snow height observations and basic meteorological measurements (air temperature, solar radiation, wind speed, relative humidity), but no reliable rain gauges are available.     

Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea

In this paper, cluster analysis (CA), principal component analysis (PCA) and the fuzzy logic approach were employed to evaluate the trophic status of water quality for 12 monitoring stations in Daya Bay in 2003. CA grouped the four seasons into four groups (winter, spring, summer and autumn) and the sampling sites into two groups (cluster DA: S1, S2, S4-S7, S9 and S12 and cluster DB: S3, S8, S10 and S11). PCA identified the temporal and spatial characteristics of trophic status in Daya Bay. Cluster DB, with higher concentrations of TP and DIN, is located in the western and northern parts of Daya Bay. Cluster DA, with the low Secchi, is located in the southern and eastern parts of Daya Bay. The fuzzy logic approach revealed more information about the temporal and spatial patterns of the trophic status of water quality. Chlorophyll a, TP and Secchi may be major factors for deteriorating water quality.     

Flood simulation using the gauge‐adjusted radar rainfall and physics‐based distributed hydrologic model

This paper reports the results of an investigation into flood simulation by areal rainfall estimated from the combination of gauged and radar rainfalls and a rainfall–runoff model on the Anseong‐cheon basin in the southern part of Korea. The spatial and temporal characteristics and behaviour of rainfall are analysed using various approaches combining radar and rain gauges: (1) using kriging of the rain gauge alone; (2) using radar data alone; (3) using mean field bias (MFB) of both radar and rain gauges; and (4) using conditional merging technique (CM) of both radar and rain gauges. To evaluate these methods, statistics and hyetograph for rain gauges and radar rainfalls were compared using hourly radar rainfall data from the Imjin‐river, Gangwha, rainfall radar site, Korea. Then, in order to evaluate the performance of flood estimates using different rainfall estimation methods, rainfall–runoff simulation was conducted using the physics‐based distributed hydrologic model, Vflo^?. The flood runoff hydrograph was used to compare the calculated hydrographs with the observed one. Results show that the rainfall field estimated by CM methods improved flood estimates, because it optimally combines rainfall fields representing actual spatial and temporal characteristics of rainfall. Copyright © 2008 John Wiley & Sons, Ltd.     

Short‐term spatial and temporal patterns of suspended sediment transfer in proglacial channels,small River Glacier,Canada

Alpine glacial basins are a significant source and storage area for sediment exposed by glacial retreat. Recent research has indicated that short‐term storage and release of sediment in proglacial channels may control the pattern of suspended sediment transfer from these basins. Custom‐built continuously recording turbidimeters installed on a network of nine gauging sites were used to characterize spatial and temporal variability in suspended sediment transfer patterns for the entire proglacial area at Small River Glacier, British Columbia, Canada. Discharge and suspended sediment concentration were measured at 5 min intervals over the ablation season of 2000. Differences in suspended sediment transfer patterns were then extracted using multivariate statistics (principal component and cluster analysis). Results showed that each gauging station was dominated c. 80% of days by diurnal sediment transfer patterns and ‘low’ suspended sediment concentrations. ‘Irregular’ transfer patterns were generally associated with ‘high’ sediment concentrations during snowmelt and rainfall events, resulting in the transfer of up to 70% of the total seasonal suspended sediment load at some gauging stations. Suspended sediment enrichment of up to 600% from channel storage release and extrachannel inputs occurred between the glacial front and distal proglacial boundary. However, these patterns differed significantly between gauging stations as determined by the location of the gauging station within the catchment and meteorological conditions. Overall, the proglacial area was the source for up to 80% of the total suspended sediment yield transferred from the Small River Glacier basin. These results confirmed that sediment stored and released in the proglacial area, in particular from proglacial channels, was controlling suspended sediment transfer patterns. To characterize this control accurately requires multiple gauging stations with high frequency monitoring of suspended sediment concentration. Accurate characterization of this proglacial control on suspended sediment transfer may therefore aid interpretation of suspended sediment yield patterns from glacierized basins. Copyright © 2004 John Wiley & Sons, Ltd.     

Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network

A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial–temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of clusters based on the Haar wavelet, which captures the occurrence of extreme hydrological events, appear in blocks while those classified by the Morlet wavelet, which indicates periodicity changes and describes fine structures, appear in strips that cross the island of Taiwan. Principal component analysis is applied to the precipitation data of each cluster. The first principal components explain 62–90% of the total variation of data. Characteristics of precipitation data for each cluster are explored using scalogram analysis. The results show that both extreme hydrological events and periodicity changes appear in the spatial and temporal precipitation data but with different characteristics for each cluster. Recognizing homogeneous hydrologic regions and identifying the associated precipitation characteristics improves the efficiency of water resources management in adapting to climate change, preventing the degradation of the water environment, and reducing the impact of climate-induced disasters. Measures for countering the stress of precipitation variation for water resources management are provided.     

Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges

In the quantitative evaluation of radar-rainfall products (maps), rain gauge data are generally used as a good approximation of the true ground rainfall. However, rain gauges provide accurate measurements for a specific location, while radar estimates represent areal averages. Because these sampling discrepancies could introduce noise into the comparisons between these two sensors, they need to be accounted for. In this study, the spatial sampling error is defined as the ratio between the measurements by a single rain gauge and the true areal rainfall, defined as the value obtained by averaging the measurements by an adequate number of gauges within a pixel. Using a non-parametric scheme, the authors characterize its full statistical distribution for several spatial (4, 16 and 36 km²) and temporal (15 min and hourly) scales.     

Spatial reconstruction of rainfall fields from rain gauge and radar data

Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.     

Using entropy theory to improve the definition of homogeneous regions in the semi-arid region of Brazil

ABSTRACT

Although the semi-arid region of Brazil appears to be homogeneous due to drought conditions, there is a great deal of variability in climatic elements in the region, so that the definition of homogeneous regions will provide the deployment of measures appropriate for each locality. However, the limited information on climatic parameters in the region makes it difficult to define these regions. This problem can, however, be alleviated by the use of entropy theory. Therefore, this study aimed to investigate the potential of the theory to identify hydrologically homogeneous regions for conditions of the semi-arid region of Brazil. Entropy-based Disorder Index (DI) data were computed, based on monthly precipitation and monthly water balance (precipitation – reference evapotranspiration) for 290 gauge stations. For defining homogeneous regions, cluster analysis was utilized, using the data on geographical information about rain gauges (latitude and longitude), annual precipitation, annual water balance, coefficient of skewness, coefficient of kurtosis and DI. The identification of homogeneous regions in the Brazilian semi-arid region was only possible when the grouping of stations was performed, based on DI for precipitation and latitude. Results showed the definition of seven homogeneous regions in the semi-arid region of Brazil.     

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.     

广东省干旱灾害空间分布特征

利用1956-2005年126个雨量站逐月降水资料,采用标准化降雨指数和经验正交函数分解法,探讨了广东省干旱灾害空间分布规律.结果表明:广东省虽然总体比较湿润,但局部干旱时有发生,且在空间上存在东西差异、南北差异、中部差异的特点;春旱大致呈自西向东、自北向南逐渐加重的趋势;秋旱空间分布特点与春旱相反,由东向西、由南向北逐渐加重;春旱、秋旱在中部地区也有微弱的差异.     

Geostatistical interpolation of space–time rainfall on Tamshui River basin,Taiwan

Taiwan suffers from heavy storm rainfall during the typhoon season. This usually causes large river runoff, overland flow, erosion, landslides, debris flows, loss of power, etc. In order to evaluate storm impacts on the downstream basin, a real‐time hydrological modelling is used to estimate potential hazard areas. This can be used as a decision‐support system for the Emergency Response Center, National Fire Agency Ministry, to make ‘real‐time’ responses and minimize possible damage to human life and property. This study used 34 observed events from 14 telemetered rain‐gauges in the Tamshui River basin, Taiwan, to study the spatial–temporal characteristics of typhoon rainfall. In the study, regionalized theory and cross‐semi‐variograms were used to identify the spatial‐temporal structure of typhoon rainfall. The power form and parameters of the cross‐semi‐variogram were derived through analysis of the observed data. In the end, cross‐validation was used to evaluate the performance of the interpolated rainfall on the river basin. The results show the derived rainfall interpolator represents the observed events well, which indicates the rainfall interpolator can be used as a spatial‐temporal rainfall input for real‐time hydrological modelling. Copyright © 2007 John Wiley & Sons, Ltd.     

Estimating the role of a ditch network in groundwater recharge in a Mediterranean catchment using a water balance approach

Water balance variables were monitored in a farmed Mediterranean catchment characterized by a dense ditch network to allow for the separate estimation of the diffuse and concentrated recharge terms during flood events. The 27 ha central part of the catchment was equipped with (i) rain gauges, (ii) ditch gauge stations, (iii) piezometers, (iv) neutron probes, and (v) an eddy covariance mast including a 3D sonic anemometer and a fast hygrometer. The water balance was calculated for two autumnal rain and flood events. We also estimated the uncertainty of this approach with Monte Carlo simulations. Results show, that although ditch area represents only 6% of the total study area, concentrated recharge appeared to be the main source of groundwater recharge. Indeed, it was 40–50% of the total groundwater recharge for autumnal events, which are the major annual recharge events. This indicate that both, concentrated and diffuse recharge should be taken into account in any hydrological modeling approach for Mediterranean catchments. This also means that, since they collect overland flow that is often largely contaminated by chemicals, ditches may be a place where groundwater contamination is likely to occur. The uncertainty analysis indicates that recharge estimates based on water balance exhibit large uncertainty ranges. Nevertheless, Monte Carlo simulations showed that concentrated recharge was higher than expected based on their area.