Integrating Ground-based Observations and Radar Data Into Gridding Sub-daily Precipitation

A new and general approach is proposed for interpolating 6-h precipitation series over large spatial areas. The outputs are useful for distributed hydrological modelling and studies of flooding. We apply our approach to large-scale data, measured between 2014 and 2016 at 159 weather stations network of Meteo Romania, using weather radar information and local topography as ancillary data. Novelty of our approach is in systematic development of a statistical model underlying the interpolation. Seven methods have been tested for the interpolation of the 6-h precipitation measurements: four regression methods (linear regression via ordinary least squares (OLS), with and without logarithmic transformation, and two models of generalized additive model (GAM) class, with logarithmic and identity links), and three regression-kriging models (one uses semivariogram fitted separately every 6-h, based on the residuals of the GAM with identity links models, and other two with pooled semivariograms, based on the OLS and GAM with identity links models). The prediction accuracy of the spatial interpolation methods was evaluated on a part of the dataset not used in the model-fitting stage. Due to the good results in interpolating sub-daily precipitation, normal general additive model with identity link followed with kriging of residuals with kriging parameters estimated from pooled semivariograms was applied to construct the final 6-h precipitation maps (PRK-NGAM). The final results of this work are the 6-h precipitation gridded datasets available in high spatial resolution (1000 m?×?1000 m), together with their estimated accuracy.

     

Spatial Interpolation of Annual Precipitation in South Africa-Comparison and Evaluation of Methods

Abstract

Data from 545 rainfall gauges were used to interpolate the spatial distribution of annual rainfall in South Africa. Several spatial interpolation methods (inverse distance weighting (IWD), ordinary kriging, universal kriging, cokriging) were tested by variation analyses and cross-validation to determine the most suitable one. The best results were achieved by ordinary kriging, whereby the setting of the parameters was determined through sensitivity analyses. The median of the errors turned out to be 61 mm (11%). The interpolation errors were generally small for the interior of the country and high for coastal and mountainous regions.     

Remote Sensing and GIS Innovation with Hydrologic Modelling for Hydroelectric Power Plant (HPP) in Poorly Gauged Basins

Within the last two decades, modelling of rainfall–runoff has become an important topic in water resources assessment due to increasing water demand and energy, particularly in the determination of hydropower potential. In addition to remote sensing (RS) and geographical information systems (GIS), with the development on satellite technologies, it becomes possible to asses rapid and economic solutions to determine a practical rainfall–runoff relation, particularly poorly gauged or ungauged basins. In this paper, Solakli Watershed which is located in Eastern Black Sea Region of Turkey is selected as the study area. To determine the hydroelectric water potential in a poorly gauged basin, basin boundary and area, minimum maximum and mean elevation, slope information of the basin have been derived from the digital elevation model (DEM) using remote sensing (RS) and geographical information systems (GIS) techniques. IRS P5 stereo satellite data with 2.5-m spatial resolution has been used for deriving the DEM. This DEM is used to produce the flow direction and flow accumulation maps of the basin. Afterward, synthetic drainage network is obtained with the analysis of these maps. Using topographical data such as area, mean basin elevation and limited point observations of rainfall data; a regression model was derived for the whole watershed. This regression model was validated on a sub-basin with satisfactory results using mean areal rainfall which was calculated isohyetal map produced by kriging method. Suggested hydropower station points are also determined.     

Geostatistical analysis of spatial variability of rainfall and optimal design of a rain gauge network

Kriging is a geostatistical estimation technique for regionalized variables that exhibit an autocorrelation structure. Such a structure can be described by a semivariogram of the observed data. The punctual-kriging estimate at any point is a weighted average of the data, where the weights are determined by using the semivariogram and an assumed drift, or lack of drift, in the data. The kriging algorithm, based on unbiased and minimum-variance estimates, involves a linear system of equations to calculate the weights. Kriging is applied in an attempt to describe the spatial variability of rainfall data over a geographical region in northern Greece. Monthly rainfall data of January and June 1987 have been taken from 20 measurement stations throughout the above area. The rainfall data are used to compute semivariograms for each month. The resulting semivariograms are anisotropic and fitted by linear and spherical models. Kriging estimates of rainfall and standard deviation were made at 90 locations covering the study area in a rectangular grid and the results used to plot contour maps of rainfall and contour maps of kriging standard deviation. Verification of the kriging estimates of rainfall are made by removing known data points and kriging an estimate at the same location. This verification is known as the jacknifing technique. Kriging errors, a by-product of the calculations, can then be used to give confidence intervals of the resulting estimates. The acceptable results of the verification procedure demonstrated that geostatistics can be used to describe the spatial variability of rainfall. Finally, it is shown how the property of kriging variance depends on the structure and the geometric configuration of the data points and the point to be estimated can also be used for the optimal design of the rain gauge network in an area.     

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.     

Error Correction Modelling of Wind Speed Through Hydro-Meteorological Parameters and Mesoscale Model: A Hybrid Approach

Accurate estimation of wind speed is essential for many hydrological applications. One way to generate wind velocity is from the fifth generation PENN/NCAR MM5 mesoscale model. However, there is a problem in using wind speed data in hydrological processes due to large errors obtained from the mesoscale model MM5. The theme of this article has been focused on hybridization of MM5 with four mathematical models (two regression models- the multiple linear regression (MLR) and the nonlinear regression (NLR), and two artificial intelligence models – the artificial neural network (ANN) and the support vector machines (SVMs)) in such a way so that the properly modelled schemes reduce the wind speed errors with the information from other MM5 derived hydro-meteorological parameters. The forward selection method was employed as an input variable selection procedure to examine the model generalization errors. The input variables of this statistical analysis include wind speed, temperature, relative humidity, pressure, solar radiation and rainfall from the MM5. The proposed conjunction structure was calibrated and validated at the Brue catchment, Southwest of England. The study results show that relatively simple models like MLR are useful tools for positively altering the wind speed time series obtaining from the MM5 model. The SVM based hybrid scheme could make a better robust modelling framework capable of capturing the non-linear nature than that of the ANN based scheme. Although the proposed hybrid schemes are applied on error correction modelling in this study, there are further scopes for application in a wide range of areas in conjunction with any higher end models.     

淮河方邱湖大堤地质勘察与空间结构分析

利用RI型静力触探设备,对淮河方邱湖段堤防进行了地质勘察工作,收集了堤防各土层大量物理与力学指标,并通过钻孔取土室内试验与常规静力触探对测试数据进行了对比验证。在地质勘察分层的基础上,利用地质统计学理论,对锥尖阻力、摩阻比、干密度、孔隙比四参数进行了空间结构分析,发现球形模型可较好模拟堤身、堤基土层参数空间变异性,土层顺堤轴线方向变程在30～50m之间,深度方向变程在0.2～0.8m之间。物理指标变程稍小于强度指标,与多数文献提供的变程范围一致,该分析结果为进一步的土工参数克立格估值与条件模拟提供了基础数据。     

Hydrologic scaling for hydrogeomorphic floodplain mapping: Insights into human‐induced floodplain disconnectivity

Hydrogeomorphic approaches for floodplain modelling are valuable tools for water resource and flood hazard management and mapping, especially as the global availability and accuracy of terrain data increases. Digital terrain models implicitly contain information about floodplain landscape morphology that was produced by hydrologic processes over long time periods, as well as recent anthropogenic modifications to floodplain features and processes. The increased availability of terrain data and distributed hydrologic datasets provide an opportunity to develop hydrogeomorphic floodplain delineation models that can quickly be applied at large spatial scales. This research investigates the performance of a hydrogeomorphic floodplain model in two large urbanized and gauged river basins in the United States, the Susquehanna and the Wabash basins. The models were calibrated by a hydrologic data scaling technique, implemented through regression analyses of USGS peak flow data to estimate floodplain flow levels across multiple spatial scales. Floodplain model performance was assessed through comparison with 100‐year Federal Emergency Management Agency flood hazard maps. Results show that the hydrogeomorphic floodplain maps are generally consistent with standard flood maps, even when significantly and systematically varying scaling parameters within physically feasible ranges, with major differences that are likely due to infrastructure (levees, bridges, etc.) in highly urbanized areas and other locations where the geomorphic signature of fluvial processes has been altered. This study demonstrates the value of geomorphic information for large‐scale floodplain mapping and the potential use of hydrogeomorphic models for evaluating human‐made impacts to floodplain ecosystems and patterns of disconnectivity in urbanized catchments.     

负荷历时曲线在流域水质特征分析中的应用

介绍了负荷历时曲线(LDC)的概念和建立方法,以及针对不同水质标准时,LDC应用于水质评价、关键水质条件和主要污染机制判断、现状及允许负荷通量估算等问题的分析。结合国外已有案例,并以洱海弥苴河流域作为典型研究区域,分别探讨了不同水质限制要求下,运用LDC具体分析和解决各类水质问题的方法。结果表明,LDC包含众多水质特征信息,可以在较少数据信息条件下,合理分析各类水环境问题,从而为流域水环境管理提供可靠决策依据。     

Hydroacoustic Estimation of Zooplankton Biomass at Two Shoal Complexes in the Apostle Islands Region of Lake Superior

Hydroacoustics can be used to assess zooplankton populations, however, backscatter must be scaled to be biologically meaningful. In this study, we used a general model to correlate site-specific hydroacoustic backscatter with zooplankton dry weight biomass estimated from net tows. The relationship between zooplankton dry weight and backscatter was significant (p < 0.001) and explained 76% of the variability in the dry weight data. We applied this regression to hydroacoustic data collected monthly in 2003 and 2004 at two shoals in the Apostle Island Region of Lake Superior. After applying the regression model to convert hydroacoustic backscatter to zooplankton dry weight biomass, we used geostatistics to analyze the mean and variance, and ordinary kriging to create spatial zooplankton distribution maps. The mean zooplankton dry weight biomass estimates from plankton net tows and hydroacoustics were not significantly different (p = 0.19) but the hydroacoustic data had a significantly lower coefficient of variation (p < 0.001). The maps of zooplankton distribution illustrated spatial trends in zooplankton dry weight biomass that were not discernable from the overall means.     

A Comprehensive Approach for Spatial and Temporal Water Demand Profiling to Improve Management in Network Areas

The aim of this paper is to present a comprehensive approach for spatial and temporal demand profiling in water distribution systems. Multiple linear regression models for estimating network design parameters and decision trees for predicting daily demand patterns are presented. Proposed approach is a four-step procedure: data collection, data processing, data characterization, and spatial and temporal demand profiling. Continuous flow measurements and infrastructure and billing data were collected from a large set of water network areas and combined with census data. Main results indicate that family structures (i.e., families with elderly or adolescents), individuals’ mobility (i.e., people employed in the tertiary sector and university graduates) and public consumption (i.e., public spaces’ irrigation) are key-variables to profile water demand. Profiling models are of the utmost importance to describe water demand in areas with no monitoring but with similar socio-demographic characteristics to the ones analyzed, to improve network operation and to support network planning and design in new areas. Obtained models have been tested for new areas, showing good prediction performances.     

Nested Augmentation of Rainfall Monitoring Network: Proposing a Hybrid Implementation of Block Kriging and Entropy Theory

One of the most influential environmental variables is rainfall which has significant effect on water resources management, agricultural development, hydrology, and climate change studies. Due to high spatiotemporal variability of rainfall, its monitoring network design can be considered as a useful tool to improve the efficiency of recorded rain gauge stations within the study area. In this study, a new methodology of augmentation of rain gauge network is developed using coupled Block Kriging (BK) and entropy theory methods. In the proposed method, a nested approach of a two-stage positioning of rain gauge stations has been demonstrated. In the first stage, large-scale or fast positioning was done in which the optimal number of candidate blocks was identified. Then, local scale or fine-tuned positioning was done in the second stage. In this stage, to develop the network, accurate locations of rain gauge stations in each block are determined. Besides the main point of this paper, the effect of two kriging estimators, BK and Ordinary Kriging (OK), on the developed network has been investigated and compared. The study area is the Namak Lake watershed with various climates and altitudes. To assess the performance of the optimal rainfall network, three diagnostics were utilized; spatial distribution of annual precipitation, Estimation Error Variance (EEV) maps and histograms. Based on the results, 30 (more than 30% percent of the current stations) rain gauge stations have been proposed scattered over the watershed. Evaluation of the results has shown that the augmented rain gauge network proposed by the BK method outperformed dramatically that of the OK method. EEV maps and also statistical analysis of EEV values confirms the EEV value reduction of almost 25% in augmented network, as well.

     

Comparison of Interpolation Methods for the Prediction of Reference Evapotranspiration—An Application in Greece

The objective of this paper is to evaluate four interpolation methods, concerning their suitability for spatial prediction of long-term mean daily reference evapotranspiration (calculated by Penman–Monteith equation) for each month in Greece. The methods studied were ordinary kriging (OK) and inverse distance squared (IDS) and their modifications in which elevation data was incorporated into the interpolation process. The modified methods were named residual kriging (RK), and gradient-plus-inverse distance squared (GIDS). Apart from interpolation methods, two different approaches were studied in order to define what the proper sequence of steps is in the case of the interpolation of reference evapotranspiration. More particularly the ‘calculate first, interpolate later’ (CI) procedure was compared to the reverse procedure, namely ‘interpolate first, calculate later’ (IC). The assessment criteria of the methods accuracy were: mean error (ME), mean absolute error (MAE) and root mean squared error (RMSE). The results revealed that the incorporation of elevation significantly improved the performance of interpolation methods. On the contrary, procedures CI and IC were very similar since they had no effect on the performance of the four interpolation methods studied.     

An improved parameter estimation procedure in lake modelling

This paper proposes an alternative parameter estimation procedure. Usually the parameters that yield the highest sensitivity are calibrated by trial and error or by an automatic calibration procedure. If the number of parameters to be calibrated is more than 3-5, this procedure is very time consuming. The presented procedure is based on the assumption that the biological components in an ecosystem model attempt to develop such properties that they become best possible survivors, i.e. develop as much biomass as possible. It has previously been proposed that the survival of the entire ecosystem can be measured by use of the thermodynamic function exergy, which measures the distance from thermodynamic equilibrium and accounts for the biomass and its information content. If these assumptions are correct, it should be possible to determine the combination of parameters which gives the highest exergy. The paper presents the use of this idea in combination with a normal calibration. The parameters, which are less known from the literature and still yield a relatively high sensitivity, are determined by use of the above-mentioned exergy principle. The parameters that are known within relatively narrow ranges from the literature are calibrated by the normal procedure. The method has been used on a concrete lake modelling study and given good results. This combination method seems therefore to offer clear advantages, particularly for models with a relatively high number of sensitive parameters (> 5), which otherwise would require a very cumbersome calibration.     

Comparison of approaches for modelling submerged aquatic vegetation in the Toronto and Region Area of Concern

In freshwater aquatic ecosystems, submerged aquatic vegetation (SAV) is critical habitat for may fish species and provides a variety of ecosystem services including nutrient filtration and substrate stabilization. Characterizing habitats and assessing their suitability for fish and other aquatic and terrestrial organisms is an important component of delisting efforts in the Toronto and Region Area of Concern (AOC). The primary objective of this study was to develop a spatial model for SAV within the AOC. A variety of modelling options were explored with a two stage random forest model identified as the most accurate approach; a two stage boosted regression tree model yielded comparable accuracy but was more complicated and processing intensive to implement. The final models for presence (modelled first) and SAV percent cover (applied only where the presence model predicted SAV to occur) incorporated directionally weighted wind fetch, water depth, and clarity (Secchi depth) with relatively high predictive accuracy (87.1% for presence). When applied across the AOC, SAV was primarily found to occur within the Central Waterfront, particularly adjacent to and among the Toronto Islands. Outside of this area, SAV was generally sparse and confined to areas that were protected from wind and wave action from Lake Ontario. Future habitat creation and remediation efforts should therefore focus on creating habitat conducive to SAV establishment.     

Transboundary Water Resources Allocation under Various Parametric Conditions: The Case of the Euphrates & Tigris River Basin

The literature on transboundary water resources allocation modelling is still short on encompassing and analyzing complex geographic multiparty nature of basins. This study elaborates the Inter Temporal Euphrates and Tigris River Basin Model (ITETRBM), which is a linear programming based transboundary water resources allocation model maximizing net economic benefit from allocation of scarce water resources to energy generation, urban, and agricultural uses. The elaborations can be categorized in two directions: First, agricultural and urban demand nodes are spatially identified with their relative elevations and distances to water resources supplies (dams, reservoirs, and lakes). Digital elevation model (DEM) database are intensely processed in geographic information system (GIS) environment. Second, the agricultural irrigable lands are restructured into a pixel based decision making units (DMUs) in order to be able to see the spatial extent of optimally irrigated land, and then optimization program is converted from linear programming (LP) to a mixed integer programming (MIP). The model applications are designed to cover a series of sensitivity analyses encompassing the various transboundary management, energy and agricultural use value, and transportation cost scenarios over the optimal uses of the Euphrates and Tigris Basin (ETRB) resources. The model results are visually presented via GIS in order to show the transboundary upstream and downstream spatial impacts of these selected parameters. The findings are i) system parameters significantly alter the spatial extent of water resources allocation in the ETRB, and ii) the magnitudes of the parameters also explains the tradeoffs between agriculture and energy sectors as much as upstream and downstream water uses of countries.     

Prediction of longitudinal dispersion coefficient in natural streams by prediction map

Longitudinal dispersion coefficient can be determined by experimental procedures in natural streams. Many theoretical and empirical equations that are based on hydraulic and geometric characteristics have been developed from the field experiments of longitudinal dispersion coefficient. Regression analysis, which carries some restrictive assumptions such as linearity, normality and homoscedasticity, was used to derive some of these equations. Generally speaking, results obtained from regression analyses are not that accurate as these assumptions are often not satisfied completely. In this study, a method called Prediction Map (PM) is developed based on geostatistics to predict longitudinal dispersion coefficient from measured discharge values, shear velocities, and other conventional parameters of the hydraulic variables and normalized velocity with the objective of overcoming the drawbacks indicated above. As part of this method, a new procedure called Iterative Error Training Procedure (IETP) was developed to minimize prediction error. The prediction error level was reduced after implementing the IETP. PM was compared with various regression models by taking analyzed errors (average relative error percentage and root mean square error), coefficient of efficiency, coefficient of determination and Scatter Index as performance evaluation criteria. The results of the study indicate that the PM approach can perform very well in predicting longitudinal dispersion coefficient by applying IETP. The presented approach yielded lower average relative error percentage, root mean square error and Scatter Indices, and higher coefficient of efficiency and coefficient of determination values compared to the regression models. One of the important advantages of the PM method is that valuable interpretations and a prediction map can be extracted from the resulting contour maps, and as a result, more accurate predictions can be obtained compared to regression analysis.     

TIMING OF REDD CONSTRUCTION BY FALL CHINOOK SALMON IN THE HANFORD REACH OF THE COLUMBIA RIVER

Spawning habits of fall Chinook salmon in the Hanford Reach of the Columbia River have been documented with annual aerial surveys since 1948. We developed a series of models analysing these data, exploring the influence of environmental factors on the timing of redd construction. These models included a logistic regression and a dynamic modelling approach, with combinations of day of year (as a surrogate for environmental cues such as day length), water temperature and discharge as potential explanatory factors. Results of these analyses indicate that day of year was the strongest predictor of the timing of redd construction, but with significant modifying effects of water temperature and discharge. The dynamic modelling approach provides substantial advantages over a traditional logistic regression, including (1) the ability to treat data collected at non‐synchronous time intervals in a consistent fashion and (2) the ability to easily implement complex functions (e.g., threshold responses) relating behaviour to environmental cues. Evaluation of the series as a whole indicates that the median date of redd construction has increased over time, from approximately day 299 in 1950 to day 307 in 2010, as has the temperature on Oct 1 (16.3 °C–18.1 °C). The degree to which these changes are caused by climate change or dam operations is uncertain, however. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparison of IDW and Physically Based IDEW Method in Hydrological Modelling for a Large Mountainous Watershed,Northwest China

Topography and spatial patterns of landscape significantly affect spatial distribution of precipitation and, in turn, hydrological modelling, especially in high elevation, mountainous watersheds of arid regions. This study incorporates a physically based inverse distance and elevation weighted (PBIDEW) method into a distributed conceptual hydrological model, distributed large basin runoff model, and compared with an inverse distance weighted (IDW) method to assess the performances of both methods in precipitation estimation for hydrological modelling at watershed scale. The PBIDEW method considers the impacts of topography using month‐dependent parameters in its interpolation of meteorological variables while the IDW method does not. Both the IDW and the PBIDEW methods are evaluated and compared in hydrological modelling at different spatial resolutions in the upper reach of the Heihe River Watershed, Northwest China. Results show that the IDW method underestimated the areal precipitation, and the PBIDEW method produced more realistic precipitation estimations in the study area. Both methods have some limitations, the performance of the IDW method was mainly influenced by the availability of observation data, while that of the PBIDEW method was mainly influenced by the representation of topographical information. Considering more detailed information for precipitation estimates, the PBIDEW method performed better at finer spatial resolution. Overall, the PBIDEW method, using month‐dependent physical interpolation parameters, seems more suitable for precipitation estimation in hydrological simulations in data‐scarce, high elevation and topographically complex mountainous watersheds in arid area. Copyright © 2017 John Wiley & Sons, Ltd.     

随机模拟田面微地形空间分布状况的方法研究

微地形是影响畦灌性能的重要要素，而实测地形数据的有限性必将影响数值模拟手段在灌溉设计和管理中的灵活运用。本文首先基于田间实测的116个典型试验田块的田面相对高程数据，采用地质统计学方法分析其空间变异特性，结果表明田面相对高程的空间变异结构函数可采用球状半方差函数进行描述，空间变异特征参数可根据田块参数进行估算；其次在同时考虑田面相对高程既具有随机性又具有空间结构相关性的基础上，将Monte-Carlo方法与Kriging插值方法相结合，构建田面微地形随机模拟方法。借助数理统计学方法解决了随机模拟方法在实际应用中所遇到的最小样本容量的确定问题。根据田间实测数据验证了基于随机生成的田面相对高程最小样本容量，采用田面微地形随机模拟方法获得任意给定田块的田面微地形数据对其灌溉性能进行评价的可行性。