Challenges of hydrological analysis for water resource development in semi-arid mountainous regions: case study in Iran

Abstract

This study modified the BTOPMC (Block-wise TOPMODEL with the Muskingum-Cunge routing method) distributed hydrological model to make it applicable to semi-arid regions by introducing an adjustment coefficient for infiltration capacity of the soil surface, and then applied it to two catchments above the dams in the Karun River basin, located in semi-arid mountain ranges in Iran. The application results indicated that the introduced modification improved the model performance for simulating flood peaks generated by infiltration excess overland runoff at a daily time scale. The modified BTOPMC was found to fulfil the need to reproduce important signatures of basin hydrology for water resource development, such as annual runoff, seasonal runoff, low flows and flood flows. However, it was also very clear that effective model use was significantly constrained by the scarcity of ground-gauged precipitation data. Considerable efforts to improve the precipitation data acquisition should precede water resource development planning.

Editor D. Koutsoyiannis     

中国区域地理、地形因子对降水分布影响的估算和分析

不同与以往基于最小二乘的多元线性回归方法,本文首次尝试将新型的第二代回归分析方法——偏最小二乘回归分析方法应用到中国区域的降水建模中．利用区域内394个气象观测站建站到2000年45年(及以上)的降水资料,建立了一个简单的年、季降水量和地理、地形因子(包括纬度、经度、地形高程、坡度、坡向和遮蔽度)的关系模型,估算了区域降水量中地理、地形的影响部分,并分析了这种影响的特征．结果表明,用此方法建立的模型能够解释70%以上的因变量的变异,相关系数基本都在0.84以上,经交叉有效性检验,模型的回归效果较显著．分析表明,在多元线性回归不适用的情况下,本文基于偏最小二乘法的简单模型能够比较准确地定性、定量地再现实际降水分布．     

A bivariate gamma distribution for use in multivariate flood frequency analysis

A gamma distribution is one of the most frequently selected distribution types for hydrological frequency analysis. The bivariate gamma distribution with gamma marginals may be useful for analysing multivariate hydrological events. This study investigates the applicability of a bivariate gamma model with five parameters for describing the joint probability behavior of multivariate flood events. The parameters are proposed to be estimated from the marginal distributions by the method of moments. The joint distribution, the conditional distribution, and the associated return periods are derived from marginals. The usefulness of the model is demonstrated by representing the joint probabilistic behaviour between correlated flood peak and flood volume and between correlated flood volume and flood duration in the Madawask River basin in the province of Quebec, Canada. Copyright © 2001 John Wiley & Sons, Ltd.     

Formation of homogeneous regions for regional frequency analysis of extreme precipitation events in the Czech Republic

Extreme high precipitation amounts are among environmental events with the most disastrous consequences for human society. This paper deals with the identification of ‘homogeneous regions’ according to statistical characteristics of precipitation extremes in the Czech Republic, i.e. the basic and most important step toward the regional frequency analysis. Precipitation totals measured at 78 stations over 1961–2000 are used as an input dataset. Preliminary candidate regions are formed by the cluster analysis of site characteristics, using the average-linkage clustering and Ward’s method. Several statistical tests for regional homogeneity are utilized, based on the 10-yr event and the variation of L-moment statistics. In compliance with results of the tests, the area of the Czech Republic has been divided into four homogeneous regions. The findings are supported by simulation experiments proposed to evaluate stability of the test results. Since the regions formed reflect also climatological differences in precipitation regimes and synoptic patterns causing high precipitation amounts, their future application may not be limited to the frequency analysis of extremes.     

A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain

Precipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for downscaling low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the downscaling algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by downscaling NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm downscaled daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the downscaling and the original NEXRAD precipitation maps. For three daily precipitation events, downscaled precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the downscaled map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on downscaling hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has potential to perform well at both daily and hourly precipitation fields, indicated from good regression performance.     

复杂山区高分辨率地震采集分析与应用—以四川盆地及周缘地区为例

震源药柱激发因素选择是影响地震资料品质的一个重要环节,在南方复杂山区高分辨率地震采集中合理选择激发岩性、井深、药量等激发因素尤为重要.本文根据复杂山区地震地质条件多样性、复杂性等特点,从理论上分析了地震激发机理、井深、药量、虚反射效应、岩性与药柱长度等对激发子波的影响,开展了基于适应近地表结构特征的动态井深设计、双微测井和综合表层结构调查（微测井和高密度电法联合）等,结合四川盆地及周缘等地区不同地震地质条件下进行了采集试验研究,获得了高品质单炮记录.

     

Estimation of potential evapotranspiration in the mountainous Panama Canal watershed

Spatially distributed hydrometeorological and plant information within the mountainous tropical Panama Canal watershed is used to estimate parameters of the Penman–Monteith evapotranspiration formulation. Hydrometeorological data from a few surface climate stations located at low elevations in the watershed are complemented by (a) typical wet‐ and dry‐season fields of temperature, wind, water vapour and pressure produced by a mesoscale atmospheric model with a 3 × 3 km² spatial and hourly temporal resolution, and (b) leaf area index fields estimated over the watershed during a few years using satellite data with two different spatial and temporal resolutions. The mesoscale model estimates of spatially distributed surface hydrometeorological variables provide the basis for the extrapolation of the surface climate station data to produce input for the Penman–Monteith equation. The satellite information and existing digital spatial databases of land use and land cover form the basis for the estimation of Penman–Monteith spatially distributed parameter values. Spatially distributed 3 × 3 km² potential evapotranspiration estimates are obtained for the 3300 km² Panama Canal watershed. Estimates for Gatun Lake within the watershed are found to reproduce well the monthly and annual lake evaporation obtained from submerged pans. Sensitivity analysis results of potential evapotranspiration estimates with respect to cloud cover, dew formation, leaf area index distribution and mesoscale model estimates of surface climate are presented and discussed. The main conclusion is that even the limited spatially distributed hydrometeorological and plant information used in this study contributes significantly toward explaining the substantial spatial variability of potential evapotranspiration in the watershed. These results also allow the determination of key locations within the watershed where additional surface stations may be profitably placed. Copyright © 2006 John Wiley & Sons, Ltd.     

Seismogeodynamics of lineament structures in the mountainous regions bordering the Scythian-Turan plate

The Scythian-Turan platform, together with the Alpine Iran-Caucasus-Anatolia and Hercynian Central Tien Shan orogenic structures adjacent to it, represents a coherent seismogeodynamic system responsible for regional seismicity features in the territory under consideration. Investigations of the spatiotemporal and energy evolution of seismogeodynamic processes along the main lineament structures of the orogen reveal characteristic features directly related to the prediction of seismic hazard in this region, as well as in southern European Russia. These characteristics primarily include kinematic features in the sequences of seismic events of various magnitudes and an ordered migration of seismic activation, enabling the more or less reliable determination of the occurrence time intervals (years) and areas of forthcoming large earthquakes (magnitudes of 7.0 ± 0.2, 7.5 ± 0.2, and 8.0 ± 0.2).     

Estimation of the effective precipitation recharge coefficient in an unconfined aquifer using stochastic analysis

Effectively managing groundwater relies heavily on estimating the amount of precipitation that may infiltrate the subsurface and supply groundwater. In this study, we present a novel estimation method based on a stochastic approach to evaluate the quantity of precipitation that may recharge groundwater. The precipitation recharge coefficient is also investigated based on an unconfined aquifer with an unbound, infinitely extended boundary condition. Moreover, a spectrum's relationship to the precipitation and groundwater level variation is also derived. The precipitation recharge coefficient can be obtained from the solution of the spectrum equation. Furthermore, sensitivity analysis is performed in order to determine the key variable on the precipitation recharge coefficient. Analysis results indicate that the location of an observation well affects the estimated precipitation recharge coefficient. If the precipitation recharge area is large enough, the precipitation recharge coefficient becomes insensitive to the location of the observation well. The spectrum's relationship between the precipitation recharge and groundwater level variation is also applied when estimating the precipitation recharge coefficient upstream of the Cho‐Shui River alluvial fan. According to those results, the precipitation recharge coefficient is 0·03 and the amount of groundwater recharge from precipitation is 35 million tons of water annually upstream of the Cho‐Shui River alluvial fan. Copyright © 2000 John Wiley & Sons, Ltd.     

Estimating recharge using relations between precipitation and yield in a mountainous area with large variability in precipitation

Estimates of recharge to bedrock aquifers from infiltration of precipitation can be difficult to obtain, especially in areas with large spatial and temporal variability in precipitation. In the Black Hills area of western South Dakota and eastern Wyoming, streamflow yield is highly influenced by annual precipitation, with yield efficiency (annual yield divided by annual precipitation) increasing with increasing annual precipitation. Spatial variability in annual yield characteristics for Black Hills streams is predictably influenced by precipitation patterns. Relations between precipitation and yield efficiency were used to estimate annual recharge from long-term records of annual precipitation. A series of geographic information system algorithms was used to derive annual estimates for 1000- by 1000-m grid cells. These algorithms were composited to derive estimates of annual recharge rates to the Madison and Minnelusa aquifers in the Black Hills area of western South Dakota and eastern Wyoming during water years 1931–1998 and an estimate of average recharge for water years 1950–1998. This approach provides a systematic method of obtaining consistent and reproducible estimates of recharge from infiltration of precipitation. Resulting estimates of average annual recharge (water years 1950–1998) ranged from 1 cm in the southern Black Hills to 22 cm in the northwestern Black Hills. Recharge rates to these aquifers from infiltration of precipitation on outcrops was estimated to range from 0.9 m³/s in 1936 to 18.8 m³/s in 1995.     

Delineation of precipitation regions in two Canadian study areas: the role of the temporal resolution of the precipitation data

The identification of homogeneous precipitation regions has value in many water resources engineering applications (infrastructure planning, design, operations; climate forecasting, modelling). The objective of this paper is to assess the sensitivity of precipitation regions to the temporal resolution (monthly, seasonal, annual and the annual maximum series) of the data. The presented method uses the fuzzy c-means clustering algorithm to partition climate sites into statistically homogeneous precipitation regions. The regions are validated using an approach based on L-moment statistics. The method is conducted in two climatically different study areas in western and eastern Canada. There does not appear to be a relationship between the spatial distributions of the regions formed using different temporal resolutions of the precipitation data. It is recommended to delineate precipitation regions that are specific to the task at hand, and to select a temporal resolution that is consistent with the final application of the regional precipitation dataset.

EDITOR A. Castellarin; ASSOCIATE EDITOR T. Kjeldsen     

Evaluation of nonlinear models for precipitation forecasting

Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.     

Delineation of precipitation regions using location and atmospheric variables in two Canadian climate regions: the role of attribute selection

The identification of homogeneous precipitation regions is essential in the planning, design and management of water resources systems. Regions are identified using a technique that partitions climate sites into groups based on the similarity of their attributes; the procedure is known as regionalization. In this paper the ability of four attribute sets to form large, coherent precipitation zones is assessed in terms of the regional homogeneity of precipitation statistics and computational efficiency. The outcomes provide guidance for effective attribute selection for future studies in Canada. The attributes under consideration include location parameters (latitude, longitude), distance to major water bodies, site elevation and atmospheric variables modelled at different pressure levels. The analysis is conducted in two diverse climate regions within Canada including the Prairie and the Great Lakes–St Lawrence lowlands regions. The method consists of four main steps: (i) formation of the attribute sets; (ii) determination of the preferred number of regions (selection of the c-value) into which the sites are partitioned; (iii) regionalization of climate sites using the fuzzy c-means clustering algorithm; and (iv) validation of regional homogeneity using L-moment statistics. The results of the attribute formation, c-value selection, regionalization and validation processes are presented and discussed in a comparative analysis. Based on the results it is recommended for both regions to use location parameters including latitude, longitude and distance to water bodies (in the Great Lakes region) to form precipitation regions and to consider atmospheric variables for future (climate change) applications of the regionalization procedure.     

Mathematical models for long-term prediction of mountainous river runoff: methods,information and results

Abstract

Unlike traditional statistical methods, a mathematical model is used to solve the problem of the long-term prediction of mountainous river runoff. The paper describes the structure of a basic model and ways of numerical presentation of macroscale rainfall fields in mountains as well as the computation of their parameters in conditions with complex relief and lack of information. The encouraging results and estimates of forecasts are discussed.     

Studying precipitation chemistry by multivariate analysis based on data of rural stations in Russian Barents Region

Multivariate analysis methods have been applied to studying variations in the concentrations of Ag, Al, As, B, Ba, Br, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Si, Sn, Sr, Th, Tl, U, V, Zn, Cl^?, NO ₃ ^? , SO ₄ ^2? as components of precipitation at 11 rural stations under project “Ecogeochemistry of Barents Region”. Hierarchic factor analysis revealed the structure and space-time distribution of seven first-order factors and two second-order factors. The combinations of ingredients that determine the composition of first-order factors characterize the sources of precipitation composition, which have been found to be specific and volatile products of fuel oil and coal combustion, marine and earth aerosols, and biogenic processes. Second-order factors showed two independent sets of components, which are typical of the chemistry of precipitation at the examined stations in winter and summer. Step-by-step discriminant and cluster analysis made it possible to classify the observational stations by precipitation chemistry and demonstrate the extent of difference between them.     

Evaluation of precipitation products over complex mountainous terrain: A water resources perspective

The availability of in situ measurements of precipitation in remote locations is limited. As a result, the use of satellite measurements of precipitation is attractive for water resources management. Combined precipitation products that rely partially or entirely on satellite measurements are becoming increasingly available. However, these products have several weaknesses, for example their failure to capture certain types of precipitation, limited accuracy and limited spatial and temporal resolution. This paper evaluates the usefulness of several commonly used precipitation products over data scarce, complex mountainous terrain from a water resources perspective. Spatially averaged precipitation time series were generated or obtained for 16 sub-basins of the Paute river basin in the Ecuadorian Andes and 13 sub-basins of the Baker river basin in Chilean Patagonia. Precipitation time series were generated using the European Centre for Medium Weather Range Forecasting (ECMWF) 40 year reanalysis (ERA-40) and the subsequent ERA-interim products, and the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset 1 (NCEP R1) hindcast products, as well as precipitation estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The Tropical Rainfall Measurement Mission (TRMM) 3B42 is also used for the Ecuadorian Andes. These datasets were compared to both spatially averaged gauged precipitation and river discharge. In general, the time series of the remotely sensed and hindcast products show a low correlation with locally observed precipitation data. Large biases are also observed between the different products. Hydrological verification based on river flows reveals that water balance errors can be extremely high for all evaluated products, including interpolated local data, in basins smaller than 1000 km². The observations are consistent over the two study regions despite very different climatic settings and hydrological processes, which is encouraging for extrapolation to other mountainous regions.     

A novel approach for estimating karst groundwater recharge in mountainous regions and its application in Switzerland

A pragmatic and simple approach for estimating the groundwater recharge of karst aquifers in mountainous regions by extrapolation of the hydrological regimes of gauged and well‐documented systems is presented. Specific discharge rates are derived using annual precipitation and spring measurements by taking into account catchment size and elevation, which are assumed to be the dominant factors. Reference sites with high data reliability are used for calibration and regional extrapolation. This is performed with normalized values employing spatial precipitation deviations and correlation with the elevation of the catchment areas. A tiered step procedure provides minimum and maximum normalized gradients for the relationship between recharge quantity and elevation for karst regions. The normalized recharge can therefore be obtained and extrapolated for any location using the spatial precipitation variability to provide an estimate of annual groundwater recharge. The approach was applied to Switzerland (approximately 7500 km² of karst terrain situated between 200 and over 4000 m a.s.l.) using annual precipitation data from meteorological stations for the years 2000 to 2011. Results show that the average recharge rates of different Swiss karst domains range from 20 to 46 L/km²s, which corresponds to an infiltration ratio between 0.6 and 0.9 of total precipitation. Despite uncertainties inherent in the approach, these results provide a benchmark for renewable karst groundwater resources in Switzerland of about 8.4 km³/year. The approach can be applied to any other mountainous karst region, that is, where a clear relationship between elevation, precipitation and recharge can be assumed. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of monthly evaporative loss using relevance vector machine,extreme learning machine and multivariate adaptive regression spline models

The forecasting of evaporative loss (E) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation (S), maximum temperature (T _max), minimum temperature (T _min), atmospheric vapor pressure (VP) and precipitation (P). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T _max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP. This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T _max, verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E. It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.     

Artificial neural network models for forecasting monthly precipitation in Jordan

Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the performance criterion under consideration.