Spatial hydrological drought characteristics in Karkheh River basin,southwest Iran using copulas

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall \(\tau \) estimation method for copulas parameter estimation. The methods were employed to study joint severity–duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The \(Q_{75}\) index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma–GEV, LN2–exponential, and LN2–gamma were selected as the best paired drought severity–duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov–Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-\(\tau \) is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall \(\tau \) estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.     

Comparing multi-objective optimization techniques to calibrate a conceptual hydrological model using in situ runoff and daily GRACE data

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a “calibration” procedure. Optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. Practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: modèle du Génie Rural à 4 paramètres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that according to MS and SP, NSGA-II performs better than other techniques for calibrating GR4J using GRACE dTWS and in situ runoff data. Considering GD as a measure of efficiency, MPSO is found to be the best technique. CGA is found to be an efficient method, while considering the statistics of the GR4J’s 4 calibrated parameters to rank the optimization techniques. The Nash-Sutcliffe model efficiency coefficient is also used to assess the predictive power of the calibrated hydrological models, for which our results indicate satisfactory performance of the assessed calibration experiments.     

Effects of human activities on hydrological drought patterns in the Yangtze River Basin,China

Natural Hazards - As an extremely important region for the socioeconomic development of China, the Yangtze River Basin (YRB) is vulnerable to climate change and natural disasters. In recent...     

旱灾风险定量评估总体框架及其关键技术

作为旱灾风险管理的核心内容和关键环节,旱灾风险评估逐渐成为旱灾研究的热点问题。从科学界定旱灾风险概念入手,辨析了干旱风险与旱灾风险之间的关系,并从机理上对旱灾风险形成机制进行了剖析和阐述;首次提出了旱灾风险定量评估总体框架,即通过建立干旱频率~潜在损失~抗旱能力之间的定量关系实现对旱灾风险的定量评估,并探讨了该评估框架所涉及的干旱频率分析技术、灾损评估技术、抗旱能力评估技术和旱灾风险表征技术等关键技术及其难点。     

基于统计理论方法的水文模型参数敏感性分析

参数敏感性分析是模型不确定性量化的重要环节,有助于有效识别关键参数,减少参数的不确定性影响,进而提高参数优化效率。利用Morris筛选方法定性识别相对重要参数,耦合方差分解的Sobol方法和统计理论的响应曲面模型构建一种新的定量敏感性分析方法——RSMSobol方法。以长江支流沿渡河流域的日降雨径流过程模拟为例,系统分析4种不同目标函数响应条件下新安江模型的参数敏感性。结果表明Morris方法和RSMSobol方法的集成应用极大地提高了全局敏感性分析的效率,Morris定性筛选结果为定量评估减少了模型参数维数,采用代理模型技术的RSMSobol方法减少了模型的计算消耗。     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Water system characteristics of Karst river basins in South China and their driving mechanisms of hydrological drought

Natural Hazards - It is common knowledge that drought is considered one of the most damaging natural disasters in terms of economic costs, societal problems, and ecological impacts. In this study,...     

A comprehensive approach to drought and desertification in Northern Nigeria

Drought has been a recurring feature of the arid and semi-arid areas of Nigeria. This paper reviews the extent, severity, and consequences of drought and desertification in Nigeria with particular emphasis on the northern part of the country. The haphazard manner in which these environmental hazards have been tackled is examined and a systematic approach for the formulation of a national policy is proposed. It is recommended that a detailed formulation and implementation of the proposed policy plan is imperative to mitigate the often devastating impacts of drought and desertification in the very prone areas of Nigeria. Until such is done, some areas of Nigeria will always be vulnerable to the whims of an inevitable climatic hazard of drought and associated land degradation in the form of desertification.     

Regional analysis of streamflow drought: a case study in southwestern Iran

Droughts are complex natural hazards that, to a varying degree, affect some parts of the world every year. The range of drought impacts is related to drought occurring in different stages of the hydrological cycle and usually different types of droughts such as meteorological, agricultural, hydrological, and socio-economical are the most distinguished types. Hydrological drought includes streamflow and groundwater droughts. In this paper, streamflow drought was analyzed using the method of truncation level (at 70 % level) by daily discharges at 54 stations in southwestern Iran. Frequency analysis was carried out for annual maximum series of drought deficit volume and duration. 35 factors such as physiographic, climatic, geologic and vegetation were studied to carry out the regional analysis. According to conclusions of factor analysis, the six most effective factors include watershed area, the sum rain from December to February, the percentage of area with NDVI <0.1, the percentage of convex area, drainage density and the minimum of watershed elevation, explained 89.2 % of variance. The homogenous regions were determined by cluster analysis and discriminate function analysis. The suitable multivariate regression models were ascertained and evaluated for hydrological drought deficit volume with 2 years return period. The significance level of models was 0.01. The conclusion showed that the watershed area is the most effective factor that has a high correlation with drought deficit volume. Moreover, drought duration was not a suitable index for regional analysis.     

Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting

In this study, application of a class of stochastic dynamic models and a class of artificial intelligence model is reported for the forecasting of real-time hydrological droughts in the Black River basin in the USA. For this purpose, the Standardized Hydrological Drought Index (SHDI) was adopted in different time scales to represent the hydrological drought index. Six probability distribution functions (PDF) were fitted to the discharge time series to obtain the best fit for SHDI calculation. Then, a dynamic linear spatio-temporal model (DLSTM) and artificial neural network (ANN) were used to forecast SHDI. Although results indicated that both models were able to forecast SHDI in different time scales, the DLSTM was far superior in longer lead times. The DLSTM could forecast SHDI up to 6 months ahead while ANN was only capable of forecasting SHDI up to 2 months ahead appropriately. For short lead times (1–6 months), the DLSTM has performed nearly perfect in test phase and CE oscillates between 0.97 and 0.86 while for ANN modeling, CE is between 0.72 and 0.07. However, the performance of DLSTM and ANN reduced considerably in medium lead times (7–12 months). Overall, the DLSTM is a powerful tool for appropriately forecasting SHDI at short time scales; a major advantage required for drought early warning systems.     

Characterization and evaluation of MODIS-derived Drought Severity Index (DSI) for monitoring the 2009/2010 drought over southwestern China

This article investigates whether the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived global terrestrial Drought Severity Index (DSI) had the capability of detecting regional drought over subtropical southwestern China. Monthly, remotely sensed DSI data with 0.05° spatial resolution were used to characterize the extent, duration, and severity of drought from 2000 to 2010. We reported that southwestern China suffered from incipient to extreme droughts from November 2009 to March 2010 (referred to as the “drought period”). The area affected by drought occupied approximately 74 % of the total area of the study region, in which a moderate drought, severe drought, and an extreme drought accounted for 20, 12.7, and 13.2 % of the total area, respectively; particularly in March 2010, droughts of severe and extreme intensity covered the largest areas of drought, which were 16.1 and 18.6 %, respectively. Spatially, eastern Yunnan, western Guizhou, and Guangxi suffered from persistent droughts whose intensities ranged from mild to extreme during the drought period. Pearson’s correlation analyses were performed between DSI and the in situ meteorological station-based Standardized Precipitation Index (SPI) for validating the monitoring results of the DSI. The results showed that the DSI corresponded favorably with the time scales of the SPI; meanwhile, the DSI showed its highest correlation (mean: r = 0.58) with a three-month SPI. Furthermore, similar spatial patterns and temporal variations were found between the DSI and the three-month SPI, as well as the agro-meteorological drought observation data, when monitoring drought. Our analysis suggests that the DSI can be used for near-real-time drought monitoring with fine resolution across subtropical southwestern China, or other similar regions, based solely on MODIS-derived evapotranspiration/potential evapotranspiration and Normalized Difference Vegetation Index data.     

GPS-derived crustal deformation in southwestern China

The Sichuan – Yunnan region is divided into nine active secondary crustal blocks, based on several GPS repeat surveys at more than 200 GPS sites during the period 1999 – 2005. Velocities of the nine secondary blocks are calculated and analysed. The strain field within the area related to the 2004 Sumatra – Andaman earthquake event is also analysed. Results indicate that the crustal movement in the northern and western areas of the Sichuan – Yunnan region is stronger than that in the south and east. The horizontal velocities change from 19 – 20 mm/y in the northern and the central rhombic block to 11.7 mm/y in the southern rhombic block. The orientations of block motion vary from 99° in the north to 126 – 150° in the central area and 156 – 188° in the south, implying that the motion of the Sichuan – Yunnan rhombic block is dominated by a clockwise rotation. The velocity differences between blocks inside and outside the rhombic block are about 6.5 – 7.7 mm/y in the northern and central Sichuan – Yunnan region. The southeastward extrusion rate of the Tibetan Plateau shows a remarkable downtrend of up to 47% along the Xianshuihe Fault, suggesting an increase in strain accumulation and hence an area prone to strong earthquakes. The horizontal coseismic deformation caused by the Mw9.0 Sumatra – Andaman earthquake is <10 mm with a south-southeast orientation towards the earthquake epicentre. The dilatational strain rates from coseismic displacements reveal a possible interaction between the extrusion from the Tibet plateau interior and the underthrust effects from the Sumatra – Andaman earthquake.     

Identifying rainfall threshold of flash flood using entropy decision approach and hydrological model method

Natural Hazards - Flash flood disaster, with strong suddenness and tremendous destructiveness, is one of the most severe natural disasters in China that seriously threaten the lives and property...     

Drought characterization using a new copula-based trivariate approach

Meteorological drought is a natural climatic phenomenon that occurs over various time scales and may cause significant economic, environmental and social damages. Three drought characteristics, namely duration, average severity and peak intensity, are important variables in water resources planning and decision making. This study presents a new method for construction of three-dimensional copulas to describe the joint distribution function of meteorological drought characteristics. Using the inference function for margins, the parameters for six types of copulas were tested to select the best-fitted copulas. According to the values of the log-likelihood function, Galambos, Frank and Clayton were the selected copula models to describe the dependence structure for pairs of duration–severity, severity–peak and duration–peak, respectively. Trivariate cumulative probability, conditional probability and drought return period were also investigated based on the derived copula-based joint distributions. The proposed model was evaluated over the observed data of a Qazvin synoptic station, and the results were compared with the empirical probabilities. For measuring the model accuracy, R ², root mean square error (RMSE) and the Nash–Sutcliffe efficiency (NSE) criteria were used. Results indicated that R ², RMSE and NSE were equal to 0.91, 0.098 and 0.668, respectively, which demonstrate sufficient accuracy of the proposed model. Drought probabilistic characteristics can provide useful information for water resource planning and management.     

A quantitative approach for the evaluation of rockfall risk on buildings

Natural Hazards - A quantitative rockfall risk analysis at local scale is a complex and difficult task because it should consider both the randomness in the natural phenomenon and the variability...     

西南“三江”成矿带北段区域成矿断裂信息定量化分析

"三江"北段成矿带是中国著名的"三江"铜多金属巨型成矿带的北延部分,构造上属特提斯巨型成矿域的重要组成部分,区域成矿条件极为优越。区内矿床主要为斑岩型铜钼矿床和热液型铅锌矿床,成矿主要受断裂构造的控制,定量分析成矿断裂信息对于指明区域找矿方向至关重要。以GIS技术为平台,采用断裂等密度、断裂中心对称度、断裂优益度、断裂交点数、断裂频数等参数定量地分析区域断裂信息,从中提取区域主干断裂发育区和次级断裂发育区。区域成矿特征表明,本区矿床沿区域主干断裂成带状分布,而次级断裂发育区为主要的矿床赋存区。通过定量分析发现,本区2个主要矿集区之间的扎青乡地区存在一个成矿断裂信息异常高值区,认为是本区今后找矿的有利区域。     

Assessing agricultural and hydrological drought vulnerability in a savanna ecological zone of Sub-Saharan Africa

Natural Hazards - Despite measures to mitigate drought, study on the extent of agricultural and hydrological drought vulnerability is still neglected in the savanna ecological zone of Nigeria. This...     

流体注入法定量表征页岩孔隙结构测试方法研究进展

页岩致密且结构复杂,对其孔隙结构进行定量表征一直是页岩储层研究的重点和难点,因其能够为页岩油气储层评价和甜点确定提供重要信息。通过系统梳理已有研究成果,介绍了以压汞法和气体吸附法为代表的流体注入法,简述了能够影响测试结果的因素,指出了目前页岩全孔径孔隙结构表征的问题及发展方向。分析表明,颗粒样气体吸附法和块（柱）压汞法表征结果的数据匹配性差,整合难度大;同时由上述两种方法联合所获得的表征结果也无法被其他独立测试所验证。进一步指出采用颗粒样代替传统块（柱）样进行页岩压汞测试可以提高数据质量,也是未来方法联用的发展方向,其中样品颗粒堆积孔进汞量校正、颗粒样粒径选择、多方法数据匹配是实现页岩全孔径孔隙结构定量表征的关键。