基于分布式水文模型的怒江流域气象干旱和水文干旱分析

为了评估气候变化对怒江流域干旱演变的影响,本研究建立了GBHM-NJ分布式水文模型,利用实测站点资料率定参数并验证模型精度,模拟了1961—2010年长时间序列流域水文过程,并分别采用标准化降水指数(SPI)和标准化径流指数(SSI)分析了流域气象干旱和水文干旱的时空演变特点。结果表明:(1)GBHM-NJ模型能较好地模拟怒江流域的径流过程和水文响应的空间特征。(2)1961—2010年间,怒江流域发生气象干旱的频率、覆盖面积和强度呈增加趋势,其中1994年和2009年气象干旱最为严重。(3)在空间上,怒江流域的年度气象干旱频率约为28%,中游地区干旱频率比较高、主要分布在左贡站和八宿站附近,上游地区次之,下游地区相对较低。(4)水文干旱进入20世纪90年代和21世纪以后明显增强,年尺度干旱以轻旱为主,季尺度干旱特旱多发生在秋冬季。总之,气候变化环境下怒江流域干旱呈现增强趋势。     

Assessment of Probabilistic Multi-Index Drought Using a Dynamic Naive Bayesian Classifier

The proper consideration of all plausible feature spaces of the hydrological cycle and inherent uncertainty in preceding developed drought indices is inevitable for comprehensive drought assessment. Therefore, this study employed the Dynamic Naive Bayesian Classifier (DNBC) for multi-index probabilistic drought assessment by integrating various drought indices (i.e., Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Vegetation Supply Water Index (NVSWI)) as indicators of different feature spaces (i.e., meteorological, hydrological, and agricultural) contributing to drought occurrence. The overall results showed that the proposed model was able to account for various physical forms of drought in probabilistic drought assessment, to accurately detect a drought event better than (or occasionally equal to) any single drought index, to provide useful information for assessing potential drought risk, and to precisely capture drought persistence in terms of drought state transition probability in drought monitoring. This easily produced an alternative method for comprehensive drought assessment with combined use of different drought indices.     

Spatio-Temporal Variation of Water Availability in a River Basin under CORDEX Simulated Future Projections

In the present study, spatio-temporal variability of hydrological components under climate change is analysed over Wainganga River basin, India. In order to address the climate change projection, hydrological modelling is carried out using a macro scale, semi-distributed three (3)-Layer Variable Infiltration Capacity (VIC-3 L) model. The high-resolution (0.5^o?×?0.5^o) meteorological variables are divided into multiple periods to calibrate and validate the VIC-3 L model. The future projections (2020–2094) of the water balance components are achieved using the high resolution hydrological variables from the COordinated Regional Downscaling EXperiment (CORDEX) dataset under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The uncertainty associated with the multi-model projections are evaluated using Reliability Ensemble Averaging (REA) and the bias correction is accomplished with non-parametric quantile mapping. A probabilistic based areal drought index is also computed for different scenarios using Standardized Precipitation Evapotranspiration Index (SPEI). From the results, it is observed that amount of rainfall, evapotranspiration, and runoff has increased over the basin with no change in the spatial pattern. However, temporal variability is noticed with an increasing trend for rainfall and runoff in the non-monsoon season than the monsoon. Streamflow is expected to increase significantly, especially for medium to low flows (those occurring between 0.2 and 0.9 probability of exceedance in a Flow Duration Curve). In addition, the area under the drought condition has decreased under the projected climate scenarios.     

Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change

This study aims to investigate the effect of climate change on the probability of drought occurrence in central Iran. To this end, a new drought index called Multivariate Standardized Drought Index (MSDI) was developed, which is composed of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Soil Moisture Index (SSI). The required data included precipitation, temperature (from CRU TS), and soil moisture (from the ESA CCA SM product) on a monthly time scale for the 1980–2016 period. Moreover, future climate data were downloaded from CMIP6 models under the latest SSPs-RCPs emission scenarios (SSP1-2.6 and SSP5-8.5) for the 2020–2056 period. Based on the normalized root mean square error (NRMSE), Cramer-von mises statistic (S_n), and Nash Sutcliffe (NS) evaluation criteria, the Galambos and Clayton functions were selected to derive copula-based joint distribution functions in both periods. The results showed that more severe and longer droughts will occur in the future compared to the historical period and in particular under the SSP5-8.5 scenario. From the derived joint return period, a drought event with defined severity or duration will happen in a shorter return period as compared with the historical period. In other words, the joint return period indicated a higher probability of drought occurrence in the future period. Moreover, the joint return period analysis revealed that the return period of mild droughts will remain the same, while it will decrease for extreme droughts in the future.

     

A New Non-stationary Hydrological Drought Index Encompassing Climate Indices and Modified Reservoir Index as Covariates

Due to accelerating climate variability and intensified anthropogenic activities, the hypothesis of stationarity of data series is no longer applicable, questioning the reliability of the traditional drought index. Thus, it is critical to develop a non-stationary hydrological drought index that takes into account the joint impacts of climate and anthropogenic changes in a drought assessment framework. In this study, using the Generalized Additive Model for Location, Scale and Shape (GAMLSS), a new Non-stationary Standardized Runoff Index (NSRI) was developed combining climate indices (CI) and modified reservoir index (MRI) as explanatory variables. This novel index was applied to the hydrological drought assessment of the Hanjiang River basin (HRB) in China, and its reliability was assessed by comparing with the traditional Standardized Runoff Index (SRI). Results indicated that the optimal non-stationary model with CI and MRI as covariates performed better than did other models. Furthermore, NSRI was more robust in identifying extreme drought events and was more effective in the study region than the conventional SRI. In addition, based on the method of Breaks for Additive Seasonal and Trend (BFAST), it was found that there were two change points in 1981 and 2003 for the NSRI series at four hydrological stations in the HRB, which indicated that hydrological drought in the basin had a prominent non-stationary behavior. Our findings may provide significant information for regional drought assessment and water resources management from a changing environment perspective.

     

Analysis of Impacts of Climate Change and Human Activities on Hydrological Drought: a Case Study in the Wei River Basin,China

Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRI_var) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRI_var is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions.     

人类活动影响下塔里木河流域气象干旱向水文干旱传播的规律

为了解人类活动影响下塔里木河流域气象干旱向水文干旱传播的规律,基于标准化降水指数(SPI)和径流干旱指数(SDI)分析了气象、水文干旱的变化特征,探讨了人类活动对气象干旱向水文干旱传播规律的影响。研究结果表明：源流区气象、水文干旱均呈减弱趋势,干流气象干旱呈减弱趋势,水文干旱与之相反;随时间尺度增大,干旱历时延长;源流区气象、水文干旱以及干流气象干旱的发生频率受人类活动影响后(1993年后)均降低,仅干流水文干旱的发生频率升高;受人类活动影响后,源流区不同季节气象干旱向水文干旱的传播时间均变长,干流干旱传播时间除春季外均缩短;源流区干旱传播时间延长与气候变化有关,而干流干旱传播时间缩短主要是受人类活动的影响。     

Trend analysis of hydrological and water quality variables to detect anthropogenic effects and climate variability on a river basin scale: A case study of Iran

In recent years, climate changeability, hydrologic regime conditions, and human interventions have become crucial issues to be assessed. In this research, two annually recorded datasets were collected to analyse the change in the trend. The first set is comprised of precipitation, streamflow, and water quality variables including Total Dissolved Solids (TDS), pH, cation, and anion and the second one contains the mean groundwater level and agricultural water demand of four main stations of Shahpour River basin in the south of Iran. To recognize the fluctuating patterns, the Mann-Kendall Trend Test (MKTT), KPSS Stationary Test, and Pettit Homogeneity Test (PHT) of statistical methods were utilized at a 5% significance level. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) were subsequently employed to detect the hydrological drought patterns. According to the statistical analysis, the streamflow and water quality depicted intensive varying trends, while there were slight decreasing trends for the precipitation series. Afterward, the abrupt changing points were identified in the first and second datasets between the years 2004 to 2007. The results of this study clarified that human activity effects (as a major factor) and climate variability (as a minor factor) have been affecting the Shahpour River basin. These effects disrupt the water chemical balance (the relationship between cations and anions) and hydrological regimes (increasing drought drivers) and consequently menace the health of the watershed.     

Hydro-Climatological Drought Analyses and Projections Using Meteorological and Hydrological Drought Indices: A Case Study in Blue River Basin, Oklahoma

Understanding the characteristics of historical droughts will benefit water resource managers because it will reveal the possible impacts that future changes in climate may have on drought, and subsequently, the availability of water resources. The goal of this study was to reconstruct historical drought occurrences and assess future drought risk for the drought-prone Blue River Basin in Oklahoma, under a likely changing climate using three types of drought indices, i.e., Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Standardized Runoff Index (SRI). No similar research has been conducted in this region previously. Monthly precipitation and temperature data from the observational period 1950?C1999 and over the projection period 2010?C2099 from 16 statistically downscaled Global Climate Models (GCM) were used to compute the duration, severity, and extent of meteorological droughts. Additionally, soil moisture, evapotranspiration (ET), and runoff data from the well-calibrated Thornthwaite Monthly Water Balance Model were used to examine drought from a hydrological perspective. The results show that the three indices captured the historical droughts for the past 50?years and suggest that more severe droughts of wider extent are very likely to occur over the next 90?years in the Blue River Basin, especially in the later part of the 21st century. In fact, all three indices display lower minimum values than those ever recorded in the past 50?years. This study also found that SRI and SPI (PDSI) had a correlation coefficient of 0.81 (0.78) with a 2-month (no appreciable) lag time over the 1950?C2099 time period across the basin. There was relatively lower correlation between SPI and PDSI over the same period. Although this study recommends that PDSI and SRI are the most suitable indices for assessing future drought risks under an increasingly warmer climate, more drought indices from ecological and socioeconomic perspectives should be investigated and compared to provide a complete picture of drought and its potential impacts on the dynamically coupled nature-human system.     

Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index

Socioeconomic drought occurs when a water shortage is caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Compared with meteorological drought, hydrological drought, and agricultural drought, socioeconomic drought has received relatively little attention. Hence, this study aims to construct a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China, as an example, we analyzed the socioeconomic drought characteristics and trends from 1985 to 2019. The return periods of different levels of drought were calculated. The relationships among socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. Results showed that: (1) SSDWI can assess the socioeconomic drought conditions well at the basin scale. Based on the SSWDI, during the 35-year study period, 29 socioeconomic droughts occurred in the basin, with an average duration of 6.16 months and average severity of 5.82. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of “∪” and “∩” for moderate drought, severe drought, and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively. (3) Because of the increasing outflow from Gaozhou Reservoir, the occurrence probability of socioeconomic drought and hydrological drought in the JJRB has declined significantly since 2008. Reservoir scheduling helps alleviate hydrological and socioeconomic drought in the basin.

     

The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions

Climate change has made many alterations to the climate of earth, including hydro-climatic extreme events. To investigate the impact of climate change on hydro-meteorological droughts in the Kamal-Saleh dam basin in Markazi province, Iran, proportional to future climate conditions, a new and comprehensive index was developed with the aim of accurately estimating drought in a more realistic condition. This aggregate drought index (ADI) represented the main meteorological and hydrological characteristics of drought. Temperature and precipitation projections for future climates were simulated by five CMIP5 models and downscaled over the study area during 2050s (2040–2069) and 2080s (2070–2099) relative to the baseline period (1976–2005). By fitting five univariate distribution functions on drought severity and duration, proper marginal distributions were selected. The joint distribution of drought severity and duration was chosen from five types of copula functions. The results revealed that in future, severe droughts are expected to frequently occur in a shorter period.

     

Uncertainty Analysis of Climate Change Impact on River Flow Extremes Based on a Large Multi-Model Ensemble

Water managers are faced with a changing climate in the decision-making process while adaptation and mitigation strategies need to be developed. The climate change impact towards the end of the century, however, is highly uncertain and coping with this is a great challenge for decision makers. Over the recent years, combined efforts of hydrologists and climatologists have led to many climate change impact studies on water resources. However, most studies only use a limited ensemble size and/or focus on only one contributing source and hence possibly underestimate the total uncertainty.

For two Belgian catchments, we simulated daily flow with five different lumped conceptual hydrological models and ten different parameter sets each, forced by the output of 24 global climate models covering four different emission scenarios, combined with 9 different downscaling methods over reference (1961–1990) and future (2071–2100) periods, resulting in a large multi-model ensemble with 41,850 members. Results show that both low and peak flows would become more extreme in the future, and these changes are stronger with increased radiative forcing. The most important uncertainty sources in low-flow projections are the global climate models (explaining 27–36% of the total variance) and the hydrological model structure (34–42%). For peak flow projections, these are global climate models (32–39%) and statistical downscaling methods (21–26%). Also, interaction effects account for a significant part of the uncertainty (24–38%). The results of this study illustrate that one might end up with biased results and overly confident conclusions when only focusing on some of the uncertainty sources in multi-model ensembles.

     

抚河流域气象干旱向水文干旱传播的影响机制研究

气象干旱是水文干旱发生的前兆,探究影响气象干旱向水文干旱传播的主要因素对建立有效的基于气象干旱的水文干旱监测预警具有重要意义。以抚河流域为例,采用标准化降水指数和标准化径流指数分别评估气象干旱和水文干旱,并构建基于流域分布式二元水循环模型的干旱传播评估方法,采用多因素综合影响贡献量分解法量化气候变化和人类活动对干旱传播变化的贡献。结果表明：建立的抚河流域分布式二元水循环模型模拟流域出口断面流量的纳什效率系数大于0.85,相对误差在5%之内;气象干旱向水文干旱传播关系变化的时间是1980s—1990s。相对于1956—1990年,1991—2019年气象干旱向水文干旱的传播率降低了8.3%;在气象干旱向水文干旱传播的影响中,气候变化的减缓作用占主导地位,贡献量为-9.9%;其次是人类活动的加剧作用,贡献量为1.6%;降水作为干旱的主要致灾因子,变化期增加了144.3 mm,这对于减弱气象干旱向水文干旱传播的敏感性起到主要作用。     

基于SAPEI的河北省玉米生育期旱涝演变特征

为探究河北省玉米生育期旱涝演变特征,通过构建标准化前期降水蒸散指数(standardized antecedent precipitation evapotranspiration index,SAPEI)和利用气象站点逐日气象数据,对河北省1980—2020年玉米生育期内旱涝特征演变进行时空分析。结果显示：1980—2020年河北省SAPEI在玉米初始生长期、快速发育期、发育中期和成熟期4个生育阶段内波动变化区间为-1.0～1.5,且初始生长期波动呈上升趋势;SAPEI空间变化总体特征表现为初始生长期和快速发育期玉米生长气候湿润化趋势表现明显,生育中期和成熟期东部及南部气象站点表现为变旱趋势;玉米生育期内多年平均旱积指数Q_dl总体呈东南高西北低的格局分布,即西北部大于东南部;多年平均渍积指数Q_wl在21.07～37.85内变化,年均旱积与渍积指数的分布格局与河北省降水分布格局相呼应。研究结果可为河北省农业气象灾害风险评估和防灾减灾管理决策提供理论依据。     

Drought Analysis in the Awash River Basin,Ethiopia

This study analyzes drought characteristics in the Awash River Basin of Ethiopia based on meteorological and hydrological variables. Standardized precipitation index is used for temporal and spatial analyses of meteorological drought and the theory of runs is used to define hydrological drought by considering streamflow as the drought indicator. Drought severity maps are generated using Arc View/GIS by summarizing the percentage of occurrence of droughts in areas within the study basin. Extreme drought category on 12-month time scale indicated that extreme events occur most frequently in the Upper and Middle Awash Basin. However, while considering the overall categories of drought, the most frequent droughts occurred in the Middle and Lower Awash Basin during the period of analysis. Similarly, results based on hydrological drought analysis shows that the severest drought events occurred in the Middle Awash Basin during May 1988 to June 1988 and April 1998 to May 1998. Analysis of the relationship between meteorological and hydrological drought indices in the basin shows that occurrence of hydrological drought event at Melka Sedi stream gauging station lags meteorological drought event in the Upper Awash on average by 7 months with a variation of 3 to 13 months.     

滦河流域气象干旱向水文干旱传播特征及风险分析

干旱是一种复杂的自然灾害,气象干旱可能导致水文干旱等灾害,严重影响社会经济发展。探明气象干旱向水文干旱传播的特征及规律,可为减小干旱损失、保障流域供水用水安全提供技术支持。本文基于滦河山区1960—2017年的气象水文数据,应用游程原理识别气象水文干旱事件,甄别二者间的传播特征,并借助Copula模型评估气象干旱向水文干旱的风险和干旱传播的临界关系。结果表明：（1）在1960—2017年期间,与气象干旱事件相比,水文干旱事件具有频率低、历时长、峰值低的特点;（2）滦河地区气象干旱和水文干旱传播关系以单场气象干旱向单场水文干旱传播为主;（3）气象干旱向水文干旱的传播存在明显的滞后效应,从上游至下游地区干旱滞后时间依次增长;（4）Gumbel Copula函数能够更准确地评估该流域干旱传播风险,结果显示上游地区发生干旱传播的风险低于下游地区,上、下游地区气象干旱发展成水文干旱的临界历时分别为0.93个月和1.26个月,临界峰值强度分别为0.66和0.44。本研究可为评价不同干旱事件间的传播与演变关系,构建流域干旱风险评价模型提供新思路。     

Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring

Due to the complexity of agricultural drought, univariate indices may not be suitable for assessing its impacts comprehensively. The main objective of this study was to develop a new multivariate drought index using the Scalogram concepts, in which the input data weights and their cluster separation were performed based on the entropy theory and fuzzy k-means algorithm, respectively. The newly developed index, named as SCI index, integrates the four weighted individual quantitative indicators such as the difference between precipitation and potential evapotranspiration (D_i), the moisture departure (d_i), the Soil Moisture index (SMI), and the Vegetation Condition Index (VCI) to quantify agricultural drought in monthly and annual timescales in the various climate conditions of Golestan province, Iran. Next, the Composite Drought Index (CDI) was calculated for the selected stations by the same variables in the SCI index as an input. According to the results a good agreement and a high behavioral similarity for the identifying moisture conditions was found between SCI index and CDI index and even other well-known drought indices such as SPEI and SPDI. But the intensity with extremes of wet and dry conditions in the CDI significantly were more than the SCI index and other ones. Comparing results obtained by the Standardized Yield Index (SYI) for rainfed wheat with the SCI index showed that at most stations when a severe drought as happened in 2000–2001 and 2007–2008, severe crops losses also occurred. The flexible structure of SCI index provides a comprehensive approach to quantify agricultural drought and can be adapted to characterize other types of drought on a practical basis.     

基于水文-作物耦合模型和CWAPI指数的农业干旱评估

基于模拟土壤含水量构建的干旱指数可反映农业干旱的时空发展过程,已被广泛用于大范围农业干旱评估中。当前模拟用于干旱评估的土壤含水量时,常采用水文模型进行模拟。然而,水文模型常过于简化作物模块甚至缺少作物模拟方案,无法模拟作物需水量,因此仅基于土壤含水量构建的土壤干旱指数因忽略作物需水而难以准确评估实际的农业干旱情况。研究构建了考虑灌溉过程影响的水文-作物耦合模型VIC-EPIC（Variable Infiltration Capacity-Environmental Policy Integrated Climate）,提出了基于VIC-EPIC模拟作物需耗水过程的作物缺水距平指数CWAPI（Crop Water Anomaly Percentage Index）,在青口河流域开展了区域农业干旱评估方法研究。验证分析表明,构建的CWAPI因考虑作物需水影响可直接反映作物的缺水状态和缺水对作物生长的累积影响;由于在干旱评估中引入了作物种植比例、轮作类型和灌溉的影响,CWAPI能够较SMAPI（Soil Moisture Anomaly Percentage Index）更客观地反映区域作物旱情。因此评估农业干旱时,需要考虑作物种植比例、轮作和灌溉过程的影响。     

Uncertainty-Driven Characterization of Climate Change Effects on Drought Frequency Using Enhanced SPI

The Standardized Precipitation Index (SPI) is a well-established drought index that is based on transforming the interannual distribution of precipitation to a standard normal distribution. Because of its robust statistical basis, SPI is readily applicable to different regions making comparisons between locations and time windows possible. Nevertheless, the usability of SPI results is undermined by shortcomings that are partly resultant from data and model uncertainties. One such shortcoming is the inability of the existing SPI model to include change in variability of interannual precipitation from non-stationary normal – mostly caused by climate change. In addition, epistemic uncertainty in the form of incompleteness in station-wide precipitation records results in heterogeneity and inconsistency in SPI results. The effects of such epistemic uncertainty on the accuracy of estimations of long-term changes in drought frequency are mostly unknown. Given such deficiency, SPI’s procedure and subsequent results remain deterministic and inadequately informative. Here, we introduce modifications to the traditional SPI using Dempster-Shafer theory (DST) to enable modeling and propagation of variability and epistemic uncertainty with the regular SPI procedure. By generalizing the SPI model from a deterministic setting to an “uncertainty-driven setting” provided by DST, this work makes possible: (a) efficiently propagating data uncertainty in interpolation of station-wide precipitation and SPI, and (b) modeling the effects of shift in precipitation normals (due to e.g., climate change) on drought frequency. In addition, the significance of this shift may then be evaluated with respect to the epistemic uncertainty by measuring how much of the surrounding epistemic uncertainty this shift encloses (i.e., “probability of enclosing”). The latter is especially important due to large unknowns already associated with climate change modeling. We implement the model on summer extreme drought for the Okanagan Basin, BC, Canada. For a single general circulation model and scenario (CGCM3 A2) a maximum 7 % increase in summer extreme drought (for 2080s, as per current definition) is estimated with a maximum probability of enclosing of 36 %.