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1.
AbstractThe combined analysis of precipitation and water scarcity was done with the use of the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), developed as a monthly, two-variable SPI-SRI indicator to identify different classes of hydrometeorological conditions. Stochastic analysis of a long-term time series (1966–2005) of monthly SPI-SRI indicator values was performed using a first-order Markov chain model. This provided characteristics of regional features of drought formation, evolution and persistence, as well as tools for statistical long-term drought hazard prediction. The study was carried out on two subbasins of the Odra River (Poland) of different orography and land use: the mountainous Nysa K?odzka basin and the lowland, agricultural Prosna basin. Classification obtained with the SPI-SRI indicator was compared with the output from the NIZOWKA model that provided identification of hydrological drought events including drought duration and deficit volume. Severe and long-duration droughts corresponded to SPI-SRI Class 3 (dry meteorological and dry hydrological), while severe but short-term droughts (lasting less than 30 days) corresponded to SPI-SRI Class 4 (wet meteorological and dry hydrological). The results confirm that, in Poland, meteorologically dry conditions often shift to hydrologically dry conditions within the same month, droughts rarely last longer than 2 months and two separate drought events can be observed within the same year. 相似文献
2.
Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series
《水文科学杂志》2013,58(4):588-598
Abstract The main aim of this study is to develop a flow prediction method, based on the adaptive neural-based fuzzy inference system (ANFIS) coupled with stochastic hydrological models. An ANFIS methodology is applied to river flow prediction in Dim Stream in the southern part of Turkey. Application is given for hydrological time series modelling. Synthetic series, generated through autoregressinve moving-average (ARMA) models, are then used for training data sets of the ANFIS. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting by using ANFIS. 相似文献
3.
Adaptive Neuro-Fuzzy Inference System for drought forecasting 总被引：3，自引：2，他引：1
Ulker Guner Bacanli Mahmut Firat Fatih Dikbas 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(8):1143-1154
Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability
of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized
Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey
are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For
all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided
into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained
and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated.
Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate
that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting. 相似文献
4.
Drought is a natural disaster that significantly affects human life; therefore, precise monitoring and prediction is necessary to minimize drought damage. Conventional drought monitoring is based predominantly on ground observation stations; however, satellite imagery can be used to overcome the disadvantages of existing monitoring methods and has the advantage of monitoring wide areas. In this research, we assess the applicability of drought monitoring based on satellite imagery, focusing on historic droughts in 2001 and 2014, which caused major agricultural and hydrological issues in South Korea. To assess the applicability and accuracy of the drought index, drought impact areas in the study years were investigated, and spatiotemporal comparative analyses between the calculated drought index and previously affected areas were conducted. For drought monitoring based on satellite imagery, we used hydro-meteorological factors such as precipitation, land surface temperature, vegetation, and evapotranspiration, and applied remote sensing data from various sensors. We verified the effectiveness of using precipitation data for meteorological drought monitoring, vegetation and land surface temperature data for agricultural drought monitoring, and evapotranspiration data for hydrological drought monitoring. Moreover, we confirmed that the Standard Precipitation Index (SPI) can be indirectly applied to agricultural or hydrological drought monitoring by determining the temporal correlation between SPI, calculated for various time scales, and satellite-based drought indices. 相似文献
5.
ABSTRACTNowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).
Editor D. Koutsoyiannis; Associate editor F. Pappenberger 相似文献
6.
ABSTRACTSince the performance of hydrological models relies on numerous factors, the selection of an appropriate modeling approach for hydrological study has always been a crucial issue. The major objective of this research is to demonstrate that data-driven models such as the Adaptive Neuro-Fuzzy Inference system (ANFIS) are more suitable in a region where spatially distributed precipitation datasets are not available. Since precipitation has a teleconnection with the El Niño Southern Oscillation (ENSO) in different parts of the world, the sea surface temperatures (SSTs) and sea level pressures (SLPs) of the equatorial Pacific can be expected to act as surrogates for the precipitation if there are insufficient raingauge stations in the watershed. Moreover, in contrast to conceptual and physically-based models, data driven models can incorporate SST and SLP in their input vectors, and hence additional forcing of SST with precipitation has been experimented with in past studies. Therefore, our second objective is to test whether the additional forcing of SST and SLP will improve the hydrologic simulation. For this, various ANFIS models for the winter season were developed considering 10 raingauge stations situated at various locations in the watershed. Rainfall from each raingauge station was considered in the ANFIS model one at a time with and without SST/SLP. The results show that the performance of the ANFIS model improved with the additional fusion of SST and SLP, especially when a raingauge station from a remote location was considered. However, this improvement was observed when the analysis was primarily focused on the winter season which is a period with a strong ENSO signal.
Editor D. Koutsoyiannis Associate editor L. See 相似文献
7.
T.C. Sharma 《水文科学杂志》2013,58(4):705-722
Abstract Hydrological drought durations (lengths) in the Canadian prairies were modelled using the standardized hydrological index (SHI) sequences derived from the streamflow series at annual, monthly and weekly time scales. The rivers chosen for the study present high levels of persistence (as indicated by values exceeding 0.95 for lag-1 autocorrelation in weekly SHI sequences), because they encompass large catchment areas (2210–119 000 km2) and traverse, or originate in, lakes. For such rivers, Markov chain models were found to be simple and efficient tools for predicting the drought duration (year, month, or week) based on annual, monthly and weekly SHI sequences. The prediction of drought durations was accomplished at threshold levels corresponding to median flow (Q50) (drought probability, q?=?0.5) to Q95 (drought probability, q?=?0.05) exceedence levels in the SHI sequences. The first-order Markov chain or the random model was found to be acceptable for the prediction of annual drought lengths, based on the Hazen plotting position formula for exceedence probability, because of the small sample size of annual streamflows. On monthly and weekly time scales, the second-order Markov chain model was found to be satisfactory using the Weibull plotting position formula for exceedence probability. The crucial element in modelling drought lengths is the reliable estimation of parameters (conditional probabilities) of the first- and second-order persistence, which were estimated using the notions implicit in the discrete autoregressive moving average class of models. The variance of drought durations is of particular significance, because it plays a crucial role in the accurate estimation of persistence parameters. Although, the counting method of the estimation of persistence parameters was found to be unsatisfactory, it proved useful in setting the initial values and also in subsequent adjustment of the variance-based estimates of persistence parameters. At low threshold levels corresponding to q < 0.20, even the first-order Markov chain can be construed as a satisfactory model for predicting drought durations based on monthly and weekly SHI sequences. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Sharma, T.C. and Panu, U.S., 2012. Prediction of hydrological drought durations based on Markov chains in the Canadian prairies. Hydrological Sciences Journal, 57 (4), 705–722. 相似文献
8.
ABSTRACTDroughts can have serious negative impacts on the water quality needed for irrigated agriculture. The Metropolitan region of Chile is a relevant producer of high-value crops and is prone to droughts. Standardized Drought Indices were used to characterize meteorological and hydrological droughts for the period from 1985 to 2015. To understand the relationship between droughts and water quality, we evaluated the correlations between daily discharge and surface water quality observations. The threshold level method was used to compare physicochemical parameters during hydrological drought periods with the Chilean water quality thresholds for agricultural uses. A significant (p < 0.05) negative relationship between discharge and electrical conductivity and major ions was found in most of the basin. Hydrological stations located in irrigation districts exceeded the official thresholds for these parameters during hydrological drought periods seriously threatening irrigated agriculture of the region. 相似文献
9.
Abstract This work investigates historical trends of meteorological drought in Taiwan by means of long-term precipitation records. Information on local climate change over the last century is also presented. Monthly and daily precipitation data for roughly 100 years, collected by 22 weather stations, were used as the study database. Meteorological droughts of different levels of severity are represented by the standardized precipitation index (SPI) at a three-monthly time scale. Additionally, change-point detection is used to identify meteorological drought trends in the SPI series. Results of the analysis indicate that the incidence of meteorological drought has decreased in northeastern Taiwan since around 1960, and increased in central and southern Taiwan. Long-term daily precipitation series show an increasing trend for dry days all over Taiwan. Finally, frequency analysis was performed to obtain further information on trends of return periods of drought characteristics. 相似文献
10.
MIKE BONELL 《水文科学杂志》2013,58(5):809-810
Abstract Since droughts are natural phenomena, their occurrence cannot be predicted with certainty and thus it must be treated as a random variable. Once drought duration and magnitude have been found objectively, it is possible to plan for the transport of water in known quantities to drought-stricken areas either from alternative water resources or from water stored during wet periods. The summation of deficits over a particular period is referred to as the drought magnitude. Drought intensity is the ratio of drought magnitude to its duration. These drought properties at different truncation levels provide significant hydrological and hydrometeorological design quantities. In this study, the run analysis and z-score are used for determining drought properties of given hydrological series. In addition, kriging is used as a spatial drought analysis for mapping. This study is applied to precipitation records for Istanbul, Edirne, Tekirdag and Kirklareli in the Trakya region, Turkey and then the drought period, magnitude and standardized precipitation index (SPI) values are presented to depict the relationships between drought duration and magnitude. 相似文献
11.
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions
ABSTRACT In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions. 相似文献
12.
Pieter R. van Oel Eduardo S. P. R. Martins Alexandre C. Costa Niko Wanders Henny A. J. van Lanen 《水文科学杂志》2020,65(Z1):979-990
ABSTRACTTo effectively manage hydrological drought, there is an urgent need to better understand and evaluate its human drivers. Using the “downstreamness” concept, we assess the role of a reservoir network in the emergence and evolution of droughts in a river basin in Brazil. In our case study, the downstreamness concept shows the effect of a network of reservoirs on the spatial distribution of stored surface water volumes over time. We demonstrate that, as a consequence of meteorological drought and recovery, the distribution of stored volumes became spatially skewed towards upstream locations, which affected the duration and magnitude of hydrological drought both upstream (where drought was alleviated) and downstream (where drought was aggravated). The downstreamness concept thus appears to be a useful entry point for spatiotemporally explicit assessments of hydrological drought and for determining the likelihood of progression from meteorological drought to a human-modified hydrological drought in a basin. 相似文献
13.
Dimitrios Myronidis Dimitrios Fotakis Konstantinos Ioannou Konstantina Sgouropoulou 《水文科学杂志》2013,58(15-16):2005-2019
ABSTRACTTen notable meteorological drought indices were compared on tracking the effect of drought on streamflow. A 730-month dataset of precipitation, temperature and evapotranspiration for 88 catchments in Oregon, USA, representing pristine conditions, was used to compute the drought indices. These indices were correlated with the monthly streamflow datasets of the minimum, maximum and mean discharge, and the discharge monthly fluctuation; it was revealed that the 3-month Z-score drought index (Z3) has the best association with the four streamflow variables. The Mann-Kendall trend detection test applied to the latter index time series mainly highlighted a downward trend in the autumn and winter drought magnitude (DM) and an upward trend in the spring and summer DM (p = 0.05). Finally, the Pettitt test indicated an abrupt decline in the annual and autumn DM, which began in 1984 and 1986, respectively. 相似文献
14.
《水文科学杂志》2012,57(2):254-268
ABSTRACTUsing regionally downscaled and adjusted outputs of three global climate models (GCMs), meteorological drought analysis was accomplished across Ankara, the capital city of Turkey. To this end, standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) were projected under (representative concentration pathway) RCP4.5 and RCP8.5 greenhouse gas scenarios. In general, our results show that Ankara experienced six severe and two extreme drought events during the reference period, 1971–2000. However, the projections indicate fewer drought events for the near-future period of 2016–2040, with no potential extreme drought events. While the RCP4.5 scenario showed that dry spells will be dominant in the second half of the near-future period, the RCP8.5 scenario projected that dry spells will be evenly distributed during the entire near-future period. 相似文献
15.
Parveen Sihag Vijay P. Singh Anastasia Angelaki Vinod Kumar Alireza Sepahvand Evangelia Golia 《水文科学杂志》2013,58(13):1647-1658
ABSTRACTInfiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area. 相似文献
16.
《水文科学杂志》2012,57(2):311-324
ABSTRACTIn semi-arid regions, reduced river flows present is a major challenge in water resources management. We present a new standardized contribution of rainfall to runoff index (SCRI) for evaluating changes in rainfall contribution to river flow. We employ the standardized precipitation index (SPI), standardized discharge index (SDI) and SCRI to characterize meteorological drought, hydrological drought and land-use change impacts on river flow, respectively. These indices are applied to the Mond River Basin (Iran), which is regulated by the Salman Farsi and Tangab dams since 2006. A new concept called “mirage water” is proposed that represents the reduced water delivery to downstream areas due to new developments and water withdrawals in headwater tributaries. In particular, mirage water accounts for changes in upstream water consumption between the planning phase and construction/operation life of dams. We recommend that this concept be used for communication with decision-makers and managers to clarify the need for revising dimensions of planned dams. 相似文献
17.
AbstractThe standardized series of monthly and weekly flow sequences, referred to as standardized hydrological index (SHI) series, from five rivers in the Canadian prairies were subjected to return period (Tr) analysis of drought length (L). The SHI series were truncated at drought probability levels q ranging from 0.5 to 0.05 with the intention of deducing drought events and corresponding drought lengths. The values of L were fitted to the Pearson 3, the gamma (2-parameter), the exponential (1-parameter), the Weibull 3 and the Weibull (2-parameter) probability density functions (pdfs). A priori assignment of one week or one month for the location parameter in the Pearson 3 pdf proved logical and also facilitated the rapid estimation of other parameters using either the method of moments or the method of maximum likelihood. The Pearson 3 turns out to be the most suitable pdf to describe and to estimate return periods of drought lengths. At the monthly and weekly time scales, it was inferred that the sample size (T, months or weeks) of SHI series could be treated equivalent to the return period of the largest recorded drought length. At the annual time scale, however, the sample size (T, years) should be modified using either the Hazen or the Gringorten plotting position formula to reflect the actual return period of the largest recorded drought length in years.
Editor D. Koutsoyiannis; Associate editor E. Gargouri 相似文献
18.
PATRICK BROCHET 《水文科学杂志》2013,58(3):393-411
Abstract The author recalls the four classical definitions of drought:
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— pluviometrie drought, numerically characterized by a pluviometric ratio
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— climatic drought, depending on the potential water budget
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— agricultural drought, referring to the easily available soil water storage
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— hydrological drought, recognized by an anomaly in the water supply to the water courses.
19.
ABSTRACT Reliable seasonal forecasting of water resources variability may be of great value for agriculture and energy management in Ethiopia. This work aims to develop statistical forecasting of seasonal total water storage (TWS) anomalies in Ethiopia using sea-surface temperature and sea-level pressure indices. Because of the spatial and temporal variability of TWS over the country, Ethiopia is divided into four regions each having similar TWS dynamics. Periods of long-term water deficit observed in GRACE TWS products for the region are found to coincide with periods of meteorological drought. Multiple linear regression is employed to generate seasonal forecasting models for each region. We find that the skill of the resulting models varies from region to region, with R 2 from 0.33 to 0.73 and correlation from 0.27 to 0.77 between predicted and observed values (using leave-one-out cross-validation). The skill of the models is better than the climatology in all regions. 相似文献
20.
The standardized precipitation index (SPI) and standardized streamflow index (SSI) were used to analyse dry/wet conditions in the Logone catchment over a 50-year period (1951–2000). The SPI analysis at different time scales showed several meteorological drought events ranging from moderate to extreme; and SSI analysis showed that wetter conditions prevailed in the catchment from 1950 to 1970 interspersed with a few hydrological drought events. Overall, the results indicate that both the Sudano and Sahelian zones are equally prone to droughts and floods. However, the Sudano zone is more sensitive to drier conditions, while the Sahelian zone is sensitive to wetter conditions. Correlation analysis between SPI and SSI at multiple time scales revealed that the catchment has a low response to rainfall at short time scales, though this progressively changed as the time scale increased, with strong correlations (≥0.70) observed after 12 months. Analysis using individual monthly series showed that the response time reduced to 3 months in October. 相似文献