A regional tree-ring width chronology of Schrenk spruce(Picea schrenkiana) was used to determine the annual(previous July to current June) streamflow of the Kuqa River in Xinjiang, China, for the period of 1414–2015....A regional tree-ring width chronology of Schrenk spruce(Picea schrenkiana) was used to determine the annual(previous July to current June) streamflow of the Kuqa River in Xinjiang, China, for the period of 1414–2015. A linear transformation of the tree-ring data accounted for 63.9% of the total variance when regressed against instrumental streamflow during 1957–2006. The model was validated by comparing the regression estimates against independent data. High streamflow periods with a streamflow above the 602-year mean occurred from 1430–1442, 1466–1492, 1557–1586, 1603–1615, 1687–1717, 1748–1767, 1795–1819, 1834–1856, 1888–1910 and 1989–2015. Low streamflow periods(streamflow below the mean) occurred from 1419–1429, 1443–1465, 1493–1556, 1587–1602, 1616–1686, 1720–1747, 1768–1794, 1820–1833, 1857–1887 and 1911–1988. The reconstruction compares well with the tree-ring-based streamflow series of the Tizinafu River from the Kunlun Mountains;both show well-known severe drought events. The streamflow reconstruction also shows highly synchronous upward trends since the 1980 s, suggesting that streamflow is related to Central Asian warming and humidification. Thus, the influences of the extremes and the persistence of low streamflows on local society may be considerable. Climatic changes in the watershed may be responsible for the change in the hydrologic regime of the Tarim Basin observed during the late twentieth century.展开更多
Reconstructing the hydrological change based on dendrohydrological data has important implications for understanding the dynamic distribution and evolution pattern of a given river. The widespread, long-living conifer...Reconstructing the hydrological change based on dendrohydrological data has important implications for understanding the dynamic distribution and evolution pattern of a given river. The widespread, long-living coniferous forests on the Altay Mountains provide a good example for carrying out the dendrohydrological studies. In this study, a regional composite tree-ring width chronology developed by Lariat sibirica Ledeb. and Picea obovata Ledeb. was used to reconstruct a 301-year annual （from preceding July to succeeding June） streamflow for the Haba River, which originates in the southern Altay Mountains, Xinjiang, China. Results indicated that the reconstructed streamflow series and the observations were fitting well, and explained 47.5% of the variation in the observed streamflow of 1957-2008. Moreover, floods and droughts in 1949-2000 were precisely captured by the streamflow reconstruction. Based on the frequencies of the wettest/driest years and decades, we identified the 19th century as the century with the largest occurrence of hydrological fluctuations for the last 300 years. After applying a 21-year moving average, we found five wet （1724-1758, 1780-1810, 1822-1853, 1931-1967, and 1986-2004） and four dry （1759-1779, 1811-1821, 1854-1930, and 1968-1985） periods in the streamflow reconstruction. Furthermore, four periods （1770-1796, 1816-1836, 1884-1949, and 1973-1997） identified by the streamflow series had an obvious increasing trend. The increasing trend of streamflow since the 1970s was the biggest in the last 300 years and coincided with the recent warming-wetting trend in northwestern China. A significant correlation between streamflow and precipitation in the Altay Mountains indicated that the streamflow reconstruction contained not only local, but also broad-scale, hydro-climatic signals. The 24-year, 12-year, and 2.2-4.5-year cycles of the reconstruction revealed that the streamflow variability of the Haba River may be influenced by solar activity and the atmosphere-ocean system.展开更多
Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly a...Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems.展开更多
基金This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20100306)the National Key R&D Program of China (Grant No. 2018YFA0606401)+1 种基金NSFC (Grant No. U1803341)the National Youth Talent Support Program and the Special Project for Key Laboratory of Autonomous Region (Grant No. 2017D04018)
文摘A regional tree-ring width chronology of Schrenk spruce(Picea schrenkiana) was used to determine the annual(previous July to current June) streamflow of the Kuqa River in Xinjiang, China, for the period of 1414–2015. A linear transformation of the tree-ring data accounted for 63.9% of the total variance when regressed against instrumental streamflow during 1957–2006. The model was validated by comparing the regression estimates against independent data. High streamflow periods with a streamflow above the 602-year mean occurred from 1430–1442, 1466–1492, 1557–1586, 1603–1615, 1687–1717, 1748–1767, 1795–1819, 1834–1856, 1888–1910 and 1989–2015. Low streamflow periods(streamflow below the mean) occurred from 1419–1429, 1443–1465, 1493–1556, 1587–1602, 1616–1686, 1720–1747, 1768–1794, 1820–1833, 1857–1887 and 1911–1988. The reconstruction compares well with the tree-ring-based streamflow series of the Tizinafu River from the Kunlun Mountains;both show well-known severe drought events. The streamflow reconstruction also shows highly synchronous upward trends since the 1980 s, suggesting that streamflow is related to Central Asian warming and humidification. Thus, the influences of the extremes and the persistence of low streamflows on local society may be considerable. Climatic changes in the watershed may be responsible for the change in the hydrologic regime of the Tarim Basin observed during the late twentieth century.
基金supported by the National Natural Science Foundation of China (41275120, 41605047)the Shanghai Cooperation Organization Science and Technology Partnership (2017E01032)+1 种基金the Special Foundation for Asian Regional Cooperation (Climate Reconstruction of Tian Shan in China, Kyrgyzstan and Tajikistan)the Autonomous Region Youth Science and Technology Innovation Talents Training Project (qn2015bs025)
文摘Reconstructing the hydrological change based on dendrohydrological data has important implications for understanding the dynamic distribution and evolution pattern of a given river. The widespread, long-living coniferous forests on the Altay Mountains provide a good example for carrying out the dendrohydrological studies. In this study, a regional composite tree-ring width chronology developed by Lariat sibirica Ledeb. and Picea obovata Ledeb. was used to reconstruct a 301-year annual （from preceding July to succeeding June） streamflow for the Haba River, which originates in the southern Altay Mountains, Xinjiang, China. Results indicated that the reconstructed streamflow series and the observations were fitting well, and explained 47.5% of the variation in the observed streamflow of 1957-2008. Moreover, floods and droughts in 1949-2000 were precisely captured by the streamflow reconstruction. Based on the frequencies of the wettest/driest years and decades, we identified the 19th century as the century with the largest occurrence of hydrological fluctuations for the last 300 years. After applying a 21-year moving average, we found five wet （1724-1758, 1780-1810, 1822-1853, 1931-1967, and 1986-2004） and four dry （1759-1779, 1811-1821, 1854-1930, and 1968-1985） periods in the streamflow reconstruction. Furthermore, four periods （1770-1796, 1816-1836, 1884-1949, and 1973-1997） identified by the streamflow series had an obvious increasing trend. The increasing trend of streamflow since the 1970s was the biggest in the last 300 years and coincided with the recent warming-wetting trend in northwestern China. A significant correlation between streamflow and precipitation in the Altay Mountains indicated that the streamflow reconstruction contained not only local, but also broad-scale, hydro-climatic signals. The 24-year, 12-year, and 2.2-4.5-year cycles of the reconstruction revealed that the streamflow variability of the Haba River may be influenced by solar activity and the atmosphere-ocean system.
基金The first author thanks the Brazilian National Council for Scientific and Technological Development for the Post-Doc scholarship(155814/2018-4).
文摘Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems.
