Monitoring the meteorological drought in the middle reaches of Heihe River basin based on TRMM precipitation data

Abstract. Drought indices have been widely used in climate classification. However, there is not enough evidence for their ability in identifying the multiple climate types in areas with complex topography and landscape, especially in those areas with a transition climate. This study compares a meteorological drought index, the aridity index (AI) defined as the ratio of precipitation (P) to potential evapotranspiration (PET), with a hydrological drought index, the evaporative stress index (ESI) defined as the ratio of actual evapotranspiration (AET) to PET. We conducted this study using modeled high resolution climate data for period of 1980–2010 in the Heihe River Basin (HRB) in the arid northwestern China. PET was estimated using the Penman–Monteith and Hamon methods. The climate classified by AI shows two distinct climate types for the upper and the middle and lower basin reaches, while three types were found if ESI was used. This difference indicates that only ESI is able to identify a transition climate zone in the middle basin. This contrast between the two indices is also seen in the inter-annual variability and extreme dry/wet events. The magnitude of variability in the middle basin is close to that in the lower basin for AI, but different for ESI. AI has larger magnitude of the relative inter-annual variability and greater decreasing rate from 1980–2010 than ESI, suggesting the role of local hydrological processes in moderating extreme climate events. Thus, the hydrological drought index is better than the meteorological drought index for climate classification in the arid Heihe River Basin where local climate is largely determined by topography and landscape. We conclude that the land–surface processes and human disturbances play an important role in altering hydrological drought conditions and their spatial and temporal variability.

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Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0129.1 ◽

2019 ◽

Vol 20 (1) ◽

pp. 59-77 ◽

Cited By ~ 8

Author(s):

Feng Ma ◽

Lifeng Luo ◽

Aizhong Ye ◽

Qingyun Duan

Keyword(s):

Climate Change ◽

River Basin ◽

Human Activities ◽

Meteorological Drought ◽

Hydrological Drought ◽

Heihe River Basin ◽

Heihe River ◽

Propagation Time ◽

Drought Characteristics ◽

Hydrological Droughts

Abstract Meteorological and hydrological droughts can bring different socioeconomic impacts. In this study, we investigated meteorological and hydrological drought characteristics and propagation using the standardized precipitation index (SPI) and standardized streamflow index (SSI), over the upstream and midstream of the Heihe River basin (UHRB and MHRB, respectively). The correlation analysis and cross-wavelet transform were adopted to explore the relationship between meteorological and hydrological droughts in the basin. Three modeling experiments were performed to quantitatively understand how climate change and human activities influence hydrological drought and propagation. Results showed that meteorological drought characteristics presented little difference between UHRB and MHRB, while hydrological drought events are more frequent in the MHRB. In the UHRB, there were positive relationships between meteorological and hydrological droughts, whereas drought events became less frequent but longer when meteorological drought propagated into hydrological drought. Human activities have obviously changed the positive correlation to negative in the MHRB, especially during warm and irrigation seasons. The propagation time varied with seasonal climate characteristics and human activities, showing shorter values due to higher evapotranspiration, reservoir filling, and irrigation. Quantitative evaluation showed that climate change was inclined to increase streamflow and propagation time, contributing from −57% to 63%. However, more hydrological droughts and shorter propagation time were detected in the MHRB because human activities play a dominant role in water consumption with contribution rate greater than (−)89%. This study provides a basis for understanding the mechanism of hydrological drought and for the development of improved hydrological drought warning and forecasting system in the HRB.

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Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China

10.5194/hess-2018-405 ◽

2018 ◽

Cited By ~ 1

Author(s):

Feng Ma ◽

Lifeng Luo ◽

Aizhong Ye ◽

Qingyun Duan

Keyword(s):

River Basin ◽

Climate Models ◽

Dominant Role ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Hydrological Drought ◽

Forecast Skill ◽

Heihe River Basin ◽

Heihe River ◽

Drought Forecasting

Abstract. Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of Anomaly Correlation (AC) and Brier skill score (BSS). The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanism. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of initial hydrologic conditions (ICs) during cold and dry seasons. During wet seasons, there's no skillful hydrological predictions since lead-2 month because the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions is the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological predictability during the wet seasons in the MHRB.

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Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0202.1 ◽

2014 ◽

Vol 15 (4) ◽

pp. 1560-1574 ◽

Cited By ~ 23

Author(s):

Xiaoduo Pan ◽

Xin Li ◽

Kun Yang ◽

Jie He ◽

Yanlin Zhang ◽

...

Keyword(s):

River Basin ◽

Spatial Resolution ◽

Heihe River Basin ◽

Precipitation Event ◽

Fusion Product ◽

Heihe River ◽

Observation Data ◽

Hydrological Process ◽

Precipitation Data ◽

The Heihe River Basin

Abstract Development of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP–MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation event verification indicators. The results indicate that 1) precipitation from the DCMAP product may not be suitable for water cycle studies at the watershed scale because of its coarser spatial resolution and 2) ITP-F, WRF-P, and DCMAP–MicroMet precipitation products generally show similar spatial–temporal patterns in HRB but have varying performances between different subbasins.

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Meteorological Drought, Hydrological Drought, and NDVI in the Heihe River Basin, Northwest China: Evolution and Propagation

Advances in Meteorology ◽

10.1155/2020/2409068 ◽

2020 ◽

Vol 2020 ◽

pp. 1-26

Author(s):

Fanglei Zhong ◽

Qingping Cheng ◽

Ping Wang

Keyword(s):

River Basin ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Condition Index ◽

Hydrological Drought ◽

Heihe River Basin ◽

Heihe River ◽

Propagation Time ◽

Peak Intensity ◽

Positive Effect

Understanding the evolution and propagation of different drought types is crucial to reduce drought hazards in arid and semiarid regions. Here, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Vegetation Condition Index (VCI) were used to investigate the spatiotemporal variation of different drought types and correlations between Pre (Pre-R)/post (Pos-R)-reservoir. Results showed that the average peak/intensity/duration/severity of meteorological droughts (MD) were greater in the Pre-R than in the Pos-R period in the upstream Heihe River Basin (UHRB), while there was little change between the Pre-R and Pos-R periods in the midstream Heihe River Basin (MHRB). The average peak/intensity/duration/severity of hydrological drought (HD) decreased in the mainstream for Yingluoxia (Ylx) but increased for Zhengyixia (Zyx) station in the Pos-R period. Propagation time decreased by 3 months (negative effect) in Ylx and increased by 8 months (positive effect) in Zyx compared with the Pre-R period. In the Pos-R period, propagation time increased (1–3 months) for tributaries (positive effect). Propagation times for the mainstream and tributaries varied for different seasons and time periods. Pearson’s correlation coefficient values were lower at short timescales (1–3 months) but higher at long timescales for the Pos-R period in Ylx and Zyx for SDI-1 with different timescales of SPI. The SDI and SPI had no lag in the UHRB and MHRB. However, VCI with SPI had a significant lag correlation at short timescales in the UHRB (lag 6 months) and MHRB (lag 4 months), and the VCI with SDI had a significant lag correlation for 1 month in the MHRB. The propagation time from MD to HD has been reduced for Pos-R in the UHRB. There was a positive effect (prolonged MD propagation HD time) in Pos-R but still faces serious drought stress in the MHRB.

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Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China

Hydrology and Earth System Sciences ◽

10.5194/hess-22-5697-2018 ◽

2018 ◽

Vol 22 (11) ◽

pp. 5697-5709 ◽

Cited By ~ 6

Author(s):

Feng Ma ◽

Lifeng Luo ◽

Aizhong Ye ◽

Qingyun Duan

Keyword(s):

River Basin ◽

Dominant Role ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Hydrological Drought ◽

Forecast Skill ◽

Heihe River Basin ◽

Brier Score ◽

Heihe River

Abstract. Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below −1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB.

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