Searching for the optimal drought index and timescale combination to detect drought: a case study from the lower Jinsha River basin, China

Summary

Drought indices based on precipitation are commonly used to identify and characterize droughts. Due to the general complexity of droughts, the comparison of index-identified events with droughts at different levels of the complete system, including soil humidity or river discharges, relies typically on model simulations of the latter, entailing potentially significant uncertainties.

The present study explores the potential of using precipitation-based indices to reproduce observed droughts in the lower part of the Jinsha River basin (JRB), proposing an innovative approach for a catchment-wide drought detection and characterization. Two indicators, namely the Overall Drought Extension (ODE) and the Overall Drought Indicator (ODI), have been defined. These indicators aim at identifying and characterizing drought events on the basin scale, using results from four meteorological drought indices (standardized precipitation index, SPI; rainfall anomaly index, RAI; percent of normal precipitation, PN; deciles, DEC) calculated at different locations of the basin and for different timescales. Collected historical information on drought events is used to contrast results obtained with the indicators.

This method has been successfully applied to the lower Jinsha River basin in China, a region prone to frequent and severe droughts. Historical drought events that occurred from 1960 to 2014 have been compiled and cataloged from different sources, in a challenging process. The analysis of the indicators shows a good agreement with the recorded historical drought events on the basin scale. It has been found that the timescale that best reproduces observed events across all the indices is the 6-month timescale.

This paper was published in February 2018 in the Hydrology and Earth System Sciences journal.

1 Introduction

Drought is a natural phenomenon that results from persistent deficiency of precipitations over an extended period of time compared with some long-term average condition (e.g., precipitation), resulting in a water shortage for some activity, group, or environmental sector (Landsberg, 1982). It generally affects larger areas than other hazards and more people than any other natural catastrophe (Keyantash and Dracup, 2002; Wilhite, 2000).

In China, droughts represent the most severe natural threat for socioeconomic development and ecosystems (Mei and Yang, 2014). Drought events occur in the Jinsha River basin (JRB) and surrounding regions with high frequency. They affect a wide range of areas and cause huge losses to the agriculture sector (He et al., 2013).

To reduce and anticipate such drought impacts, a comprehensive characterization of the phenomenon is essential. Effective and accurate analysis of hydrometeorological data is a key input. Drought indices are useful for tracking droughts and providing a quantitative assessment of the severity, location, timing and duration of such events (World Meteorological Organization and Global Water Partnership, 2016), but also for real-time monitoring (Niemeyer, 2008), risk analysis (Hayes et al., 2004) and drought early warning (Kogan, 2000). Some organizations and agencies already rely on the use of indices in their decision-making processes, thus enhancing proactive drought management policies (Wilhite, 2000).

The choice of index should be based on the type of drought (meteorological, agricultural, hydrological or socioeconomical), the climate regime and the regions affected, as well as the available data. It was found that measured meteorological data were limited in the study region and that precipitation was the single most reliable type of exploitable information. The present study thus focuses on the use of meteorological indices based only on precipitation data. The main advantages are their ease of use, the limited data requirements and the capacity for early detection of drought events, while extensive literature and calculation tools are widely accessible (World Meteorological Organization and Global Water Partnership, 2016). It has been decided to base this study on the standardized precipitation index (SPI, McKee et al., 1993, 1995), the rainfall anomaly index (RAI, Van Rooy, 1965), the percent of normal precipitation (PN, Barua et al., 2011) and the deciles index (DEC, Gibbs and Maher, 1967).

To fill the lack of specific drought-related information, most studies assess the performance of drought indices against results from hydrological soil water models (Halwatura et al., 2016; Hao and AghaKouchak, 2013; Trambauer et al., 2014; Vasiliades et al., 2011; Wanders et al., 2010). However, the performance of these types of studies depends on the accuracy of the models. Their limitations and uncertainties represent an important drawback and should be addressed (Mishra and Singh, 2011). An alternative that often requires more time-consuming work is the compilation of historical records of drought events from different sources. Consequently, their duration, the water scarcity levels, and the drought impacts on population and agriculture can be estimated and then integrated into the analysis. This enables one to identify other types of droughts such as socioeconomical droughts that are hard to assess with hydrological models.

Regarding their spatial resolution, the available drought indices may be based on local measurements (Zhou et al., 2012) and index calculations are usually applied to stations or cells of gridded precipitation datasets; overall spatial patterns on catchment or sub-catchment scales are thus hardly captured. As stated above, droughts affect large areas whose limits are often vaguely demarcated. In addition, water resources are part of a more complex interrelated network which links the source to the point of consumption, where isolated rainfall deficiencies do not necessarily imply a shortage of water availability or even a drought event. Some work (Bhalme and Mooley, 1980; Fleig et al., 2011; Mitchell et al., 1979) suggests the use of drought area indices for the study of droughts that considers areal coverage. The use of overall indicators capable of capturing in a single value the effect of the rainfall deficiency at a regional level is thus convenient and will be applied in this study based on the abovementioned work.

The objective of this study is to capitalize on the collection of drought events that the authors have registered in the lower part of the JRB since 1960 to evaluate and calibrate two indicators capable of identifying drought occurrence and characterizing their intensity on the catchment scale. These indicators are based on commonly used meteorological drought indices for particular timescales.

2 Investigation area and data

The JRB is a sensitive zone in terms of water resources, food security, ecosystem management and human well-being where glacier and climatic variability greatly influence the water regimes and availability. The JRB constitutes the upper part of the Yangtze River Basin and is located between 24°28′N–35°46′N longitude and 90°23′E–104°37′E latitude in southwestern China, with a catchment area of 473’200 km2 (Fig. 1).

The lower part of JRB is a hot-dry valley region characterized by a southwest monsoon climate. The hydrologic regime is characterized by a pronounced seasonal cycle with an annual average precipitation of 600–800 mm/year. Dry season (November to April) precipitation accounts for 10% to 22% of the annual precipitation.

Figure 1 shows the division of the JRB in three parts (Upper, Middle and Lower), and the locations of the meteorological stations used. This study focuses on the analysis of drought events in the lower JRB. The precipitation data needed in this study have been obtained from the China Meteorological Data Service Center (CMA), and downloaded from its data sharing service system (CMDC, 2017). A preliminary quality check and correction of datasets (including data gap-filling) is performed by CMA before uploading them to the system. The monthly precipitation data of 29 meteorological stations within or around JRB, recorded from 1960 to 2014, have been collected and processed. More than 50 years of continuous data are thus available. The spatial distribution of the stations is supposed adequate for the purposes of the study: the stations are distributed relatively evenly both in the zonal and meridional directions, with no zones having a significantly denser presence of stations that could overestimate their importance.

Figure 5. PSS results for different ODE thresholds, with the black error bars representing the 95% confidence interval (±1.96 standard errors) when sampling uncertainties are considered.

Attending to the PSS values (Fig. 5), results show a consistent tendency across all ODE thresholds of higher PSS at the 3- and the 6-month timescales. Moreover, there is no single index that clearly produces better results. Indeed, based on the PSS values and taking into account their uncertainty, there are no statistically significant differences across the different indices for the 3- and 6-month timescales. This indicates that, for these timescales, all the indices perform similarly well in capturing the events. However, it is worth mentioning that, in general, higher PSS values for the 3- and the 6-month timescales are produced using ODE thresholds between 0.4 and 0.6.

Regarding the 6-month ODE series (cf. article), it is important to highlight some relevant aspects:

• All the observed drought events have their corresponding ODE peaks.
• Although event VIII has an estimated duration of 3 months, ODE and ODI results consistently show a longer drought. The exact period of this drought is not well defined as indicated in the catalog, leaving room for a longer duration of the real episode.
• In general, all the indices are well correlated, identifying most of the recorded droughts.
• Several droughts are consistently detected between event I (1962) and II (1979) even if no drought has been chronicled (false alarms). This may correspond to the above-mentioned scarcity of reliable information on droughts prior to 1980.
• The drought events IX, X, XI, XII and XIII are well captured. The different events during this period (2009–2014) match with the consecutive increases in the ODE values for all the indices (DEC, PN, RAI, SPI).
• However, the 6-month series of ODE suggest some false positive detections: more drought events than the observed are calculated.

Regarding the 3-month ODE series, results suggest an overestimation of the number of detected events, as sometimes several detected events combine into one (longer) observed event. The 6-month timescale appears as more appropriate.

In summary, according to the ODE series presented in this article and to the forecast verification carried out with the Peirce skill score (Fig. 5), it seems that the best timescale for the identification of droughts is at 6-months. Results show an equally effective performance of the ODE series for all the indices. However, the risk of false positives must be addressed carefully, as the observation record likely misses events, in particular between 1962 and 1979.

Despite the good performance shown by the overall indicator ODE in drought detection, caution is advised. In particular, the choice of meteorological indices as a basis for the calculation of the ODE and ODI can lead to errors when assessing drought occurrence. Temperature variability, not considered here, can play a significant role in the onset of agricultural drought. Meteorological indices may not be fully capable of capturing the impacts on water scarcity and could be complemented with other types of indices, such as agricultural or hydrological. The same approach proposed in this study is recommended using more comprehensive indices in order to better capture the complex drought processes.

6 Conclusions

• This study aims at applying overall drought indicators representing the drought status within the entire lower JRB investigation area. This work represents an attempt at building a tool for drought monitoring and risk management purposes on the basin scale. It is based on established meteorological indices for the identification of droughts and a method for a catchment-wide drought assessment and characterization, which is compared to historical drought events of the lower JRB.
• The information used for the identification and characterization of major historic droughts was compiled from different sources. A total of 13 major droughts between 1960 and 2014 were identified in the lower JRB and cataloged using a web-based registration platform, allowing for a comparison of the different events.
• Drought indices typically assess local water deficits while available historical records usually refer to regional droughts. To overcome this problem, two drought area indicators, the Overall Drought Extension and the Overall Drought Indicator, have been used to characterize the occurrence and intensity of an event within a specific investigation area. These indicators are based on four common meteorological indices on different timescales: the standardized precipitation index, the rainfall anomaly index, the percent of normal precipitation and the deciles index. By relying exclusively on precipitation, the proposed procedure serves as a basis for further studies in other regions where only precipitation data are available.
• The performance of the ODE in drought detection has been assessed by contrasting the results of this indicator with historical recorded events, offering promising results. It seems that the best results are independent of the index used and produced using the 6-month timescale. Although results suggest the same patterns for all ODE thresholds, it has been noticed that the highest PSS values are produced for thresholds between 0.4 and 0.6, which can be defined as a trigger to detect the occurrence of a drought in the lower JRB.
• Considering the challenge that the compilation of historical drought information represents and the identified limitations, this is a good method for the monitoring of drought episodes within an entire catchment. The use and contrast of drought indicators on the basin scale with historical collected information represent the main innovative aspects of this study. Since meteorological droughts are the first stage in the progression of subsequent agricultural or hydrological droughts, this methodology could be used to activate a management response for a drought event, which starts at a specific threshold value. Additionally, this methodology can be used to complete lacking information on droughts’ duration, geographical extension or intensity.

Figure 4. ODE and ODI values using the 6-month time scales of SPI and RAI indices, compared with the 13 detected historical droughts (in orange).

The objective is to establish a combination of timescale and index that offers an optimum identification of historical droughts. As stated before, the main criteria used to contrast the performance of the forecasts is that a drought event is supposed to happen when the ODE value exceeds a threshold that is to be defined. The combination finally retained should maximize the number of hits and minimize the misses between the forecasts and the observed events.

The 1-month-scale overall indices show rapid fluctuations that correspond to short periods of precipitation deficiency not captured in the catalog of historical droughts. This is mainly due to punctual, large rainfall events that have an important influence in the indices, which may indicate that the drought had ceased when it is not the case (Barua et al., 2011). The opposite effect occurs when using the 48-month scale. The inertia of the rainfall shortage tendencies may mask shorter droughts and overestimate their durations. Since most of the episodes last 1 year or less (Table 1), they are hardly detected using the 48-month scale. Therefore, using the 1- and 48-month scales do not provide any substantial information about the occurrence and duration of the droughts and have been excluded from the performance analysis.

For the rest of the timescales (3-, 6-, 12- and 24-month timescale), the ODE threshold indicating the occurrence of a drought is required for the computing of the PSS that will serve as a support for the selection of the best combination of the index and timescale. A sensitivity analysis was performed using the same threshold across all of the combinations and exploring the effect of varying it in a reasonable range (in this case, from 0.3 to 1 by 0.1 steps). The resulting PSS values are shown in Fig. 5. The black bars indicate the statistical error estimates (confidence intervals) at 95% confidence, due to sampling uncertainties, assessed with the statistical significance test described in Sect. 4.4, which allows indicating whether the score is significantly different from zero. According to the results, most of the PSS confidence intervals do not include zero, disproving that skill scores could have identified drought events by chance sampling fluctuations. For some of the indices on the 24-month scale (e.g., RAI-24 for an ODE threshold=0.7), results cannot assert that skill scores are significantly different from zero and thus these combinations should not be considered.