Flash droughts in a typical humid and subtropical basin: A case study in the Gan River Basin, China

Accepted Manuscript Research papers Flash droughts in a typical humid and subtropical basin: a case study in the Gan River Basin, China Yuqing Zhang, Qinglong You, Changchun Chen, Xin Li PII: DOI: Reference:

S0022-1694(17)30337-2 http://dx.doi.org/10.1016/j.jhydrol.2017.05.044 HYDROL 22035

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

10 September 2016 27 March 2017 22 May 2017

Please cite this article as: Zhang, Y., You, Q., Chen, C., Li, X., Flash droughts in a typical humid and subtropical basin: a case study in the Gan River Basin, China, Journal of Hydrology (2017), doi: http://dx.doi.org/10.1016/ j.jhydrol.2017.05.044

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Flash droughts in a typical humid and subtropical basin: a case study in the Gan River Basin, China Yuqing Zhang a, Qinglong You a,*, Changchun Chen b, Xin Li a a

Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/ Joint International

Research Laboratory of Climate and Environmental Change (ILCEC)/ Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China b

School of Geography and Remote Sensing, NUIST, Nanjing, 210044, China

* Corresponding author E-mail address: [email protected] First author E-mail address: [email protected]

Submitted to Journal of Hydrology, September 10, 2016 1st revision submitted to Journal of Hydrology, January 23, 2017 2nd revision submitted to Journal of Hydrology, March 27, 2017 1

Abstract:

As opposed to traditional drought events, flash droughts evolve rapidly and are characterized

by soil moisture deficits. The general lack of high resolution soil moisture and evapotranspiration data makes identifying flash droughts at short-term scales (pentads or weeks) nearly impossible, particularly at the basin scale. In this study, we investigated the spatial patterns, temporal characteristics, and related mechanisms of flash droughts in a humid and subtropical basin (Gan River Basin) in China. The variable infiltration capacity (VIC) model can accurately reflect hydrological processes in the Gan River Basin at daily and monthly time scales; here, flash droughts were defined based on VIC outputs (soil moisture and evapotranspiration) and meteorological observations (maximum temperature and precipitation) during the growing season (March-October) from 1961 to 2013. We classified flash droughts into two categories (heat wave and precipitation deficit flash droughts) based on the formation mechanisms. Heat wave flash droughts are high temperature driven events, high temperatures (heat waves) cause evapotranspiration to increase and soil moisture to decrease rapidly. The main driver of precipitation deficit flash droughts is precipitation deficits, which cause soil moisture to drop and in turn cause evapotranspiration anomalies to decrease and temperature to increase. The northern part of the basin is apparently vulnerable to heat wave flash droughts, whereas precipitation deficit flash droughts tend to occur across the central and southern parts of the basin. Precipitation deficit flash droughts are more common than heat wave flash droughts in general. Both types of flash droughts became significantly more frequent from 1997 to 2013. These increases in both types of flash droughts are likely attributable to climate-related variables such as temperature, precipitation, evapotranspiration, and soil moisture during 1997-2013. As evidenced by our investigation of the evolution of the two types of flash droughts and the example of 2003 summer flash drought across the Gan River Basin, flash droughts can evolve into prolonged droughts.

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Keywords: Flash drought; Variable infiltration capacity (VIC); Soil moisture; Temperature and precipitation; Gan River Basin; China

1. Introduction Drought events have continually become more common across the globe since 1970, particularly in tropical and subtropical regions (IPCC, 2007). The fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC AR5) provided a comprehensive assessment of variations in drought events, suggesting that different types of droughts merit consideration separately (IPCC, 2013; Liu et al., 2015; Sehgal et al., 2017). Climate warming increases the land surface temperature, causing land surfaces to dry out; droughts occur more quickly under these dry climate conditions, and tend to be more intense and longer-lasting (Trenberth et al., 2013). A traditional drought is considered a slowly evolving phenomenon caused by long-term (months or longer) changes in both precipitation and evapotranspiration. However, droughts can occur rapidly if extreme atmospheric anomalies (e.g., geopotential height and water vapor flux) persist even for a short duration (days or weeks). Recently, the term “flash drought” has been popularized to describe these rapidly evolving droughts (Mo and Lettenmaier, 2015; Otkin et al., 2013). Flash droughts are most likely to occur in the north hemispheric subtropical regions during the growing/warm season (March-October) when daily temperatures are higher than normal and precipitation is lower, both of them cause a decrease in soil moisture (Mo and Lettenmaier, 2015, 2016). Flash droughts suddenly occur with little warning, imposing substantial harm on human society and the economy especially in regards to agricultural crops. For example, the drought event in the south-central United States (U.S.) during May-June 2012 was 3

characterized by its unusually rapid onset; it inflicted widespread agricultural crop failure and drastically cut down the livestock population, making it one of the most costly natural hazards in the U.S. history at a nearly $30 billion economic loss (Otkin et al., 2016; Paimazumder and Done, 2016). Traditional drought indices do not readily allow for identifying flash droughts. For example, both the standardized precipitation evapotranspiration index (SPEI) (Vicente-Serrano et al., 2010) and Palmer drought severity index (PDSI) (Palmer, 1965) use precipitation and temperature observations to capture long-term (months or longer) droughts, but cannot be utilized to monitor flash droughts due to the relatively untimely response to monthly input data versus immediate prevailing weather conditions. It is possible to monitor a meteorological drought (i.e., a period with an abnormal precipitation deficit) via solely precipitation and temperature observations, but flash droughts cannot readily be detected based on long-term soil moisture and evapotranspiration observations. Unfortunately, there currently exist very few high-resolution observations of drought-related variables such as soil moisture and evapotranspiration. Some recent researchers have applied remotely sensed observations and/or reanalysis products to obtain short-term soil moisture and evapotranspiration conditions for identifying flash droughts. For example, Otkin et al. (2013) examined rapid drought onset across the U.S. using evaporative stress index (ESI) with evapotranspiration on the basis of remote-sensing data; they likewise focused on soil moisture evolution and vegetation conditions during the 2012 U.S. flash drought using the ESI based on several model and satellite drought metrics (Otkin et al., 2016). Ford et al. (2015) evaluated the utility of in situ soil moisture observations for flash drought early warning in Oklahoma (U.S.). Mo and Lettenmaier (2015) explored heat wave flash droughts based on temperature measurements and model-reconstructed soil moisture and evapotranspiration during growing seasons (April-September) from 1916 to 2013. The same researchers identified precipitation deficit flash drought accordingly in a subsequent study (Mo and Lettenmaier, 2016). 4

In China, Yuan et al. (2015) assessed the capability of microwave remote sensing datasets in monitoring short-term agricultural droughts, which are defined based on soil moisture deficit and affect crop production or ecosystem function during the growing season. Wang et al. (2016) investigated the temporal and spatial distribution of heat wave flash droughts over China from 1979 to 2010. It is necessary to further study flash droughts and their mechanisms for the sake of early prediction. There has been limited research to date in regards to identifying and attributing spatial variations in flash drought events, especially at the basin scale in China. As mentioned above, there are two separate categories of flash droughts worthy of research attention: the heat wave flash drought, and the precipitation deficit flash drought. A heat wave flash drought is mainly caused by high temperatures, strong evapotranspiration, and anomalously low soil moisture; although precipitation plays an important role, precipitation anomalies are not responsible for heat wave flash droughts (Mo and Lettenmaier, 2015, 2016). A precipitation deficit flash drought, conversely, is caused by high temperature and decrease in evapotranspiration due to severe lack of precipitation. As demonstrated by Wang et al. (2016), flash droughts are most likely to occur across southern China; however, this study only focused on heat wave flash droughts, while changes in precipitation deficit flash drought over China have not been addressed. There has also been very little research on flash droughts in humid and subtropical basin areas in China. The Gan River Basin is the largest sub-basin of the Poyang Lake Basin, a typical humid and subtropical basin in China. In this study, we used the Gan River Basin as an example to study flash drought variations in terms of spatial and temporal scales. The primary goals of this study include: 1) evaluating the performance of hydrological processes using the variable infiltration capacity (VIC) model; 2) examining the frequency at which heat wave and precipitation deficit flash droughts occur across the Gan River Basin; 5

3) detecting trends in these flash droughts; and 4) investigating the mechanisms behind flash drought onset by focusing on the 2003 drought as an example. The findings presented here may provide a scientific basis for improving the detection and prediction of flash drought, mitigating their associated potential losses, and optimizing basin-scale water resources management techniques.

2. Materials and methods 2.1. Study region The Gan River Basin is located mostly within the south-central part of Jiangxi province, China, with an approximate area of 83,500 km2. The Waizhou hydrological station (outlet) in Nanchang controls 97% of the Gan River Basin, which is bounded by the 113°42′ E - 116°38′ E longitudes and the 24°30′ N - 28°42′ N latitudes (Fig. 1). Landforms in this basin are quite complex: there are mountainous areas with subordinate hills in the central and south parts of the basin, while alluvial plains dominate the lower basin. The basin is dominated by a humid subtropical climate and mainly influenced by the East Asian monsoon. The basin receives annual mean precipitation of 1600.1 mm and has an annual mean temperature of 18.2 °C, with distinct seasonal variations. The dry season (July-September) is prone to drought, while the wet season (April-June) is vulnerable to flooding (Zhang et al., 2015). The total mean annual streamflow volume of the Gan River is around 687×108 m3, representing the largest sub-basin both in area (51%) and runoff volume (50%) within the Poyang Lake Basin. The runoff is mainly precipitation-driven and exhibits strong seasonality, with an increase in the first half of the year followed by a decline in the second half of the year, typically reaching its peak in June and its lowest level in December. 2.2. Hydrometeorological data

6

Daily streamflow data at Waizhou hydrological station for the period 1960-2012 were provided by the Jiangxi Hydrological Bureau. The quality of daily streamflow data was carefully verified and controlled before its release and the dataset contained no missing values. Daily maximum/minimum temperature, precipitation, and surface wind speed data sets for 1960-2013 were taken from 47 meteorological stations provided by the National Climate Center (NCC) of the China Meteorological Administration (CMA). The quality and homogeneity of the climate data sets were again fully verified and controlled before their release, and there were no gaps in the data. The station-based data were interpolated to each grid (0.125o spatial resolution) for the VIC hydrological model via the Thiessen polygon method (Fiedler, 2003), which is well-suited to the uneven spatial distribution of the stations. Daily 500 hPa geopotential heights originated from the NOAA-CIRES Twentieth Century Reanalysis (V2c) (Compo et al., 2006; Compo et al., 2011) at a horizontal resolution of 2° for the period 1851-2014. We used the 500 hPa geopotential heights lat/lon for the region 0-80°N, 70-180°E from 1960 to 2013. 2.3. Geospatial data The geospatial data applied in this study included the digital elevation model (DEM), river network, soil information, and land cover information. The 90 × 90 m SRTM (Shuttle Radar Topography Mission) DEM

was

obtained

from

the

International

Scientific

and

Technical

Data

Mirror

Site

(http://www.gscloud.cn). Land cover data at 1 × 1 km resolution (including vegetation-related parameters such as architectural resistance of vegetation type, minimum stomatal resistance of vegetation type, root zone thickness, and leaf-area index of vegetation) were taken from the University of Maryland's Global Land Cover Facility (http://glcf.umd.edu/data/landcover/data.shtml), which contains a total of 14 different land cover types (Hansen et al., 2000). Soil data (1:1 million scale) were downloaded from the Food and Agriculture Organization (FAO) with the standard depth of 0-30 cm for the upper soil layer and 30-100 cm 7

for the lower soil layer (FAO, 2009). The hydrological attributes (e.g., saturated hydrological conductivity, bulk density of soil layer, and soil moisture diffusion parameter) of each soil type were calculated using Soil-Plant-Atmosphere-Water (SPAW) software and relevant empirical formulas. DEM, land cover, and soil data were uniformly gridded into 0.125 o spatial resolution according to the requirements of the Variable Infiltration Capacity model. 2.4. Variable Infiltration Capacity (VIC) model The physically-based VIC model (Liang et al., 1994) can balance both water and energy budgets for land surface-atmosphere hydrometeorological processes within a given grid cell. The key characteristics of the grid-based VIC are the representation of vegetation heterogeneity, multiple soil layers with variable infiltration, and non-linear base flow. The water balance in the VIC model can be calculated as follows:

where ds/dt, P, E, and R are the water storage variation (mm), precipitation (mm), evapotranspiration (mm), and runoff (mm), respectively. The energy balance equation for the surface is:

where

is the net radiation flux (W·m-2), H is the sensible heat flux (W·m-2),

water (kg·m-3),

is the latent heat of vaporization (J·kg-1),

is the density of liquid

represents the latent heat flux

(W·m-2), and G is the ground heat flux (W·m-2). The total evapotranspiration within a grid cell is calculated as follows:

where

denotes the vegetation fractional coverage for the nth vegetation tile,

from the canopy layer of each vegetation tile, tiles,

is the bare soil fraction, and

is the evaporation

denotes the transpiration from each of the vegetation

represents the evaporation from the bare soil. All three types 8

of evaporation are given in millimeters. The Penman-Monteith method (Allen et al., 1998) is mainly used to estimate evapotranspiration. The total evapotranspiration is the sum of the canopy evaporation and transpiration values from each vegetation tile plus evaporation from the bare soil tile, which is weighted by the coverage fraction for each surface cover type. In order to accurately estimate soil evapotranspiration and to establish the mechanisms for moisture migration from the lower to upper soil layer, the three-layer VIC model (VIC-3L) framework (Liang et al., 1996) includes an additional 10 cm soil layer situated above the upper soil layer. Bare soil evaporation only occurs on the top thin layer, while the surface runoff is generated from the upper two soil layers based on the infiltration capacity curve as described by the Xinanjiang hydrological model (Zhao Renjun, 1980). This capacity curve equation is:

where

and

denote the point and maximum soil infiltration capacity (mm), respectively, A is the

fraction of area for which the soil infiltration capacity is less than

, and b is the soil infiltration shape

parameter. The runoff from the bottom soil layer (third soil layer, maximum depth around 1.5 m) is defined based on the drainage in the Arno model (Franchini and Pacciani, 1991); the base flow formulation is defined as follows:

where

is the maximum base flow (mm d-1),

is the fraction of

the fraction of maximum soil moisture (soil porosity)

, and

,

is the soil porosity,

is the current soil moisture of the third

layer (bottom soil layer). The base flow recession curve is linear/nonlinear below/above a threshold ( 9

is

).

Typically, each grid cell of the VIC results is post-processed with a separate routing model to simulate streamflow (Lohmann et al., 1996; Lohmann et al., 1998) based on a linear transfer function. In the routing model, we applied a channel-routing program based on the linearized Saint-Venant equation to simulate the discharge at the basin outlet. The linearized Saint-Venant equation is:

where

is discharge,

denotes time,

is the space coordinate along the channel axis, the D and C are

diffusivity and wave velocity parameters, respectively. The key features of the grid-based VIC include an accurate representation of land cover/vegetation heterogeneity, non-linear base flow, and multiple soil layers with variable infiltration. Further detail regarding the VIC model is described by Liang et al. (1994) and Gao et al. (2010). The VIC model (version 4.1.2.g) is applied to simulate hydrological processes at a daily temporal and a 0.125° spatial resolution in the Gan River Basin. Briefly, we calibrated six parameters: a shape of the VIC curve b, three base flow parameters ( where non-linear base flow begins, and

is the maximum velocity of base flow,

is the fraction of

is the fraction of maximum soil moisture where non-linear

base flow occurs), and two soil layer parameters (d2 is the thickness of the second soil moisture layer, and d3 is the thickness of the third soil moisture layer) (Bao et al., 2012). These parameters were calibrated by the Nash-Sutcliffe Efficiency (NSE), determination coefficient (R2), and relative bias (Bias), which are expressed as follows:

10

where

and

temporal scales);

are the observed and simulated streamflow at each time step i (in daily and monthly and

are the mean values of observed and simulated streamflow, and n is

the number of time steps, respectively. The closer NSE and R2 are to 1, the better the VIC performs. 2.5. Definition of flash droughts We used meteorological observations (daily maximum temperature and precipitation) and VIC simulation outputs such as daily soil moisture (with soil total column depth of around 1.5 m), evapotranspiration, and runoff to explore flash drought characteristics. As mentioned above, the base period of the data set was from 1961 to 2013. We used pentads (five day means) to appropriately account for the brevity of flash drought events. We focused on pentads from March to October (49 pentads per year). There were a total of 2597 pentads in the 53-year record (1961-2013). According to the definitions provided by Mo and Lettenmaier (2015, 2016), we applied two respective scenarios for heat wave and precipitation deficit flash droughts based on pentad data. For heat wave flash droughts: Case 1: Tmax anomaly > 1 STD, ET anomaly > 0, and SM < 40th percentile; Case 2: Tmax anomaly > 1 STD, ET anomaly > 0, and P anomaly < 0. and for precipitation deficit flash droughts: Case 3: Tmax anomaly > 1 STD, ET anomaly < 0, and P < 40th percentile; Case 4: Tmax anomaly > 1 STD, ET anomaly < 0, and SM < 40th percentile. STD denotes the standard deviation, SM% is expressed as a percentile relative to the long-term record, and P% is defined similarly but for precipitation. The main difference between a heat wave flash drought and precipitation deficit flash drought is the required range of anomalous evapotranspiration values.

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For each grid and pentad, a flash drought event was identified when all of the above requirements were met. We defined the frequency of occurrence (FOC) as the percentage of pentads under both types of flash droughts for each grid.

where

is the total number of pentads and N is the number of pentads in which each type of flash

drought occurred from 1961 to 2013.

3. Results 3.1. VIC performances in hydrological processes In order to accurately simulate hydrological processes after calculating each grid cell of the VIC results (i.e., flux files), the flow direction must be introduced to the routing model as it determines all of the grid cells that are connected in the routing net. We ensured the appropriate VIC flow direction using the ArcGIS9.3 software package based on the measured river network (Fig. 2). Calibration and validation were performed for daily and monthly streamflows using the discharge values at the basin outlet (Waizhou gauge station). Comparisons among observed and simulated streamflows at various timescales during calibration and validation periods are shown in Fig. 3. The VIC model results were in accordance with the hydrological observations, despite a few differences in the peaks. Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (R2), and relative bias (Bias) were then adopted to evaluate VIC model performance. Table 1 shows the results of statistical evaluations at daily and monthly timescales. During the calibration period, NSE values were 0.90 and 0.83 and R2 values were 0.95 and 0.84 at monthly and daily timescales, respectively, indicating that simulations at coarser timescales yielded better performance in representing the effects of hydrological processes. Bias 12

values were 7.0% and 6.9% at monthly and daily timescales, suggesting that the simulated values were close to the observed values. During the validation period, NSE and R2 values were slightly higher than those of the calibration period at monthly timescales, but slightly lower at daily timescales; further, the Bias values indicated that the validation period slightly outperformed the calibration period. According to these statistical evaluation results, we concluded that the VIC model can indeed reasonably represent the characteristics of hydrological processes in the Gan River Basin. Therefore, the outputs (e.g., soil moisture and evapotranspiration) of the VIC are reliable and reasonable to study flash droughts. 3.2. Flash drought frequency of occurrences (FOCs) As shown in Fig. 4a, the FOC for heat wave flash droughts across the Gan River Basin had a pronounced north-south gradient. The FOC across most southern parts of the basin was less than 2%, implying that heat wave flash droughts do not occur commonly in this region. Conversely, the maximum FOC reached 4-5% of the total pentads over the north, suggesting that they suffer heat wave flash droughts than other parts of the basin. A heat wave flash drought is defined based on maximum temperatures, so precipitation is not a key characteristic of heat wave flash drought. The precipitation anomalies for pentads under heat wave flash droughts are presented in Fig. 4b; precipitation anomalies were almost negative over the entire Gan River Basin, and the distribution of precipitation anomalies and heat wave flash droughts was broadly consistent. The greater the negative precipitation anomalies, the greater the frequency of heat wave flash droughts. Because precipitation anomalies are not a requirement for heat wave flash droughts, when soil moisture percentile was lower than 40% (Fig. 4a), the precipitation anomalies were almost negative (Fig. 4b). Compared to the heat wave flash drought criteria shown in Fig. 4a, after changing the precipitation anomaly criteria to below zero (Fig. 4c), the FOC pattern did not significantly change but the magnitudes markedly increased (more than 10% of the total pentads’ FOC in northern parts of the basin). 13

However, the soil moisture percentiles under this heat wave flash drought criteria was higher than 40% (Fig. 4d), which is too high to fall under the definition of agricultural drought (soil moisture deficit is an important indicator of flash drought, and soil moisture is the proximate determinant of agricultural drought, therefore, flash drought is the category of agricultural drought). We ultimately adopted Case 1 (Tmax anomaly > 1 STD, ET anomaly > 0, and SM < 40th percentile) to define heat wave flash drought events. In order to distinguish precipitation deficit flash droughts from heat wave flash droughts, we set the evapotranspiration anomaly requirement to below zero and calculated FOC again. Precipitation deficit flash droughts were most likely to occur over the central and southern parts of the basin (Fig. 5a and Fig. 5c). The maximum FOC (6-7%) was located over the central and southern parts of the basin (Fig. 5a), indicating that the number of precipitation deficit flash droughts in that location was much greater than that of the heat wave flash droughts. As shown in Fig. 5b, the soil moisture for pentads under precipitation deficit flash droughts was below 30% over the whole basin; these conditions are consistent with the definition of agricultural drought. When we changed the soil moisture criteria to below 40%, the FOC pattern was consistent with Case 3 (Section 2.5) but the number of events slightly increased (Fig. 5c). As shown in Fig. 5d, the precipitation percentiles for pentads under this precipitation deficit flash drought criteria was lower than the 36th percentile. Case 4 has more relaxed requirements overall compared to Case 3 because its FOC for precipitation deficit flash drought is greater. We defined these drought events by precipitation anomalies instead of soil moisture percentiles and ultimately adopted Case 3 (Tmax anomaly > 1 STD, ET anomaly < 0, and P < 40th percentile) to characterize them appropriately. 3.3. Flash drought trends To explore the interannual variability in the data described above, we plotted the average number of pentads under heat wave and precipitation deficit flash droughts per year over the entire Gan River Basin 14

(Fig. 6). There were many more pentads for heat wave and precipitation deficit flash droughts overall from 1962 to 1969 and from 2007 to 2011, but fewer pentads for both types of flash droughts from 1973 to 1977 and from 1993 to 1997. Precipitation deficit flash droughts occurred more frequently than heat wave flash droughts according to their respective quantities of pentads. Interestingly, both types of flash droughts significantly increased in frequency from 1997 to 2013; further, both types of flash droughts increased around one pentad per decade. To determine FOC patterns and related forcing variables (e.g., maximum temperature, precipitation, evapotranspiration, and soil moisture) from 1997 to 2013 over the entire basin, we ran a Mann-Kendall test (Kendall, 1975; Mann, 1945; Sen, 1968) on the FOC and related variables for each grid cell. As shown in Fig. 7, there were several regions where trends were statistically significant from 1997 to 2013. There were upward trends in both heat wave and precipitation deficit flash droughts, especially in the central and southern parts of the basin (Fig. 7a and Fig. 7b). The frequency of precipitation deficit flash droughts increased to a greater extent than heat wave flash droughts. There were no statistically significant downward trends in either type of flash drought. Maximum temperature in almost the entire Gan River Basin significantly increased during the growing season, ranging from 0.4 to 1°C per decade across the basin (Fig. 7c). Precipitation and evapotranspiration values exhibited significant downward trends from 1997 to 2013, though the areas most keenly affected by these changes are mainly located in the central basin (Fig. 7d and Fig. 7e). There were decreasing trends in precipitation and evapotranspiration in the areas where precipitation deficit flash droughts tend to occur. 3.4. Flash drought mechanisms To explore the characteristics of flash droughts in further detail, we investigated the evolution of both types of flash droughts by examining the anomalies in certain hydrometeorological variables from one 15

pentad prior to onset (T-1) to one pentad post-onset (T+1). The evolution of heat wave flash droughts that are driven mainly by elevated temperature is shown in Fig. 8. When a heat wave flash drought event occurs, maximum temperature anomalies rapidly increase (about 1-2°C higher than pre-onset). Maximum temperatures in the north increased to a greater extent than temperatures in the south, indicating that the northern part of the basin is more vulnerable to heat wave flash droughts (Fig. 4a). High temperatures also increased evapotranspiration and quickly decreased soil moisture below 40%. Precipitation deficits do not increase temperature or evapotranspiration, but do increase soil moisture deficits and make the climatic conditions more favorable for heat wave flash droughts to occur. During any heat wave drought, precipitation in the south was greater than that in the north, but the extent of increase in maximum temperature in the south was lower than that in the north. This resulted in weak evapotranspiration (and therefore fewer droughts) in the south than the north. Runoff (one of the direct indicators of surface wet/dry conditions) had similar distribution to precipitation. Temperature and evapotranspiration anomalies decreased on average after heat wave flash drought onset, but soil moisture continually showed negative anomalies across the whole Gan River Basin; this may have been caused by a lack of precipitation in general. Figure 9 shows the life cycle of a precipitation deficit flash drought associated with maximum temperature, evapotranspiration, soil moisture, precipitation, and runoff. When this type of flash drought begins, precipitation has declined rapidly and corresponding evapotranspiration decreases. Increased temperature anomalies are caused by evapotranspiration decreases throughout the whole basin. Evapotranspiration and soil moisture showed significant negative anomalies due to precipitation deficits and rapid responsive increase in temperature. The spatiotemporal distribution of runoff anomalies was consistent with that of precipitation anomalies because precipitation is the direct source of runoff in the 16

humid basin. During onset, soil moisture reached a minimum value while temperature reached a maximum value. Precipitation anomalies also markedly decreased to negative values leading to large deficits in runoff and evapotranspiration anomalies. After the onset period was complete, temperature returned to a normal state while the remaining four variables took a few pentads to return to normal. These persistently negative values indicated that precipitation deficit flash droughts were likely to develop into lengthier (i.e., not flash) drought events. Precipitation, runoff, soil moisture, and evapotranspiration values were closely interrelated throughout the precipitation deficit flash drought evolution and were strongly affected by decreases in evapotranspiration and increases in maximum temperature. Jiangxi Province suffered rampant and unprecedented high temperatures and drought disasters during the summer of 2003. This severe drought caused economic losses directly tied to agricultural production by 6.7 billion yuan across the province (Chen, 2005). As shown in Fig. 6, in 2003, the numbers of pentads for flash droughts (especially precipitation deficit type) were quite high. To further probe the characteristics and mechanisms of this particular drought event, we examined the evolution of the 2003 summer droughts associated with maximum temperature, evapotranspiration, soil moisture, and precipitation. As shown in Fig. 10, maximum temperature anomalies increased from July 4-8 onward until reaching a maximum around July 14-18, at which maximum temperature anomalies were greater than 1.6 times standard deviation. There was a rapid increase from the period before one pentad (July 9-13) to that after one pentad (July 14-18) over the entire basin. Evapotranspiration anomalies were negative in the central and southern parts of the basin, albeit with positive values in the north. Soil moisture decreased rapidly from July 9-13 to July 14-18, at below 20% in the most of the basin. Precipitation values were almost entirely below the 40th percentile in the whole basin during July 9-28, and the precipitation percentiles decreased rapidly from July 4-8 to July 9-13. We selected July 14-18, 2003 as a case study to further explore these anomalies. 17

Evapotranspiration anomalies were negative in the most central and southern parts of the basin in response to the insufficient soil moisture caused by precipitation deficits. The decreased (negative values) evapotranspiration caused the Bowen ratio and temperature to increase. Despite high temperature and evapotranspiration anomalies in the northern parts of the basin, however, soil moisture was in excess of 40% – too high to fall under the definition of heat wave flash drought. To this effect, the 2003 summer drought can be defined as a precipitation deficit flash drought event. Sustained high temperatures and soil moisture deficits during this time also caused flash droughts to evolve into prolonged droughts. The composite of 500 hPa geopotential height anomalies for July 4-28, 2003, is shown in Fig. 11. Positive values in 500 hPa geopotential height anomalies field readily strengthened anticyclone properties, causing precipitation to decrease and temperature to increase across the whole Gan River Basin and further creating conditions where precipitation deficit flash drought events were highly likely to occur.

4. Discussion Flash droughts by definition fall into the agricultural drought category. They are characterized by rapid increase in temperatures (i.e., heat waves), high evaporative demand, and soil moisture deficit (Mo and Lettenmaier, 2015). In this study, we took a novel approach to exploring the distinct patterns and trends in two types of flash droughts in the Gan River Basin (a typical humid and subtropical basin); we also explored the unique characteristics and causes of these droughts according to the climatic conditions under which they occur. Heat wave flash droughts tended to occur most often in the northern parts of the Gan River Basin while precipitation deficit flash droughts occurred mostly over the central and southern parts of the basin. Precipitation deficit flash droughts were more universal than heat wave flash droughts according to the FOC values. The humid and subtropical basin climate is prone to flash droughts, as evidenced here and in 18

accordance with the results of a previous study by Wang et al. (2016) on southern Chinese climatic characteristics. Figure 12 shows the distribution of the number of hot days alongside precipitation patterns during March to October months from 1961 to 2013. Heavily precipitation deficient and hot days, which are more common in central and southern parts of the basin, caused more precipitation deficit flash droughts in these areas than the northern parts of the basin. The maximum temperature anomalies we observed were much greater than one standard deviation under precipitation deficit flash droughts, but the corresponding evapotranspiration anomalies were less than zero. The northern part of the basin showed higher heat wave flash drought FOC values, as well as relatively more hot days and abundant precipitation (Fig. 12) related to positive evapotranspiration anomalies and rapid soil moisture deficits. The concurrence of droughts and heat waves have substantially increased in recent years across the U.S. (Aghakouchak et al., 2015; Mazdiyasni and Aghakouchak, 2015). Though climate warming appears to have stalled across most of China in recent years (Duan and Xiao, 2015), flash drought trends are still largely uncertain in areas like the Gan River Basin. El Niño-Southern Oscillation (ENSO) is the main source of variability in tropical and subtropical precipitation, thus ENSO variations may have affected the drought occurrence and strength (Trenberth et al., 2013). There was an obvious upward trend in precipitation deficit and heat wave flash droughts in the Gan River Basin from 1997 to 2013. The Mann-Kendall test results indicated upward trends in both types of flash droughts, especially in central and southern parts of the basin. This phenomenon may be attributable to the significant increase in maximum temperature anomalies over almost the entire basin during the growing season, whereas precipitation, evapotranspiration, and soil moisture substantially declined in the central and southern parts of the basin. We utilized three land use maps specific to different time scales (Fig. 13) from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn) for 19

the purposes of this study. Over the past 20 years, although some land use types have changed, the basic distributions have remained consistent over the study area: the largest proportion of land use is forest, so vegetation cover is relatively high in the Gan River Basin during the growing season. Flash droughts are by nature very likely to occur in a humid and subtropical basin that is as densely vegetated as the Gan River Basin. When temperatures across the basin were high enough to constitute a heat wave, it was not vegetation but precipitation that was most responsible for flash drought onset. A schematic representation of the two types of flash droughts over the Gan River Basin is shown in Fig. 14. When a heat wave flash drought event occurs, there is a significant and rapid increase in temperature. As discussed above, high temperature anomalies cause evapotranspiration to increase and soil moisture to decrease; there is no prepositional precipitation requirement because this drought is defined based on soil moisture deficits instead of precipitation deficits. Per its namesake, precipitation deficit is a major driver in precipitation deficit flash drought; precipitation deficit causes decrease in soil moisture and evapotranspiration, which in turn results in increase in temperature (the Bowen ratio and temperature increased in response to the decreased evapotranspiration). Heat wave flash droughts were more likely to occur in northern parts of the basin while precipitation deficit flash droughts were more common in central and southern parts of the basin throughout the entire study period. The total precipitation (Fig. 12b) in the northern parts of the basin was relatively high. The runoff caused by precipitation should be controlled to minimize agricultural crop damage caused by heat wave flash droughts. However, the total precipitation in the central and southern parts of the basin is relatively low, so agricultural crop damage caused by precipitation deficit flash droughts can be mitigated by artificial precipitation enhancement or inter-basin water transfer projects.

20

Temperature and evapotranspiration anomalies typically return to normal levels by the end of the flash drought onset, while soil moisture and runoff deficits may persist for several weeks. These conditions can transform a flash drought into a prolonged drought if soil moisture is deficient while temperature is continually high. We found that the 2003 summer drought was ultimately caused by precipitation deficit. During a typical summer in a humid and subtropical basin with high vegetation cover, any rapid soil moisture loss resulting from enhanced evaporative ability caused by precipitation deficits can lead to temperature increases, ultimately resulting in a precipitation deficit flash drought.

5. Conclusions Flash droughts tend to occur suddenly with little warning in humid and subtropical basins that may be populated by communities that are unprepared for their severe impacts on society and the economy. These communities would benefit considerably from improved drought prediction and mitigation techniques. This study used the Gan River Basin as an example to investigate the spatial and temporal characteristics of two types of flash droughts and to explore their mechanisms as associated with a series of hydrometeorological variables. The most notable conclusions of this study can be summarized as follows: 

According to the NSE, R2, and Bias results, the VIC model can reasonably represent the characteristics of hydrological processes in the Gan River Basin at daily and monthly time scales. Heat wave flash droughts can be defined accordingly as concurrent Tmax anomaly > 1 STD, ET anomaly > 0, and SM < 40th percentile. The northern part of the basin is more likely to suffer heat wave than precipitation deficit flash droughts. We adopted Tmax anomaly > 1 STD, ET anomaly < 0, and P < 40th percentile as the criteria for precipitation deficit flash droughts, to which the central and southern parts of the basin are more prone than heat wave flash droughts. 21



Precipitation deficit flash droughts were more common than heat wave flash droughts in the Gan River Basin according to the calculated FOC values. Interestingly, both types of flash droughts have increased significantly from 1997 to 2013 across the whole basin. This upward trend may be attributable to increases in temperature and decreases in precipitation, evapotranspiration, and soil moisture from 1997 to 2013.



The heat wave flash drought is temperature-driven event. Elevated temperature causes rapid increase in evapotranspiration and decrease in soil moisture, so heat wave flash droughts are more likely to occur when these conditions are met. Conversely, though evaporative capability is high under elevated temperature, decrease in evapotranspiration caused by precipitation deficit and the corresponding rapid declines in soil moisture are conditions responsible for precipitation deficit flash droughts. As evidenced by our investigation of the 2003 summer flash drought across the Gan River Basin, a flash drought may easily transform into a prolonged drought under certain conditions.

Acknowledgements This study is supported by the National Key Research and Development Program of China (2016YFA0601700), State Key Program of National Natural Science Foundation of China (41230528), Jiangsu Specially-Appointed Professor (R2013T07), Jiangsu Natural Science Funds for Distinguished Young Scholar “BK20140047”, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the National Natural Science Foundation of China (NO.41471425)，the Research and Innovation Project for College Graduates of Jiangsu Province (No.1344051501007), and the National

22

Natural Science Foundation of China (NO.41401017). We are very grateful to the reviewers for their constructive comments and thoughtful suggestions that have improved this paper substantially.

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Figure:

Fig.1. (a) Location of Gan River Basin with hydrological and meteorological stations, (b) Monthly mean precipitation and (c) temperature from 1960 to 2013 .

25

Fig.2. VIC flow network to the basin outlet. 10000 VIC

3

Monthly streamflow (m /s)

Observed 8000

6000

4000

2000

0 25000 Validation

3

Daily streamflow (m /s)

Calibration 20000

15000

10000

5000

0

1961

1971

1981

1991

2001

2011

Year

Fig.3. Comparison between observed and simulated streamflows at monthly (upper panel) and daily (lower panel) timescales during calibration and validation periods. The correlation coefficients of observed and simulated streamflows are 0.97 and 0.91 for monthly and daily timescales, respectively. 26

Fig. 4. (a) Frequency of occurrence (FOC) of pentads under heat wave flash droughts. (b) Precipitation anomalies for pentads under heat wave flash drought as defined in (a). (c) FOCs under different definitions of heat wave flash drought. (d) SM% for pentads under heat wave flash drought as defined in (c). Anomalies are calculated based on the difference between each variable and climate mean for each variable from March to October, 1961-2013.

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Fig. 5. (a) FOCs of pentads under precipitation deficit flash droughts. (b) SM% for pentads under precipitation deficit flash drought as defined in (a). (c) FOCs under different definitions of precipitation deficit flash drought. (d) Precipitation percentiles for pentads under precipitation deficit flash drought as defined in (c). Anomalies are calculated based on the difference between each variable and climate mean for each variable from March to October, 1961-2013.

28

Fig. 6. Number of pentads under heat wave flash drought (left panel) and precipitation deficit flash drought (right panel) per year averaged over the Gan River Basin. Red lines indicate the trends exceeding the 0.05 significance level. Black lines indicate the trends failing the 0.05 significance level.

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Fig. 7. Trends in the number of events (pentads) per decade under heat wave flash droughts (a) and precipitation deficit flash drought (b) during 1997-2013. Trends in maximum temperature mean values (c), precipitation (d), evapotranspiration (e), and soil moisture (f) from March to October. Trends which are statistically significant at p<0.05 level determined by Mann-Kendall test are filled in color. Areas in white indicate trends are not statistically significant at p<0.05 level.

Fig. 8. Maximum temperature anomalies for heat wave flash droughts (a) one pentad before onset, (b) onset, and (c) one pentad after onset (oC/day). (d)-(f) Evapotranspiration anomalies (mm/day). (g)-(i) Soil moisture percentiles. (j)-(l) Precipitation anomalies, and (m)-(o) runoff anomalies ( mm/day).

30

Fig. 9. Maximum temperature anomalies for precipitation deficit flash drought (a) one pentad before onset, (b) onset, and (c) one pentad after onset (oC/day). (d)-(f) Evapotranspiration anomalies (mm/day). (g)-(i) Soil moisture percentiles. (j)-(l) Precipitation anomalies, and (m)-(o) runoff anomalies ( mm/day).

31

Fig. 10. Maximum temperature anomalies (standardized) for pentad (a) July 4-8, (b) July 9-13, (c) July 14-18, (d) July 19-23, and (e) July 24-28, 2003. (f)-(j) Evapotranspiration anomalies (mm/day). (k)-(o) Soil moisture (percentiles). (p)-(t) Precipitation (percentiles).

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Fig. 11. The 500 hPa geopotential height anomalies for (a) 4-8 July, (b) 9-13 July, (c) 14-18 July, (d) 19-23 July, (e) 24-28 July, and (f) 4-28 July, 2003. Unit is in geopotential height metre. The red rectangle represents the study area.

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Fig. 12. Distribution of (a) number of hot days under Tmax>=35 oC and (b) precipitation during March-October (1961-2013).

Fig. 13. Land use for (a) 1990, (b) 2000, and (c) 2010.

Fig. 14. The schematic representation of two types of flash droughts over the Gan River Basin.

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Table: Table 1 Performance statistics during calibration and validation periods. NSE

R2

Bias(%)

Calibration(1960-1990)

0.90

0.95

7.0

Validation(1991-2012)

0.92

0.95

4.8

Calibration(1960-1990)

0.83

0.84

6.9

Validation(1991-2012)

0.81

0.81

4.7

Period Monthly

Daily

35

Highlights

1. The northern parts of the Gan River Basin suffer heat wave flash droughts.

2. The central and southern parts of the basin are prone to precipitation deficit flash droughts.

3. Precipitation deficit flash droughts are more common than heat wave flash droughts in the basin.

4. Both types of flash droughts have increased significantly since 1997 across the entire basin.

5. Precipitation deficit flash droughts can develop into prolonged drought events

36