remote sensing

Article Drought and Flood Monitoring of the Liao Basin in Northeast Using Extended GRACE Data

Xinhui Chen, Jinbao Jiang * and Hui Li

College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China; [email protected] (X.C.); [email protected] (H.L.) * Correspondence: [email protected]; Tel.: +86-131-2686-0872

 Received: 20 May 2018; Accepted: 20 July 2018; Published: 24 July 2018 

Abstract: In recent years, alternating periods of floods and droughts, possibly related to climate change and/or human activity, have occurred in the Basin of China. To monitor and gain a deep understanding of the frequency and severity of the hydro-meteorological extreme events in the Liao River Basin in the past 30 years, the total storage deficit index (TSDI) is established by the Gravity Recovery and Climate Experiment (GRACE)-based terrestrial water storage anomalies (TWSAs) and the general regression neural network (GRNN)-predicted TWSA. Results indicate that the GRNN model trained with GRACE-based TWSA, model-simulated soil moisture, and precipitation observations was optimal, and the correlation coefficient and the root mean square error (RMSE) of the predicted TWSA and GRACE TWSA for the testing period equal 0.90 and 18 mm, respectively. The drought and flood conditions monitored by the TSDI were consistent with those of previous studies and records. The extreme climate events could indirectly reflect the status of the regional hydrological cycle. By monitoring the extreme climate events in the study area with TSDI, which was based on the TWSA of GRACE and GRNN, the decision of water resource management in the Liao River Basin could be made reasonably.

Keywords: droughts and floods; TSDI; GRACE; TWSA; GRNN; previous researches; records

1. Introduction The Liao River Basin in China is characterized by an arid and semi-arid climate [1]. Rainfall in the area mainly occurs in July or August [2–4]. The Liao River Basin, affected by the alternating effects of warm and moist air in the Southeast Pacific and cold air from the west or north, is prone to rainstorms [5]. Rainstorms account for a large proportion of annual precipitation, and cause frequent flooding in the basin. Moreover, the river basin suffers from different degrees of drought intensity nearly each year; spring drought is particularly severe [6]. These hazards harm the national economy, human life, and property. The frequency of hydro-meteorological extreme events has increased substantially due to global warming and high-intensity human activity, such as deforestation and unsustainable use of water resources [1,7–9]. Hence, a need is created to monitor and/or predict occurrences of drought and flood timely and effectively. Total water storage change (TWSC) integrates the change of water storage in the vertical direction and includes variations in groundwater storage (GWS), soil moisture storage (SMS), snow water equivalent (SWE), and biomass water content. TWSC is derived from the Gravity Recovery and Climate Experiment (GRACE) total water storage anomalies (TWSAs) by default with a monthly temporal resolution. TWSC represents the difference in TWS (i.e., water flux) between two consecutive months, while TWSA is the anomaly with relative to the average during the time period. As a result,

Remote Sens. 2018, 10, 1168; doi:10.3390/rs10081168 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 1168 2 of 21 the GRACE satellites can be used to monitor floods and droughts by analyzing temporal variations in TWSC [10–16]. Droughts and floods in the Liao River Basin are mostly analyzed by statistical data of disasters in Province, which may prove difficult to acquire. As an alternative, we used GRACE TWSAs to monitor droughts and floods. The Earth is a dynamic system that changes over time and space. The changes in the Earth’s gravitational field are mainly caused by the Earth’s mass redistribution. GRACE is established on the basis of the time-varying gravity field of the Earth [17,18]. The GRACE satellite adopted a low-low satellite to satellite tracking technology to launch two low-orbit satellites simultaneously that are approximately 220 km apart on the same orbit. The satellite-borne GPS receiver was used to determine the orbit position of the two low-orbit satellites accurately; then, the k-band ranging system was utilized to continuously monitor the distance variation between the two satellites; thus, the change in the Earth’s gravitational field was obtained [19]. By removing the effects of tidal (solid, oceanic, and polar) and non-tidal (atmospheric and oceanic) influences, in terms of land areas, GRACE time-varying gravity fields mainly reflect the changes in Terrestrial Water Storage (TWS) on a seasonal or short time scale [20]. Low-pass filtering is required to reduce high-frequency noise in processing the GRACE spherical harmonic coefficient product. Low-pass filtering (e.g., truncation, destriping and Gaussian smoothing), however, may cause partial signal loss, which can be restored by two signal restoration methods [21]: The first is based on TWS derived from land surface models (LSMs), e.g., an additive correction approach [22,23] and scaling factor [24–26]; the other is less dependent on LSMs, such as forward modeling [21,27] and multiplicative correction approaches [28]. The multiplicative correction approach, however, needs to assume that the distribution of TWS changes is uniform, if not, large biases are introduced. The recently released Jet Propulsion Laboratory (JPL) mass concentration block (mascon) solutions is based on a priori constraints in space and time without additional destriping filter, thereby minimizing the effect of measurement and leakage errors [29]. The mascons can be applied at regional to global scales and do not require prior information provided by LSMs to restore signal loss [30,31]. Hence, both the JPL mascon solutions and GRACE spherical harmonic coefficient products are used here. Drought monitoring systems require (near) real-time inputs of GRACE TWS changes; in addition, developing a drought index requires a long-term time series of data (more than 30 years) [32]. Monthly GRACE satellite data, however, are only available since April 2002, with a latency of two to six months [13]. Moreover, the first generation GRACE satellites stopped functioning before the launch of the GRACE follow-on mission in May 2018. Therefore, a method to extend the time series is required. By assimilating the information obtained from remote sensing into the hydrological model, the integrated soil moisture (SM) can be obtained, and then the monitoring of floodplain inundations can be completed [33–35]. However, the vertical accuracy and spatial resolution of remote sensing images constrained the use of this method. Pan et al. [36] combined the terrestrial water budget estimated from different data sources, including in situ observations, remote sensing retrievals, LSM simulations, and global re-analyses, to enforce the water balance constraint of 32 globally distributed major basins from 1984 to 2016 using data assimilation techniques. In his article, TWS beyond the GRACE period is estimated by the variable infiltration capacity (VIC) model, which lacks GWS change and is arguably ill-suited to simulate the interactions between water storage compartments. Long et al. [13] reconstructed the GRACE TWSA data for a large karst plateau in Southwest China over the past three decades by developing an artificial neural network (ANN) model and combining this model with the monthly mean temperature, monthly precipitation and SMS to obtain the frequency and severity of droughts and floods. Sun [37] predicted the groundwater level changes by developing an ANN and combining it with GRACE products. However, the convergence rate of the ANN algorithm is slow and is prone to over-fitting. In comparison with ANN, a general regression neural network (GRNN) is superior to ANN network in terms of learning speed, function approximation, pattern recognition and classification ability [38]. Remote Sens. 2018, 10, x FOR PEER REVIEW 3 of 21

GRNN demonstrates a strong nonlinear mapping capability, flexible network structure and high degree of fault tolerance and robustness [39]. The prediction effect also performs well when the sample data size is restricted, which may be exploited for unstable data processing; this effect can be used to process unstable data [40]. However, overfitting may occur with the GRNN method reconstructing TWS changes for the training period, resulting in poor performance in other periods, especially over the hindcasting period. To avoid this, cross validation is applied over the training period and early stopping is considered. This paper aims to (1) compare TWS changes of JPL mascon solutions with GRACE spherical harmonic solutions, (2) predict TWSA for the Liao River Basin in China beyond the GRACE period with GRNN models, and (3) monitor droughts and floods across Remotethe Sens. Liao2018 River, 10, 1168Basin with a drought index, where the total storage deficit index (TSDI), which is3 of 21 based on a long-term TWSA time series. The flowchart of this study as follows:

Figure 1. Flowchart of drought and flood disaster monitoring in the Liao River Basin. Note: the time span of the hydrological model and meteorological data from January 1985 to March 2016 was divided into three parts: the first part was from January 1985 to March 2002, the second part was from April 2002 to January 2014, and the last part was from February 2014 to March 2016. By contrast, the GRACE satellite data from April 2002 to March 2016 were divided into two parts: one was from April 2002 to January 2014, and the other was from February 2014 to March 2016. The part from April 2002 to January 2014 was used for training (75%) and validating (25%) the GRNN model; during this process, 4-fold cross validation was applied. However, the part from February 2014 to March 2016 was utilized for testing the predicted model to avoid overfitting. The remaining part of the hydrological model and meteorological data, from January 1985 to March 2002, was used as the input variables of the optimal GRNN model to obtain the TWSA from January 1985 to March 2002. Remote Sens. 2018, 10, 1168 4 of 21 Remote Sens. 2018, 10, x FOR PEER REVIEW 4 of 21

GRNN demonstrates a strong nonlinear mapping capability, flexible network structure and high Figure 1. Flowchart of drought and flood disaster monitoring in the Liao River Basin. Note: the time degreespan of faultof the tolerance hydrological and robustnessmodel and [meteorol39]. Theogical prediction data from effect January also performs 1985 to wellMarch when 2016 the was sample datadivided size is restricted, into three whichparts: the may first be part exploited was from for January unstable 1985 data toprocessing; March 2002,this the effectsecond can part be was used to processfrom unstable April 2002 data to [ 40January]. However, 2014, and overfitting the last maypart was occur from with February the GRNN 2014 methodto March reconstructing 2016. By TWScontrast, changes the for GRACE the training satellite period, data from resulting April in2002 poor to March performance 2016 were in divided other periods, into two especially parts: one over the hindcastingwas from April period. 2002 to To January avoid 2014, this, crossand the validation other was isfrom applied February over 2014 the to training March 2016. period The and part early stoppingfrom isApril considered. 2002 to January This paper2014 was aims used to for (1) traini compareng (75%) TWS and changes validating of JPL(25%) mascon the GRNN solutions model; with GRACEduring spherical this process, harmonic 4-fold solutions,cross validation (2) predict was applied. TWSA However, for the Liao the part River from Basin February in China 2014 beyond to the GRACEMarch 2016 period was withutilized GRNN for testing models, the predicted and (3) monitor model to droughts avoid overfitting. and floods The acrossremaining the part Liao of River Basinthe with hydrological a drought model index, and where meteor theological total storage data, from deficit January index 1985 (TSDI), to March which 2002, is based was used on a as long-term the TWSAinput time variables series. of The the flowchart optimal GRNN of this model study to is obtain shown the in TWSA Figure from1. January 1985 to March 2002.

2. Study Area The LiaoLiao RiverRiver originates originates from from Guangtoushan Guangtoushan in Qilaotuin Qilaotu Mountain Mountain in in Hebei Province Province and flows and flowsthrough through Hebei Hebei Province, Province, Inner , Mongolia, Province Jilin Province and Liaoning and Liaoning Province Province [2,3,41 [2,3,41].]. The LiaoThe ◦ 0 ◦ 0 ◦ 0 ◦ 0 LiaoRiver River Basin Basin (117 00(117°00E–125′E–125°3030 E, 40′E,30 40°30N–45′N–45°1010 N) is′N) situated is situated in the in southwest the southwest of Northeast of , China,east of east the of Di’er the Di’er Songhua Songhua and Yaluand Yalu , Rivers, west west of theof the Inner Inner Mongolia Mongolia Plateau, Plateau, southsouth ofof the , Daling River Basin, and Bohai, and north of the SonghuaSonghua River; the total area equals 2 221,100 kmkm2 [42,43].[42,43]. Figure 2 2 illustrates illustrates the the location location of of the the Liao Liao River River Basin Basin and and the the distribution distribution of the of themeteorological meteorological stations. stations.

Figure 2. Distribution map of the Liao River Basin. Figure 2. Distribution map of the Liao River Basin. The main of the Liao River Basin are the West Liao, East Liao, and Liao and Hun Tai riversThe [5]. main Most tributaries of the regions of the Liaoin this River basin Basin are are characterized the West Liao, by East a temperate Liao, and Liaosemi-humid and Hun and Tai semi-aridrivers [5]. Mostmonsoon of the climate. regions inSeve thisrity basin of aredroughts characterized and floods by a temperateis related semi-humid to precipitation and semi-arid [6]. The frequencymonsoon climate.of droughts Severity is high of droughts in spring and due floods to dry is related weather to precipitationconditions when [6]. Thesand frequency winds are of common.droughts isPrecipitation high in spring is highly due to drylocalized weather in conditionssummer, resulting when sand in alternating winds are common. droughts Precipitation and floods. Precipitationis highly localized is virtually in summer, absent resulting in autumn in alternatingand winter, droughtswhich makes and floods.droughts Precipitation likely to occur is virtually during theseabsent seasons in autumn [44]. and winter, which makes droughts likely to occur during these seasons [44].

Remote Sens. 2018, 10, 1168 5 of 21

3. Materials and Methods

3.1. Data and Processing

3.1.1. GRACE Data The GRACE TWSA is derived using two solutions. The first method, namely, the traditional GRACE spherical harmonic solutions, is based on the GRACE spherical harmonic coefficient products; the second method is the JPL mascon solutions. The GRACE spherical harmonic coefficient products are currently available in three centers: the Center for Space Research (CSR) at the University of Texas at Austin, the NASA Jet Propulsion Laboratory and the German Research Center for Geoscience. Here, we used the GRACE Level-2 RL05 data provided by the CSR and recorded from April 2002 to March 2016, covering 151 months (the data from June and July 2002; June 2003; January 2004; January and June 2011; May and October 2012; March, August, and September 2013; February, July, and December 2014; and June, October, and November 2015 are missing but can be restored by linear interpolation [26]). To reduce the error of the high-order spherical harmonic coefficient, the GRACE spherical harmonic coefficients are truncated at the maximum degree and order of 60, and the degree-one spherical harmonic coefficients (C10, C11 and S11) are replaced with those calculated by Swenson et al. [45]. The C20 coefficients in the GRACE data were replaced with Satellite Laser Ranging (SLR) C20 to reduce measurement errors [46]. The multiyear mean gravity field is subtracted from the monthly time-varying gravity fields to obtain the monthly anomalous gravity field. The north–south stripes were removed by destriping [47], and the high-frequency noise in the spherical harmonic coefficients was reduced by 300-km Gaussian smoothing. Furthermore, the TWS in the study area is obtained by applying the regional kernel function to the spherical harmonic coefficients [48]. Moreover, bias (signal lost to the surrounding area) and leakage (signal gained from the surrounding area) corrections were performed through the scaling factor method. This method computes multiple factors by least squares fitting between the filtered and unfiltered TWSA at the basin scale [25,26]. The TWSAs are then converted into TWSC in the form of equivalent water height (EWH) by deriving the TWSAs with respect to time, for example, TWSCi = (TWSA(i+1) − TWSA(i−1))/2, where i is the month during the study period [49]. Additionally, the newly released JPL mascon solutions in the form of equal-area 3-degree spherical cap are also used to obtain the TWS changes. The data represent TWS anomalies relative to the baseline average from January 2004 to December 2009. It is critical that the same time average is used while comparing with other products. At last, the TWSA is obtained by applying mascon gain factors on the mascon fields [29].

3.1.2. Hydro-Meteorological Data (1) Statistical bulletin data

The total storage of water resources was obtained from the Water Resources Bulletin of the Song Liao River Basin from 2002 to 2016, which could be used to verify the accuracy of GRACE TWSA. The data sources of the water resources bulletin are mainly the collection and utilization of existing information in the relevant departments, supplemented by the necessary typical investigation, observation experiments and special research work [The compilation of technical outline of China Water Resources Bulletin]. The yearly anomalies of the total storage of water resources were obtained and were relative to the mean of data from 2002 to 2016. The GRACE TWSA in this study was presented on a monthly scale and expressed in the form of EWH, and the total storage of water resources provided by the bulletin was expressed on an annual scale and in volume. Therefore, to achieve a unity in them, the expression of the statistical bulletin data was converted to the form of EWH, and the time scale of GRACE TWSA was transformed into year. The GRACE TWSAs in 2002 and 2016 were not used in the comparison because data of several months in these 2 years are missing. Moreover, Grubbs statistical test [50] was used to detect outliers before the comparison between the Remote Sens. 2018, 10, 1168 6 of 21 two solutions and the statistical bulletin data. The statistical results showed that the bulletin data had a large anomaly in 2010. Therefore, the original data of 2010 will be listed separately as follows for key analysis.Remote Sens. 2018, 10, x FOR PEER REVIEW 6 of 21

(2) Meteorologicalcomparison between data the two solutions and the statistical bulletin data. The statistical results showed that the bulletin data had a large anomaly in 2010. Therefore, the original data of 2010 will be listed Theseparately meteorological as follows data for key (e.g., analysis. precipitation and temperature) were acquired and set as the input parameters(2) forMeteorological the following data GRNN models. Daily precipitation and temperature data were gathered from the ChinaThe meteorological Meteorological data Data (e.g., Sharing precipitation Network and temperature) (URL: http://data.cma.cn/ were acquired and set) from as the January input 1985 to Marchparameters 2016. Figure for the2 illustrates following theGRNN distribution models. Daily of the precipitation meteorological and temperature stations in data the were Liao River Basin. Thegathered daily from meteorological the China Meteorological station data Data were Sh interpolatedaring Network by (URL: kriging; http://data.cma.cn/) as a result, the from grid daily precipitationJanuary (or 1985 temperature) to March 2016. was Figure obtained. 2 illustrates By summingthe distribution all the of the daily meteorological precipitation stations (ortemperature) in the Liao River Basin. The daily meteorological station data were interpolated by kriging; as a result, the grid data for the same month of the same year, the grid monthly precipitation (or temperature) data grid daily precipitation (or temperature) was obtained. By summing all the daily precipitation (or could betemperature) achieved. grid Finally, data thefor regionalthe same month precipitation of the same (or temperature)year, the grid monthly data on precipitation a monthly (or scale was derivedtemperature) by averaging data the could grid be monthlyachieved. Finally, precipitation the regional (or temperature).precipitation (or temperature) The monthly data precipitation on a productsmonthly from the scale Tropical was derived Rainfall by Measuringaveraging the Mission grid monthly (TRMM) precipitation from April (or 2002 temperature). to March The 2016 were used tomonthly verify the precipitation reliability products of the spatiallyfrom the Tropical averaged Rainfall in situ Measuring precipitation Mission (Figure (TRMM)3). from The April correlation coefficient2002 of to the March two 2016 sources were used was to 0.98, verify and the the reliability root mean of the square spatially error averaged (RMSE) in situ was precipitation 9 mm month-1, (Figure 3). The correlation coefficient of the two sources was 0.98, and the root mean square error indicating a high consistency. (RMSE) was 9 mm month-1, indicating a high consistency.

Figure 3. Comparison between the in situ precipitation from meteorological stations and the Figure 3. Comparison between the in situ precipitation from meteorological stations and the precipitation from satellite (TRMM 3B43) over the Liao River Basin from April 2002 to March 2016. precipitation from satellite (TRMM 3B43) over the Liao River Basin from April 2002 to March 2016. 3.1.3. Hydrological Model Data 3.1.3. HydrologicalThe hydrological Model Datamodel has been widely used to obtain the hydrological parameters, such as TheSM hydrological and SWE, due model to the has long been time widely span used and toglobal obtain grid the scale hydrological [13,32]. The parameters, following three such as SM hydrological models for the time span from January 1985 to March 2016 were used in this article: and SWE,Noah, due Mosaic to the and long VIC time from span the Global and global Land gridData scaleAssimilation [13,32 ].System The following(GLDAS) [51]. three Here, hydrological the modelsCLM for the model time in spanGLDAS from was Januarynot used 1985because to it March considerably 2016were underestimates used in this the amplitude article: Noah, of SM Mosaic and VICvariations from the across Global the land Land surface Data and Assimilation frequently deviates System from (GLDAS)other LSMs [[31].51]. GLDAS Here, can the provide CLM model in GLDASoptimal was near-real-time not used because land surface it considerably states which underestimatescome from the fusion the amplitudeof satellite and of SMground variations across theobservation land surface data, andsuch frequentlyas SM and deviatesSWE [27,31]. from The other model LSMs excludes [31]. the GLDAS groundwater can provide and the optimal Antarctic data [52,53]. The processing method of the hydrological model was the same as that of the near-real-time land surface states which come from the fusion of satellite and ground observation data, GRACE spherical harmonic coefficient products (e.g., truncation, monthly anomalies, and 300 km such asGaussian SM and smoothing). SWE [27,31 The]. specific The model processing excludes proces thes of groundwaterthe hydrological and model the was Antarctic as follows: data the [52,53]. The processinggrid data methodof hydrological of the models hydrological were expanded model wasto the the spherical same asharmonic that of coefficients the GRACE with sphericala harmonicmaximum coefficient degree products and order (e.g., of 60; truncation, the monthly monthly anomaly anomalies, was calculated, and which 300 km was Gaussian relative to smoothing). the The specificmean processing of the grid data process between of the April hydrological 2002 and March model 2016, was and as a follows:300 km Gaussian the grid smoothing data of hydrological was models were expanded to the spherical harmonic coefficients with a maximum degree and order of 60; the monthly anomaly was calculated, which was relative to the mean of the grid data between April Remote Sens. 2018, 10, 1168 7 of 21

Remote Sens. 2018, 10, x FOR PEER REVIEW 7 of 21 2002 and March 2016, and a 300 km Gaussian smoothing was applied; the decorrelation filter was not usedapplied; because the decorrelation no north–south filter stripes was werenot used observed because in theno north–south hydrological stripe models datawere [observed27]. The depths in the ofhydrological SM recorded model by Noah, data [27]. Mosaic, The anddepths VIC of were SM recorded different, by which Noah, were Mosaic, 2, 3.5, and and VIC 1.9 m,were respectively. different, Here,which the were SMS 2, of3.5, different and 1.9 hydrological m, respectively. models Here, was comparedthe SMS of with different the GRACE hydrological TWSA, andmodels the SMSwas thatcompared best fit with the GRACE the GRACE TWSA TWSA, was used and asthe the SMS input that parameter best fit the for theGRACE following TWSA GRNN was used models. as the input parameter for the following GRNN models. 3.2. GRNN Model 3.2. GRNN Model The GRNN model demonstrates the advantages of good topology, high precision and fast convergenceThe GRNN speed model [54] and demonstrates performs a nonlinearthe advantages functional of good mapping topology, from pasthigh observations precision andX = fast (x1, T T xconvergence2, ..., xn) to futurespeed [54] values andY performs= (y1, y2, ...a nonlinearyk) . In Equationfunctional (1), mappingF is the from mapping past byobservations the GRNN, X and = (xw1, isx2, the ..., x vectorn)T to future of all parameters values Y = ( [y391, ].y2 Here,, … yk) theT. In GRNN Equation model (1), wasF is the used mapping to predict by thethe monthlyGRNN, and TWSAs w is fromthe vector January of 1985all parameters to March 2002.[39]. Here, the GRNN model was used to predict the monthly TWSAs from January 1985 to March 2002. Y = F(X, w) (1) 𝑌=𝐹(𝑋,𝑤) (1) The GRNN model consists of four layers,layers, namely,namely, input,input, pattern,pattern, summation,summation, and output [[40]40] (Figure(Figure4 4).). In In this this study, study, three three types types of of predicti predictionsons were were set set to toforecast forecast TWSAs, TWSAs, namely, namely, SMS SMS and andprecipitation precipitation (Prediction (Prediction 1), 1),SMS SMS and and temperatur temperaturee (Prediction (Prediction 2), 2), and and SMS, SMS, precipitation, andand temperaturetemperature (Prediction(Prediction 3) 3) due du toe theto highthe high correlation correlation between between the GRACE the GRACE TWSAs andTWSAs SMS anomaliesand SMS andanomalies the direct and effect the direct of precipitation effect of precipitation on the SMS anomalieson the SMS and anomalies the indirect and reflectionthe indirect of temperaturereflection of ontemperature evaporation. on evaporation.

Figure 4. Structure of GRNN for predicting the monthly GRACE TWSAs from January 1985 to March Figure 4. Structure of GRNN for predicting the monthly GRACE TWSAs from January 1985 to March 2002. The input layer xn is the combination of mean precipitation and temperature from in situ 2002. The input layer xn is the combination of mean precipitation and temperature from in situ observations and SMS from thethe NoahNoah hydrologicalhydrological model;model; n isis thethe numbernumber ofof inputinput variables.variables. TheThe output layer yk is the time series of the predicted monthly GRACEGRACE TWSATWSA andand kk isis thethe time span on a monthly scale. Pn, SD, and SNT are the neuronalneuronal transfertransfer functionsfunctions betweenbetween layers.layers.

The output of the pattern layer neurons i is the exponential form of the exponential square of The output of the pattern layer neurons i is the exponential form of the exponential square of the Euclidian distance square between the input variable and its corresponding learning sample X the Euclidian distance square between the input variable and its corresponding learning sample X (Equation (2)). In Equation (2), Pi is the transfer function of the pattern layer neurons, X is the input (Equation (2)). In Equation (2), Pi is the transfer function of the pattern layer neurons, X is the input variable, and Xi is the corresponding learning sample of neurons i. variable, and Xi is the corresponding learning sample of neurons i. (X − X )T (X − X ) = − iT i = Pi exp[ (X − X ) (X2 − X ) ] i 1,2,,n (2) P = exp[− i 2σ i ] i = 1, 2, ··· , n (2) i 2σ2 Two typestypes of of neurons neurons in thein summationthe summation layer laye werer usedwere to used conduct to conduct the summation the summation (as expressed (as inexpressed Equations in (3)Equations and (4)). (3) One and way (4)). is byOne performing way is by anperforming arithmetic an summation arithmetic of summation the outputs of of the all theoutputs neurons of all in the the neurons pattern in layer; the pattern the connection layer; the weight connection between weight the patternbetween layer the pattern and each layer neuron and iseach 1. Theneuron other is method1. The other is the method weighted is the summation weighted ofsummation all the pattern of all layerthe pattern neurons; layer the neurons; connection the connection weight between the ith and jth neurons in the pattern and summation layers, weight between the ith and jth neurons in the pattern and summation layers, respectively. Yij is the jrespectively.th element from Yij is the theith jth output element sample. from the Moreover, ith output the sample. subscript Moreover,j in Equation the subscript (4) is the timej in Equation series to be(4) predicted.is the time series to be predicted. n S = P (3) D i=1 i

Remote Sens. 2018, 10, 1168 8 of 21

= n SD ∑i=1 Pi (3) = n = ··· SNj ∑i=1 yi jPi j 1, 2, , k (4) The prediction results could be retrieved by dividing the output from the summation layer (Equation (5)). The output neuron is the monthly GRACE TWSA.

SNj TWSAj = (5) SD However, in neural network training, overfitting may occur, that is, the prediction error of the neural network for the training set is considerably small, but the error for the testing set is considerably large. Here, earthly stopping, which divided the data sets into training, validation, and testing sets, was adopted to avoid overfitting [40]. Thus, after repeated experiments, the GRACE TWSA data from April 2002 to January 2014 (85% of all samples) were used for training (75%) and validating (25%) the GRNN model. The 4-fold cross validation method was applied during the training period. The GRACE TWSA data from February 2014 to March 2016 (15% of all samples) were used as testing set, which was mainly used to compare the simulation effects of different GRNN models. The resulting optimal GRNN model was then utilized to predict the GRACE TWSAs in the Liao River Basin from January 1985 to March 2002.

3.3. Total Storage Deficit Index (TSDI) The meteorological disaster indexes used currently include the Palmer drought severity index (PDSI) [55], standardized precipitation index (SPI) [56], and soil moisture deficit index (SMDI) [57]. PDSI is the most widely used meteorological drought index. This index is used to evaluate long-term abnormal drought or humidity at a certain area. Moreover, PDSI assumes that the input parameters, such as land use, land coverage and soil characteristics, are uniform in the entire climate zone. The calculation process of the SPI drought index is simple but disregards the impact of temperature anomalies. SMDI is an agricultural drought index that demonstrates the advantage of high temporal and spatial resolutions. However, this index requires a high quantity and quality of SM. Droughts or floods may occur when a dry or wet state exceeds a certain period [32]. The SMDI developed by Narasimhan and Srinivasan [57] was renamed by Yirdaw et al. [15] as the TSDI. This index can be used to describe long-term dry–wet conditions. The TWSAs derived from the GRACE satellites contain all the water storage changes, such as SWS, SMS, SWE, and GWS. The TSDI can be computed using the GRACE TWSAs and can be estimated by calculating the total storage deficit (TSD, %) [15].

TSAi,j−MTSAj TSDi,j = × 100 MaxTSAj−MinTSAj (6) i = 1985, 1986, ··· , 2015 j = 1, 2, ··· , 12 where TSDi,j is the TSD (%) and TSAi,j is the TWSA (mm) for the jth month of the ith year derived from the GRACE data; MTSAj, MaxTSAj and MinTSAj are the long-term mean, maximum, and minimum TWSA, respectively, for the jth month (mm). Palmer [55] suggested that the computation of the TSDI can be completed in accordance with the previous drought status and the current TSD condition.

TSDIj = p × TSDIj−1 + q × TSDj, (7) where parameters p and q are the drought severity and duration factors, respectively, which can be determined from the monthly cumulative TSD plot on the basis of the following relation during the drought or flood period.

m C p = 1 − , q = (8) m + b m + b Remote Sens. 2018, 10, 1168 9 of 21 where m and b indicate the slope and intercept of the best fitting line of the cumulative TSD curve during the drought (or flood) period, respectively; and C stands for drought intensity (drought monograph), which can be retrieved in accordance with the best fitting line. According to the definition of Palmer [55], C can be any value of the four drought classifications, namely, −4.0 for extreme, −3.0 for severe, −2.0 for moderate and −1.0 for mild droughts. Palmer [55] also highlighted that the criteria for defining the drought monograph are non-uniform. Table1 shows that the severity classifications of the TSDI drought were similar to those of PDSI.

Table 1. Severity classification of TSDI drought and wetness.

TSDI Category TSDI TSDI Category TSDI Extreme wetness ≥ 4 Mild drought (−2,−1) Severe wetness (3,4) Moderate drought (−3,−2) Moderate wetness (2,3) Severe drought (−4,−3) Mild wetness (1,2) Extreme drought ≤ −4 Normal (−1,1)

4. Results

4.1. Evaluation of the GRACE TWSAs

4.1.1. Comparison between GRACE Spherical Harmonic Solutions and JPL Mascon Solutions In this article, the acquisition of TWSA from April 2002 to March 2016 adopted two methods: GRACE spherical harmonic solutions and JPL mascon solutions. The Pearson correlation coefficient and RMSE between the two solutions were 0.83 and 18 mm, respectively, indicating a high correlation. The amplitudes of GRACE spherical harmonic-based TWSA and JPL mascon-based TWSA varied from −8.38 cm to 8.46 cm and −5.50 cm to 8.56 cm respectively. Figure5 show that the difference in amplitude of the two methods was insignificant.

Figure 5. TWSA of GRACE spherical harmonic solutions and JPL mascon solutions over the Liao River Basin from April 2002 to March 2016.

4.1.2. Comparison between the Two Solutions and the Statistical Bulletin Data Prior to the prediction of the TWSAs from January 1985 to March 2002, the TWSAs of the Liao River Basin from April 2002 to March 2016, which were restored by GRACE, were verified by the water resources storage from the Water Resources Bulletin of the Song Liao River Basin [58]. Considering the special characteristics of TWSA in the Water Resources Bulletin in 2010, the TWSAs from 2003 to 2015 were divided into three parts for analysis: 2003–2009, 2010, and 2011–2015.

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Table2 and Figure6 show that the two solutions correlated well with the statistical bulletin data for the two periods; the RMSE between the two solutions and the statistical bulletin data was small. Moreover, the difference in the RMSEs of JPL mascon solutions and the RMSEs of GRACE spherical harmonicRemote solutions Sens. 2018, was10, x FOR insignificant PEER REVIEW for the same period. However, the correlation between 10 of 21 the JPL mascon solutions and the statistical bulletin data was slightly higher than that for GRACE spherical harmonic solutions.Table 2 and Figure 6 show that the two solutions correlated well with the statistical bulletin data for the two periods; the RMSE between the two solutions and the statistical bulletin data was Tablesmall. 2. Moreover,Comparison the of difference TWSA between in the RMSEs different of GRACEJPL mascon solutions solutions and and the the statistical RMSEsbulletin of GRACE data. spherical harmonic solutions was insignificant for the same period. However, the correlation between the JPL mascon solutions and the statistical bulletin data was slightly higher than that for Variables r RMSE (cm) GRACE spherical harmonic solutions. Bulletin vs. SH 0.64 2.08 2003–2009 Table 2. Comparison of TWSA betweenBulletin different vs. mascon GRACE solutions 0.65 and the statistical 3.05 bulletin data. Bulletin vs. SH 0.64 2.54 2011–2015 Variables r RMSE (cm) BulletinBulletin vs. vs. mascon SH 0.64 0.76 2.08 2.21 2003–2009 Note: the number of all TWSA samples fromBulletin 2003 tovs. 2009 mascon is 7 (n 1 0.65= 7), and the 3.05 number of all TWSA samples from 2003 to 2009 is 5 (n2 = 5). “SH” stands for GRACEBulletin sphericalvs. SH harmonic 0.64 solutions; 2.54 “mascon” represents the JPL mascon solutions. 2011–2015 Bulletin vs. mascon 0.76 2.21

Figure 6. Comparison between the two solutions of GRACE TWSA and the Water Resources Bulletin Figure 6. Comparison between the two solutions of GRACE TWSA and the Water Resources Bulletin of the Song Liao River Basin. of the Song Liao River Basin. Figure 6 shows that the TWSA in 2010 derived from GRACE spherical harmonic solutions and FigureJPL mascon6 shows solutions that the indicated TWSA ina similar 2010 derived change trend from with GRACE the TWSA spherical of the harmonic Water Resources solutions and Bulletin; specifically, both increased significantly in 2010 and then decreased in 2011. Combining JPL mascon solutions indicated a similar change trend with the TWSA of the Water Resources Bulletin; with the precipitation in Figure 3, the precipitation showed a considerable increase in 2010; a specifically,relatively both high increased precipitation significantly peak in summer in 2010 and and also then a secondary decreased peak inin 2011.spring Combiningwere observed. with the precipitationThe increased in Figure precipitation3, the precipitation might lead to showedan increase a considerablein TWS in 2010. increase in 2010; a relatively high precipitationHowever, peak in the summer amplitude and of TWSA also afrom secondary the Water peak Resources in spring Bulletin were was extremely observed. larger The than increased precipitationthe two might different lead GRACE to an solutions increase in in 2010. TWS The in diff 2010.erence could be explained by the following: the precipitation in the study area was relatively high and concentrated from July 19 to August 22 in However, the amplitude of TWSA from the Water Resources Bulletin was extremely larger than 2010. GRACE satellites measured gravity anomalies on a monthly scale and might fail to capture the the twomoment different with GRACE the maximum solutions precipitation. in 2010. The Howe differencever, the couldtotal water be explained resources by of thethe following: Water the precipitationResources in theBulletin study were area obtained was by relatively a statistic highof the andreal-time concentrated measurement from data. July 19 to August 22 in 2010. GRACEAll satellitesof these results measured indicated gravity that the anomalies TWSAs ob ontained a monthly from the scale two solutions and might were fail accurate, to capture the momentand with the theJPL maximummascon solution precipitation. might be slightly However, better than the totalthe GRACE water spherical resources harmonic of the Watersolution. Resources BulletinNote: were the obtained number of by all a statisticTWSA samples of the from real-time 2003 to measurement 2009 is 7 (n1 = 7), data. and the number of all TWSA samples from 2003 to 2009 is 5 (n2 = 5). “SH” stands for GRACE spherical harmonic solutions; All of these results indicated that the TWSAs obtained from the two solutions were accurate, and “mascon” represents the JPL mascon solutions. the JPL mascon solution might be slightly better than the GRACE spherical harmonic solution. 4.1.3. Error Estimation 4.1.3. Error Estimation To further determine a better solution of TWSA, the uncertainty of the GRACE spherical Toharmonic further determine solutions and a better JPL solutionmascon solutions of TWSA, was the estimated. uncertainty The of error the GRACEof GRACE spherical spherical harmonic solutions and JPL mascon solutions was estimated. The error of GRACE spherical harmonic solutions Remote Sens. 2018, 10, 1168 11 of 21 mainly includes measurement and leakage errors. The method of Chen et al. [59] was used to estimate the measurement error of GRACE, which calculates the RMS of the residual of mass variation in the ocean region that has the same latitude as the study area. In addition, the method of Landerer and Swenson [24] method was applied to obtain the leakage error. By calculating the RMS of the sum of squares of the measurement and leakage errors, the total error of GRACE spherical harmonic solutions in the study area could be derived and was 28 mm. In addition, the uncertainty of JPL mascon solutions could be obtained from the JPL website and was 23 mm in the study area. From the uncertainty of the two solutions and the comparison with the statistical bulletin data in Section 4.1.2, we can conclude that the accuracy of JPL mascon solutions was slightly higher than that of GRACE spherical harmonic solutions. Therefore, in the following process, the JPL mascon solutions will be used to simulate the GRNN models.

4.2. Selection of the Optimal Hydrological Model SMS derived from the hydrological models was needed in the simulation of GRNN models. Thus, selecting an optimal hydrological model that best fits the change trend of TWSA is necessary. Here, all the three hydrological models were compared with JPL mascon solutions from the perspective of correlation and amplitude, and Table3 presents the corresponding correlation coefficients. Table3 shows that the SMS from the Noah hydrological model demonstrated the highest correlation with the JPL mascon-based TWSA (r = 0.69), which was consistent with the result of Long et al. [13].

Table 3. Correlation coefficients of different hydrological models and JPL mascon-based TWSA (n = 168).

Variable r Noah vs. JPL mascon solutions 0.69 VIC vs. JPL mascon solutions 0.47 Mosaic vs. JPL mascon solutions 0.56 Note: “n” is the total number of samples of TWSA (or SMS) from April 2002 to March 2016; here, n = 168.

Figure7 depicts the comparison between JPL mascon-based TWSA and the SMS from different hydrological models. The three hydrological models of GLDAS and JPL mascon-based TWSA provided consistent time series changes. However, the difference in amplitude was relatively large in summer and winter. The TWSA of JPL mascon varied from −5.50 cm to 8.56 cm, whereas those of the Noah, VIC, and Mosaic hydrological models varied from −3.48 cm to 6.95 cm, −3.57 cm to 5.91 cm, and −3.44 cm to 5.86 cm, respectively. Among the amplitudes of all hydrological models, the amplitude of the Noah hydrological model was the closest to the JPL mascon solutions.

Figure 7. Comparison between the JPL mascon-based TWSA and SMS of different hydrological models over the Liao River Basin from April 2002 to March 2016.

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The SMS derived from the Noah hydrological model fits well with the JPL mascon-based TWSA whetherRemote in terms Sens. 2018 of, 10 the, x FOR correlation PEER REVIEW or the amplitude. Therefore, Noah of GLDAS was 12 the of 21 optimal hydrological model to describe the TWS changes in the study area. As a result, the SMS anomaly from The SMS derived from the Noah hydrological model fits well with the JPL mascon-based TWSA Noah waswhether selected in terms as the of the input correlation of the GRNNor the amplit model.ude. Therefore, Noah of GLDAS was the optimal However,hydrological the datamodel during to describe the periodthe TWS of changes 1995–1997 in the in study GLDAS-1 area. As are a result, subject the to SMS large anomaly uncertainties due to erroneousfrom Noah was forcing selected fields as the as input described of the onGRNN the model. NASA website [60]. The time span of GLDAS-2.0 was from 1948However, to 2010. the Here, data during the SM the of period GLDAS-2.0 of 1995–199 from7 in 1985 GLDAS-1 to 2010 are was subject selected to large to uncertainties compare with that of GLDAS-1due to (Figureerroneous8). forcing Figure fields8 show as described that the on SMs the NASA of GLDAS-1 website and[60]. The GLDAS-2 time span showed of GLDAS-2.0 an opposite was from 1948 to 2010. Here, the SM of GLDAS-2.0 from 1985 to 2010 was selected to compare with trend fromthat 1995of GLDAS-1 to 1997. (Figure However, 8). Figure from 8 1985show to that 1994, the theSMs correlation of GLDAS-1 coefficient and GLDAS-2 and showed RMSE ofan the two modelsopposite were 0.68 trend (n from = 120) 1995 and to 58.691997. However, mm, respectively. from 1985 to The 1994, corresponding the correlation coefficient correlation and coefficient RMSE and RMSE fromof the 1998two models to 2010 were were 0.68 0.79 (n = (n 120) = 156)and 58.6 and9 mm, 101.41 respectively. mm, respectively. The corresponding The difference correlation between the amplitudescoefficient ofand the RMSE two from models 1998 might to 2010 be were caused 0.79 by(n = the 156) different and 101.41 forcing mm, respectively. fields used The in the two models.difference However, between the overall the amplitudes change trendsof the two of models SM in themight two be modelscaused by were the different consistent forcing except fields for those used in the two models. However, the overall change trends of SM in the two models were in 1995–1997. Thus, the SMS from Noah of GLDAS-2 during the period of 1995–1997 was used to consistent except for those in 1995–1997. Thus, the SMS from Noah of GLDAS-2 during the period of replace1995–1997 that of GLDAS-1. was used to replace that of GLDAS-1.

550 GLDAS-1 500 GLDAS-2

450

400

350 SM(mm)

300

250

200 1985 1989 1993 1997 2001 2005 2009 Time(month) Figure 8. SM of GLDAS-2 and GLDAS-1 over the Liao River Basin from 1985 to 2010. Figure 8. SM of GLDAS-2 and GLDAS-1 over the Liao River Basin from 1985 to 2010. 4.3. Evaluation of the TWSAs Predicted by the GRNN Models 4.3. Evaluation of the TWSAs Predicted by the GRNN Models The JPL mascon-based and GRNN method-predicted TWSAs in the testing period from TheFebruary JPL mascon-based 2014 to March and 2016 GRNN were method-predicted analyzed. Table 4 TWSAsand Figure in the9 present testing the period results. from The February 2014 toGRNN-predicted March 2016 were TWSAs analyzed. for the testing Table 4period and Figurecorrelated9 present well with the the results. JPL mascon-based The GRNN-predicted TWSAs except for the last dots. The difference in the last dots might be attributed to the trend in SMS TWSAsanomalies for the testing at the periodcorresponding correlated location. well The with difference the JPL mascon-basedin correlation among TWSAs the exceptthree prediction for the last dots. The differencemodels in in Table the last4 was dots insignificant. might be Therefore, attributed to toselect the a trendreliable in and SMS stable anomalies model, the at F the tests corresponding on the location.three The predictions difference were in correlation performed further among under the three the significance prediction level models of 0.05. in TableTable 44 wasshows insignificant. that Therefore,Prediction to select 1 had a reliable the highest and F stable(F = 5.51). model, The TWSA the F from tests Prediction on the three 1 had predictions a high correlation were performed(r = further0.90) under (n the= 26) significance with GRACE level TWSA of 0.05.from February Table4 shows 2014 to that March Prediction 2016. The 1 RMSE had the between highest the Ftwo ( F = 5.51). was 18 mm, which was lower than the uncertainty of the JPL mascon-based TWSA of 23 mm. As a The TWSA from Prediction 1 had a high correlation (r = 0.90) (n = 26) with GRACE TWSA from result, the GRNN model of Prediction 1 was regarded as the optimal model and could be used for Februaryfurther 2014 analysis. to March 2016. The RMSE between the two was 18 mm, which was lower than the uncertainty of the JPL mascon-based TWSA of 23 mm. As a result, the GRNN model of Prediction 1 was regardedTable as the4. Accuracy optimal assessment model andof TWSA could predicted be used by forGRNN further models analysis. for the testing period from February 2014 to March 2016 (n = 26).

r (TWSA-GRNN vs. RMSE F-test Prediction Model TWSA-JPL Mascon) (mm) (α = 0.05) SMS 1 0.90 18 5.51 Precipitation SMS 2 0.91 22 4.03 Temperature

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Table 4. Accuracy assessment of TWSA predicted by GRNN models for the testing period from February 2014 to March 2016 (n = 26).

r (TWSA-GRNN vs. RMSE F-Test Prediction Model TWSA-JPL Mascon) (mm) (α = 0.05) SMS 1 0.90 18 5.51 Precipitation Remote Sens. 2018, 10, x FOR PEER REVIEWSMS 13 of 21 2 0.91 22 4.03 Temperature SMS SMS 3 3 PrecipitationPrecipitation 0.87 0.87 2424 3.163.16 TemperatureTemperature Note: “TWSA-GRNN”“TWSA-GRNN” indicates indicates the GRNN the GRNN model-predicted model-pr TWSAedicted for TWSA the testing for period the testing from February period2014 from to FebruaryMarch 2016. 2014 “n” to is theMarch total 2016. number “n” of is samples the total (n =number 26). of samples (n = 26).

Figure 9. JPL mascon-based TWSAs TWSAs and and SM anomalies (g (generatedenerated by GLDAS Noah) over the Liao River Basin fromfrom AprilApril 20022002 to to March March 2016. 2016. The The hollow hollow triangles triangles from from February February 2014 2014 to Marchto March 2016 2016 are arethe the GRNN-generated GRNN-generated TWSAs TWSAs with with three three types types of predictions. of predictions.

4.4. Drought/flood Drought/Flood Status Status of of the the Liao Liao River River Basin Basin Monitored Monitored by by the the TSDI TSDI

(1) Calculation of TSD and cumulative TSD By using the TWSA time series, which was obtained from the optimal GRNN model (Prediction 1 in TableBy using 4) and the the TWSA JPL timemascon series, solutions, which wasand obtainedcombining from with the Equation optimal GRNN(6), the modelTSD (Figure (Prediction 10) in 1 thein Table Liao4 )River and theBasin JPL from mascon 1985 solutions, to 2016 andwas combiningcalculated. withFigure Equation 10 depicts (6), thethat TSD the (FigureTWS followed 10) in the a clearLiao Riverdownward Basin trend from 1985from toMarch 2016 was2000 calculated.to May 2002, Figure and 10the depicts TSD was that negative the TWS from followed May 2000 a clear to Maydownward 2002. The trend study from area March was 2000 relatively to May 2002,dry and and theprone TSD to was droughts negative from from May May 2000 2000 to to May May 2002 2002. becauseThe study the area TWS was had relatively been decreasing dry and prone for more to droughts than 3 consecutive from May 2000months. to May Assuming 2002 because that May the TWS2000 washad beenthe beginning decreasing of forthe more dry state, than 3the consecutive monthly cumu months.lative Assuming TSD (red that solid May dot 2000 in Figure was the 11) beginning could be calculatedof the dry state,with the monthlyTSD from cumulative May 2000 TSDto May (red 2002 solid. Figure dot in Figure11 describes 11) could the betemporal calculated patterns with the of historicalTSD from droughts May 2000 and to May wetness. 2002. The Figure monthly 11 describes cumulative the temporal TSD gradually patterns decreased of historical from droughts May 2000 and towetness. May 2002 The and monthly then began cumulative to increase, TSD graduallythereby indi decreasedcating that from the Maydry state 2000 of to the May Liao 2002 River and Basin then hadbegan ended to increase, in May thereby2002. indicating that the dry state of the Liao River Basin had ended in May 2002.

50 30 10 -10 TSD(%) -30 -50 -70 1月-85 1月-88 1月-91 1月-94 1月-97 1月-00 1月-03 1月-06 1月-09 1月-12 1月-15 Time (month)

Figure 10. TSD (%) for the Liao River Basin during the period of 1985–2016.

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SMS 3 Precipitation 0.87 24 3.16 Temperature Note: “TWSA-GRNN” indicates the GRNN model-predicted TWSA for the testing period from February 2014 to March 2016. “n” is the total number of samples (n = 26).

Figure 9. JPL mascon-based TWSAs and SM anomalies (generated by GLDAS Noah) over the Liao River Basin from April 2002 to March 2016. The hollow triangles from February 2014 to March 2016 are the GRNN-generated TWSAs with three types of predictions.

4.4. Drought/flood Status of the Liao River Basin Monitored by the TSDI (1) Calculation of TSD and cumulative TSD By using the TWSA time series, which was obtained from the optimal GRNN model (Prediction 1 in Table 4) and the JPL mascon solutions, and combining with Equation (6), the TSD (Figure 10) in the Liao River Basin from 1985 to 2016 was calculated. Figure 10 depicts that the TWS followed a clear downward trend from March 2000 to May 2002, and the TSD was negative from May 2000 to May 2002. The study area was relatively dry and prone to droughts from May 2000 to May 2002 because the TWS had been decreasing for more than 3 consecutive months. Assuming that May 2000 was the beginning of the dry state, the monthly cumulative TSD (red solid dot in Figure 11) could be calculated with the TSD from May 2000 to May 2002. Figure 11 describes the temporal patterns of historical droughts and wetness. The monthly cumulative TSD gradually decreased from May 2000 to May 2002 and then began to increase, thereby indicating that the dry state of the Liao River Basin hadRemote ended Sens. 2018 in May, 10, 1168 2002. 14 of 21

50 30 10 -10 TSD(%) -30 -50 -70 Jan-85 Jan-88 Jan-91 Jan-94 Jan-97 Jan-00 Jan-03 Jan-06 Jan-09 Jan-12 Jan-15 Time (month)

Remote Sens. 2018, 10Figure, x FOR 10.PEERTSD REVIEW (%) for the Liao River Basin during the period of 1985–2016. 14 of 21 Figure 10. TSD (%) for the Liao River Basin during the period of 1985–2016.

0

-100 -1

-200 -2

-300 y=-26.752t+6.642 -3 -400 R2=0.997 -500 -4 Cumulative TSD (%) Historical Dryness and Wetness -600 Drought Monograph

-700 May-00 Sep-00 Jan-01 May-01 Sep-01 Jan-02 May-02 Time (month)

Figure 11. Cumulative TSD of the Liao River Basin in China.

(2) Calculation of the parameters m, b, p, and q (2) Calculation of the parameters m, b, p, and q As described in the previous section, the study area began to dry up in May 2000 and the As described in the previous section, the study area began to dry up in May 2000 and the cumulative TSD reached its maximum value in May 2002. Thus, the monthly cumulative TSD from cumulative TSD reached its maximum value in May 2002. Thus, the monthly cumulative TSD from the the beginning of May 2000 to May 2002 was used to conduct linear fitting, through which the beginning of May 2000 to May 2002 was used to conduct linear fitting, through which the best-fitting best-fitting line was obtained. The slope and intercept of the best-fitting line (black line in Figure 11) line was obtained. The slope and intercept of the best-fitting line (black line in Figure 11) were were m = −26.752 and b = 6.642, respectively. m = −26.752 and b = 6.642, respectively. Parameter C could be estimated from the drought monograph of the best-fitting line. The Parameter C could be estimated from the drought monograph of the best-fitting line. The drought drought monograph of the Liao River Basin in China from May 2000 to May 2002 needs to be monograph of the Liao River Basin in China from May 2000 to May 2002 needs to be determined. determined. In 2012, Jiang et al. [61] calculated the temporal and spatial characteristics of droughts In 2012, Jiang et al. [61] calculated the temporal and spatial characteristics of droughts and floods and floods in Liaoning Province in the past 50 years using the Z index. The results indicated that the in Liaoning Province in the past 50 years using the Z index. The results indicated that the drought drought and flood disaster degrees in Liaoning Province in 2000, 2001, and 2002 were great drought, and flood disaster degrees in Liaoning Province in 2000, 2001, and 2002 were great drought, partial partial drought and partial drought respectively. In 2016, Wu et al. [62] used the meteorological drought and partial drought respectively. In 2016, Wu et al. [62] used the meteorological drought drought comprehensive monitoring index to monitor the meteorological drought in Liaoning comprehensive monitoring index to monitor the meteorological drought in Liaoning Province from Province from 1951 to 2014. They highlighted that a strong drought occurred in 2000 and 2001 in 1951 to 2014. They highlighted that a strong drought occurred in 2000 and 2001 in Liaoning Province, Liaoning Province, especially in the central part. Wang et al. (2007) [63] indicated that from 1951 to especially in the central part. Wang et al. (2007) [63] indicated that from 1951 to 2005, a total of 2005, a total of 55 years, the time with the largest drought coverage was 2000, reaching 68%, followed 55 years, the time with the largest drought coverage was 2000, reaching 68%, followed by 2002 and by 2002 and 2001. The greater the drought coverage, the more severe the drought. Zou et al. (2010) 2001. The greater the drought coverage, the more severe the drought. Zou et al. (2010) [64] found [64] found that the Liao River Basin had a continuous drought area exceeding 40% from 1951 to 2008, that the Liao River Basin had a continuous drought area exceeding 40% from 1951 to 2008, of which of which the drought area in 2001 reached 54.8%. Therefore, on the basis of the above-mentioned the drought area in 2001 reached 54.8%. Therefore, on the basis of the above-mentioned studies, the studies, the drought monograph of the drought events from May 2000 to May 2002 was defined as drought monograph of the drought events from May 2000 to May 2002 was defined as severe drought, severe drought, with a C value of −3. with a C value of −3. The best-fitting line of the cumulative TSD (solid black line in Figure 11) was defined as the The best-fitting line of the cumulative TSD (solid black line in Figure 11) was defined as the upper upper limit of severe drought in the drought monograph. As the horizontal line of zero in Figure 11 limit of severe drought in the drought monograph. As the horizontal line of zero in Figure 11 represents represents “normal”, the interval from normal to severe was divided into three equal intervals. “normal”, the interval from normal to severe was divided into three equal intervals. Moreover, the body Moreover, the body of the graph above the best-fitting line was correspondingly divided by two dashed lines labeled “−1” (green line) and “−2” (blue line), which represent the upper limits of mild and moderate droughts. The fourth line below the best-fitting line was derived with an equal interval and labeled “−4” (magenta line), which represents the upper limits of extreme drought. In accordance with the values of parameters m, b and C combined with Equation (8), parameters p = −0.330 and q = 0.149 could be obtained. (3) Calculation of the TSDI Equation (9) could be obtained by substituting parameters p and q into Equation (7). Then, the value of the TSDI for a given month in the study area could be calculated. TSDI = −0.330×TSDI + 0.149×TSD j j−1 j (9) when j = 1, the method of Narasimhan and Srinivasan [56] was adopted to determine the initial value of the TSDI, that is, multiplying TSD1 by 0.02. Figure 12 illustrates the TSDI histogram.

Remote Sens. 2018, 10, 1168 15 of 21 of the graph above the best-fitting line was correspondingly divided by two dashed lines labeled “−1” (green line) and “−2” (blue line), which represent the upper limits of mild and moderate droughts. The fourth line below the best-fitting line was derived with an equal interval and labeled “−4” (magenta line), which represents the upper limits of extreme drought. In accordance with the values of parameters m, b and C combined with Equation (8), parameters p = −0.330 and q = 0.149 could be obtained.

(3) Calculation of the TSDI

Equation (9) could be obtained by substituting parameters p and q into Equation (7). Then, the value of the TSDI for a given month in the study area could be calculated.

TSDIj = −0.330 × TSDIj−1 + 0.149 × TSDj (9) when j = 1, the method of Narasimhan and Srinivasan [56] was adopted to determine the initial value Remote Sens. 2018, 10, x FOR PEER REVIEW 15 of 21 of the TSDI, that is, multiplying TSD1 by 0.02. Figure 12 illustrates the TSDI histogram.

6 3 0

TSDI -3 -6 -9 Jan-85 Jan-89 Jan-93 Jan-97 Jan-01 Jan-05 Jan-09 Jan-13 Time (month)

Figure 12. Monthly TSDI of the Liao River Basin. Figure 12. Monthly TSDI of the Liao River Basin.

(4)(4) AssessmentAssessment of of meteorological meteorological hazards hazards by by the the TSDI TSDI TheThe TSDI TSDI ranged ranged between between −8.70−8.70 and and 6.50 6.50 from from January January 1985 1985 to to March March 2016 2016 (Figure (Figure 12).12). For For three three consecutiveconsecutive months months or or over, over, the the value value of of the the TSDI was less than −1−1 or or greater greater than than 1, 1, and and then then this this periodperiod was was defined defined as as a a drought drought or or flood flood event, respectively respectively [32]. [32]. In In accordance accordance with with the the previous previous definition,definition, 14 14 flood flood and and six six drought drought events events with with different different intensities intensities and and durations durations were were detected. detected. TableTable 55 listslists thethe correspondingcorresponding periods.periods. By calculatingcalculating the best-fittingbest-fitting line of thethe cumulativecumulative TSDITSDI withwith different different disaster disaster events, events, and and comparing comparing the the slope slope of of the the best-fitting best-fitting line line with with the the categorization categorization schemescheme in in Table Table 11,, thethe disasterdisaster severityseverity couldcould bebe obtained.obtained.

TableTable 5 5.. StatisticsStatistics of of disaster disaster events events identified identified by by the the TSDI. TSDI.

IDID Time Time Span Span Duration/Month Duration/Month Cumulative Cumulative TSDI Slope TSDI Slope Disaster Disaster Level Level 1 1985.08−1985.10 3 1.62 Mild wetness 1 1985.08–1985.10 3 1.62 Mild wetness 2 1985.12−1986.03 4 1.35 Mild wetness 2 1985.12–1986.03 4 1.35 Mild wetness 33 1986.08−1987.07 1986.08–1987.07 12 12 2.62 2.62 Moderate Moderate wetness wetness 44 1987.09−1989.05 1987.09–1989.05 21 21 2.50 2.50 Moderate Moderate wetness wetness 55 1990.03−1991.07 1990.03–1991.07 17 17 2.96 2.96 Moderate Moderate wetness wetness 66 1991.09−1992.07 1991.09–1992.07 11 11 2.93 2.93 Moderate Moderate wetness wetness 77 1992.09−1993.06 1992.09–1993.06 10 10 2.21 2.21 Moderate Moderate wetness wetness 8 1993.08–1994.05 10 2.31 Moderate wetness 8 1993.08−1994.05 10 2.31 Moderate wetness 9 1994.07−1997.12 42 3.32 Severe wetness 10 1998.08−1999.07 12 2.65 Moderate wetness 11 2000.06−2002.03 22 −3.03 Severe drought 12 2003.02−2003.07 6 −3.14 Severe drought 13 2005.02−2005.12 11 3.52 Severe wetness 14 2006.06−2006.09 4 1.55 Mild wetness 15 2007.06−2010.04 35 −5.08 Extreme drought 16 2010.06−2010.09 4 −4.20 Extreme drought 17 2011.03−2012.07 17 −5.41 Extreme drought 18 2012.10−2013.05 8 3.80 Severe wetness 19 2013.09−2013.12 4 3.12 Severe wetness 20 2014.07−2016.03 21 −5.33 Extreme drought

5. Discussion

5.1. Noah-Based SM Versus JPL Mascon-Based TWSA Figure 7 shows that the amplitudes of the TWSAs obtained from GRACE and Noah exhibited a certain difference, especially in mid-2014. This result could be explained from two aspects: one is that the Noah hydrological model excludes surface water and groundwater variables; the other is that the Liao River Basin suffered a severe drought in 2014, and the drought lasted until the mid-2015 [41], which resulted in severe deficit in the TWS during this period. However, the seasonal

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Table 5. Cont.

ID Time Span Duration/Month Cumulative TSDI Slope Disaster Level 9 1994.07–1997.12 42 3.32 Severe wetness 10 1998.08–1999.07 12 2.65 Moderate wetness 11 2000.06–2002.03 22 −3.03 Severe drought 12 2003.02–2003.07 6 −3.14 Severe drought 13 2005.02–2005.12 11 3.52 Severe wetness 14 2006.06–2006.09 4 1.55 Mild wetness 15 2007.06–2010.04 35 −5.08 Extreme drought 16 2010.06–2010.09 4 −4.20 Extreme drought 17 2011.03–2012.07 17 −5.41 Extreme drought 18 2012.10–2013.05 8 3.80 Severe wetness 19 2013.09–2013.12 4 3.12 Severe wetness 20 2014.07–2016.03 21 −5.33 Extreme drought

5. Discussion

5.1. Noah-Based SM versus JPL Mascon-Based TWSA Figure7 shows that the amplitudes of the TWSAs obtained from GRACE and Noah exhibited a certain difference, especially in mid-2014. This result could be explained from two aspects: one is that the Noah hydrological model excludes surface water and groundwater variables; the other is that the Liao River Basin suffered a severe drought in 2014, and the drought lasted until the mid-2015 [41], which resulted in severe deficit in the TWS during this period. However, the seasonal variations and trend between the TWSAs from the GRACE and Noah hydrological model agreed well and showed peaks in summer (June–August) and valleys in winter (December–February). Figure7 displays some systematic differences between mascon solutions and the hydrological models. The differences were mainly because the hydrological models only contained SM, which was only one component of TWS. Positive anomalies of the mascon solutions were much higher than those of the hydrological models, especially in summer, when more precipitation was observed. This case was a portion of the precipitation is converted into surface runoff, most of which is intercepted by the soil, and the infiltration is converted into soil water. Under the effect of gravity, part of the infiltration is converted into groundwater. As a result, the runoff is stored in soil water and groundwater. Thus, positive anomalies of the mascon solutions (TWS) were considerably higher than those of the hydrological models (which only contains SM). Besides, Seasonal biases may exist in the TWSA between hydrological models and GRACE solutions [65], and may bring some systematic differences between mascon solutions and hydrological models. The biases may have some impact on the final meteorological disaster monitoring results, and we will test it in the future work.

5.2. Analysis of TSDI Results TSDI results from January 1985 to March 2016 were divided into two parts for analysis due to the limited materials of meteorological disaster. The first part was from January 1985 to December 2000, which was compared with the previous researches. The second part was from January 2001 to March 2016, which was analyzed with the records from the National Water Report and Bulletin of Flood and Drought Disasters in China. Under the constraints of the current experimental conditions, the drought and flood disasters in the Liao River Basin that we can obtain from the previous studies and records were recorded on an annual scale. Thus, the analysis was yearly flood or drought occurrences.

(1) TSDI versus previous studies

Table6 presents the disaster situation of the study area, from January 1985 to March 2016, monitored by the previous research. The flood and drought degrees, for the past studies, were Remote Sens. 2018, 10, 1168 17 of 21 evaluated with the measured runoff or model (which was based on the measured runoff). For example, Cao (2013) [6] used standardized discharge index (SDI) to show the dry or wet period in the Liao River Basin.

Table 6. Disaster situation in the study area obtained from previous studies.

Disaster Classification Time References Drought 1997 −−, 1999 −−−, 2000 −−−−, Cao (2013) [6]; Sun et al. (2012, 2015) [4,43]; Cui et al. 1985 +++, 1986 ++++, 1987 ++++, 1988 ++, (2009) [5]; Tu et al. (2012) [66]; Zhang et al. (1993) Flood 1989 ++, 1991 +, 1992 +, 1994 +++, 1995 +++, [67]; Zhao et al. (2011) [68]; Xu et al. (2010) [69] 1998 ++, Note: ‘−’ represents the drought degree, ‘+’ represents the flood degree.

The comparison between Tables5 and6 revealed that the drought and flood disasters detected by TSDI were roughly the same with those of the previous research. For example, the floods in 1985–1989, 1991–1992, 1994–1995, and 1998 and the droughts in 2000 were all monitored using the TSDI. However, certain differences were also observed in several locations. For example, the TSDI miscalculated the droughts in 1997, and 1999 as flood events, which might be due to the relatively high TWSA developed in previous months. However, the disaster severity determined by the TSDI was slightly different from those of the previous studies. For example, the flood severity from 1985 to 1987, indicated by the TSDI, was slightly lower than that of the previous studies. However, the flood severities in 1991 and 1992 monitored by TSDI were slightly higher than those of the previous studies. The differences could be explained by the different drought indexes that used to monitor the flood and drought disasters. As a result, the severity of some drought or flood disasters might be lower or higher than the actual disaster degree. Moreover, the difference in the input variables of different drought indexes might lead to differences in results. The TSDI drought index was constructed on the basis of TWSA, and the TWSA was recovered by the GRACE satellite and contained all the components of water storage, including SMS, GWS, SWE, and so on. The drought index used in other papers was measured runoff. However, the drought and flood monitoring results of TSDI were generally consistent with those of the previous studies, indicating the accuracy of TSDI results from January 1985 to December 2000.

(2) TSDI versus records

The TSDI results from January 2001 to March 2016 were compared with records such as the National Water Report and Bulletin of Flood and Drought Disasters in China. Table7 shows the disaster situation obtained from records. Most of the flood and drought disasters monitored by TSDI (Table5) were the same as those of the records.

Table 7. Disaster situation in the study area obtained from records.

Disaster Classification Time References Drought 2001, 2002, 2007, 2008, 2009, 2014, 2015 National Water Report (2000–2016); Bulletin of Flood and Drought Disasters in China Flood 2005, 2006, 2010, 2011,2012, 2013 (2006–2016)

However, the flood disasters in 2010 and 2011 were classified as droughts by the TSDI. By combing the precipitation in Figure3 and TWSA in Figure5, we can see that the precipitation increased in 2010 and 2011, which replenished the deficit of water resources to a certain extent. However, the state of water resources in the Liao River Basin changed from deficit to surplus not until July 2012. From Figure6, we can see that the Water Resources Bulletin shows a much higher EWH than both GRACE solutions in 2010. However, the flood event was happening on a smaller temporal and/or spatial scale than what GRACE is able to resolve. Moreover, the flood in 2010 and 2011 mainly occurred on the tributaries of the West Liao River, Liao River, and so on. When precipitation is concentrated, it may Remote Sens. 2018, 10, 1168 18 of 21 cause flooding in local areas, but local flooding cannot play a decisive role in dry and wet conditions of the whole basin. Besides, the National Meteorological Disaster Monitoring Map, provided by the National Climate Center, shows that the study area was in a state of moderate drought in 2010 and 2011, which was consistent with the results monitored by TSDI. Overall, the monitoring results of TSDI for the period from January 1985 to March 2016 were generally consistent with those of the previous studies and records, indicating the accuracy of TSDI results.

6. Conclusions A reliable TWSC product is important for regional hydrological cycle and other related studies. In this study, the newly released JPL mascon solutions were compared with the GRACE spherical harmonic solutions. The JPL mascon solutions showed higher accuracy than the GRACE spherical harmonic solutions in terms of the uncertainty and the comparison with the Water Resources Bulletin of the Song Liao River Basin. The TSDI, which was established by the JPL mascon and GRNN-predicted TWSAs, monitored the drought and flood patterns in the semi-arid and semi-humid land of China. The findings indicated that the monitoring results of drought and flood disasters were consistent with those of previous studies and records, and the frequency and severity of drought and floods had intensified in the Liao River Basin in the past 30 years. The comparison between TSDI and previous studies showed that TSDI was more reliable than previous studies in drought and flood monitoring. The reason was that the TSDI drought index was constructed on the basis of TWSA, and the TWSA was recovered by the GRACE satellite and contained all the components of water storage, including SMS, GWS, SWE, and so on. However, the drought index used in previous studies contains only one single variable runoff. Thus, the GRACE satellite and TSDI can provide a solid theoretical basis for drought and flood monitoring in areas where data are lacking or even non-existent. Extreme climate events are a serious threat to human life and property and reflect the irrational use of water resources in the region to a certain extent. Therefore, strengthening the early warning system for drought and flood disasters in the study area, and establishing a reasonable water resources management mechanism are necessary to reduce the losses caused by meteorological disasters and to develop and utilize water resources rationally. The GRNN method is useful in predicting TWSAs beyond the GRACE period. By combing GRACE TWSA, the extended GRACE TWSAs and TSDI, drought and flood disasters in the Liao River Basin in China and other semi-arid and semi-humid regions can be monitored and predicted.

Author Contributions: X.C. and J.J. conceived and designed the experiments; X.C. performed the experiments and analyzed the data; H.L. collected the data; X.C. wrote the manuscript; J.J. proposed suggestions to improve the quality of the paper; X.C. revised the manuscript. Acknowledgments: This work was sponsored by the National Natural Science Foundation of China through Grant 41571412. The authors thank GRACE data processing centers (CSR, JPL, and GFZ) for providing the GRACE data and GSFC/NASA for providing GLDAS data, and the Ministry of Water Resources of the People’s Republic of China for providing National Water Report and Bulletin of Flood and Drought Disasters in China. The authors thank four anonymous reviewers for their valuable comments and suggestions on the improvement of this manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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