Ecological risk assessment for water scarcity in China s Yellow River Delta Wetland

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1 Stoh Environ Res Risk Assess (2) 25:697 7 DOI.7/s ORIGINAL PAPER Eologil risk ssessment for wter srity in Chin s Yellow River Delt Wetlnd Yn Qin Zhifeng Yng Wei Yng Pulished online: 27 April 2 Ó Springer-Verlg 2 Astrt Wetlnds re eologilly importnt due to their hydrologi ttriutes nd their role s eotones etween terrestril nd quti eosystems. Bsed on 2-yer study in the Yellow River Delt Wetlnd nd Mrkov-hin Monte Crlo (MCMC) simultion, we disovered temporl nd sptil reltionships etween soil wter ontent nd three representtive plnt speies (Phrgmites ustrlis (Cv.) Trin. ex Steud., Sued sls (Linn.) Pll, nd Tmrix hinensis Lour.). We seleted eight indies (iodiversity, iomss, nd the uptke of TN, TP, K, C, Mg, nd N) t three sles (ommunity, single plnt, nd miro-sle) to ssess eologil risk. We used the eologil vlue t risk (EVR) model, sed on the three sles nd eight indies, to lulte EVR nd generte three-level lssifition of eologil risk using MCMC simultion. The high-risk res t ommunity sle were ner the Bohi Se. The high-risk res t single-plnt sle were ner the Bohi Se nd long the northern nk of the Yellow River. At miro-sle, we found no onentrtion of high-risk res. The results will provide foundtion on whih the wtershed s plnners n llote environmentl flows nd guide wetlnd restortion. Keywords Eologil risk ssessment EVR model Wter srity Yellow River Delt Wetlnd Y. Qin Z. Yng (&) W. Yng Stte Key Lortory of Wter Environment Simultion, Shool of Environment, Beijing Norml University, 9 Xinjiekouwi Street, Beijing 875, Chin e-mil: zfyng@nu.edu.n Introdution Wetlnds re eologilly importnt due to their hydrologi ttriutes nd their role s n eotone etween terrestril nd quti eosystems (Psoe 993). Sientists round the world hve noted lrming hnges in these importnt eosystems. Eologil risk ssessment provides sientifi foundtion for mnging the risks tht will result from suh hnges in projets designed to protet nd onserve nturl eosystems nd iodiversity; the importne of suh tools hs inresingly een reognized y oth demi reserhers nd environmentl mngers. There re three min soures of risk tht re ommonly onsidered in wetlnds reserh: hevy metls nd nonmetl elements, suh s Cd, Cr, Cu, Hg, Ni, P, Zn, As, Bo, nd Se (Powell et l. 997; Overesh et l. 27; Pollrd et l. 27; Suntornvongsul et l. 27; Nulo et l. 28; Bi et l. 2; Brix et l. 2); orgni pollutnts, suh s hevy oil ompounds nd persistent orgni pestiides (Ji et l. 27; Chen et l. 28; Dimitriou et l. 28; Rumold et l. 28; Go et l. 29; Yng et l. 29); nd nturl prmeters suh s wter vilility nd slinity (Speelmns et l. 27; Sun et l. 29; Xie et l. 2). Sine wter is the defining feture of wetlnds, nd is essentil to their helth, wter risks tke the form of too muh or too little wter flooding nd drought (Ni nd Xue 23; Smith et l. 23; Boum et l. 25; Ci et l. 29; Huer et l. 29; Niolosi et l. 29). Most reserhers hve foused on wter srity in wetlnds t mro sle (Boum et l. 25; Sun et l. 28; Huer et l. 29; Yng et l. 29), ut there hs een little reserh on the risk to wetlnd speies used y drought (Smith et l. 23) euse wetlnds (y definition) mostly hve suffiient wter. However, drought is n importnt prolem in wetlnds in res where regionl drought or

2 698 Stoh Environ Res Risk Assess (2) 25:697 7 exessive withdrwls of wter upstrem of wetlnd redue the environmentl flows elow the level required to sustin wetlnd helth. In this pper, we foused on ssessment of the eologil risks tht result from wter srity; tht is, we ssessed the eologil risk used y wter srity. However, euse some wetlnd plnts nnot survive long-term flooding, we lso onsidered the risk used y exess wter. The vlue t risk (VR) model ws first used in eonomis reserh to ssess the insurne, investment, nd stok risks rising from eosystem hnges y Morgn Gurnry Trust Compny (996). In reent yers, it hs een used in eologil reserh from primrily eologil rther thn eonomi perspetive (Shi et l. 24); for exmple, eologil vlue t risk (EVR) hs een used to ssess the risk to fisheries resoures used y redued wter volumes (Wey et l. 27). This reserh demonstrted tht the models nd onepts of VR re dptle to eologil systems in the form of EVR. Mny methods hve een used to lulte EVR, with different methods suitle for different onditions, dtsets, nd preision requirements. In generl, these n e lssified into three types (Dowd 998): vrine ovrine methods, historil simultion methods, nd Monte Crlo simultion methods. The Monte Crlo simultion methods pper to e most suitle for eosystem reserh euse they re inherently proilisti, nd therefore ount for the stohsti nture of eosystems (Srinivsn nd Shh 2; Alexnder 22). In this pper, we nlyzed dt for plnts nd soils in Chin s Yellow River Delt Wetlnd using the Mrkov-hin Monte Crlo (MCMC) method. We lssified soil wter ontent in the wetlnd into three vilility levels (drought, suffiient, nd flooding) nd exmined the impts of these levels on severl eosystem indies, t three different sles (ommunity, single plnt, nd mirosle). Bsed on these results, we lulted the eologil risk to the wetlnd posed y eh of the three wter vilility levels using the EVR model. 2 Mterils nd methods 2. Study re The Yellow River Delt (8 33 E to 9 2 E, nd Nto38 2 N), the world s fstest-growing delt, is loted in northestern Shndong Provine, on the southern shore of the Bohi Se (Fig. ). The region hs temperte, semi-humid, ontinentl monsoon limte. The verge nnul temperture is 2. C. The verge nnul preipittion is 552 mm, of whih 7% flls during the summer. However, the verge nnul evportion is 962 mm. Sine the erly 97s, flow in the Yellow River hs een deresing due to omintion of drought nd exessive withdrwls in upstrem regions, nd the frequeny of omplete drying or ephemerl flow hs een inresing (Liu nd Zhng 22). The worst flow interruption ourred in 997, when there ws no flow in the Yellow River for 226 dys (Yng nd Yng 2). During the pst 3 yers, the wetlnds in the Yellow River Delt deresed in extent y more thn 3 km 2 (Zong et l. Fig. Lotions of the study re nd the smpling sites

3 Stoh Environ Res Risk Assess (2) 25: ). Wter srity is therefore one of the iggest threts to the wetlnd s iodiversity nd to plnt growth in the wetlnd. 2.2 Study design To study the eologil risk of the wetlnd t three sles, we hoose reeds (Phrgmites ustrlis (Cv.) Trin. ex Steud.), sued (Sued sls (Linn.) Pll), nd sltedr (Tmrix hinensis Lour.) s our study speies. These three plnts re the most widely distriuted nd representtive speies in the delt (He et l. 29) Soils nd plnts The field study ws rried out in the spring, summer, nd utumn (from April to Otoer) in 28 nd 29, euse ll the plnts in the wetlnds re ded or dormnt during the winter nd do not resume growth until April. We seleted 3 smpling sites for investigtion, eh m, whih previous studies hve shown to e suitle sle for the study re (Xio et l. 2). At eh site, we rndomly estlished qudrts (eh 9 m). Figure shows the lotions of the smpling sites. At eh site, we ounted the numer of speies nd used this dt to represent the iodiversity. We lso olleted plnt nd soil smples from ll 3 qudrts. In eh qudrt, we olleted ll the oveground plnt prts to mesure the totl iomss. We seprted the reeds, sued, nd sltedr so tht we ould determine seprte iomss vlues for eh speies. We lso otined two soil smples from eh site t depth of 3 m: one ws stored in seled luminum smple ox nd used to lulte soil wter ontent, nd the other ws stored in plsti g nd used to determine vrious element ontents. In ddition, we otined soil smples t 2-m depth intervls until we rehed the wter tle t eh site. We lso instlled neutron proe ess tue t site 3, whih ws the most representtive of the sites, so tht we ould mesure soil wter ontent t -m intervls to depth of 5 m every dys Dt nlysis Plnt nd soil smples were ir-dried for 2 dys, then the dry soil smples were ground with mortr nd pestle nd pssed through mesh-sieve (prtile size \54 lm) to provide smples with homogeneous prtile size nd size distriution. Dry plnt smples were mehnilly ground (FW, Anrui, Shnghi, Chin) to pss through -mm sieve, nd were then stored in drk vils until nlysis. The plnt smples were digested in ultrpure HNO 3 (p.. 65%) nd HF (p.. 45%), in 2: v/v rtio, wheres soil smples were digested in ultrpure HNO 3 (p.. 65%) nd HClO (p.. 7%) in 2: v/v rtio. Element onentrtions (P, K, C, Mg, N) in soils nd plnts were mesured y mens of indutively oupled plsm-emission mss spetrometry (JY-ULTIMA spetrometer, Join Yvon, Longjumeu, Frne). The C nd N ontents in the soil nd plnt smples were determined using Vrio EL Anlyzer (Elementr, Hnu, Germny). 2.3 MCMC We used MCMC nlysis to simulte the sptil vriility in wter vilility. The ore of suh nlyses is to determine the Mrkov-hin trnsition proilities. The trnsition proility (t) is defined s follows: n o t jk ðh U Þ¼Pr ðx þ h U Þ k jðxþ j ðþ where vetor x is sptil lotion, h U represents lg (whih is lso seprtion vetor) in diretion U, j nd k represent different levels of wter vilility, nd (x) j mens tht the level of wter vilility t point x is j. A Mrkov-hin model is then pplied to one-dimensionl tegoril dt in diretion U, whih ssumes n exponentil mtrix form: Tðh U Þ¼expðR U h U Þ ð2þ where R U denotes trnsition rte mtrix, R U ¼ r ;U... r K;U B..... A; with entries r jk,u representing the r K;U r KK;U rte of hnge from tegory j to tegory k (ssuming tht j is present) per unit length in diretion U. An eigenvlue nlysis must e rried out to evlute exp(r U h U ), whih nnot e omputed diretly. Sine the sptil vriility of wter vilility is ontinuous, we used ontinuous-lg Mrkov-hin model to desrie this trnsition mtrix: Tðh U Þ¼expðR U h U Þ¼ Xk i¼ h i ðdh U Þ h U=Dh U Z i ð3þ where T(h U ) denotes the disrete-lg trnsition proilities, whih n e otined y totling the trnsition ount mtrix N(Dh U ) for wter vilility, then dividing eh row y the row sum, h i (Dh U ) for i = tok denoting the eigenvlues of N(Dh U ), nd Z i denotes spetrl omponent mtrix ssoited with eh eigenvlue, h i (Dh U ). 2.4 Indies for eologil risk ssessment We used three kinds of indies to ssess the eologil risk to the Yellow River Delt Wetlnd t different sles used y wter srity. Biodiversity represented the

4 7 Stoh Environ Res Risk Assess (2) 25:697 7 index t ommunity sle, nd ws defined s the existene of wide vriety of vsulr plnt speies in the wetlnd during speifi time period. On this sis, we lulted the iodiversity index s: V i ¼ M i =M ð4þ where V i is the iodiversity of smple site i, M i is the numer of speies in smple i, nd M is the totl numer of speies in the study re. Biomss (C) represented the index t the single-plnt sle, sine this prmeter diretly reflets the eologil risk to plnt used y insuffiient wter. It is lulted s follows: C ¼ C i =S i ð5þ where C i is the ir-dry iomss of plnts in smple i nd S i is the size of the smple site. We hose six indies t the miro-sle: uptke of nutrients, whih we represented y the totl nitrogen (TN), totl phosphorus (TP), K, C, Mg, nd N ontents of the plnts: A i ¼ A ij =A i ðj ¼ ; 2; 3Þ ð6þ where A i is the sorption (uptke) index for element i, A ij is the ontent of element i in plnt speies j, nd A i is the ontent of element i in the soil. These six indies represent the utiliztion effiieny of the six elements, whih re importnt for the growth nd development of ll three plnt speies. 2.5 EVR model EVR is defined s the mximum expeted loss of n eologil index t given onfidene level, nd DP is the possile loss of vlue for the eologil index: PrfDP [ EVRg ¼ ð7þ In suh nlyses, the onfidene level is usully set t 95%, so we mesured the mximum loss for this level nd defined the high, medium, nd low risks for eh index (Tle ). 2.6 Dt soures nd proessing We estimted the distriutions of the three plnt speies using version 4.2 of the ENVI softwre (ITT Visul Informtion Solutions, using dt extrted from Lndst TM stellite imges sed on supervised lssifition. The soil wter ontent ws simulted using version 6. of the GMS softwre (Aquveo, for MCMC. All dt were summrized using Mirosoft Exel 27 nd tested for signifine using version 8 of SPSS (SPSS In., Chigo, IL). For eh smpling dte, we lssified the 3 soil wter ontent vlues into three wter vilility levels using K-men luster nlysis in SPSS. Bsed on this lssifition, we determined how wter srity ffeted the plnts using regression nlysis for the eight indies. We used the oeffiient of vrition (CV) to represent the vrition in soil wter ontent for eh speies. Then we used the wter vilility t eight of the smpling sites (2 5 nd 7 ) s inputs for the GMS softwre, nd lulted the mtrix of trnsition proilities (MTP). We used the other five sites to verify the simultion results. The result of the MCMC simultion ws dtse with more thn 2 million dt points tht inluded the soil wter ontent for three levels of wter vilility, nd eight indies for eh of the three wter vilility levels for the three plnt speies in the spring, summer, nd utumn. In the lst step of this nlysis, we rsterized the mp of the study re using the GIS module of GMS. The resulting mp ontined pixels, eh m in size. We then ssigned the wter vilility level nd vlues of the eight indies to eh pixel in the grid. Tle The eologil vlue t risk (EVR) of eight indies in three tegories in the spring, summer, nd utumn Seson Level Biodiversity Biomss (kg/m 2 ) Uptke of nutrients (mg/mg) TN TP K C Mg N Spring Low \. \.4 \4 \.6 \. \. \.4 \.5 Medium High [.3 [.8 [75 [.2 [.3 [.2 [.7 [2.5 Summer Low \. \.7 \5 \.4 \.2 \. \.5 \.5 Medium High [.4 [ [3 [.7 [.4 [.2 [. [.5 Autumn Low \.2 \.8 \5 \.3 \. \.3 \. \. Medium High [.4 [. [5 [ [.3 [.5 [.2 [2

5 Stoh Environ Res Risk Assess (2) 25: Verifition of the model We used dt from eight smple sites to simulte the distriution of soil wter ontent in the Yellow River Delt Wetlnd, nd used dt from the remining five sites to verify the results. Figure 2 indites the MTP dt fit using the disretelg pproh t.-m lg. We found tht the Mrkovhin dt fit the mesured dt well in ll ses nd for most lg vlues sed on the smoothness of the urves nd the onvergene results, whih indited tht our lssifition of the soil wter ontent levels ws eptle. We used MCMC to simulte the distriution of soil wter ontent times, nd more thn 75% of the simulted dt orresponded to our field dt. 4 Results nd disussion 4. Soil wter ontent t different depths For the whole growing period, we otined soil wter ontent dt from different depths t smple site 3. We ompred the soil wter ontents t depths of 3, 5, nd m from April to Otoer in 28 (Fig. 3). The three urves followed similr ptterns, with mxim nd minim Fig. 2 Trnsition proilities for different wter vilility levels (DS drought stress, SW suffiient wter, FS flooding stress) s funtion of the lg in the spring, the summer, nd the utumn

6 72 Stoh Environ Res Risk Assess (2) 25:697 7 Soil wter ontent (% v/v) 48% 44% 4% 36% 3 m 5 m m 32% April My June July August Septemer Otoer Novemer Fig. 3 Soil wter ontent t depths of 3, 5, nd m from April to Otoer in 28 ourring t lose to the sme time for ll three depths. Mximum wter vilility ourred in lte April, lte July, nd lte August, wheres minimum wter vilility ourred in mid-june nd mid-august. The soil wter ontents t depths of 3 nd 5 m were strongly orrelted (r 2 =.75, P \.5). The soil wter ontents t depths of 3 nd m were lso strongly orrelted (r 2 =.76, P \.5). Beuse of the strengths of these orreltions nd the ft tht most plnt roots were loted in the top 3 m of the soil, we hose to use the 3-m wter ontent for the reminder of our nlysis. 4.2 Distriution of wter vilility in the Yellow River Delt Wetlnd To relily simulte soil wter ontent, we lssified the soil wter ontent into three wter vilility levels for eh seson using the K-men luster module in SPSS, with the vlues djusted to ount for the reltive importne of wter for plnt growth during eh seson (Tle 2). The rnge of soil wter ontent ws smllest in the spring, nd gretest in the utumn. The summer ws the wettest seson. We imported the wter vilility level from eight of the smple sites into the GMS softwre, nd lulted the MTP. We then lulted the wter distriution y mens of MCMC simultion nd imported prt of these dt into the GMS softwre to represent the wter vilility level for eh pixel in the rster mp (Fig. 4). Figure 4 indites tht more sites experiened drought during the spring nd utumn thn during the summer. The soil wter ontent did Tle 2 The wter vilility levels in three sesons Level Soil wter ontent (% v/v) Drought stress Suffiient wter Flooding stress Spring \2 2 3 [3 Summer \ [35 Autumn \ [32 not show distint sptil zontion t lrge sles; tht is, the three wter vilility levels were lerly intermingled. 4.3 The distriution nd soil wter ontent levels for the three speies Bsed on our field dt nd the Lndst TM dt, we were le to define the distriution of the three plnts (Fig. 5). We found tht the soil wter ontent levels differed for res with reeds, sued, nd sltedr. Reeds need more wter thn the other two speies. The reeds were le to survive t soil wter ontents rnging from 3 to 54%; s result, reeds were found t sites with men soil wter ontent of 33%. The sued needs less wter thn reeds, nd ws found t sites with men soil wter ontent of 28%, whih is 5% lower thn the men for reeds. Sltedr ws the most drought-tolernt speies, nd ws found t sites with soil wter ontent rnging from 8 to 4%, with men of 27%. The CVs of soil wter ontent for the reed, sued, nd sltedr sites were.28,.2, nd.34, respetively. Bsed on the lssifition of CV vlues y Cmrdell et l. (994) (CV\., low vriility;. B CV \, medium vriility; CV C, high vriility), ll three plnts inhited sites with medium vriility. The sltedr sites hd the lrgest CV nd therefore exhiited the gretest vrition in soil wter ontent, wheres sued inhited sites with lower vrition. 4.4 Effet of wter vilility on the Yellow River Delt Wetlnd The different levels of wter vilility ffeted the ommunity struture nd distriution of the plnts t the three sles we ssessed. At the ommunity sle, we foused on the effets of wter vilility on iodiversity. Figure 6 indites tht inresing wter vilility led to inresed iodiversity, nd tht iodiversity inresed signifintly from spring to utumn t ll levels of vilility. We used ANOVA to test whether there ws signifint differene etween wter levels in the sme seson. We found tht iodiversity ws generlly highest in utumn when plnts were under flooding stress, ut the differene ws not signifint when the plnts hd suffiient wter. Hene, the iodiversity differed signifintly mong wter vilility levels within seson. At the single-plnt sle, we ompred the iomss of the three plnts t different wter vilility levels (Fig. 7). Reeds hd the most iomss under flooding stress, wheres sued nd sltedr hd the highest iomss when wter ws suffiient. Hene, reeds ould tolerte wetter onditions thn sued nd sltedr during ll three sesons. However, reeds lso showed the lrgest derese

7 Stoh Environ Res Risk Assess (2) 25: Fig. 4 Distriution of wter vilility levels sed on the MCMC simultion in the spring, the summer, nd the utumn in iomss under drought onditions, inditing tht they were more sensitive thn the other speies to drought. For sued, iomss ws signifintly greter with suffiient wter in ll three sesons, nd ws signifintly greter with suffiient wter thn under drought in summer nd utumn. For sltedr, iomss did not differ signifintly etween drought stress nd flooding stress in the utumn, ut iomss ws signifintly higher with suffiient wter thn with drought or flooding, nd ws signifintly higher with drought thn with flooding, in oth summer nd

8 74 Stoh Environ Res Risk Assess (2) 25:697 7 Fig. 5 The distriution of the three min plnt speies in the Yellow River Delt Wetlnd Biodiversity utumn. For ll three speies, the net inrese in iomss ws gretest during the summer, nd iomss susequently delined. At the miro-sle, we foused on the uptke of six elements y the plnts. Figure 8 shows how wter vilility ffeted the uptke of these elements in eh seson. Reeds hd the highest uptke of eh element under flooding stress for most indies nd most sesons, wheres sltedr generlly hd the highest uptke under drought stress in ll three sesons. The uptke of Mg nd N y sued ws higher when wter ws suffiient, nd the uptke of TP nd K ws higher under drought stress. The uptke of TN y sued ws highest under drought stress in utumn, nd the uptke of C ws highest under flooding stress. 4.5 The helthy sttes for the three plnt speies Bsed on the preeding disussion, we tried to identify helthy stte for eh plnt sed on the vlues of the eight Spring Summer Autumn Fig. 6 Reltionships etween iodiversity nd wter vilility levels (DS drought stress, SW suffiient wter, FS flooding stress) in the spring, summer, nd utumn in the Yellow River Delt Wetlnd. Vlues represent mens ± SD (n = ). Brs leled with different letters differ signifintly (ANOVA, P \.5) mong the three wter vilility levels indies t the three sles. We found tht for reeds, the indies were generlly highest in the utumn, exept for N nd K, whih were highest under drought stress in the summer. Sued survived under rnge of onditions. Biomss ws highest with suffiient wter in ll sesons. The uptke of Mg nd N ws highest when wter ws suffiient in ll three sesons, wheres TP nd K were highest under drought stress. Considering the iomss nd uptke of TN nd C, sued grew etter with suffiient wter. Sltedr ws unle to survive t sites where the soil wter ontent ws too high (soil wter ontent[4%), ut survived nd grew well under drier onditions (i.e., soil wter ontent \2%). Five indies for sltedr (TN, TP, K, C, nd Mg) were highest in ll three sesons under drought stress. Bsed on these results, we ssumed tht the optiml soil wter ontent for helthy onditions existed under flooding stress for reeds, under suffiient wter for sued, nd under drought stress for sltedr. 4.6 EVR t different sles After the simultion, we ssigned level of wter vilility nd reltive degree of helth for eh speies to eh pixel in the rster mp. We lulted the EVR for eh speies using the 95% onfidene intervl. Figure 9 shows the resulting distriutions of EVR for eh index in the spring, summer, nd utumn for this onfidene intervl. At the ommunity sle, there were 93 speies of vsulr plnts in the study re. We oserved 2 speies during our investigtion, ut some only grew y the sides of rods, nd others were rre. Only 9 speies were ommonly found t our smple sites: Phrgmites ustrlis (Cv.) Trin. ex Steud., Sued sls (Linn.) Pll, Tmrix hinensis Lour., Cynnhum hinense, Artemisi

9 Stoh Environ Res Risk Assess (2) 25: () Biomss(kg/m 2 ) () Biomss(kg/m 2 ) () Biomss(kg/m 2 ) CB: NIB: CB: NIB: rvifoli, Limonium sinense, Sued glu, Cyperus glomertus, Glyine soj, Melilotus offiinlis, Sonhus rvensis, Apoynum venetum, Tripolium vulgre, Clmgrostis pseudophrgmites, Elipt prostrt, Trirrhen shriflor, Typh orientlis, Slix mtsudn, nd Myriophyllum spitum. Thus, we only used these 9 speies to lulte the iodiversity of the smple sites. Figure 9 shows tht the iodiversity risk inreses over time, eoming muh higher in utumn thn in spring. The highest-risk res re ner the Bohi Se, overing n re of out 4 km 2 in utumn. Some res long the northestern side of the Yellow River hve medium level of risk in spring ut hnge to high-risk res in utumn. Some res long the northern nk of the Yellow River Spring Summer Autumn NIB: Spring Summer Autumn CB: Spring Summer Autumn Fig. 7 Cumultive iomss (CB) nd net inrese in iomss (NIB) for reeds, sued, nd sltedr t different wter vilility levels (DS drought stress, SW suffiient wter, FS flooding stress). Vlues represent mens ± SD (n = ). Brs leled with different letters differ signifintly mong the wter vilility levels (ANOVA, P \.5) re low-risk res in spring ut hnge to medium-risk res in utumn. At single-plnt sle, iomss showed high degree of vrition (Fig. 9). In the spring, the high-risk re for iomss ws ner the Bohi Se, nd overed n re of more thn 72 km 2. The medium-risk re ourred long the northern nk of the Yellow River nd towrds the enter of the study re. In the upper Yellow River delt, there is lrge re with low to medium iomss risk. In the summer, the high-risk re moves loser to the northestern nk of the Yellow River, nd the re deresed to 63 km 2. The medium-risk re lies long the northern nk of the Yellow River, nd most of the spring high-risk re ner the Bohi Se eme low-risk re y the summer. The distriution of risk levels did not hnge gretly in the utumn. However, the high-risk re in utumn inresed to 86 km 2. At the miro-sle, the eologil risk ws represented y six uptke indies (TN, TP, K, C, Mg, nd N). The high-risk re for TN ws less onentrted thn those of the other five risks, ut ws minly found etween the Yellow River nd the Bohi Se in spring nd summer (Fig. 9). The high-risk re in the north-entrl prt of the study re overed n re of 3 km 2. The risk for TP uptke differed mong the sesons (Fig. 9d). The high-risk re ws gin loted northest of the Yellow River in the spring, nd ws reltively strongly onentrted. In the summer, the distriution eme more dispersed. The highrisk re deresed from 7 km 2 in summer to 32 km 2 in utumn. The K uptke risk hnged reltively little over time (Fig. 9e). The high-risk re ws long the northern nk of the Yellow River, y the Bohi Se, or in the northentrl prt of the study re nd the upper rehes of the Yellow River. The high-risk re ws lrgest (42 km 2 )in the summer. The C uptke risk ws highest in summer, when the high-risk re overed 66 km 2 (Fig. 9f). In spring, the high-risk re ws loted long the northestern side of the Yellow River, with n re of 7 km 2,s ws the se for Mg (Fig. 9g). For the N uptke risk, spring nd utumn hd the highest risk (Fig. 9h). In the spring, the high-risk re ly long the northern nk of the Yellow River, wheres in utumn, the high-risk re hd moved towrds the Bohi Se. The risk distriutions for TP, C, Mg, nd N were similr. The high-risk res were found on oth sides of the Yellow River or in the southentrl prt of the study re. The low-risk res were loted long the northern nk nd upper rehes of the Yellow River, or on the northestern side of the high-risk re y the Bohi Se, or south towrds the downstrem rehes of the Yellow River. Figure 9 shows tht the iodiversity risk moves wy from the shores of the river nd the northern rehes towrds the Bohi Se nd the southern rehes t

10 76 Stoh Environ Res Risk Assess (2) 25:697 7 Fig. 8 Nutrient uptke indies for the totl nitrogen (TN), totl phosphorus (TP), K, C, Mg, nd N y reeds, sued, nd sltedr t different wter vilility levels (DS drought stress, SW suffiient wter, FS flooding stress) in the spring, summer, nd utumn. Vlues represent mens ± SD (n = ). Brs leled with different letters differ signifintly (ANOVA, P \.5) mong the wter vilility levels () Uptke of TN Uptke of K Uptke of TP Uptke of C.2. Uptke of Mg () Uptke of TN DS SW FS Uptke of N Uptke of TP Uptke of K 3..5 Uptke of C Uptke of Mg 4 2 Uptke of N 2 6 ommunity sle. Tht suggests the lol hydrology uses wter to drin towrds the river (nd wy from the Bohi Se) from spring to utumn s the river dries out. In the spring, the high-risk re ppers mostly where sued grew, wheres in summer nd utumn, the high-risk re ppers mostly where sltedr grew t single-plnt sle. Spring ws the droughtiest seson. As new growth of sued needed suffiient wter, the spring drought stress ould ple this speies t risk in some res. Beuse of rinfll nd wter sediment regultion y the wtershed s

11 Stoh Environ Res Risk Assess (2) 25: Fig. 8 ontinued () Uptke of TN 4 7 Uptke of TP 6 3 Uptke of K..5 Uptke of C..5.. Uptke of Mg 3..5 Uptke of N 2. mngers in the summer nd utumn, sltedr experiened flooding stress during these sesons, whih ws not the most suitle ondition for its growth. At the miro-sle, the ptterns were less ovious, nd the risk res were not s onentrted s they were t the ommunity nd single-plnt sles. In spring, the high-risk re ws smllest for K (22 km 2 ), wheres the high-risk re ws lrgest for Mg (7 km 2 ). In summer, the high-risk re rnged from 26 to 7 km 2, with men of 57 km 2. In utumn, the totl high-risk re ws the smllest of ll three sesons, with men of 37 km 2. The distriutions of the eologil risk onfirm tht different kinds of plnts need different wter onditions to remin helthy, nd provide sientifi foundtion for the llotion of eologil flows during eh seson. Wetlnds, inluding those of the Yellow River Delt, re one of the most importnt eosystems in the world euse of the mny eosystem servies they provide. In future reserh, we hope to lulte the eosystem servies provided y the Yellow River Delt Wetlnd nd hnges in their vlues in response to different levels of wter vilility using the tehnique of eologil risk ssessment. The risk to the vlue of these eosystem servies ould provide n intuitive nd strightforwrd result from the eologil risk ssessment tht will help the wtershed s plnners to llote eologil flows nd restore degrded res of the wetlnd. 5 Conlusions In this pper, we demonstrted how the EVR model ould e used to study the eologil risks within the Yellow River Delt Wetlnd under different levels of wter vilility. Our nlysis reveled the reltionships etween wter vilility levels nd eight indies t three sles for three representtive plnt speies t different times of yer. We used these indies to lulte the EVR nd generte three-level distriution of eologil risk y mens of MCMC simultion. The eologil risk tended to e highest in utumn t the ommunity nd single-plnt sles. At miro-sle, the summer hd the highest uptke risk for TP, K, C, Mg, nd N, wheres the riskiest seson for TN ws spring. Sptilly, the high-risk res were ner the Bohi Se t ommunity sle nd ner the Bohi Se nd long the northern nk of the Yellow River t single-plnt sle. At the miro-sle, the high-risk res were more dispersed thn they were t other sles. The nlysis desried in this pper provided new method to study wetlnd s eologil risk s result of wter srity t different sles. We introdued the EVR method, omined with MCMC simultion, nd provided wy to identify res t high risk so tht wtershed plnners n mnge the eologil flows to redue the risks posed y flututions in wter vilility s result of wter mngement in regions upstrem of the wetlnd.

12 78 Stoh Environ Res Risk Assess (2) 25:697 7 Fig. 9 Eologil risks of wter srity for iodiversity, iomss, nd uptke of TN, d TP, e K, f C, g Mg, nd h N for 95% onfidene intervl in the spring, summer, nd utumn

13 Stoh Environ Res Risk Assess (2) 25: Fig. 9 ontinued

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