寧夏回族自治區(qū)生態(tài)系統(tǒng)服務(wù)時空變化及其權(quán)衡研究

楊強強,章 翩,邱小琮,趙增鋒,趙睿智,周瑞娟

寧夏回族自治區(qū)生態(tài)系統(tǒng)服務(wù)時空變化及其權(quán)衡研究

楊強強1,章 翩2,邱小琮3*,趙增鋒1,趙睿智1,周瑞娟4

(1.寧夏大學(xué)土木與水利工程學(xué)院,寧夏 銀川 750021；2.皖江工學(xué)院管理學(xué)院,安徽 馬鞍山 243000；3.寧夏大學(xué)生命科學(xué)學(xué)院,寧夏 銀川 750021；4.寧夏回族自治區(qū)生態(tài)環(huán)境監(jiān)測中心, 寧夏 銀川 751100)

綜合土地利用、高程及降水等多源數(shù)據(jù),采用InVEST模型、PLUS模型及相關(guān)性分析、熱點分析等方法,探究2000～2020年寧夏生態(tài)系統(tǒng)服務(wù)(水質(zhì)凈化、土壤保持、碳存儲及生境質(zhì)量)時空特征及其權(quán)衡/協(xié)同關(guān)系,并進(jìn)行2030年生態(tài)系統(tǒng)服務(wù)的模擬.結(jié)果表明:研究期內(nèi),近10年土地利用類型變化相對較大,且人類活動為其主要驅(qū)動因素;2000～2020年,除土壤保持服務(wù)外,寧夏的水質(zhì)凈化、碳存儲及生境質(zhì)量均呈下降趨勢;相關(guān)性分析表明,氮輸出與土壤保持及碳存儲之間呈協(xié)同效應(yīng),土壤保持與碳存儲之間存在協(xié)同關(guān)系,生境質(zhì)量與氮輸出及碳存儲之間存在權(quán)衡關(guān)系;熱點分析顯示,水質(zhì)凈化服務(wù)與生境質(zhì)量之間熱點重疊區(qū)相對較高(均值達(dá)6.5%),但單位面積能夠同時提供多種生態(tài)系統(tǒng)服務(wù)的區(qū)域占比較低且呈下降趨勢;除土壤保持服務(wù)外,生態(tài)保護(hù)情景下更有利于生態(tài)系統(tǒng)功能的改善.研究結(jié)果凸顯了生態(tài)保護(hù)在土地利用管理中的重要性,可為區(qū)域土地格局優(yōu)化及資源的有效配置提供基礎(chǔ)參考.

生態(tài)系統(tǒng)服務(wù)；權(quán)衡；InVEST模型；PLUS模型；寧夏回族自治區(qū)

生態(tài)系統(tǒng)服務(wù)(Ecosystem services, ESs, 人類直接或間接從自然環(huán)境中獲取的各種惠益)作為人與自然聯(lián)系的紐帶[1],在促進(jìn)生態(tài)安全及維持社會經(jīng)濟(jì)的可持續(xù)發(fā)展中扮演重要角色[2-3].然而,現(xiàn)階段對ESs轉(zhuǎn)化速度、規(guī)模及驅(qū)動因素的認(rèn)知仍無法滿足全球可持續(xù)發(fā)展的需要[4].研究發(fā)現(xiàn),人類活動已導(dǎo)致全球超過70%的陸地表面發(fā)生不同程度的改變[5],其動態(tài)變化通過作用于生態(tài)系統(tǒng)的組成及結(jié)構(gòu)進(jìn)一步影響ESs,致使約60%的ESs因不合理的土地利用處于衰退狀態(tài),生態(tài)系統(tǒng)功能退化[6-8].此外,預(yù)計至2100年,全球新擴張城市用地約51%～63%轉(zhuǎn)換自耕地,27%～44%轉(zhuǎn)換自林地及草地,對生物多樣性保護(hù)及水、碳、氮的循環(huán)將產(chǎn)生深遠(yuǎn)影響[9],不利于自然—經(jīng)濟(jì)—社會系統(tǒng)的和諧發(fā)展[10].相關(guān)學(xué)者及決策者從不同角度對ESs展開研究,但ESs具有時空異質(zhì)性,且不同服務(wù)間存在此消彼長的權(quán)衡關(guān)系或相互增益的協(xié)同關(guān)系[11],厘清ESs間的關(guān)系并制定可持續(xù)生態(tài)系統(tǒng)管理策略以削弱或消除權(quán)衡關(guān)系,實現(xiàn)經(jīng)濟(jì)建設(shè)及ESs維護(hù)的雙贏從而提升人類福祉的研究仍有待于進(jìn)一步豐富[6].

目前,ESs評估生態(tài)模型主要包括CASA[12]、RULSE[13]、SWAT[14]、SolVES[15]及InVEST[16]等.其中,InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型作為可獨立運行的開源軟件,具有界面友好、數(shù)據(jù)需求小、可視化能力強等優(yōu)勢,在ESs評估中得以廣泛應(yīng)用[3, 7, 17].如Xue等[16]基于InVEST模型分析了內(nèi)蒙古巴林左旗2000～ 2020年四種ESs的時空變化及權(quán)衡關(guān)系;Huang等[18]運用CASA、RULSE及InVEST模型綜合分析了我國ESs特點,并借助莫蘭指數(shù)及統(tǒng)計指數(shù)等方法探究了ESs與城市化之間的關(guān)系.盡管上述研究對提升ESs空間狀態(tài)及其變化的認(rèn)知和促進(jìn)研究區(qū)內(nèi)冷熱點的識別具有較好的推動作用,但對區(qū)域ESs多目標(biāo)優(yōu)先保護(hù)區(qū)的設(shè)定研究尚存不足[19].此外,為了更好的理解ESs與土地利用之間的關(guān)系,學(xué)者嘗試通過動態(tài)模擬多類情景的土地利用格局以實現(xiàn)土地資源優(yōu)化配置,從而提高生態(tài)安全水平[20].其中,Liang等[21]2020年基于土地利用擴張分析策略及多類隨機斑塊種子生成機制開發(fā)的土地利用模擬軟件——PLUS(Patch-generating Land Use Simulation)模型,較現(xiàn)有預(yù)測模型(ANN-CA、Logistic-CA、CLUE-S及FLUS等)具有更高的模擬精度,在土地利用研究領(lǐng)域得到廣泛認(rèn)可.如Wei等[22]基于PLUS模型預(yù)測新疆艾比湖流域土地景觀格局,并結(jié)合InVEST模型分析了研究區(qū)生境質(zhì)量變化態(tài)勢.Chen等[23]耦合PLUS-InVEST模型分析了廣東佛山市歷史及未來土地利用及碳儲量變化,發(fā)現(xiàn)在模擬情景中生態(tài)保護(hù)情景下城市擴張對耕地及林地的侵占最少,且碳儲量下降率最低.相關(guān)研究對促進(jìn)區(qū)域生態(tài)保護(hù)與經(jīng)濟(jì)發(fā)展的動態(tài)平衡具有指導(dǎo)意義[20].

寧夏回族自治區(qū)(以下簡稱寧夏)位于濕潤與干旱的過渡地帶,生態(tài)類型多樣,面臨著西北地區(qū)普遍存在的環(huán)境問題(如水土流失、草地退化等),改善寧夏生態(tài)環(huán)境以構(gòu)建生態(tài)安全屏障對于我國具有重要意義[24].本研究以寧夏為研究對象,借助InVEST模型、PLUS模型及相關(guān)性分析、熱點分析等方法旨在:(1)明晰ESs時空特征;(2)厘清ESs間的權(quán)衡關(guān)系并識別熱點區(qū);(3)探究2030年研究區(qū)土地利用格局及ESs變化趨勢.研究結(jié)果以期為黃河流域生態(tài)工程的實施及高質(zhì)量發(fā)展提供基礎(chǔ)參考.

1 材料與方法

1.1 研究區(qū)概況

寧夏(35°14′～39°23′N,104°17′～107°39′E)地處我國西北地區(qū)東部,位于黃河中上游,總面積66,400km2,地勢南高北低,西部高差大于東部[25],土地利用格局如圖1所示.該區(qū)域?qū)贉貛Т箨懶愿珊蛋敫珊禋夂騾^(qū),年均溫約5～9℃,年均降水量150～ 600mm[26].至2020年,寧夏共包括5市(銀川市、石嘴山市、吳忠市、固原市及中衛(wèi)市),721萬人,城市化率為64.96%,地區(qū)生產(chǎn)總值3920.55億元.

圖1 研究區(qū)位置及2020年土地利用格局

1.2 數(shù)據(jù)來源

30m分辨率的2000、2010、及2020年土地利用數(shù)據(jù)來源于(國家基礎(chǔ)地理信息中心)GlobeLand 30(http://globeland30.org),依據(jù)《土地利用現(xiàn)狀分類》(GB/T 21010-2017)并結(jié)合研究區(qū)實況將其分為耕地、林地、灌木地、草地、建設(shè)用地、水域、濕地及裸地八類.DEM數(shù)據(jù)(30m)來源于ASTER GDEM V003(https://search.earthdata.nasa.gov).1km分辨率的降水量、氣溫和潛在蒸散發(fā)數(shù)據(jù)[27]及30m分辨率的降雨侵蝕力[28]和土壤可蝕性因子[29]均來自國家地球系統(tǒng)科學(xué)數(shù)據(jù)中心(http://www.geodata. cn).100m分辨率人口密度數(shù)據(jù)來源于WorldPop (http://hub.worldpop.org).1km分辨率的GDP空間分布數(shù)據(jù)來源于中科院資源環(huán)境科學(xué)與數(shù)據(jù)中心(https://www.resdc.cn).30m分辨率的NDVI國家生態(tài)科學(xué)數(shù)據(jù)中心(http://www.nesdc.org.cn).道路空間分布數(shù)據(jù)來源于Open Street Map(http://www. openstreetmap.org).柵格數(shù)據(jù)經(jīng)ArcGIS 10.8處理統(tǒng)一為30m分辨率,空間坐標(biāo)系采用WGS_1984_ UTM_Zone_48N.

1.3 研究方法

1.3.1 水質(zhì)凈化 InVEST模型“Nutrient Delivery Ratio, NDR”模塊以生態(tài)系統(tǒng)截留及轉(zhuǎn)換徑流中營養(yǎng)物質(zhì)的能力反映凈化服務(wù)的強弱[30].選取氮輸出指標(biāo)評估水質(zhì)凈化服務(wù),河流中總氮輸入量越低則凈化服務(wù)水平越高[31].同時,將氮輸出低值區(qū)視為水質(zhì)凈化能力強的區(qū)域.主要計算公式[32]如下:

式中:export為像元的總氮輸出量,kg/pixel;load為修正的像元的營養(yǎng)物負(fù)荷,kg;NDR指像元的營養(yǎng)物輸出率.模型運行參數(shù)主要查看參考文獻(xiàn)[33].

1.3.2 土壤保持 該服務(wù)揭示了生態(tài)系統(tǒng)控制侵蝕及截留泥沙的能力[34],InVEST模型“Sediment Delivery Ratio, SDR”模塊用土壤保持量(潛在土壤侵蝕量與實際土壤侵蝕量之差[35])進(jìn)行評估,計算公式:

式中:SR是土壤保持量,t/hm2;為潛在土壤侵蝕, t/hm2;為實際土壤侵蝕,t/hm2;為降雨侵蝕性因子,(MJ·mm)/(hm2·h·a);為土壤可蝕性因子, (t·hm2·h)/(hm2·MJ·mm);為坡長因子,為坡度因子;為植被覆蓋和作物管理因子,為水土保持措施因子[35-36].

1.3.3 碳存儲 碳儲量與氣候調(diào)節(jié)及陸地生態(tài)系統(tǒng)生產(chǎn)力密切相關(guān),是生態(tài)系統(tǒng)服務(wù)功能的重要指標(biāo)之一[37].InVEST模型“Carbon Storage and Sequestration, CS”模塊通過簡化碳循環(huán)過程并結(jié)合土地利用類型及不同地類的碳密度實現(xiàn)研究區(qū)碳存儲的量化[38],參考前人研究進(jìn)行不同地類碳密度設(shè)置[39-41],計算公式[42]:

式中:為總碳儲量,t;代表土地利用類型;C_above、C_below、C_soil及C_dead分別為第種地類的地上生物量碳密度、地下生物量碳密度、土壤有機碳密度及死亡有機碳密度,t/hm2;A是土地利用類型的面積,hm2.

1.3.4 生境質(zhì)量 實地調(diào)查生物樣本能夠更為準(zhǔn)確的定量評價生物棲境質(zhì)量及其生存適宜性,但存在操作難度大、評價結(jié)果不易推廣與比較及僅能實現(xiàn)小范圍特定目標(biāo)的評價等局限[43].本文采用InVEST模型“Habitat Quality, HQ”模塊評估生境質(zhì)量以反映區(qū)域生物多樣性水平[44],主要計算公式如下:

式中:Q代表土地利用類型在柵格處的生境質(zhì)量;H為地類的生境適宜性;D為地類中柵格受到的總脅迫水平;是半飽和參數(shù),默認(rèn)值為0.05,通常設(shè)置為生境退化度最大值的一半[42],本文值為0.16;是歸一化常量,默認(rèn)為2.5.結(jié)合研究區(qū)實況,本模塊運行所需主要參數(shù)[45-48]及其他模塊參數(shù)設(shè)置如表1所示.

1.3.5 土地利用模擬 基于15種驅(qū)動因素(圖2)、鄰域權(quán)重(表2,由不同地類擴張面積占總土地擴張的比率獲取)、轉(zhuǎn)換矩陣(表3)及限制轉(zhuǎn)換區(qū)(自然保護(hù)區(qū))等數(shù)據(jù)應(yīng)用Markov-PLUS模型實現(xiàn)土地利用模擬.在預(yù)測之前,基于已有的2010及2020年土地利用數(shù)據(jù)進(jìn)行2020年土地利用模擬及驗證,結(jié)果顯示,kappa系數(shù)為0.77,總體分類精度為0.85,模擬精度能夠滿足研究需要.依據(jù)《黃河(寧夏段)生態(tài)保護(hù)治理攻堅戰(zhàn)行動實施方案》等政策,本文設(shè)定了自然發(fā)展(Natural development, ND, 無限制條件)、耕地保護(hù)(Cultivated land protection, CLP, 限制耕地向其它地類的轉(zhuǎn)換)及生態(tài)保護(hù)(Ecological protection, EP,限制其它地類向耕地、建設(shè)用地及裸地轉(zhuǎn)換)三種發(fā)展情景模擬預(yù)測2030年土地利用格局,并通過耦合InVEST-PLUS模型探究未來ESs變化(圖3).

表1 InVEST模型使用的主要參數(shù)

圖2 土地利用變化驅(qū)動因素

年均降水量、年均氣溫及年均潛在蒸散發(fā)均為1995—2021年數(shù)據(jù);NDVI為2020年數(shù)據(jù);人口密度為2020年數(shù)據(jù);GDP為2019年數(shù)據(jù);路網(wǎng)數(shù)據(jù)下載于2022年9月

表2 鄰域權(quán)重

表3 不同情景下轉(zhuǎn)換矩陣設(shè)置

注: ND:自然發(fā)展情景, Natural development scenario; CLP: 耕地保護(hù)情景, Cultivated land protection scenario; EP: 生態(tài)保護(hù)情景, Ecological protection scenario; a為耕地; b為林地; c為灌木地; d為草地; e為建設(shè)用地; f為水域; g為濕地; h為裸地; 0代表地類間不允許相互轉(zhuǎn)換, 1代表允許轉(zhuǎn)換.

1.3.6 權(quán)衡分析 Spearman相關(guān)性分析能夠有效識別ESs間權(quán)衡關(guān)系的方向及強度[13,49],相關(guān)系數(shù)為正則表示兩類服務(wù)間為協(xié)同關(guān)系,若為負(fù)則為權(quán)衡關(guān)系.本研究借助ArcGIS 10.8 “Create Random Points”工具創(chuàng)建1000個隨機點,并用隨機點基于“Extract Values to Points”工具提取不同ESs的值進(jìn)行相關(guān)性分析.

1.3.7 熱點分析 “熱點”指特定ESs的高值區(qū)[50].開展熱點區(qū)識別研究,有助于了解不同區(qū)域ESs供給能力的強弱,對自然資源保護(hù)、生物多樣性維持及精準(zhǔn)空間規(guī)劃具有重要意義[19,51].本文將研究區(qū)面積的15%對應(yīng)的每項ESs高值區(qū)定義為熱點區(qū)域.

2 結(jié)果與分析

2.1 土地利用變化及其驅(qū)動因素

基于土地利用轉(zhuǎn)移矩陣?yán)L制的?；鶊D(圖4)顯示,近20年耕地及草地為研究區(qū)的主要地類,兩者之和占總面積的85%左右,濕地面積最小,低于0.14%.從土地利用轉(zhuǎn)移看,前10年變幅相對較小.2010～ 2020年間建設(shè)用地擴增1552.37km2(主要由耕地及草地轉(zhuǎn)變而來),其次為耕地(主要由草地及裸地轉(zhuǎn)變而來);草地面積減少1533.16km2,裸地減少203.34km2,其余地類波幅較小.

圖4 2000～2020年土地利用轉(zhuǎn)換?；鶊D

*代表在0.05水平上顯著

基于PLUS模型“Land Expansion Analysis Strategy”模塊隨機森林模型挖掘2010～2020年各驅(qū)動因子對不同地類擴張的貢獻(xiàn)度,并利用Spearman相關(guān)性分析探究了首要驅(qū)動因子與相應(yīng)地類發(fā)展?jié)摿Φ南嚓P(guān)性(圖5,僅展示了面積波動較大的四種地類).結(jié)果顯示,GDP為耕地、裸地及建設(shè)用地擴張影響最大的驅(qū)動因子(貢獻(xiàn)度分別為0.126、0.138及0.135),其與前兩種地類之間存在顯著的負(fù)相關(guān)關(guān)系,與建設(shè)用地之間為正相關(guān)關(guān)系,表明GDP較高的區(qū)域不利于耕地及裸地的發(fā)展,但能夠促進(jìn)建設(shè)用地擴張;草地面積擴張貢獻(xiàn)度最大的為年均降水量,貢獻(xiàn)度為0.130,兩者之間為顯著的正相關(guān)關(guān)系,相關(guān)系數(shù)達(dá)0.60.

2.2 ESs時空變化

由圖6及圖7可知,2000～2020年寧夏氮輸出量呈現(xiàn)南北高、中部低的空間格局,高值區(qū)與耕地分布較為一致;單位面積氮輸出量的最高值由24.42kg/ hm2上升至26.00kg/hm2,輸出總量增加511t,水質(zhì)凈化能力下降.2020年較2000年泥沙輸出量減少了3.08×106t,土壤保持功能增強.研究發(fā)現(xiàn),林地單位面積土壤保持量約57.87t/hm2,遠(yuǎn)高于其它地類,其次為草地(21.17t/hm2),形成了土壤保持強度南高北低的梯度特征.碳存儲低值區(qū)主要位于寧夏中西部,以西部騰格里沙漠最為明顯.碳存儲總量不斷下降,20年間減少2.5×106t,主要因為建設(shè)用地擴張導(dǎo)致碳密度較高的耕地及草地面積縮減.2000、2010及2020年的生境質(zhì)量得分低于0.6的斑塊分別占研究區(qū)總面積的69.81%、70.00%及73.02%,均值得分為0.421、0.420及0.408,整體生境質(zhì)量偏低且呈下降的趨勢,表明區(qū)域受人類活動的干擾程度增強,生物多樣性水平下降[52].

圖6 2000～2020年寧夏生態(tài)系統(tǒng)服務(wù)空間分布

2.3 ESs權(quán)衡協(xié)同分析

2.3.1 相關(guān)性分析 Spearman相關(guān)性分析(雙尾)結(jié)果顯示(圖8),不同ESs間的作用關(guān)系存在差異性.其中,氮輸出與土壤保持及碳存儲之間呈協(xié)同效應(yīng),與生境質(zhì)量之間為權(quán)衡關(guān)系,總體來看,兩兩服務(wù)間作用強度均呈波動下降的趨勢.土壤保持與碳存儲之間存在顯著的正相關(guān)關(guān)系,相關(guān)系數(shù)的均值為0.3,兩種服務(wù)間的協(xié)同強度具有上升趨勢.碳存儲與生境質(zhì)量之間存在顯著的負(fù)相關(guān)關(guān)系,相關(guān)系數(shù)均值的絕對值為0.22,權(quán)衡強度降低.2000～2020年土壤保持與生境質(zhì)量間均未通過顯著性檢驗(<0.5).

2.3.2 熱點分析 研究期內(nèi),ESs間熱點區(qū)重疊占比波動較小(圖9).其中,水質(zhì)凈化服務(wù)與生境質(zhì)量之間重疊區(qū)相對較高,均值達(dá)6.5%,與相關(guān)性分析結(jié)果一致,表明水質(zhì)凈化服務(wù)的改善有利于生物多樣性的維持[53];而水質(zhì)凈化與土壤保持之間的重疊區(qū)持續(xù)下降,這是因為土壤保持的高值區(qū)主要集中于寧夏南部山區(qū),植被覆蓋度相對較高,水土流失量較低,但該區(qū)域受城市化及耕地擴張影響,水質(zhì)凈化服務(wù)能力較低[54].各類服務(wù)熱點區(qū)空間疊置分布如圖10所示,其中單位面積生態(tài)系統(tǒng)提供綜合服務(wù)能力最強的被賦值為“4”,最弱(無熱點區(qū))為“0”.研究發(fā)現(xiàn), 2000～2020年“0”與“1”之和分別占研究區(qū)總面積的92.57%、92.41%及92.21%,為主要斑塊類型,而“4”僅占0.56%、0.55%和0.52%,表明單位面積能夠同時提供多種生態(tài)系統(tǒng)服務(wù)的區(qū)域占比較低且呈下降趨勢.

圖8 2000～2020年生態(tài)系統(tǒng)服務(wù)Spearman相關(guān)系數(shù)及其變化趨勢

NE: 氮輸出, Nitrogen export; SR: 土壤保持, Soil retention; CS: 碳存儲, Carbon storage; HQ: 生境質(zhì)量, Habitat quality; **代表在0.01水平上顯著

圖9 生態(tài)系統(tǒng)服務(wù)熱點區(qū)重疊百分比

WP:水質(zhì)凈化, Water purification

2.4 ESs模擬預(yù)測

基于PLUS模型在不同情景下土地利用的模擬結(jié)果(圖11),結(jié)合InVEST模型實現(xiàn)未來ESs的預(yù)測分析(圖12及表4).結(jié)果顯示,相較于2020年,三種發(fā)展情景下水質(zhì)凈化能力均上升,其中EP情景下,氮輸出量降低最為顯著,減少了481t,表明生態(tài)保護(hù)政策能夠有效的降低非點源污染,改善水環(huán)境質(zhì)量[55].同時,碳存儲量及生境質(zhì)量均是在EP情景下達(dá)到最高值,其中碳儲量增加3.46×106t,生境質(zhì)量提升0.016,究其原因,可能在于耕地、建設(shè)用地及裸地面積減少,草地及水域等生態(tài)用地擴張,人類活動對生態(tài)環(huán)境的影響減弱[16].就泥沙輸出而言,土壤保持服務(wù)南高北低的總體格局基本穩(wěn)定,但三種情景下的土壤保持總量均呈下降趨勢,可能由于林地及灌木地面積的減少導(dǎo)致植被覆蓋度降低,進(jìn)而使得水土流失加劇[56-57].

圖10 不同生態(tài)系統(tǒng)服務(wù)熱點區(qū)重疊時空分布

圖11 寧夏2030年不同情景下土地利用模擬

ND:自然發(fā)展情景, Natural development scenario; CLP:耕地保護(hù)情景, Cultivated land protection scenario; EP:生態(tài)保護(hù)情景, Ecological protection scenario

表4 不同情景下生態(tài)系統(tǒng)服務(wù)及其相較于2020年的變化

3 討論

3.1 土地利用及ESs演變特征

在氣候劇烈變化環(huán)境下,快速城市化及人口增長成為土地利用變化及生態(tài)系統(tǒng)退化的主要驅(qū)動因素[3].研究發(fā)現(xiàn),耕地、裸地、建設(shè)用地及草地為寧夏的主要擴張地類.其中,GDP為前三種地類變化的首要影響因素,其次為人口密度及至城市的距離(圖5),表明人類活動是寧夏土地利用變化主導(dǎo)因素.草地與年均降水量之間為顯著的正相關(guān)關(guān)系,結(jié)合寧夏干旱半干旱氣候特征[26],知降水量為該地區(qū)草地擴張的主要制約因子,與付樂等[58]對黃河流域土地利用變化特征的研究結(jié)果相吻合.

ESs在生活中普遍存在,其時空變化受自然條件(氣候及地形等)及人為因素(人口密度等)的綜合作用,具有復(fù)雜性及異質(zhì)性特點[24].通過耦合InVEST- PLUS模型,本研究對比分析了寧夏歷史及未來ESs的時空特征,發(fā)現(xiàn)EP情景下生態(tài)系統(tǒng)的水質(zhì)凈化能力最強,主要因為限制了建設(shè)用地的擴張及生態(tài)用地向耕地及裸地的轉(zhuǎn)換,污染“源”的消極影響減弱,一定程度上揭示了生態(tài)約束背景下的土地利用優(yōu)化對友好型環(huán)境的構(gòu)建更有利[20].碳存儲量的高值區(qū)主要集中于寧夏南部山區(qū),與Xu等[59]以CASA模型計算的NPP(Net Primary Productivity)表示的碳儲量空間格局基本一致,但不同土壤或植被的碳密度存在差異,且野火及木材采伐等均會導(dǎo)致碳密度的變化[60],本文僅基于區(qū)域相近性及前人研究成果的可取性等原則實現(xiàn)不同地類碳密度的設(shè)置,未進(jìn)行野外調(diào)研以更新相關(guān)參數(shù)[61],后續(xù)可增加相關(guān)實驗等以提升研究的合理性及可靠性.值得注意的是,模擬情景下生境質(zhì)量指數(shù)微幅上升(EP情景下達(dá)到最高值),但平均水平均處于中等偏下狀態(tài),亟需加強生態(tài)建設(shè)以提升區(qū)域生態(tài)安全[48].

3.2 ESs權(quán)衡/協(xié)同分析

本文權(quán)衡與協(xié)同分析研究顯示,氮輸出與土壤保持服務(wù)之間存在顯著的協(xié)同關(guān)系,也即水質(zhì)凈化與土壤保持之間為權(quán)衡關(guān)系,這與Liang等[32]于湘江流域的研究結(jié)果具有較好的一致性.碳存儲與土壤保持服務(wù)之間在不同時段均處于協(xié)同狀態(tài),與Zhang等[62]在黃河流域的相關(guān)研究相符,而胡影等[54]基于同一方法在寧夏縣域尺度上探討了兩種服務(wù)間的關(guān)系,發(fā)現(xiàn)兩者并未通過顯著性檢驗,產(chǎn)生此分歧的原因可能在于研究尺度的不同,印證了ESs間的關(guān)系具有尺度效應(yīng)[24].此外,碳存儲與生境質(zhì)量之間為權(quán)衡關(guān)系,與已有研究[44]存在差異,主要原因在于寧夏耕地面積占比較大(超過42.5%)且該地類的碳密度較高(僅次于林地),具有較強的碳固存能力,但耕地受人類干擾強度較大[16],對生物棲境產(chǎn)生威脅[52].后續(xù)仍需進(jìn)一步優(yōu)化景觀結(jié)構(gòu)以尋求ESs總效益最大化的平衡點.

同時,基于熱點分析本文實現(xiàn)了寧夏ESs高值區(qū)的可視化,相較于傳統(tǒng)的ESs“熱點”識別方法(如單變量空間自相關(guān)[46]及統(tǒng)計指數(shù)[54])或?qū)⒏哂谀稠椃?wù)均值的區(qū)域定義為“熱點”展開相關(guān)研究[63],本文采用的閾值法更有利于決策者在考慮財政等條件基礎(chǔ)上進(jìn)行區(qū)域多目標(biāo)規(guī)劃方案的制定,便于推廣.通過ESs間熱點區(qū)重疊比例統(tǒng)計及空間疊置可視化兩種視角分析發(fā)現(xiàn),寧夏熱點區(qū)斑塊的ESs以權(quán)衡關(guān)系為主,高協(xié)同效應(yīng)(斑塊得分為“4”)占比不足1%,服務(wù)之間存在效益沖突[64],仍需因地制宜改善生態(tài)質(zhì)量,落實生態(tài)優(yōu)先戰(zhàn)略.此外,本文雖然從土地利用方式等角度分析了ESs間權(quán)衡/協(xié)同關(guān)系形成的可能原因,但仍無法完全反映其內(nèi)在驅(qū)動機制,今后可嘗試基于地理探測器等[65]方法作深入分析,進(jìn)一步弱化發(fā)展矛盾及優(yōu)化權(quán)衡決策的目標(biāo),實現(xiàn)沖突的最小化.

3.3 不確定性

空間政策限制轉(zhuǎn)換區(qū)是影響PLUS模型運行精度的關(guān)鍵基礎(chǔ)數(shù)據(jù)之一,本文將自然保護(hù)區(qū)(國內(nèi)外公認(rèn)的保護(hù)生物多樣性及維持生態(tài)系統(tǒng)功能的有效區(qū)域,為最嚴(yán)格的指定保護(hù)類型[66-67])作為其運行所需數(shù)據(jù)具有較好的科學(xué)性,但在政策、資源開采等因素驅(qū)動下,自然保護(hù)區(qū)升級或精簡,土地景觀格局將隨之改變[68],且研究區(qū)內(nèi)的水域、生態(tài)保護(hù)紅線及基本農(nóng)田等[20,23]何種或是綜合協(xié)調(diào)哪幾類空間約束數(shù)據(jù)的組合能夠避免或降低過度約束或約束不足現(xiàn)象以更為準(zhǔn)確的實現(xiàn)土地利用模擬,仍莫衷一是.此外,本研究從“自然發(fā)展”、 “耕地保護(hù)”及“生態(tài)保護(hù)”3個不同角度進(jìn)行了發(fā)展情景的設(shè)置,具有一定的代表性,但未來發(fā)展模式具有多樣性及不確定性,如何縮小與客觀現(xiàn)實的差距有待于進(jìn)一步豐富與研究.

4 結(jié)論

4.1 研究區(qū)近10年土地利用變幅較大,以耕地、裸地、建設(shè)用地及草地最為明顯,其中前3種地類主要受GDP的驅(qū)動,草地的擴張主要受年均降水量的制約.

4.2 2000～2020年,寧夏4種ESs變化具有時空分異特征.其中,氮輸出總量增加511t,水質(zhì)凈化能力下降;土壤保持呈南高北低的梯度特征,泥沙輸出量減少了3.08×106t;碳存儲總量不斷下降,20年間減少2.5×106t;生境質(zhì)量整體偏低且呈下降趨勢.

4.3 ESs間權(quán)衡關(guān)系存在時空差異性.相關(guān)性分析顯示,ESs間的作用強度以下降為主.其中,氮輸出與土壤保持及碳存儲之間呈協(xié)同效應(yīng),土壤保持與碳存儲之間存在協(xié)同關(guān)系,生境質(zhì)量與氮輸出及碳存儲之間存在權(quán)衡關(guān)系.熱點分析表明,單位面積能夠同時提供多種ESs的區(qū)域占比較低且呈下降趨勢.

4.4 在ND、CLP及EP三種土地利用動態(tài)情景模擬中,除土壤保持外,區(qū)域水質(zhì)凈化、碳存儲及生境質(zhì)量均在EP情景中達(dá)到最大值,表明生態(tài)保護(hù)政策的實施有利于生態(tài)系統(tǒng)功能的改善.

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致謝：安徽師范大學(xué)地理與旅游學(xué)院徐光來副教授在論文寫作及修改過程中提供了幫助與指導(dǎo),特此致謝.此外,感謝國家基礎(chǔ)地理信息中心(https://www.ngcc.cn)、國家地球系統(tǒng)科學(xué)數(shù)據(jù)共享服務(wù)平臺-黃土高原科學(xué)數(shù)據(jù)中心(http://loess.geodata.cn)、中科院資源環(huán)境科學(xué)與數(shù)據(jù)中心(https://www.resdc.cn)及國家科技基礎(chǔ)條件平臺-國家生態(tài)科學(xué)數(shù)據(jù)中心(http://www.nesdc.org.cn)等提供數(shù)據(jù)支撐.

Spatiotemporal changes and trade-off analysis of ecosystem services in Ningxia Hui Autonomous Region.

YANG Qiang-qiang1, ZHANG Pian2, QIU Xiao-cong3*, ZHAO Zeng-feng1, ZHAO Rui-zhi1, ZHOU Rui-juan4

(1.School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China；2.School of Management Engineering, Wanjiang University of Technology, Maanshan 243000, China；3.School of Life Sciences, Ningxia University, Yinchuan 750021, China；4.Ningxia Ecological and Environmental Monitoring Center, Yinchuan 751100, China)., 2023,43(10)：5453～5465

Based on the multi-source data such as land use, elevation and precipitation, this paper adopted methods such as InVEST model, PLUS model, correlation analysis and hotspot analysis, investigated the spatio-temporal characteristics of ecosystem services (water purification, soil conservation, carbon storage, and habitat quality) in Ningxia from 2000 to 2020 as well as their trade-off/synergies, and simulated ecosystem services in 2030. As shown by results, during the research period, land use types changed relatively greatly in the past decades, and human activities were the main driving factor. From 2000 to 2020, except for soil conservation services, water purification in Ningxia, carbon storage and habitat quality all presented a declining trend. According to correlation analysis, there was a synergistic effect between nitrogen export and soil conservation and carbon storage. In addition, there was a synergistic relationship between soil conservation and carbon storage, as well as a trade-off between habitat quality and nitrogen export and carbon storage. As shown by hotspot analysis, hot spots overlapping areas between water purification services and habitat quality were relatively high (average value of 6.5%), but the proportion of regions that can simultaneously provide various ecosystem services per unit area was relatively low and showed a declining trend. In addition to soil conservation services, the ecological protection scenario was more favorable to improve ecosystem function. Research results, which highlight the importance of ecological protection in land use management, can provide a basic reference for the optimization of regional land patterns and the effective allocation of resources.

ecosystem services；tradeoffs；InVEST model；PLUS model；Ningxia Hui Autonomous Region

X171

A

1000-6923(2023)10-5453-13

2023-03-13

寧夏高等學(xué)校一流學(xué)科建設(shè)(水利工程)基金資助項目(NXYLXK2021A03);寧夏回族自治區(qū)生態(tài)環(huán)境廳科研項目(2022015)

* 責(zé)任作者, 教授, qiu_xc@nxu.edu.cn

楊強強(1995-),安徽宿州人,寧夏大學(xué)博士研究生,主要從事生態(tài)系統(tǒng)服務(wù)及水域生態(tài)學(xué)方面的研究.qqy1995@126.com.

楊強強,章 翩,邱小琮,等.寧夏回族自治區(qū)生態(tài)系統(tǒng)服務(wù)時空變化及其權(quán)衡研究 [J]. 中國環(huán)境科學(xué), 2023,43(10):5453-5465.

Yang Q Q, Zhang P, Qiu X C, et al. Spatiotemporal changes and trade-off analysis of ecosystem services in Ningxia Hui Autonomous Region [J]. China Environmental Science, 2023,43(10):5453-5465.