Study on the Bagmati Basin of Nepal

sustainability

Article An Assessment of Environmental Impacts on the Ecosystem Services: Study on the Bagmati Basin of Nepal

Shiksha Bastola 1, Sanghyup Lee 1, Yongchul Shin 2 and Younghun Jung 1,* 1 Department of Advanced Science and Technology Convergence, Kyungpook National University, 2559, Gyeongsangdaero, Sangju 37224, Korea; [email protected] (S.B.); [email protected] (S.L.) 2 School of Agricultural Civil & Bio-Industrial Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-54-530-1253; Fax: +82-54-530-1258

Received: 10 August 2020; Accepted: 28 September 2020; Published: 4 October 2020

Abstract: The upsurges in population, internal migration, and various development works have caused significant land use and land cover (LULC) changes in the Bagmati Basin of Nepal. The effects of climate change such as increased precipitation and temperature are affecting the provision of ecosystem services (ES). In this regard, this study particularly treated water yield (WY), soil loss, nitrogen export, and carbon fluctuation in the basin. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools were used to carry out a comparative analysis of ES based on LULC data for 2000 and 2010 and corresponding climate data. To analyze the future period (2010–2099), we have used climate data from the multi-model ensemble (MME) of statistically downscaled and bias-corrected 12 best global climate models (GCMs). A raw GCM analysis (based on historical observational data) from 29 GCMs was done first. The results shows with a subsequent degradation of ES providers like forests and an increment in agricultural and urban areas, ES are on a verge of degradation. Furthermore, a projection of future climate patterns depicts increased precipitation and temperature. Thus, urgent measures are required for the sustainable provision of ES. Outcomes of the study are expected to help in the incorporation of ES in development policies promoting low-impact development along with maintaining ecological and economic goals. The study closes by presenting a recommendation for model application and future study needs.

Keywords: ecosystem services; land use; climate change; GCMs; Bagmati Basin; Nepal

1. Introduction Ecosystem services (ES) are defined as services provided by nature that directly and indirectly benefit the well-being of humans. The concept of ES has been developed in the scientific literature since the end of the 1970s [1], and the Millennium Ecosystem Assessment (MEA) report 2005 [2] is considered the milestone in the field of mapping ES. The MEA report evaluated the impacts on human beings as the consequences of changes in the ecosystem. The core finding of the MEA report is that human activities are depleting the Earth’s natural resources, which is inducing stress on the self-sustaining capability of the natural environment. This phenomenon urges immediate measures for checking and prevention in order to enhance the sustainability of ecosystems for future generations. To meet the requirements of a globally increasing population for food, shelter, timber, etc., land use changes are occurring but at the expense of a degraded environment and ecosystems. Land use and land cover (LULC) change is considered the major factor for the degradation of ES and loss in biodiversity [3,4]. Climate change is another factor impacting natural systems, and ultimately affects the flow of ES [5]. With the increasingly evident effects of climate change, which is especially impacting

Sustainability 2020, 12, 8186; doi:10.3390/su12198186 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 8186 2 of 22 developing countries [6–8], the challenge to attain sustainable economic development goals while preserving the natural resources and ES is getting more difficult [9]. ES have significance in both developed and developing nations. However, as the livelihood of people in developing nations is highly dependent on them, the risk of ES losses is high in developing nations. In addition, developing nations are more prone to the impacts of climate change. In this scenario, identifying where ES originate and to whom the benefits flow under current and future climate conditions is critical information [9]. Land cover refers to the biophysical attributes of Earth’s surface such as water, soil, vegetation, etc. It refers to human purposes or intents applied to these attributes such as building construction, forestry, etc. [10]. Changes in LULC is a substantial phenomenon and is occurring globally since time immemorial [11]. Urbanization is necessary for regional economic growth [12] and is one of the most important drivers of change worldwide [13]. The recent decade has seen a major increase in the rate of worldwide urbanization. As per the 2011 census of the Central Bureau of Statistics of Nepal, the urban population has increased to 17% in 2011 from 2.9% in 1952/1954 [14]. Recorded among the fastest urbanizing countries of the world [15], Nepal has faced notable LULC changes especially in urban and semi-urban areas. The Bagmati Basin, incorporating the capital city Kathmandu, has significant socio-economic importance because of centralized job opportunities, education, health facilities, etc. Issues such as decreased availability of freshwater resources, urban floods, degraded water quality, solid waste management, etc. are major problems in the basin especially in urban and semi-urban areas in the upper part of the basin [16]. The basin has been facing many flood events in its lower belt [16] and urban floods have become a re-occurring phenomenon every monsoon season in major city cores like in Kathmandu and Bhaktapur. Water availability and accessibility are also issues and due to the lack of effective plans for waste management by households, industries, and agricultural areas, water quality is on the constant verge of degradation [17]. Likewise, unchecked LULC changes for activities such as agriculture and road expansion are increasing soil loss rates. Industrialization and urbanization mainly in the upper part of the basin are impacting the overall ES of the basin. The optimum provision of ES under changing LULC and climatic conditions would be questionable if proper mitigation and preservation plans are not formulated in time. Several studies on ES have been carried out in Nepal to inform biodiversity conservation and to promote local and national decision making [18,19]. Some examples are LULC dynamics and ES valuation in the Gandaki Basin of Nepal [20] and effects of LULC change on ES in the Koshi Basin, which focused on food production, carbon storage, habitat quality, etc. [21]. Several international organizations such as the International Union for Conservation of Nature (IUCN), the International Centre for Integrated Mountain Development (ICIMOD), the World Bank, and the Asian Development Bank are currently working toward the assessment of ES and the implementation of Payments for Ecosystem Services (PES) in Nepal [19,22]. The implementation of PES schemes in sectors such as drinking water, irrigation, and tourism already exists in Nepal, but these payments are being made only for mandatory requirements, not for the sustained supply of services [19]. Many studies have independently assessed freshwater availability [23], water pollution issues [24], and soil loss cases [25] in the Bagmati Basin of Nepal. However, to the extent of our knowledge, a collective effort to access in terms of ES, LULC impacts on them, and projections based on climate data are not yet available. Thus, to overcome this gap, the concept of this study was devised. Climate change and LULC are major drivers of changes in ES and ES are itself interrelated [26]. This study aims to analyze the impacts on ES owing to changes in LULC and to provide an overview of the provision of ES in the context of a changing climate. This study thus treats four regulating ES, namely, water yield (WY) for a freshwater provision and flood control, soil loss indicating soil degradation and impacts on interrelated services, nitrogen export for surface water quality, and carbon storage for climate regulation. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) [27] ES tools and the Revised Universal Soil Loss Equation (RUSLE) [28] method along with ArcGIS were used to map ES of the basin, and APCC Integrated Modelling Solution (AIMS) software developed by the Asia Pacific Economic Cooperation Sustainability 2020, 12, 8186 3 of 22 SustainabilitySustainability 20202020,, 1122,, xx FORFOR PEERPEER REVIEWREVIEW 33 ofof 2222

ClimateEconomicEconomic Centre CooperationCooperation (APCC) wasClimateClimate used CentreCentre for statistical (APCC(APCC)) waswas downscaling usedused forfor statisticalstatistical of bias-corrected downscalidownscali globalngng ofof biasbias climate--correctedcorrected model (GCM)globalglobal data. climateclimate modelmodel ((GCMGCM)) data.data.

2.2. Materials2. MaterialsMaterials and andand Methods MethodsMethods

2.1.2.1.2.1. Study StudyStudy Area AArearea

TheTheThe Bagmati BagmatiBagmati Basin BasinBasin lies lieslies in inin the thethe middle middlemiddle mountain mountainmountain region region ofof NepalNepal Nepal (Figure(Figure (Figure 1)1)1 ) atat at latitudelatitude latitude 2626 26°°4242◦42′′ toto0 to 27◦272750°°50050N′′ NN and andand longitude longitudelongitude 85 8585◦°2°220′′ to toto 85 8585◦°°58580′′ E.E. The The BagmatiBagmati RiverRiver River isis is thethe the principalprincipal principal riverriver river ofof of thethe the basinbasin basin andand and itit it originatesoriginatesoriginates from fromfrom the thethe north northnorth of ofof the thethe capital capitalcapital city, citycity Kathmandu,,, KathmanduKathmandu at,, atat Shivpuri ShivpuriShivpuri (Bagdwar) (Bagdwar)(Bagdwar) at atat an anan altitude altitudealtitude ofofof 26902690 m. Them.m. river TheThe ﬂowsriverriver flowsflows through throughthrough Kathmandu KathmanduKathmandu and and providesand providesprovides most mostmost of the ofof the city’sthe city’scity’s drinking drinkingdrinking water waterwater in in thein thth upperee upperupper part ofpart thepart basin, ofof thethe supportsbasin,basin, supportssupports hydropower hydropowerhydropower generation generationgeneration in the inin middle thethe middlemiddle basin basinbasin and inandand the inin lower thethe lowerlower part partpart of the ofof basin,thethe andbasin,basin, supports andand supportssupports large-scale largelarge irrigation--scalescale irrigationirrigation for agriculture forfor agricultureagriculture [29]. The [29].[29]. climate TheThe climate ofclimate the basin ofof thethe varies basinbasin with variesvaries elevation; withwith elevation; higher mountains have a cold temperate climate, mid-elevation levels have a warm higherelevation; mountains higher have mountains a cold temperate have a cold climate, tempera mid-elevationte climate, levelsmid-elevation have a warm levels temperate have a warm climate, temperatetemperate climateclimate,, andand thethe southernsouthern lowlandslowlands havehave aa subtropicalsubtropical climateclimate [30].[30]. TheThe basinbasin hashas aa meanmean and the southern lowlands have a subtropical climate [30]. The basin has a mean annual temperature annualannual temperature temperature of of 20 20––3030 °C°C andand the the mean mean annual annual precipitation precipitation is is about about 180 18000 m mmm [30]. [30]. The The of 20–30 C and the mean annual precipitation is about 1800 mm [30]. The Bagmati River is a spring BagmatiBagmati◦ RiverRiver isis aa sspringpring andand monsoonmonsoon rainrain--fedfed riverriver andand consistsconsists ofof manymany tributariestributaries suchsuch asas thethe and monsoon rain-fed river and consists of many tributaries such as the Hanumante, Manohara, Dhobi Hanumante,Hanumante, Manohara,Manohara, DhobiDhobi Khola,Khola, etc.etc. TheThe totaltotal areaarea ofof thethe basinbasin isis 375,000375,000 haha andand,, owingowing toto thethe Khola, etc. The total area of the basin is 375,000 ha and, owing to the good data availability for this goodgood datadata availabilityavailability forfor thisthis study,study, aa 276,897276,897 haha areaarea waswas selected.selected. ThisThis reprepresentsresents thethe areaarea aboveabove study,thethe PandredhovanPandredhovan a 276,897 ha area gaugegauge was stationstation selected. ((FigureFigure This 2). represents2). WeWe chosechose the thethe area BagmatiBagmati above BasinBasin the Pandredhovan asas ourour studystudy areaarea gauge asas station itit isis (Figurefacingfacing2). hugehuge We choseanthropogenicanthropogenic the Bagmati pressurespressures Basin associated asassociated our study mostlymostly area withwith as it iscentralizedcentralized facing huge facilities,facilities, anthropogenic urbanurban expansion,expansion, pressures associatedandand increasedincreased mostly agriculturalagricultural with centralized activity.activity. facilities, urban expansion, and increased agricultural activity.

FigureFigureFigure 1. 1.1. River RiverRiver basinbasinbasin and and elevation elevation mapmap map ofof of NepalNepal Nepal...

FigureFigureFigure 2. 2.2. Sub-basins SubSub--basinsbasins of of thethe BagmatiBagmati BasinBasin Basin... Sustainability 2020, 12, 8186 4 of 22 Sustainability 2020, 12, x FOR PEER REVIEW 4 of 22

2.2. Model2.2. Model Description Description and Dataand Data For theFor assessmentthe assessment of ES of provision ES provision in the in basin,the basin, four four ES, namely,ES, namely WY,, WY, soil loss,soil loss, nitrogen nitrogen export, export, andand carbon carbon storage, storage were, were evaluated evaluated on the on sub-basinthe sub-basin scale scale in the in Bagmatithe Bagmati Basin Basin of Nepal. of Nepal. We haveWe have donedone a comparative a comparative study study of ES of for ES the for years the 2000years and 2000 2010 and based 2010 onbased the LULCson the LULCs of 2000 andof 2000 2010 and and 2010 correspondingand corresponding climate data climate and data for a and future for period,a future ES period, were mappedES were basedmapped on based the LULC on the map LULC of 2010 map of and2010 projected and projected climate data. climate The data. LULC The map LULC was obtainedmap was fromobtained the ICIMOD from the Nepal ICIMOD Geospatial Nepal Geospatial Portal (http:Portal//rds.icimod.org (http://rds.icimod.org/)/) which is prepared which using is public prepared domain using Landsat public Thematic domain mapper(TM) Landsat Thematic data. Uddinmapper( et al. [TM31]) havedata. discussedUddin et theal. [31] detailed have description discussed the of thedetail dataed including description LULC of the classification, data including accuracyLULC assessment, classification, and accuracy limitations, assessment hence we, and have limitations, used the hence LULC we data have from used the the ICIMOD LULC portaldata from referringthe ICIMOD to the findings portal from referring Uddin to et the al. findings [31]. Likewise, from Uddin the rainfall et al. and [31]. temperature Likewise, the data rainfall for the and periodtemperature of 1987–2016 data from for 25 the stations period from of all 198 over7–2016 Nepal from (Table 25 S1, stations Supplementary from allMaterials) over Nepal have (Table been S1, usedS toupplement downscaleary GCM Materials data for) have Nepal. been The used missing to downscale data were GCM extracted data for from Nepal. MERRA2 The missing grid data data [32 ]were afterextracted doing quality from control MERRA2 (checking grid daily data correlation [32] after doingcoefficient) quality using control R package. (checking GCM daily data from correlation 10 stationscoefficient) (stations using in bold R package. in Table GCM S1, Supplementary data from 10 stations Materials) (stations have in been bold used in Table for aS1, future Supplementary period (2010–2099)Materials ES) have evaluation been used of the for Bagmati a future Basin. period As well,(2010 for–2099 the) analysisES evaluation of the Bagmatiof the Bagmati Basin for Basin the. As periodwell, 2000 for and the 2010, analysis a precipitation of the Bagmati raster Basinis prepared for the using period data 2000 from and 23 rain 2010, stations a precipitation (Figure3) and, raster is for preparationprepared using of an data evapotranspiration from 23 rain stations raster, (Figure precipitation 3) and,and for preparation temperature of data an fromevapotranspiration 9 stations (stationsraster, in boldprecipitation in Table S1,and Supplementary temperature data Materials) from have 9 stations been used. (stations The datain bold period in forTable the S1, comparativeSupplementary study of Materials 2000 and) 2010have isbeen 1996–2015 used. The which data is dividedperiod for into the two comparative time periods study of 1996–2005 of 2000 and and2010 2006–2015 is 1996 to–2015 coincide which with is availabledivided into LULC two data time of periods 2000 and of 20101996 periods,–2005 and respectively. 2006–2015 Allto coincide the rainfallwith and available temperature LULC datadata wereof 2000 acquired and 2010 from period the Departments, respectively. of HydrologyAll the rainfall and Meteorologyand temperature (DHM)data Nepal. were acquired from the Department of Hydrology and Meteorology (DHM) Nepal.

FigureFigure 3. Location 3. Location of meteorological of meteorological stations stations (rainfall (rainfall data) data) with with data data from from 1996–2015. 1996–2015.

For theFor downscalingthe downscaling of GCMsof GCMs for for future future climate climate data, data, we we have have used used APCC’s APCC’s AIMSAIMS software. We We havehave usedused the the multi-model multi-model ensemble ensemble (MME) (MME) of of 12 12 best best GCMs GCMs of Coupledof Coupled Model Model Intercomparison Intercomparison ProjectProject 5 (CMIP5) 5 (CMIP5) under under two Representativetwo Representative Concentration Concentration Pathways Pathways (RCP) scenarios(RCP) scenario namelys namely RCP 4.5 RCP and4.5 RCP and 8.5 RCP to study 8.5 to the study projection the projection of ES in of the ES future. in the Thefuture. descriptions The description of RCPs 4.5of RCP and RCP4.5 and 8.5 RCP are 8.5 givenare in given Figure in4 .Figure InVEST 4. InVEST tools were tools used were to used map to WY, map nitrogen WY, nitrogen export, export, and carbon and carbon storage, storage, and a and RUSLEa RUSLE method method was used was toused map to soilmap loss. soil loss. As carbon As carbon storage storage computation computation by the by InVESTthe InVEST carbon carbon modelmodel is independent is independent of the of climatethe climate data, data, carbon carbon storage storage was was evaluated evaluated with with only only LULC LULC change change and and the correspondingthe corresponding climate climate scenario scenario whereas whereas WY, WY, soil loss,soil loss and, and nitrogen nitrogen export export were were evaluated evaluated for for bothboth scenarios scenarios of LULC of LULC change change and and future future climate climate change change scenarios. scenarios. We chooseWe choose the InVESTthe InVES modelT model in ourin our study study due due to itsto its capability capability to to give give a a quick quick overview overview of of ES,ES, especiallyespecially in regions regions with with low low data availability [33] in developing nations like Nepal. InVEST is a suite of a free and open source software models. Its modular design provides an effective tool for evaluating the possible outcomes of Sustainability 2020, 12, 8186 5 of 22

Sustainability 2020, 12, x FOR PEER REVIEW 5 of 22 data availability [33] in developing nations like Nepal. InVEST is a suite of a free and open source softwarealternative models. management Its modular and design climate provides scenarios an and eﬀective it helps tool in for the evaluating selection the of possible the best outcomes decision ofscenarios alternative [27]. management and climate scenarios and it helps in the selection of the best decision scenarios [27].

Figure 4. FigureGlobal 4 temperature. Global temperature projections projections for various for RCP various scenarios RCP Adaptedscenarios fromAdapted Intergovernmental from Panel onIntergovernmental Climate Change (IPCC) Panel Fifth on Climate Assessment Change Report, (IPCC 2013.) Fifth Assessment Report, 2013.

2.2.1. Water YieldYield EstimationEstimation The InVEST water yieldyield model was used for the WY estimation and this model is based on the water balancebalance principleprinciple andand BudykoBudyko curve.curve. It determines the amount of water running ooffﬀ each pixel as thethe precipitationprecipitation minus minus the the fraction fraction of of the the water water that that undergoes undergoes evapotranspiration. evapotranspirati Theon. inputThe input data includeddata include precipitation,d precipitation, evapotranspiration, evapotranspiration, LULC, soil LULC, properties, soil properties, basin boundary, basin and boundary, biophysical and attributesbiophysical (Table attributes1). The (Table spatial 1). resolution The spatial of LULC,resolution soil of depth, LULC, and soil plant-available depth, and plant water-available content water used incontent the study used was in the 30 study m. was 30 m.

Table 1. Table 1. Data description for InVEST Water YieldYield Model.Model.

SNSN Data Data Format Format Data Data Source Source 1 1Annual Annual mean mean precipitation precipitation Raster Raster map map DHM DHM * 2 2Annual Annual reference reference evapotranspiration evapotranspiration Raster Raster map map Temperature Temperature data fromfrom DHM DHM 3 Land use and land cover (LULC) Raster map ICIMOD ** Nepal 3 Land use and land cover (LULC) Raster map ICIMOD ** Nepal 4 Soil depth Raster map SOTER *** database 4 5 Plant-availableSoil depth water content (PAWC)Raster Raster map map SOTER SOTER *** database database 5 6Plant-available Basin water and content sub-basin (PAWC) Raster Shapeﬁles map SOTER ArcGIS database 6 7Basin Biophysicaland sub-basin table Shapefiles Excel CSV table PublishedArcGIS literature 7SN—Serial Number.Biophysical * DHM, Department table of HydrologyExcel and Meteorology.CSV table ** ICIMOD,Published International literature Centre for Integrated Mountain Development. *** SOTER, Soil and Terrain Database. SN—Serial Number. * DHM, Department of Hydrology and Meteorology. ** ICIMOD, International Centre for Integrated Mountain Development. *** SOTER, Soil and Terrain Database. The annual WY Y(x) on each pixel of the landscape (x) is determined as follows: The annual WY Y(x) on each pixel of the landscape !(x) is determined as follows: AET(x) Y(x) = 1 퐴퐸푇(푥) P(x) (1) 푌(푥) = (1−− P(x) )×× 푃(푥) (1) 푃(푥) AET(x) = Annual actual evapotranspiration for pixel x AET(x) = Annual actual evapotranspiration for pixel x P(x) = Annual precipitation on pixel x. 퐴퐸푇(푥) is the evapotranspiration portion of the water balance for vegetated LULC types. This is 푃(푥) computed based on an expression of the Budyko curve proposed by Fu [34] and Zhang et al. [35]: Sustainability 2020, 12, 8186 6 of 22

AET(x) P(x) is the evapotranspiration portion of the water balance for vegetated LULC types. This is computed based on an expression of the Budyko curve proposed by Fu [34] and Zhang et al. [35]:

" !ω#1/ω AET(x) PET(x) PET(x) = 1 + 1 + (2) P(x) P(x) − P(x) where PET(x) = potential evapotranspiration calculated using Equation (3); ω(x) = non-physical parameter (characterizes the natural climatic-soil properties). Calculation of PET is as follows:

PET(x) = Kc(lx) ETo(x) (3) ∗ where ETo(x) = reference evapotranspiration from pixel x. Kc(lx) = plant evapotranspiration coeﬃcient, which is associated with the LULC lx on pixel x. ω(x) is an empirical parameter. It is determined as the expression given by Donohue et al. [36]:

AWC(x) ω(x) = Z + 1.25 (4) P(x) where ω(x) is based on plant-available water content (PAWC), precipitation, and the Z constant. The Z constant defines the local precipitation pattern and additional hydrogeological characteristic of the basin [27]. The temperature-based Hargreaves equation was used for the computation of reference evapotranspiration, given that this method generates superior results to the Pennman–Montieth method in the case when long-term data is limited [37]. An average soil depth and PAWC raster map was prepared using the Soil and Terrain (SOTER) database of Nepal and ArcGIS. The biophysical information on the LULC code and its descriptive names, the maximum root depth for vegetated land use classes in mm, and the plant evapotranspiration coefficient for each LULC class was required (Table2). In this study, the root depth of main vegetation types was obtained following Chen et al. [38] and the evapotranspiration coefficient of each LULC type used on the model was based on Allen et al. [39] and the InVEST user manual [27].

Table 2. Biophysical attributes used for InVEST model.

LULC Biophysical Attribute Forest Shrubland Grassland Agriculture Land Barren Area Water Body Built-Up Area Related Model Land use code 1 2 3 4 5 6 8 Kc 1 0.398 0.65 0.65 0.5 1 0.3 Water Yield Root_depth 7000 2000 2000 1500 500 0 0 LULC_veg 1 1 1 1 0.001 0.001 0.001 Aboveground biomass 90 8 6 3 0 0 0 Belowground biomass 60 8 6 2 0 0 0 Carbon Storage Soil organic matter 95 25 20 8 0 0 0 Dead organic matter 29 3 2 1 0 0 0 Load_n 1.8 2 4 11 4 0.001 7.25 Nutrient Eff_n 0.8 0.4 0.5 0.25 0.05 0.05 0.05 Delivery Kc = plant evapotranspiration coefficient. Root_depth = maximum root depth measured for vegetated LULC (mm). Load_n = nutrient loading for each land use measured in kg/ha/year. Eff_n = nutrient retention efficiency for each LULC class (floating point value, 0–1).

2.2.2. Soil Loss Estimation The RUSLE model coupled with ArcGIS was used for the computation of soil loss in the basin. Due to data simplicity and the provision of scenario analysis and taking measures against erosion, RUSLE is widely used at large scales for soil loss assessment [40]. It computes soil erosion as the Sustainability 2020, 12, 8186 7 of 22 product of six factors representing rainfall erosivity, soil erodibility, slope length, slope steepness, cover and management practices, and supporting conservation practices:

A = R K L S C P (5) × × × × × where

A = average annual soil loss amount in (t/ha/yr) R = rainfall–runoﬀ erosivity factor (MJ mm/h/ha/yr) K = soil erodibility factor L = slope length factor S = slope steepness factor C = land cover management factor P = support practice factor

Among several equations available for the rainfall–runoﬀ erosivity factor (R), we have used the equation by Singh et al. [41] as it is the one recommended for the Himalayan region:

R = 79 + 0.363P (6)

where

1 1 1 R = rainfall erosivity factor (MJ mm ha− h− yr− ) P = mean annual precipitation (mm).

The K factor is determined as per the Food and Agriculture Organization (FAO) database adapted to Nepal by World Soil Information (ISRIC). The soil unit map was extracted from the SOTER database of Nepal and the K factor was computed based on diﬀerent published literature on mountainous areas [42,43] and other countries [44]. The K factor of the study area varied from 0.19 to 0.49. The slope formula based on slope length was used for computation of slope length factor (L) based on references [45,46] which is given as:

λ m L = (7) 22.13 where λ indicates a field slope length and 22.13 is the slope length of a unit runoff plot (m). m = slope length exponent. The slope steepness factor (S) represents the effect of slope steepness on the intensity of soil erosion. The factor is calculated using Equation (8) as described by Wischmeier and Smith [45]:

0.43 + 0.30 s + 0.043 s2 S = × × (8) 6.613 where s = slope in percent, which is determined from a digital elevation model (DEM). Likewise, the cover management factor (C) and the support practice factor (P) were obtained from various published reports [25,47] in a similar area. Separate raster layers for each factor were prepared in GIS and the average annual soil erosion rate was determined by multiplying the respective factors in the ArcGIS environment.

2.2.3. Nitrogen Export Estimation The nutrient delivery ratio (NDR) model was used to map nitrogen export in the basin. The model uses a simple mass balance approach and describes the movement of nutrient mass through space and aims to quantify nutrient (nitrogen and phosphorous) export. The model maps the flow of nutrients from various sources to the stream network. Sources of nutrients are determined based on a LULC map Sustainability 2020, 12, 8186 8 of 22 and associated loading rates. The nutrient loads are divided into sediment-bound and dissolved parts, which will be carried by surface and subsurface flow respectively and stops when they reach a stream. Next, the delivery factors were computed for each pixel, based on the properties of pixels belonging to the same flow path (their slope and retention efficiency of each land use) [27]. All pixel-level contributions were summed at the basin/sub-basin outlet to compute the nutrient export: X Xexpton = Xexpi (9) i

X = Load NDR + Load NDR (10) expi Sur f , i × Sur f , i Subs, i × Subs, i where

1 Xexpton = total export amount of nutrients in the basin (kg. yr− ) Xexpi = export amount of nutrients from each grid 1 1 LoadSur f , i = surface nutrient load (kg. ha− . yr− ) 1 1 LoadSubs, i = subsurface nutrient load (kg. ha− . yr− ) NDRSur f , i = surface nutrient transfer rate.

NDRSubs, i = subsurface nutrient transfer rate.

The raster layers required as an input for running the model are raster maps of the DEM, LULC, and precipitation. The model requires the biophysical table having a coeﬃcient for nitrogen loading for each LULC category (Table2). These values were obtained following Sharp et al. [ 27] and Line et al. [48].

2.2.4. Carbon Storage Estimation The InVEST carbon model used in this study maps carbon storage densities to the LULC raster by aggregating the amount of carbon stored on four major carbon pools. The carbon pools considered were aboveground biomass, belowground biomass, soil, and dead organic matter to produce the total amount of carbon storage. The carbon storage Cm,i,j for a given grid cell (i, j) with land use type m can be calculated as: C = A Ca + Cb + Cs + Cd (11) m,i,j × m,i,j m,i,j m,i,j m,i,j where A = actual area of each grid cell (ha); Cam,I,j, Cbm,I,j, Csm,I,j, Cdm,I,j are the aboveground, belowground, soil organic, and dead organic matter carbon density in MgC/ha for grid cell (i, j) with land use type m (Table2). The carbon pool data were obtained from published literature [ 49,50] and InVEST user guidelines.

2.3. GCM Downscaling and Future Climate Scenarios For this study, 29 GCMs of CMIP5 were statistically downscaled and bias-corrected using the simple quantile mapping (SQM) [51] method. We adopted RCP 4.5 and RCP 8.5 concentration pathways (Table3, adapted from reference [ 52]) for this study. The future projections of daily precipitation and temperature were performed at 25 meteorological stations of Nepal. Statistical downscaling makes it possible to perform a quantitative comparison with observational data through bias correction using the observations and it is easy to convert them into high-resolution data. The multi-model ensemble was prepared for the study basin using 12 best GCMs after doing raw GCM analysis of the downscaled GCMs. For the downscaling, we used the AIMS developed by the APCC. The AIMS module is a free and open source module available online from www.aims.apcc21.org[53]. Sustainability 2020, 12, 8186 9 of 22

Table 3. Overview of representative concentration pathways in IPCC AR5.

Name Radiative Forcing CO2 Concentration (ppm) Pathways Peak in radiative forcing at ~3 RCP 2.6 Peak at 490 before 2100 and then decline Peak and decline W/m2 before 2100 and decline RCP 4.5 4.5 W/m2 at stabilization after 2100 ~650 (at stabilization after 2100) Stabilization without overshoot RCP 6 6 W/m2 at stabilization after 2100 ~850 (at stabilization after 2100) Stabilization without overshoot

RCP 8.5 >8.5 W/m2 in 2100 >1370 CO2 equivalent in 2100 Rising Source: Van Vuuren et al. [52].

3. Results

3.1. Evaluation of Land Use and Land Cover Change (LULC) A total of 37,487.32 ha (13.54% of the study area) of land cover has faced conversion from one land use type to another in the period between 2000 and 2010. Among all the conversions from one type to another, conversion to agriculture and built-up areas is highest on the basin (Tables4 and5). The highest overall increment is in the agricultural area (increased by 9232 ha), followed by the built-up area (increased by 4087.28 ha). Similarly, the highest reduction is on grassland which decreased by 6012.12 ha. The built-up area in 2010 had increased by 4087.28 ha compared to 2000 and in this conversion, 82.71% (3380.96 ha) came from the agricultural area only. This decrement in agricultural area is mostly concentrated around major cities of the Kathmandu valley, changing the major traditional agricultural pattern in the valley. Likewise, 9362.92 ha of forest land and 7555.8 ha of grassland were converted to agricultural areas from 2000 to 2010. However, as the conversion from agricultural to other land use types like built-up and forest areas is notable, the net gain on agricultural areas was 9232.00 ha in 2010. The grassland land use faced the highest decrement contributing to other land use types mostly for the agriculture land use (7555.8 ha) and barren land has also faced similar conversion with the highest (3194.24 ha) conversion to agricultural land use types. The upper part of the basin has faced signiﬁcant expansion on urban and agricultural areas and this transition has resulted due to several factors like internal migration of people from other parts of the country, increased agricultural practices, ongoing infrastructural activities, and economic movements in the basin.

Table 4. Change in LULC between 2000 and 2010.

LULC CLASS LULC 2000 (ha) LULC 2010 (ha) Change Percent Change (%) Forest 181,246.64 177,803.92 3442.72 1.90 − − Shrubland 2025.96 662.92 1363.04 67.28 − − Grassland 11,414.60 5402.48 6012.12 52.67 − − Agriculture area 60,545.56 69,777.56 9232.00 15.25 Barren area 7655.64 5003.60 2652.04 34.64 − − Water body 884.20 1034.84 150.64 17.04 Built-up area 13,124.52 17,211.80 4087.28 31.14 Total 276,897.12 276,897.12

Table 5. Conversion of LULC in 2000 and 2010 (in ha).

2000 LULC LULC Forest Shrubland Grassland Agriculture Barren Water Built-up Total Forest 171,000.16 106.48 283.76 9362.92 95.24 89.2 308.88 181,246.64 Shrubland 551 473.92 241.2 674.52 42.16 34.56 8.6 2025.96 Grassland 886.04 47.84 1963.28 7555.8 543.72 156.12 261.8 11,414.6 2010 Agriculture 5202.24 14.88 2653.44 48,755.88 391.12 147.04 3380.96 60,545.56 LULC Barren 133 12.08 213.08 3194.24 3737.16 253.04 113.04 7655.64 Water 31.48 7.72 47.72 234.2 194.2 354.88 14 884.2 Built-up 0 0 0 0 0 0 13,124.52 13,124.52 276,897.12 Sustainability 2020, 12, 8186 10 of 22

3.2. Climate Data Analysis Twelve GCMs (Table6) were selected for the study basin after raw GCM analysis (Figure5) compared to the historical observation data (data period 1987–2016). With reference to the ﬁndings from several studies, multi-model ensemble (MME) accounted for the uncertainty of the single model [51,54], MME was prepared with an ensemble average of 12 best GCMs for the study area. The data were analyzed based on six scenarios, three each for RCP 4.5 and RCP 8.5 (Table7).

Table 6. Twelve GCMs of CMIP5 used in the study.

SN GCM Name Resolution Institution 1 HadGEM2-AO 1.875 1.250 Met Office Hadley Centre × 2 HadGEM2-CC 1.875 1.250 Met Office Hadley Centre × 3 CNRM-CM5 1.406 1.401 Centre National de Recherches Meteorologiques × 4 IPSL-CM5A-MR 1.875 1.865 Institut Pierre-Simon Laplace × 5 CESM1-CAM5 1.250 0.942 National Centre for Atmospheric Research × 6 FGOALS-s2 2.813 1.659 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences × 7 GFDL-CM3 2.500 2.00 Geophysical Fluid Dynamics Laboratory, NOAA × 8 HadGEM2-ES 1.875 1.250 Met Office Hadley Centre × 9 CESM1-BGC 1.250 0.942 National Centre for Atmospheric Research × 10 MPI-ESM-MR 1.875 1.865 Max Planck Institute for Meteorology (MPI-M) × 11 BCC-CSM1-1-M 1.125 1.122 Beijing Climate Centre, China Meteorological Administration × Centro Euro-Mediterraneo sui Cambiamenti Climatici 12 CMCC-CMS 1.875 1.865 Sustainability 2020, 12, x FOR PEER REVIEW × (Euro-Mediterranean Centre on Climate Change) 10 of 22

Figure 5. Overall rank of used Coupled Model Intercomparison Project 5 (CMIP5) global climate model Figure 5. Overall rank of used Coupled Model Intercomparison Project 5 (CMIP5) global climate (GCM) for Nepal. model (GCM) for Nepal.

Table 6. Twelve GCMs of CMIP5 used in the study.

SN GCM Name Resolution Institution 1 HadGEM2-AO 1.875 × 1.250 Met Office Hadley Centre 2 HadGEM2-CC 1.875 × 1.250 Met Office Hadley Centre 3 CNRM-CM5 1.406 × 1.401 Centre National de Recherches Meteorologiques 4 IPSL-CM5A-MR 1.875 × 1.865 Institut Pierre-Simon Laplace 5 CESM1-CAM5 1.250 × 0.942 National Centre for Atmospheric Research 6 FGOALS-s2 2.813 × 1.659 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences 7 GFDL-CM3 2.500 × 2.00 Geophysical Fluid Dynamics Laboratory, NOAA 8 HadGEM2-ES 1.875 × 1.250 Met Office Hadley Centre 9 CESM1-BGC 1.250 × 0.942 National Centre for Atmospheric Research 10 MPI-ESM-MR 1.875 × 1.865 Max Planck Institute for Meteorology (MPI-M) 11 BCC-CSM1-1-M 1.125 × 1.122 Beijing Climate Centre, China Meteorological Administration Centro Euro-Mediterraneo sui Cambiamenti Climatici (Euro- 12 CMCC-CMS 1.875 × 1.865 Mediterranean Centre on Climate Change)

The patterns of two climate parameters (temperature and precipitation) were observed for the period 2010–2099 compared with the baseline historical period of 1987–2016 (Table 7). The average annual precipitation for the historical period was 1609.57 mm. It was observed that the average annual precipitation is expected to increase by 94.70 mm, 245.73 mm, and 356.20 mm in RCP 4.5 and 121.47 mm, 294.17 mm, and 562.47 mm under RCP 8.5 by the ends of 2030, 2060, and 2090, respectively (Figure 6). Likewise, the annual average maximum and minimum temperature for the baseline period (1987–2016) was 25.92 °C and 13.92 °C, respectively. Compared to the baseline period, Sustainability 2020, 12, 8186 11 of 22

Table 7. Precipitation and temperature in various climate scenarios.

Climate Scenario RCP Period Precipitation (mm) Tmax (◦C) Tmin (◦C) Observed Period 1987–2016 1609.57 25.92 13.92 S1Rcp4.5 RCP 4.5 2010–2039 1704.27 27.239 15.833 S2Rcp4.5 RCP 4.5 2040–2069 1855.30 28.123 16.767 SustainabilityS3Rcp4.5 2020, 12, x FORRCP PEER 4.5 REVIEW 2070–2099 1965.76 28.764 17.06411 of 22 SustainabilityS1Rcp8.5 2020, 12, x FOR RCPPEER 8.5 REVIEW 2010–2039 1731.04 27.228 15.9211 of 22 S2Rcp8.5 RCP 8.5 2040–2069 1903.74 28.65 17.593 the averageS3Rcp8.5 maximum RCPtemperature 8.5 2070–2099 (Tmax) is projected 2172.03to increase by 1.32 30.298°C, 2.20 °C, and 19.284 2.84 °C theunder average RCP 4.5maximum and 1.31 temperature °C, 2.73 °C, and(Tmax 4.38) is projected°C under toRCP increase 8.5 by bythe 1.32end s°C, of 2030,2.20 °C 2060,, and and 2.84 2090 °C, under RCP 4.5 and 1.31 °C, 2.73 °C, and 4.38 °C under RCP 8.5 by the ends of 2030, 2060, and 2090, respectively (Figure 7). The increment in the average minimum temperature (Tmin) is high for all Therespectivelyscenarios patterns compared (Figure of two to 7). climate the The maximum increment parameters temperature. in the (temperature average The minimum average and minimum precipitation) temperature temperature (T weremin) isobserved highis projected for all for the periodscenariosto 2010–2099increase compared by 1.91 compared °C, to 2.85 the with°C,maximum and the 3.14 baseline temperature. °C under historical RCP The 4.5 average and period 2.00 minimum of°C, 1987–2016 3.67 °C, temperature and (Table 5.36 °C7 is ).under projected The RCP average annualto8.5 increase precipitationby the end by 1.91s of 2030,°C, for 2.85 the 2060, °C, historical andand 20903.14 period,°C respectively, under was RCP 1609.57 compared 4.5 and 2.00 mm. to °C, the It 3.67 baseline was °C, observed and period. 5.36 °C that under the RCP average 8.5 by the ends of 2030, 2060, and 2090, respectively, compared to the baseline period. annual precipitation is expected to increase by 94.70 mm, 245.73 mm, and 356.20 mm in RCP 4.5 and Table 7. Precipitation and temperature in various climate scenarios. 121.47 mm, 294.17 mm,Table and 562.477. Precipitation mm under and temp RCPerature 8.5 by in thevarious ends climate of 2030, scenarios 2060,. and 2090, respectively (Figure6). Likewise,Climate Scenario the annual RCP average maximumPeriod Precipitation and minimum (mm) temperature Tmax (°C) for Tmin the baseline (°C) period Climate Scenario RCP Period Precipitation (mm) Tmax (°C) Tmin (°C) (1987–2016)Observed was 25.92 Period◦C and 13.92 ◦C,198 respectively.7–2016 Compared1609.57 to the25.92 baseline period,13.92 the average ObservedS1Rcp4.5 Period RCP 4.5 19820170––20162039 16091704.57.27 27.23925.92 15.83313.92 maximum temperature (Tmax) is projected to increase by 1.32 ◦C, 2.20 ◦C, and 2.84 ◦C under RCP 4.5 and S1Rcp4.5S2Rcp4.5 RCPRCP 4.54.5 20120400––20392069 17041855.27.30 27.23928.123 15.83316.767 1.31 ◦C, 2.73 ◦C, and 4.38 ◦C under RCP 8.5 by the ends of 2030, 2060, and 2090, respectively (Figure7). S2Rcp4.5S3Rcp4.5 RCPRCP 4.54.5 20420700––20692099 18551965.30.76 28.12328.764 16.76717.064 The increment in the average minimum temperature (T ) is high for all scenarios compared to the S3Rcp4.5S1Rcp8.5 RCPRCP 4.58.5 20720100––20992039 19651731min.76.04 28.76427.228 17.06415.92 maximum temperature. The average minimum temperature is projected to increase by 1.91 C, 2.85 C, S1Rcp8.5S2Rcp8.5 RCPRCP 8.58.5 20120400––20392069 17311903.04.74 27.22828.65 17.59315.92 ◦ ◦ and 3.14 ◦C underS2Rcp8.5S3Rcp8.5 RCP 4.5 andRCPRCP 2.00 8.58.5 ◦C,204207 3.6700––20692099◦C, and 5.3619032172◦C.74.03 under RCP30.29828.65 8.5 by the ends17.59319.284 of 2030, 2060, and 2090, respectively,S3Rcp8.5 comparedRCP 8.5 to the207 baseline0–2099 period.2172.03 30.298 19.284

FigureFigure 6. Precipitation6. Precipitation projection projection for future future climate climate scenarios scenarios.. Figure 6. Precipitation projection for future climate scenarios.

Figure 7. Temperature projection for future climate scenarios. Figure 7. Temperature projection for future climate scenarios. Figure 7. Temperature projection for future climate scenarios. 3.3. Impacts on Ecosystem Services (ES) 3.3. Impacts on Ecosystem Services (ES) Sustainability 2020, 12, x FOR PEER REVIEW 12 of 22

3.3.1. Water Yield The average annual precipitation was 1879.53 mm and 1766.59 mm for the periods of 2000 and 2010, respectively. Comparing the two periods, the WY is observed to decrease by 106.592 MCM (million cubic meters) from 2000 to 2010. Figure 8 shows a raster map for the difference of WY between 2000 and 2010. In 2000, the total average annual WY was observed to be 3278.609 MCM and in 2010, WY was 3172.017 MCM. Performing a sub-basin-scale evaluation, WY was observed to have decreased in 2010 in all sub-basins except in sub-basin 2 (Table 8). As the computation of WY is a factor of evapotranspiration along with precipitation and LULC change, increment in the water yield in sub-basin 2 is attributable to an increase in the built-up area, which comes up with several other consequences like increased nitrogen load and reduced carbon storage. The assessment of WY on climate scenarios based on the baseline land use of 2010 and the corresponding climate data obtained from MME of GCMs shows increased WY with an increased projection of precipitation in future scenarios (Table 9). Compared to 2010, the total WY of the basin is observed to decrease in S1Rcp4.5 and S1Rcp8.5. However, even with the baseline LULC of 2010, the total WY was observed to increase from period S1 to S2 to S3 under both RCP 4.5 and RCP 8.5 scenarios. The WY per ha is highest on sub-basin 5 and lowest on sub-basin 1 in 2010 but, in future scenarios, sub-basin 6 will have the highest WY and sub-basin 2 will have the lowest water yield per ha of land (Table 9). This provides a general estimate and trend of WY but as WY is a function of evapotranspiration, highly affected by LULC, projections based on future LULC scenarios can give a Sustainabilitybetter estimate.2020, 12 , 8186 12 of 22

Table 8. Water yield comparison for 2000 and 2010. 3.3. Impacts on Ecosystem Services (ES) 2000 2010 3.3.1.Sub Water- YieldArea Precipitation WY_per ha Precipitation WY_per ha Basin (ha) (mm) (m3/ha) (mm) (m3/ha) The average annual precipitation was 1879.53 mm and 1766.59 mm for the periods of 2000 and 1 21,393.24 1775.44 11,037.84 1672.57 10,651.14 2010, respectively. Comparing the two periods, the WY is observed to decrease by 106.592 MCM 2 66,396.00 1695.71 10,659.63 1671.58 11,013.60 (million cubic meters) from 2000 to 2010. Figure8 shows a raster map for the di ﬀerence of WY between 3 43,520.56 1945.75 12,539.44 1850.32 12,231.78 2000 and 2010. In 2000, the total average annual WY was observed to be 3278.609 MCM and in 4 35,195.92 1837.08 11,594.40 1758.57 11,425.20 2010, WY was 3172.017 MCM. Performing a sub-basin-scale evaluation, WY was observed to have 5 36,103.44 2223.06 15,275.12 2045.25 14,087.72 decreased in 2010 in all sub-basins except in sub-basin 2 (Table8). As the computation of WY is a 6 52,777.16 1738.20 10,711.38 1612.74 10,085.51 factor of evapotranspiration along with precipitation and LULC change, increment in the water yield 7 21,510.80 1941.49 12,278.39 1755.11 11,042.91 in sub-basin 2 is attributable to an increase in the built-up area, which comes up with several other Average 1879.53 12,013.74 1766.59 11,505.41 consequences like increased nitrogen load and reduced carbon storage.

Figure 8. 2000 and 2010 water yield (mm) didifferenceﬀerence map.map.

Table 8. Water yield comparison for 2000 and 2010. Table 9. Water yield per ha computation for period S1-S3 under RCP 4.5 and RCP8.5 (m3/ha). 2000 2010 Sub-Basin Area (ha) Precipitation (mm) WY_per ha (m3/ha) Precipitation (mm) WY_per ha (m3/ha) 1 21,393.24 1775.44 11,037.84 1672.57 10,651.14 2 66,396.00 1695.71 10,659.63 1671.58 11,013.60 3 43,520.56 1945.75 12,539.44 1850.32 12,231.78 4 35,195.92 1837.08 11,594.40 1758.57 11,425.20 5 36,103.44 2223.06 15,275.12 2045.25 14,087.72 6 52,777.16 1738.20 10,711.38 1612.74 10,085.51 7 21,510.80 1941.49 12,278.39 1755.11 11,042.91 Average 1879.53 12,013.74 1766.59 11,505.41

The assessment of WY on climate scenarios based on the baseline land use of 2010 and the corresponding climate data obtained from MME of GCMs shows increased WY with an increased projection of precipitation in future scenarios (Table9). Compared to 2010, the total WY of the basin is observed to decrease in S1Rcp4.5 and S1Rcp8.5. However, even with the baseline LULC of 2010, the total WY was observed to increase from period S1 to S2 to S3 under both RCP 4.5 and RCP 8.5 scenarios. The WY per ha is highest on sub-basin 5 and lowest on sub-basin 1 in 2010 but, in future scenarios, sub-basin 6 will have the highest WY and sub-basin 2 will have the lowest water yield per ha of land (Table9). This provides a general estimate and trend of WY but as WY is a function of evapotranspiration, highly aﬀected by LULC, projections based on future LULC scenarios can give a better estimate. Sustainability 2020, 12, x FOR PEER REVIEW 13 of 22

Sub- 2010 S1_Rcp4.5 S2_Rcp4.5 S3_Rcp4.5 S1_Rcp8.5 S2_Rcp8.5 S3_Rcp8.5 Basin 1 10,651.14 10,006.49 11,396.52 12,368.50 10,302.09 11,854.92 14,171.04 2 11,013.60 9949.46 11,106.20 12,040.53 10,225.30 11,420.34 13,454.66 Sustainability 2020, 12, 8186 13 of 22 3 12,231.78 10,990.14 12,347.25 13,368.02 11,278.17 12,779.34 15,100.02 4 11,425.20 11,420.95 12,782.41 13,792.02 11,689.52 13,202.42 15,614.55 5 Table14,087.72 9. Water yield11,694.02 per ha computation 13,173.62 for period14,185.08 S1-S3 under11,966.76 RCP 4.5 and13,668.95 RCP8.5 (m 3/ha).16,210.80 6 10,085.51 11,869.07 13,279.60 14,299.19 12,109.70 13,725.27 16,340.63 Sub-Basin7 11,042.91 2010 11,526.35 S1_Rcp4.5 12,989.38 S2_Rcp4.5 13,993.72 S3_Rcp4.5 11,772.17 S1_Rcp8.5 S2_Rcp8.513,475.75 S3_Rcp8.516,133.65 1 10,651.14 10,006.49 11,396.52 12,368.50 10,302.09 11,854.92 14,171.04 3.3.2. 2Soil Loss 11,013.60 9949.46 11,106.20 12,040.53 10,225.30 11,420.34 13,454.66 3 12,231.78 10,990.14 12,347.25 13,368.02 11,278.17 12,779.34 15,100.02 The4 annual 11,425.20 average soil 11,420.95 loss is reduced 12,782.41 in 2010 (20.46 13,792.02 Mt/yr) 11,689.52compared to 13,202.42 2000 (21.38 Mt/yr) 15,614.55 and is attributable5 14,087.72 to decreased 11,694.02 annual average 13,173.62 precipitation. 14,185.08 Figure 11,966.76 9 shows 13,668.95 a raster map 16,210.80 for the 6 10,085.51 11,869.07 13,279.60 14,299.19 12,109.70 13,725.27 16,340.63 difference of soil loss between 2000 and 2010. In 2000, agriculture accounted for the highest average 7 11,042.91 11,526.35 12,989.38 13,993.72 11,772.17 13,475.75 16,133.65 rate of soil loss (198.40 t/yr/ha) whereas, in 2010, barren land was responsible for the highest soil loss (225.62 t/yr/ha) (Table 10). In both periods, the highest average rate of soil loss was as a result of 3.3.2. Soil Loss agricultural land use causing 56.07% and 56.06% of total soil loss in 2000 and 2010, respectively. TheThe annualassessment average of soil loss ison reduced future inperiods 2010 (20.46 under Mt RCP/yr) 4.5 compared and RCP to 20008.5 was (21.38 computed Mt/yr) and with is attributablethe baseline to LULC decreased map annualof 2010 average and projected precipitation. MME of Figure climate9 shows data. a rasterThe incre mapment for the in diprecipitationﬀerence of soilwill loss result between in increased 2000 and rainfall 2010. erosivity In 2000, agricultureand, hence, accountedsoil loss is forprojected the highest to increase average in rate all consequent of soil loss (198.40future scenarios. t/yr/ha) whereas, Soil loss in increased 2010, barren by 1.53, land 1.74, was and responsible 3.1 Mt/yr for in theS1, S2 highest, and S3 soil periods loss (225.62, respectively t/yr/ha), (Tableunder 10 the). RCP In both 4.5 periods,scenario. the Likewise, highest averageunder RCP rate 8.5, of soilsoil lossloss waswas asprojected a result to of increase agricultural by 3.63, land 4.23, use causingand 6.59 56.07% Mt/yr in and S1, 56.06% S2, and of S3 total periods, soil loss respectively in 2000 and (Table 2010, 11). respectively.

FigureFigure 9.9. 2000 and 2010 soilsoil lossloss (t(t/ha/yr)/ha/yr) didifferenceﬀerence map.map.

Table 10. Soil loss comparison for various land use types. Table 10. Soil loss comparison for various land use types. 2010 2000 2010 2000 Average Rate Soil Loss Average Rate Soil Loss Land Use Soil Loss Average Rate Soil Loss Land Use Average(t/yr/ha) Rate (t/yr/ha)(Mt /yr) (t/yr/ha) (Mt/yr) (Mt/yr) (t/yr/ha) (Mt/yr) ShrublandShrubland 199.93199.93 0.140.14 110.65110.65 0.230.23 Water 65.83 0.07 51.36 0.05 BarrenWater 225.6265.83 1.120.07 121.2351.36 0.920.05 GrassBarren 108.05225.62 0.591.12 75.09121.23 0.860.92 BuiltGrass 9.98108.05 0.170.59 9.2275.09 0.120.86 ForestBuilt 35.669.98 6.300.17 40.139.22 7.220.12 AgricultureForest 173.5835.66 12.096.30 198.4040.13 11.997.22 Total 20.46 21.38

The assessment of soil loss on future periods under RCP 4.5 and RCP 8.5 was computed with the baseline LULC map of 2010 and projected MME of climate data. The increment in precipitation will result in increased rainfall erosivity and, hence, soil loss is projected to increase in all consequent Sustainability 2020, 12, 8186 14 of 22 future scenarios. Soil loss increased by 1.53, 1.74, and 3.1 Mt/yr in S1, S2, and S3 periods, respectively, under the RCP 4.5 scenario. Likewise, under RCP 8.5, soil loss was projected to increase by 3.63, 4.23, and 6.59 Mt/yr in S1, S2, and S3 periods, respectively (Table 11).

Table 11. Soil loss computation for period S1-S3 under RCP 4.5 and RCP 8.5 (Mt/yr).

Sub-basin Sl_2010 S1_Rcp4.5 S2_Rcp4.5 S3_Rcp4.5 S1_Rcp8.5 S2_Rcp8.5 S3_Rcp8.5 1 2.20 2.21 2.34 2.49 2.20 2.40 2.70 2 4.09 4.23 4.56 4.77 4.32 4.65 5.19 3 6.61 7.21 7.74 8.13 7.30 7.92 8.87 4 4.00 4.40 4.67 4.95 4.40 4.78 5.37 5 1.03 1.12 1.22 1.25 1.14 1.25 1.41 6 2.22 2.47 2.67 2.73 2.50 2.73 3.09 7 0.31 0.35 0.37 0.39 0.35 0.38 0.43 Sum 20.47 22.00 22.21 23.57 24.10 24.70 27.06

3.3.3. Nitrogen Export Nitrogen export from the various LULC types directly aﬀects the watershed health, humans, and aquatic life processes. The comparative study (Table 12) of nitrogen export between LULC of 2000 and 2010 (Figure 10) showed that the total nitrogen export of the basin increased by 34,490.90 kg in 2010 compared to 2000. The sub-basin-scale evaluation shows that nitrogen export was the highest from sub-basin 2 in both periods. The assessment of nitrogen export on future scenarios with projected MME of climate data and the baseline LULC of 2010 did not show a precise pattern like other ES. The nitrogen export was determined based on LULC and associated loading rates. As the study was conducted on baseline land use of 2010, the comparative value of nitrogen export on climate scenarios was observed to be less than that of the 2010 case. The estimation with future land use scenarios can enhance the accuracy of estimation. However, in all cases (Table 13), nitrogen export was highest in sub-basin 2 and lowest in sub-basin 7 like in the baseline period of 2010.

Table 12. Nitrogen export comparison for 2000 and 2010.

2000 2010 Sub-Basin Area (ha) Nitrogen Export per ha (kg/ha) Nitrogen Export per ha (kg/ha) 1 21,393.24 0.817 0.915 2 66,396 1.352 1.402 3 43,520.56 0.676 0.755 4 35,195.92 0.545 0.640 5 36,103.44 0.680 0.987 6 52,777.16 0.581 0.717 7 21,510.8 0.251 0.442

Table 13. Nitrogen export for period S1-S3 under RCP 4.5 and RCP 8.5.

S1RCP4.5 S2RCP4.5 S3Rcp4.5 S1Rc8.5 S2Rcp8.5 S2Rcp8.5 Sub-Basin Area (ha) N/ha (kg/ha) N/ha (kg/ha) N/ha (kg/ha) N/ha (kg/ha) N/ha (kg/ha) N/ha (kg/ha) 1 21,393.24 0.941 0.947 0.947 0.943 0.949 0.949 2 66,396.00 1.344 1.336 1.337 1.346 1.331 1.322 3 43,520.56 0.72 0.719 0.72 0.721 0.72 0.716 4 35,195.92 0.658 0.657 0.657 0.658 0.657 0.656 5 36,103.44 0.875 0.879 0.879 0.875 0.882 0.885 6 52,777.16 0.806 0.807 0.807 0.804 0.807 0.813 7 21,510.80 0.455 0.457 0.457 0.454 0.458 0.462 247,744.57 247,536.45 247,605.62 247,844.51 247,393.51 247,095.67 Sustainability 2020, 12, x FOR PEER REVIEW 14 of 22

Agriculture 173.58 12.09 198.40 11.99 Total 20.46 21.38

Table 11. Soil loss computation for period S1-S3 under RCP 4.5 and RCP 8.5 (Mt/yr).

3.3.3. Nitrogen Export Nitrogen export from the various LULC types directly affects the watershed health, humans, and aquatic life processes. The comparative study (Table 12) of nitrogen export between LULC of 2000 and 2010 (Figure 10) showed that the total nitrogen export of the basin increased by 34,490.90 kg in 2010 compared to 2000. The sub-basin-scale evaluation shows that nitrogen export was the highest from sub-basin 2 in both periods. The assessment of nitrogen export on future scenarios with projected MME of climate data and the baseline LULC of 2010 did not show a precise pattern like other ES. The nitrogen export was determined based on LULC and associated loading rates. As the study was conducted on baseline land use of 2010, the comparative value of nitrogen export on climate scenarios was observed to be less than that of the 2010 case. The estimation with future land Sustainabilityuse2020 scenarios, 12, 8186 can enhance the accuracy of estimation. However, in all cases (Table 13), nitrogen export 15 of 22 was highest in sub-basin 2 and lowest in sub-basin 7 like in the baseline period of 2010.

Sustainability 2020, 12, x FOR PEER REVIEW 15 of 22

5 36,103.44 0.680 0.987 6 52,777.16 0.581 0.717 7 21,510.8 0.251 0.442

Table 13. Nitrogen export for period S1-S3 under RCP 4.5 and RCP 8.5. FigureFigure 10. 2000 10. 2000 and and 2010 2010 nitrogen nitrogen export difference difference map map (kg/pixel) (kg/pixel).. S1RCP4.5 S2RCP4.5 S3Rcp4.5 S1Rc8.5 S2Rcp8.5 S2Rcp8.5 3.3.4. CarbonSub- StorageArea TableN/ha 12. NitrogenN/ha export comparisonN/ha for 2000N/ha and 2010. N/ha N/ha TheB totalasin modeled (ha) carbon (kg/ha) was reduced(kg/ha)2000 by 969,923.33(kg/ha) Mg(kg/ha) from 2000(kg/ha)2010 to 2010. The(kg/ha) reduction in 1 21,393.24 0.941 0.947 0.947 0.943 0.949 0.949 forest cover,Sub shrubland,-Basin Area and (ha) grassland Nitrogen caused Export theper overallha (kg/ha) reduction Nitrogen of Export carbon per storageha (kg/ha) on the basin. 2 166,396.00 21,393.24 1.344 1.336 0.817 1.337 1.346 0.9151.331 1.322 Likewise, a sub-basin-scale evaluation of carbon storage shows that, in both periods, carbon storage 3 243,520.56 66,3960.72 0.719 1.352 0.72 0.721 1.4020.72 0.716 per ha was4 the highest335,195.92 43,520.56 in sub-basin0.658 7 and0.657 lowest 0.676 in0.657 sub-basin 0.658 2 (Table 140.755).0.657 Figure 110.656 shows a raster file for the di5 fference436,103.44 in35,195.92 carbon 0.875 storage between0.879 0.545 2000 0.879 and 2010.0.875 There were0.6400.882 increased 0.885 anthropogenic activities in6sub-basin 52,777.16 2 which 0.806 is mostly comprised0.807 of0.807 residential 0.804/built-up areas0.807 and agricultural0.813 areas of the basin.7 This21,510.80 represents the0.455 area with0.457 the lowest0.457 storage despite0.454 having0.458 thehighest 0.462 area. Likewise, 247,744.57 247,536.45 247,605.62 247,844.51 247,393.51 247,095.67 comparing total carbon storage in 2000 and 2010, sub-basin 2 marks the highest loss with a 315,381.00 Mg reduction3.3.4. Carbon and sub-basin Storage 6 has increased storage by 168,886.91 Mg. The total modeled carbon was reduced by 969,923.33 Mg from 2000 to 2010. The reduction in Table 14. Carbon storage comparison for 2000 and 2010 LULC map. forest cover, shrubland, and grassland caused the overall reduction of carbon storage on the basin. Sub-BasinLikewise, Area a (ha)sub-basin Carbon-scale Storage evaluation 2000 (Mg) Carbonof carbon Storage storage 2010 (Mg) shows Carbon that Storage, in both 2000 (Mgperiods/ha),Carbon carbon Storage storage 2010 (Mg/ha) 1per ha was 21,393.24 the highest 4,192,540.21 in sub-basin 7 and lowest 4,099,479.62 in sub-basin 2 (Table 195.98 14). Figure 11 shows a 191.62 raster 2file for 66,396.00the difference in 7,789,910.70 carbon storage between 7,474,529.70 2000 and 2010. There 117.33 were increased anthropogenic 112.57 3 43,520.56 9,43,3081.38 9,151,285.75 216.75 210.27 4activities 35,195.92 in sub-basin 2 8,087,142.52 which is mostly comprised 7,809,974.65 of residential/built- 229.78up areas and agricultural 221.90 areas 5of the 36,103.44basin. This represents 7,134,039.74 the area with 7,051,904.42 the lowest storage despite 197.60 having the highest 195.32 area. 6 52,777.16 10,687,374.90 10,856,261.81 202.50 205.70 7Likewise, 21,510.80 comparing total 5,060,953.47 carbon storage in 4,971,683.652000 and 2010, sub-basin 235.282 marks the highest loss 231.12with a Total315,381.00276,897.12 Mg reduction52,385,042.92 and sub-basin 6 has 51,415,119.60 increased storage by 168,886.91 Mg.

FigureFigure 11. 200011. 2000 and and 2010 2010 carbon carbon storage difference diﬀerence map map (Mg/Pixel). (Mg/Pixel).

Sustainability 2020, 12, 8186 16 of 22

4. Discussion The global concern for the conservation and promotion of ES is rising along with an awareness that projected extreme events as a result of climate change will hamper the provision of services. Normally, ES are renewable if they can be managed sustainably but can be depleted or degraded if mismanaged. The future climate will continue to deliver ES; in some cases, ES are increased and, in some cases, decreased—mostly decreased compared to historical supply. Along with climate change, another important factor is LULC transformation, which alters the provision of ES. Thus, mapping and evaluation of ES are crucial for future land use plans and for strengthening the capability of various services and facilities, directly or indirectly associated with ES. Provisioning services are often readily appreciated by the public as they have direct market values. However, other ES (regulating services, supporting services, and cultural services) are often not prioritized in decision making as these services do not hold immediate monetary value. To emphasize the significance of regulating services in the production and sustainability of other ES, we focused on four major regulating ES, namely, WY, soil loss, nitrogen export, and carbon storage. The provision of these services was compared on sub-basin scales and compared based on LULC data of two time periods, namely, 2000 and 2010. We also computed the ES provision on future climate scenarios with the baseline LULC data of 2010 and downscaled climate data for 2010–2099. The LULC change analysis between the periods 2000 and 2010 shows that the conversion to built-up and agriculture areas from other LULC is significantly high on the Bagmati Basin. This is because of increased anthropogenic activities for food sustainability and economic upliftment. These conversions come at the huge cost of increased WY, soil loss, loss of carbon sink, and increased nitrogen export to water resources. Meantime, the concept of a Payments for Ecosystem Services (PES) scheme is being promoted in the basin with the realization of the need for ecosystem conservation [55]. This scheme has increased protected areas and community forests in the basin and has a twin objective of promoting ecosystem conservation and development earnings [55]. Likewise, climate projection shows increased precipitation and temperatures for the future period. The mean annual average precipitation in the case of 2010 was lower than that of 2000 and as WY and soil loss are highly affected by precipitation amount, its magnitude was decreased in 2010 compared to 2000. Though the overall WY was decreased in 2010 compared to 2000, the WY on sub-basin 2 increased. Sub-basin 2 faced a higher increment of built-up areas and, consequently, evaporative loss and water retention were reduced, causing an overall increase of WY. This phenomenon was also observed by another study [56] when computing the impacts of LULC on WY provision. The prediction of WY on future climate scenarios shows an overall increase in all sub-basins in consecutive periods S1 to S3 under both RCP 4.5 and RCP 8.5 scenarios (Table9). Although precipitation determines the amount of water provided by nature, LULC plays a significant role in determining the amount of water that flows as runoff or is retained as storage [57]. Owing to this phenomenon, urbanization has induced frequent urban floods in Nepal and the lack of sufficient water retention structures has increased flood peaks and flood volumes. In terms of ES, changes in WY caused by LULC changes have two major effects, namely, contribution to the water available for consumption and/or increased flood risks during storms [58]. Infrastructural capability for water collection, delivery, and treatment may work temporarily to counter increased WY. In the long term, however, management should consider human–nature interactions to avoid unintended environmental and socio-economic consequences which are caused as a result of rapid and large-scale development [58]. Several studies have recommended ecosystem-based “green” infrastructures such as wetlands, well-structured soils, and forest patches to enhance water storage and flood regulation [59]. The mapping of WY and its tendencies in future climate scenarios can hence provide an outline for such sustainable land use plans to mitigate flood risks and water scarcity problems. Although soil loss due to erosion and sedimentation are natural processes in a healthy ecosystem, excess loss mainly due to changes in LULC is a threat to the security of water, food, and energy [60]. Sustainability 2020, 12, 8186 17 of 22

Soil loss is observed to be highest from agricultural land use in the study basin. Agriculture is the major activity sustaining the economy of Nepal. Thus, the traditional agricultural system demands improved methods to sustain topsoil-containing organic matter and essential plant nutrients that are otherwise removed from the soil during erosion. Likewise, the predicted increment of soil loss in the future climate scenarios also indicates the necessity for improved farm management practices that incorporate the management of increased WY. In addition to the stress on food production, soil erosion also reduces water and nutrient retention, biodiversity, and water resources/downstream power generation [61]. Therefore, for sustainable management of soils and related services, spatial mapping of soil erosion on different land use and future scenarios can provide guidelines and mark areas for immediate actions to mitigate the loss. The loss of carbon storage is another significant phenomenon observed in the study basin with a reduction in forest areas and increases in agricultural and built-up areas. Terrestrial-based carbon storage and sequestration are directly affected by LULC. The concentrated anthropogenic activities on the upper part of the basin, especially in sub-basin 2, contribute to the lowest carbon storage and highest nitrogen export per ha of land in this sub-basin. Most developing nations have serious problems with the degradation of forests and soils which have crucial implications for changing the global C pools [62]. The information on carbon storage and fluctuations can help land managers to choose among sites for protection, harvest, or development. Furthermore, these maps can support multiple decisions by governments, NGOs, and stakeholders to offer incentives to landowners in exchange for forest conservation. Sub-classification of land use and consideration of altitudinal variation in various carbon pools produces more accurate results. Poudel et al. [63] have found higher carbon stocks at the higher altitudinal gradient in the study conducted in the Panchase Conservation Area in Nepal. The study indicated that due to human disturbance, carbon stocks were low at low altitude. Temperature and precipitation also have significant effects on the carbon pool of various biomass [64]. Thus, a detailed study with climate and elevation variation helps to produce an accurate estimate of carbon value. Similarly, with increased agricultural activities, and reduced vegetation covers, nutrient retention diminishes and, hence, the amount of nitrogen entering the river network/water resources increases. This severely increases water contamination, thereby affecting human and aquatic health. Additionally, because of increased WY in future climate scenarios, nitrogen export is expected to increase in all sub-basins. In this scenario, spatial information on nutrient export and areas with the highest filtration can help land use planners to integrate the contribution of ecosystems in order to mitigate water pollution. To understand the temporal change of ecosystem services in 2000 and 2010, the minimum value was set to 0 and the maximum value was set to 1 for each ecosystem service in two periods. This shows a comparative provision of ES in all sub-basins in the 2000 and 2010 periods which depicted that sub-basin 2 had the lowest combined ES provision in both 2000 and 2010 (Figure 12). Thus, this indicates the need for urgent policies in order to restore the services and to promote its sustainability. Likewise, the computation of ES with the projection of climate data in the 2010–2099 period shows increased WY, soil loss, and nitrogen export from the study area. The case of sub-basin 2, especially of the Kathmandu Valley area, can be referenced for the planning of other emerging cities. As Nepal is prioritizing decentralization and focusing on the development of many other smart cities on the outskirts of the Kathmandu Valley and other parts of the country, studies like this can present risk analyses and help decision making that prioritizes the conservation of ES for the optimum utilization and preservation of natural capital. Sustainability 2020, 12, x FOR PEER REVIEW 18 of 22

of the Kathmandu Valley area, can be referenced for the planning of other emerging cities. As Nepal is prioritizing decentralization and focusing on the development of many other smart cities on the outskirts of the Kathmandu Valley and other parts of the country, studies like this can present risk

Sustainabilityanalyses and2020 help, 12, 8186 decision making that prioritizes the conservation of ES for the optimum utilization18 of 22 and preservation of natural capital.

Figure 12. Relative comparison of ES in 2000 and 2010. Figure 12. Relative comparison of ES in 2000 and 2010. 5. Limitations of the Study 5. Limitations of the Study The InVEST tools used for the evaluation of ES have their own modeling limitations. The InVEST WY ModelThe InVEST is based tools on annual used for averages the evaluation and neglects of ES thehave extremes their own and modeling the spatial limitations. distribution The of InVEST LULC. TheWY InVEST Model Carbonis based Storage on annual Model averages assumes and that neglects each LULC the isextremes at the fixed and carbon the spatial storage distribution level and the of fluctuationLULC. The on InVEST carbon Carbon storage Storage is only dueModel to a assumes change in that LULC each from LULC one is type at the to another.fixed carbon Due tostorage data unavailabilitylevel and the fluctuation in the study on area, carbon tabular storage values is only were due obtained to a change from in theLULC InVEST from manual.one type Theto another. study usedDue referenceto data unavailability data for various in the LULC study types area, fromtabular InVEST values user were guidelines obtained from and thethe variationInVEST manual. in the amountThe study of carbonused reference storage data in di forfferent various carbon LULC pools types due from to elevationInVEST user have guidelines not been and acknowledged the variation inin the the study amount at this of stage. carbon In storage addition, in for different climate carbon data, uncertainties pools due remain to elevation from climate have not change been dataacknowledged itself and from in the the study selected at thismethods stage. of In downscaling addition, for and climate bias correction. data, uncertainties Nevertheless, remain careful from attentionclimate changewas given data to the itself preparation and from of the data selected and modeling. methods Despite of downscaling the model and and data bias limitations, correction. theNevertheless, results from careful the study attention provide was an given overview to the of preparation a general tendency of data and of the modeling. provision Despite of ES, includingthe model fluctuationsand data limitations, with a change the results in climate from and the LULC. study Itprovide is, thus, an expected overview to helpof a ingeneral decision tendency making of and the scenarioprovision analysis. of ES, including The results fluctuations of this study with could a change be improved in climate if ground and observationLULC. It is, datathus are, expected available to forhelp the in accurate decision analysis making ofand InVEST scenario models. analysis. The results of this study could be improved if ground observation data are available for the accurate analysis of InVEST models. 6. Conclusions 6. ConclusionsThis study attempted to evaluate ES and their fluctuations with LULC and climate change on the BagmatiThis study Basin attempted of Nepal. to The evaluate study firstES and assessed their ESfluctuations based on with the 2000 LULC and and 2010 climate LULC change map and on thenthe Bagmati with the Basin baseline of Nepal. LULC The map study of 2010 first and assessed projected ES based climate on datathe 2000 from and the 2010 MME LULC of 12 map GCMs, and ESthen provision with the on baseline the future LULC period map was of 2010 estimated. and projected The overall climate provision data from of combined the MME ES of on12 sub-basinGCMs, ES 2provision was lowest on inthe 2000, future 2010, period and onwas all estimated. future climate The overall scenarios. provision In addition, of combined the provision ES on sub of ES-basin was 2 observedwas lowest to be in decreasing 2000, 2010, in and all other on all sub-basins. future climate Outcomes scenarios. like increased In addition, WY, the reduced provision carbon of storage,ES was increased nitrogen export, and soil loss suggest that immediate actions are required from policy makers for the sustainable management of natural capital. The ES are interrelated and the absence of adequate designs to sustain one can hamper other provisions as well. The availability of spatially explicit information on ecosystems and their interrelated services serves for the prioritization of ES into policy Sustainability 2020, 12, 8186 19 of 22 and decision making. The maps produced as an outcome of this study can help land use planners, government organizations, and concerned stakeholders to recognize areas where the ecosystems are produced and help in the decision making for low impact development maintaining ecological balance and economic goals. Further studies on national scale future LULC scenarios could estimate accurate ES provisions integrating national priorities.

Supplementary Materials: The following are available online at http://www.mdpi.com/2071-1050/12/19/8186/s1, Table S1: list of stations with rainfall and temperature data along with their location and data period. The data has been duly collected from Department of Hydrology and Meteorology (DHM) Nepal. Author Contributions: Conceptualization, Y.J. and S.B.; methodology, S.B. and S.L.; formal analysis, S.B. and S.L.; writing—original draft preparation, S.B.; writing—review and editing, Y.S. and Y.J.; project administration, Y.J.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. Funding: This subject is supported by the Korea Ministry of Environment as “The SS (Surface Soil conservation and management) projects; 2019002820002”. Acknowledgments: The authors send special thanks to the Korean Environment Institute (KEI) for their data collection efforts. Conflicts of Interest: The authors declare no conflict of interest.

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