Nitrogen leaching from catchments in the Middle Hills of Nepal; an application of the INCA model

The Science of the Total Environment 228 Ž1999. 259]274

Nitrogen leaching from catchments in the Middle Hills of Nepal; an application of the INCA model R. Collins a,U , P. Whitehead b, D. Butterfield b a

Institute of Hydrology, Wallingford, Oxon, OX10 8BB, UK Aquatic En¨ ironments Research Centre, Uni¨ ersity of Reading, Reading, UK

b

Received 10 January 1999; accepted 12 February 1999

Abstract Application of a semi-distributed integrated nitrogen model ŽINCA. to forested and cultivated catchments of the Middle Hills, Nepal is described. The model simulations have determined the fate and distribution of multiple sources of nitrogen including atmospheric inputs and fertiliser applications to terraced agriculture. Predicted stream NH 4-N and NO 3-N concentrations broadly reproduce the low observed concentrations but discrepancies are evident which probably arise due to the variability in both rainfall N concentrations and land management practices. The impact of predicted future increases in N input to catchments throughout this region is assessed. Stream N concentrations are predicted to increase. However, the predominance of gaseous loss of N from terraced agriculture is likely to ensure that future concentrations remain relatively low. Q 1999 Elsevier Science B.V. All rights reserved. Keywords: INCA; Nitrogen leaching; Terraced agriculture; Nepal

1. Introduction The sustainable use of land, water and agricultural resources is an essential component in the future development of many Asian countries. This is especially so for Nepal where, in response to population growth, it is estimated that agricultural production will need to more than double by the year 2000 even though virtually all land suitU

Corresponding author.

able for cultivation is being used ŽHMG Nepal, Ministry of Forests and Soil Conservation, 1988.. This population pressure is greatest in the Middle Hills region and has resulted in an expansion of terraced agriculture onto steeper hill-slopes, previously forested or grassed, and supported by applications of inorganic and organic fertilisers. Results from a number of studies of land-use change indicate a 10]25% increase in agricultural land over the last 30 years and more than 60% of the Middle Hills region is now cultivated. There is concern that inappropriate timing and application

0048-9697r99r$ - see front matter Q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 0 4 8 - 9 6 9 7 Ž 9 9 . 0 0 0 5 0 - 9

260

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

of fertilisers coupled with their increased use may result in high nutrient concentrations being leached to drainage waters. This leaching causes an economic loss to farmers and increases the susceptibility of rivers to eutrophication. In addition, the widespread use of ammonium-based fertilisers may cause soil acidification, reducing soil fertility and, in the longer term, promote stream acidification with a consequent loss in aquatic biodiversity. The potential for degradation of the water resource may be increased further throughout the region by increased atmospheric deposition of acidic oxides. Such emissions are predicted to increase in response to the rapid industrialisation of many Asian countries. Indeed, studies of Himalayan glacier chemistry ŽNijampurkar et al., 1993., have found significant concentrations of NO 3 and SO4 suggesting the existence of longrange transboundary pollution to high-altitude regions previously considered pristine. Although an initial evaluation of stream chemistry in the Nepal Middle Hills ŽCollins and Jenkins, 1996. suggests that nutrient concentrations are relatively low, no assessment of future nutrient status has been undertaken. Such an assessment requires the use of dynamic models to predict how future changes in deposition, land use, management and climate will affect N loading to rivers. An integrated approach is necessary in order to incorporate multiple N sources and address the spatial variation across catchments, e.g. in land use, vegetation and hydrology, which influences N concentrations down the river profile. The model INCA Žintegrated nitrogen in catchments . has been developed to incorporate all these elements together within a single model enabling assessment of the fate and distribution of nitrogen in both the aquatic and terrestrial environment. The semi-distributed nature of INCA provides the potential for its use as a tool to contribute to the formulation of catchment management strategies, improvement of fertiliser efficiency and reduction of N loss to rivers. The model has been designed for, and previously applied to, western European catchments, for example, Whitehead et al. Ž1998a,b.. In this paper, its first application to catchments of the Middle Hills, Nepal, is described. The ability of the model to

simulate present-day stream nutrient concentrations is evaluated in catchments subject to a monsoonal climate and with contrasting coverages of forestry and terraced agriculture. Scenario analysis is described which predicts the impact of future increases in atmospheric and fertiliser inputs of N.

2. The INCA model A comprehensive and detailed description of INCA is provided by Whitehead et al. Ž1998a,b. and briefly summarised here. INCA is process based and uses kinetic equations to simulate the principal mechanisms operating in both catchment and river. The derivation of flow pathways within the model enables N inputs from the atmosphere and fertiliser to be tracked through catchment soils to the river. Spatial variations in land use and management, effluent discharges and N deposition can be incorporated enabling application in a semi-distributed manner. The different land-use classes and subcatchment boundaries are modelled simultaneously and information fed sequentially into a multi-reach river model. Nitrogen concentrations and fluxes are produced as a daily time-series, incorporating temporal variation in hydrological flow paths and N transformations in both the catchment and river. Three components of INCA were utilised in the application to Nepalese catchments v

v

The hydrological model which determines the flow of hydrologically effective rainfall ŽHER. in the reactive and groundwater zones of the catchment and within the river itself. This component of the model drives N fluxes through the catchment. The catchment N process model which simulates N transformations in the soil and groundwater of the catchment. This component of the model includes plant uptake, nitrogen fixation and microbial processes such as mineralisation, nitrification and denitrification. Daily time series of air temperature and soil moisture deficit ŽSMD. are input to this component since microbial N transformations

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

v

are temperature- and moisture-dependent. Process rate parameters can be determined for up to six land-use classes, although parameters for a particular land-use type cannot be varied between subcatchments. The river N process model simulates dilution and in-river N nitrification and denitrification. Net N output from each subcatchment provides the N flux into the corresponding river reach.

3. Study sites The study catchments ŽGR 278509 N, 858209 E. are located in the Likhu Khola watershed which lies within the Middle Hills topographical region of Nepal, about 10 km to the north of the Kathmandu Valley, from which it is separated by the Sheopuri hills. The Likhu Khola is a tributary of the Trisuli drainage system which flows from the High Himalayas into the Ganges. The study catchments were selected to cover the range of land-use and management regimes representative of the Middle Hills. Altitude ranges from 600 to 1990 m. The area experiences a monsoonal climate and precipitation totals approximately 2000 mm yeary1 , 80% of which falls during the period

261

May]September. Mean monthly rainfall is highest in July and August; although a substantial volume of rainfall may occur during the premonsoon period, April]June. The geology of the watershed is fairly uniform and characterised by basic, high-grade gneisses of the Kathmandu complex. Soils are predominantly Cambisols with Fluvisols common on river terrace deposits at lower altitudes. All cultivated land is either sloping and rain-fed terrace Žbari. or level terrace, irrigated through a complex network of canals fed from streamflow Žkhet.. The bari supports maize and millet through the monsoon season and either wheat or mustard during the winter months. Ditches beneath each bari terrace drain excess runoff into ravines and gullies at the side of each set of terraces. Two rice crops are grown upon the khet, the first from April to June, the second from July to NovemberrDecember. Extensive application of fertilisers, in the form of farmyard manure and inorganic nitrogen and phosphorus compounds, is common on khet, with less frequent or intensive application to bari. The application of large amounts of imported inorganic fertiliser in areas near to Kathmandu started in the late 1960s following the introduction of highyield rice varieties. Three subcatchments were chosen for the application of INCA ŽTable 1.. The Jogi is predomi-

Table 1 Summary of physical and chemical characteristics of the subcatchments a

2

Area Žkm . Elevation range Žm. Forest Ž%. Grazing Ž%. Khet Ž%. Bari Ž%. NO3 -N wet deposition NO3-N dry deposition NH4-N wet deposition NH4-N dry deposition Observed mean NH4 -N stream concentration Žmg ly1 . Observed mean NO3 -N stream concentration Žmg ly1 . a

Chinnya

Bore

Jogi

1.14 975]1990 100 0 0 0 4.5 4.5 9.0 9.0 0.17

3.09 700]1990 52 2 23 23 4.5 4.5 9.0 9.0 0.15

2.35 600]1063 2 1 87 10 4.0 4.0 8.0 8.0 0.22

0.015

0.024

0.038

Deposition values are in units of kgrharsimulation duration.

262

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

nantly cultivated and provides a marked contrast to the forested Chinnya which receives no significant fertiliser input. The Bore is moderately cultivated, providing an intermediate comparison, and includes the Chinnya as part of its headwaters enabling both reaches to be modelled in the same simulation. Simulations were undertaken which encompass both the pre-monsoon and monsoon seasons in 1992 for the Bore and Chinnya, and 1993 for the Jogi.

4. Data requirements 4.1. Hydrology A simple hydrological model was developed to provide daily time series of HER and SMD for subsequent input to INCA. The approach adopted attempts to account for the marked seasonal variation in soil moisture in the Nepal Middle Hills, which can be a fundamental control upon runoff. Initial estimates of HER which assumed all infiltrating rainfall to be hydrologically effective led to large overestimates of stream flow within INCA, particularly during the pre-monsoon period of a simulation. Large rainfall events during the pre-monsoon can give rise to a relatively small rise in flow since much of the rainfall simply contributes to the low antecedent soil water store. In the model, all rainfall of an intensity that exceeds a specified infiltration rate is considered to be hydrologically effective. Conceptually this rainfall is the origin of overland flow which is rapidly transported to the main channel via ditches, gullies, ravines and irrigation canals. This theory is consistent with erosion plot and tensiometer data ŽGardner and Jenkins, 1995. which indicates that Hortonian or infiltration excess runoff is predominant in these catchments. The threshold infiltration rate is a crude, catchment lumped value, derived from rainfall and runoff data ŽGardner and Jenkins, 1995. which takes no account of seasonal variation in infiltration rate influenced, for example, by tillage practices. The value chosen for the Bore and Chinnya Ž15 mm hy1 . catchment is higher than that of the cultivated Jogi Ž10 mm hy1 . to account for the pres-

ence of more freely-drained forest soils. Rainfall at an intensity lower than the catchment infiltration rate is assumed to infiltrate and is added to the soil water store, however, the proportion that contributes to streamflow and which can therefore be considered hydrologically effective, is dependent upon soil moisture. At or below field capacity, none of the infiltrated rainfall is available to contribute to runoff, whilst as the soil approaches saturation, 100% becomes hydrologically effective. If the soil reaches saturation, subsequent rainfall at any intensity is hydrologically effective. Infiltrated and direct runoff components of rainfall recorded half-hourly are summed to derive daily HER. Prior to the analysis of infiltration, 1 mm of rainfall in each event is assumed to be lost through interception. Once rainfall has been accounted for, the soil water store is subject to loss through vertical drainage to deeper layers and evapotranspiration ŽEt.. In the absence of reliable data to quantify these loss processes, a characteristic curve was derived from tensiometer data which describes, in the absence of further rainfall inputs, an exponential decline in the soil water store over time;

u s 0.2q 0.3exp Ž y 0.05)T . where u s soil moisture content; and T s time Ždays.. Once the soil water loss is calculated, daily SMD is derived from the remaining soil water store assuming field capacity to be 0.27 m3 my3 . SMD is a catchment lumped value representing soil moisture integrated from different land cover types. However, within INCA the user can specify a threshold soil moisture content for denitrification which is specific to each land-use type. To determine the flux of HER through the catchment system, INCA requires the residence time of water in soil and groundwater zones. Dry season flow is minimal in the study catchments and base-flow from a groundwater source is therefore excluded from the Bore and Jogi. However, for the forested Chinnya, a deep soil source, contributing to 50% of streamflow, was incorporated. The software package CAPTAIN ŽVenn and Day, 1977. was used to undertake a recursive

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

analysis of rainfall runoff time series data and to identify residence times. The technique identified an optimum first order model structure for the Bore and Jogi which supports the exclusion of a groundwater zone from the subsequent INCA simulations. Residence times are shortest in the Jogi Žapprox. 2 days. where they reflect not just flow through the soil horizons but quick-flow along the drains and ditches associated with the terraced agriculture. This contrasts with the forested Chinnya Žapprox. 6 days. where quick flow is limited and most streamflow is derived from water that has infiltrated into the soil. The Bore Ž4 days. provides an intermediate comparison reflecting the more heterogeneous land use. These relative differences in residence time are qualitatively supported by results from hydrographic separation of these streams ŽCollins and Neal, 1998. which showed that terraced agriculture increased the proportion of rainwater contributing quickly and directly to streamflow. Soil water residence times for each land-use type ŽTable 2. were estimated using the catchment-derived values. Residence time for deep soil water in the forested Chinnya was arbitarily set to 30 days. The time series analysis also indicates that residence times are subject to seasonal variation; the wetter soils of the monsoon period decrease residence time relative to the pre-monsoon. INCA, however, does not allow for incorporation of a seasonally dynamic residence time, limiting the accuracy of simulated flow in this application.

263

Modelling flow through each reach within INCA requires the inclusion of a velocity flow relationship. This was established in the Bore Ž V s 2.76 Q 0.586 . using a current meter in a range of flows. This relationship is also applied to the Chinnya and Jogi although variation in channel morphology ideally requires the derivation of a unique relationship for each stream. 4.2. Deposition inputs The total input load of NH 4 and NO 3 from atmospheric deposition ŽTable 1. was derived from mean concentrations in bulk precipitation and the amount of pre-monsoon and monsoon rainfall. The bulk collector was located in the Bore catchment and the resulting mean concentrations were also used for the Jogi application. Samples were collected whenever the sample container was full, or at least weekly during the wet season, and so frequency of sampling reflects the intensity and amount of rainfall. Total bulk precipitation of NH 4 and NO 3 was divided equally into wet and dry proportions. Bulk collectors do not, however, effectively catch dry deposition and this flux is likely to be underestimated. Within INCA, daily wet deposition load is determined from total wet deposition by calculating the proportion of daily rainfall to total rainfall. Dry deposition load is divided equally over the simulation period. This methodology can lead to significant variation between derived and observed daily wet loads,

Table 2 Process rate coefficients Ždayy1 ., fertiliser addition Žkgrharsimulation duration. and soil water residence times Žday. for the Bore and Jogi Žitalics . catchments a Forest Denitrification rate N fixation rate NO3 uptake rate Nitrification rate Mineralisation rate NH4 uptake rate Immobilisation rate Fertiliser NH4 -N Fertiliser NO3 -N Residence time a

0.6 0.0007 0.4 0.002 0.035 0.35 0.05 0.0 0.0 5.0

Grazing 0.1 0.001 0.13 0.04 0.04 1.0 0.05 0.0 0.0 5.0

0.2 0.001 0.45 0.0014 0.05 0.4 0.05 0.0 0.0 3.0

Additional NH 4 input to forestry in the Bore is not included.

Khet 0.18 0.002 0.16 0.03 0.08 1.0 0.05 0.0 0.0 3.0

0.6 0.05 0.45 0.002 0.15 0.65 0.12 70.0 5.0 2.0

Bari 0.6 0.012 0.35 0.014 0.4 2.6 0.15 45.0 5.0 2.0

0.6 0.002 0.45 0.002 0.15 0.65 0.12 20.0 5.0 2.0

0.55 0.003 0.33 0.016 0.16 1.6 0.1 12.0 5.0 2.0

264

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

Fig. 1. NH 4-N and NO 3-N rainfall concentrations.

principally due to the temporal variability in rainfall concentrations ŽFig. 1.. The periodically high concentrations of both NH 4 and NO 3 indicate the potential for high atmospheric inputs of pollutants which probably originate from the heavily polluted Kathmandu valley nearby. In addition, gaseous loss of N from terraced agriculture is likely to subsequently contribute significantly to the N load in deposition.

steep exponential decay in fertiliser availability is an inappropriate representation. Consequently, the duration of fertiliser application in the model is set to twice the length of the crop growth period, thus excluding the exponential decay in availability. This leads to a slight under-representation of application rates during crop growth and a small additional input was incorporated to both khet and bari.

4.3. Fertiliser inputs

4.4. Air temperature

Fertiliser N inputs ŽTable 2. are calculated for each catchment from data collected as part of a questionnaire survey which provided information for a given crop and season from approximately 20 individual farms per catchment. Urea and ammonium sulphate are the main forms of mineral N fertiliser used in the watershed and, as a consequence, the predominant fertiliser input to the model is in the form of NH 4 . Fertiliser is applied in April or May to the pre-monsoon rice crop and in August to the monsoon crop. In addition, applications are made to the maize and millet crops in May and August, respectively. Significant secondary dressings are also applied to both types of terraces. This pattern of application is not matched within INCA which assumes a single application, evenly available over the first half of the growing season, followed by an exponential decay in availability for the remaining half. The repeated fertiliser applications to each crop throughout the growing period suggest that the

All rate coefficients within INCA are temperature dependent and soil temperature is estimated from a seasonal relationship with air temperature. In this application, the seasonality is partially suppressed since mean daily air temperature typically only varies from 198C to 288C between May and October. Daily time series air temperature was obtained from an automatic weather station located within the Bore catchment and utilised in each simulation. Values for the Bore and Chinnya simulation were modified to incorporate the slightly lower temperatures at higher altitude in the Chinnya. 4.5. Obser¨ ed data Observed mean daily flow data for the Bore was derived from stage recorded continuously on a half-hourly basis, dilution gauging was undertaken throughout a range of flows to determine the stage discharge relationship. Instrument mal-

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

function prevented the derivation of an unbroken flow record for the Chinnya and an acceptable flow discharge relationship was not determined for the Jogi, consequently simulated flow cannot be fully validated against observed data for these two streams. Observed stream NO 3-N and NH 4-N concentrations were derived from a weekly programme of spot sampling. The samples were transported to a laboratory in Kathmandu within 24 h of collection and were therefore potentially subject to some biological and chemical alteration. NH 4-N and NO 3-N concentrations were determined spectrophotometrically, the former using distillation and a Nessler method, the latter using a brucine]sulphate method.

5. Calibration procedure Process parameters were chosen that generated sensible fluxes, generally comparable to fieldderived values in the literature, for each of the catchment N processes. In addition, the relative differences in these processes between land-use types was incorporated. For example, the saturated nature of the khet soils is likely to suppress nitrification relative to the more freely drained forest soils. The process parameters were then perturbed to obtain the best fit, by eye, between observed and simulated stream nutrient concentrations.

6. Results 6.1. Bore A simulation was undertaken to the Bore catchment for the period 5 May]31 October 1992, encompassing most of the pre-monsoon and all of the monsoon seasons. Two reaches were modelled: the Chinnya, draining the forested headwaters; and the Bore, draining more cultivated land downstream. Flow simulation for the Bore ŽFig. 4. is relatively successful in reproducing observed data although an overestimation is evident during the pre-monsoon period. This may reflect the diversion of streamflow to rice terraces

265

and large evapotranspiration losses from the ponded terrace surfaces. Alternatively, despite the incorporation of a large soil moisture store in the hydrological pre program, SMD may remain underestimated during May and June. Simulated flow for the Chinnya cannot be fully validated but is broadly consistent with short periods of observed flow data. The drier soils of the premonsoon period are reflected in the estimation of SMD ŽFig. 2.. By 10 July ŽDay 67. the deficit is satisfied and the soil remains at saturation until October when the monsoon rains have ceased. This pattern differs slightly from that of the Jogi where the predominance of terraced agriculture maintains soil saturation for the duration of the simulation. These patterns of simulated SMD broadly reflect observed soil moisture time series data ŽGardner and Jenkins, 1995; Wu, 1998.. Mean observed stream nutrient concentrations ŽTable 1. are generally low, reflecting significant plant uptake of N and loss through gaseous mechanisms. Mean NH 4-N concentrations are significantly higher than NO 3-N, reflecting the use of ammonium-based fertilisers and a higher atmospheric input, although the mean NH 4-N concentration in the Bore is slightly lower than that of the Chinnya, despite the fertiliser usage. This anomaly is not apparent within the longer-term dataset ŽCollins and Jenkins, 1996.. No strong relationship exists between NH 4-N or NO 3-N with flow. This reflects the daily time step which reduces peak flows, and the collection of samples which were not generally undertaken during periods of high flow. The simulated concentrations ŽFigs. 3 and 4. broadly reproduce the low observed values in both reaches, although discrepancies are evident. No marked trend in nutrient concentrations is apparent for the duration of the simulation. Initial simulations were unable to reproduce the relatively high NH 4 -N concentrations observed during August ŽDays 90]135. in both reaches. The occurrence of high NH 4-N concentrations in the reach draining the forested Chinnya indicates that the source is likely to be atmospheric rather than input from fertilisers. This inference is supported by cross reference to Fig. 1 which indicates high atmospheric input of NH 4

266

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

Fig. 2. HER, SMD, air and soil temperature daily time series, Bore.

during the late monsoon period of 1992. An elevated input of atmospheric NH 4 to INCA would lead to increased concentrations throughout the

whole simulation and, as a consequence, the additional NH 4 required was incorporated as a 40-kghay1 fertiliser input to forestry and released over

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

267

Fig. 3. Simulated flow, observed and simulated stream N concentrations, Chinnya.

a 25-day period. Although this is conceptually incorrect, predicted stream NH 4 concentrations are improved during this period. However, the

additional NH 4 available leads to a very large but transient rate of nitrification and NH 4 uptake under forested land ŽFigs. 5 and 6.. The additio-

268

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

Fig. 4. Observed and simulated flow and stream N concentrations, Bore.

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

269

Fig. 5. Catchment N processes, daily flux time series, Bore. Fig. 6. Catchment N processes, daily flux time series, Bore.

nal NH 4-N requirement represents a significant input highlighting the difficulty caused by such highly variable atmospheric concentrations. The optimum process rate coefficients ŽTable 2. and daily process fluxes ŽFigs. 5 and 6. reflect the well-documented influence of land use upon

N transformations. The saturated and anaerobic nature of catchment soils under the monsoon rainfall regime inhibit nitrification and accentuate denitrification ŽBouldin, 1986., particularly under the terraced land where soils remain slightly wet-

270

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

ter than those under forested or grazed land. Denitrification and nitrification rates in the model therefore reflect the influence of soil water and are optimised to enable simulation of the low observed stream NO 3 concentrations. The daily flux time series ŽFig. 6. indicates that denitrification is limited by soil moisture availability during the early pre-monsoon until the SMD is satisfied in early July. Loss of N from rice terraces through ammonia volatilisation may be as much as 56% of fertiliser-applied N ŽWatanabe et al., 1987., and although this gaseous mechanism is not directly included within INCA, the process is represented within this simulation by incorporating high NH 4 immobilisation rates under khet and bari. This flux therefore represents the sum of immobilisation and volatilisation processes. The differences in mineralisation rates between land-use types account for the occurrence of more rapid mineralisation in flooded than non-flooded soils ŽPatrick and Wyatt, 1964.. Although the presence of soil moisture enhances mineralisation rates within INCA, the model does not incorporate more complex variation such as the enhancement of mineralisation under alternate wetting and drying of soils ŽReddy and Patrick, 1975., a process that may be apparent during the pre-monsoon. In addition, the role of soil C and N in determining the immobilisation and mineralisation of N ŽSahrawat, 1983. is not incorporated. The daily nutrient uptake flux ŽFig. 5. reflects the greater availability of and plant affinity for, NH 4 . Rate coefficients are set highest under the cultivated land and the process under khet includes algal uptake of nutrients. The fixation of N by algae within the rice terrace floodwater may provide a significant N source ŽMikkelsen, 1987. and therefore a relatively large fixation rate coefficient is set for khet. However, algal growth during the crop cycle may utilise N, although the store is returned if, once the rice crop is harvested, the algae is reapplied as a mulch. Nutrient leaching is highest from cultivated land as a result of fertiliser inputs. The network of quick flow-paths associated with terraced agriculture and represented in the model by short residence times, are the dominant contributors to stream nutrient

concentrations. The optimised in-stream denitrification rate is high Ž4.0 dayy1 . and the nitrification rate relatively low Ž0.01 dayy1 . to account for the continued loss of N not only within the main channel but within irrigation canals and ditches. 6.2. Jogi Model application to the Jogi catchment was undertaken for the period 1 May]3 October 1993 using a single-reach structure. Observed stream nutrient concentrations are significantly higher in the Jogi relative to the Bore and Chinnya reaches ŽTable 1., reflecting the application of fertiliser to an extensively cultivated catchment. Simulated nutrient concentrations reflect the low observed values but significant discrepancies are apparent ŽFig. 7.. Higher NO 3-N and NH 4-N concentrations are observed during August ŽDays 80]105. and probably reflect fertiliser application to the monsoon rice crop or enhanced deposition input, or both. The model is unable to simulate the higher NH 4-N concentrations. Although the simulated flow cannot be validated, the process fluxes for the Jogi ŽFigs. 8 and 9. are generally comparable, for each land-use type, to those used in the Bore application, providing some confidence in model performance.

7. Validation Assessment and partial validation of the model can be undertaken by comparison of the simulated catchment N fluxes with those reported in the literature. Summation of each daily process flux enables derivation of a seasonal flux and, consequently, estimation of N budgets. It is pertinent to note, however, that neither simulation encompasses the final weeks of growth of the monsoon rice crop Žharvested in Novemberr December. and all simulated fluxes will reflect this. Total N uptake on khet Žpredominantly as NH 4-N. is approximately 80 kg hay1 and 76 kg hay1 for the duration of each simulation in the Jogi, and Bore and Chinnya, respectively. Khet N uptake in the Likhu Khola watershed has been calculated previously from grain yield information

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

271

Fig. 7. Simulated flow, observed and simulated stream N concentrations, Jogi.

collected as part of the farm questionnaire survey. These estimates range from 50 to 175 kg hay1 over the duration of the both crops ŽGardner and Jenkins, 1995., encompassing the values simulated by the model. In addition, Cassman et al. Ž1993. report N uptake of 25]200 kg hay1 from irrigated rice crops in the Philippines. Simulated mineralisation of NH 4 under terraced agri-

culture ranges between 63 and 78 kg hay1 for each simulation duration. These values are difficult to validate, however, given the paucity of field-derived values in the literature. Assuming the simulation of mineralisation is broadly accurate, immobilisation of NH 4 under khet is estimated to be 48% and 41% of total N input through deposition, fertilisers and mineralisation

272

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

Fig. 8. Catchment N processes, daily flux time series, Jogi.

Fig. 9. Catchment N processes, daily flux time series, Jogi.

in the Bore and Chinnya, and Jogi, respectively. This flux represents the processes of both immobilisation and NH 3 volatilisation and therefore precludes accurate validation of each individual process. However, the simulated combined losses appear to be broadly consistent with Mikkelsen Ž1987., who reports that 20]80% of added fertiliser N can be immobilised, and Fillery and Vlek

Ž1986., who indicate that 11]53% of applied N can be lost through volatilisation.

8. Scenario analysis Future increases in population and per capita energy consumption throughout Asia are likely to

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

result in increased emissions of anthropogenic nitrogen. For example, Galloway Ž1995. projects a doubling of nitrogen deposition from fossil fuel sources alone. Furthermore, the growth in population is predicted to intensify the use of fertilisers in order to meet the demand for higher crop yields, potentially increasing the N load to catchments. To assess the impact of future changes in deposition and fertiliser usage, three scenarios were undertaken using the Bore and Chinnya catchment. Simulated stream nutrient concentrations under each scenario were compared with present day simulated Žbaseline . values. The scenarios were: Ži. a doubling of total N input from atmospheric deposition, including both wet and dry inputs; Žii. a doubling of the fertiliser NH 4-N and NO 3-N applied to both khet and bari; and Žiii. a ‘worse case’ combination of scenarios Ži. and Žii.. The scenarios were all undertaken using the same process rate coefficients, HER, SMD, air temperature and additional input of NH 4-N in August used in the baseline simulation. These scenarios do not, however, account for a cumulative increase in N load over a number of years. Table 3 illustrates the impact of each scenario upon mean and maximum NO 3-N and NH 4-N stream concentrations in the Bore reach. Each scenario significantly increases the N load to the stream, however, even in the worst case scenario, nutrient concentrations remain relatively low. These results emphasise the important role of N loss through gaseous mechanisms within these catchments.

9. Discussion Stream nutrient concentrations in the Middle Hills of Nepal reflect a highly complex catchment

273

N system. Spatial and temporal variations in land management including the utilisation of irrigation water and method, timing and rate of fertiliser application, are difficult to incorporate within a modelling framework. However, the integrated, semi-distributed approach adopted within INCA does enable representation of some of the variation in N sources and catchment processes, enabling simulation of the low observed stream N concentrations in catchments with contrasting land use. Process fluxes typically lie within broad ranges reported from other studies and there is consistency in the magnitude of each flux between catchments. The determination of wet deposition load proved a key limitation of the model in this application. Daily wet deposition load within INCA is determined from total wet deposition over the course of a simulation using the proportion of daily rainfall to total rainfall. This methodology is not able to represent the extremely large temporal variation in rainfall N concentrations within the watershed, which may be strongly influenced by gaseous loss of N from terraces. Modification of the model to incorporate this variation would therefore provide a better representation of the N input dynamics and potentially, improve the simulation of stream concentrations. In addition, the incorporation of multiple fertiliser applications would provide a more accurate representation of land management practices however, lack of field data precludes the derivation of patterns of fertiliser availability during the growing season. These patterns are likely to be complex and masked by the leaching of fertiliser during large storm events. The investigation of scenarios is a key utility of INCA and has enabled the impact of future changes in deposition and agricultural intensifi-

Table 3 The impact of deposition and land-use scenarios upon stream N concentrations in the Bore reach Žconcentrations are in mg ly1 .

Baseline Scenario Ži. Scenario Žii. Scenario Žiii.

Mean NO3 -N

Mean NH4 -N

Max. NO3 -N

Max. NH4-N

0.01 0.02 0.16 0.21

0.17 0.18 0.4 0.43

0.04 0.06 0.38 0.51

0.38 0.42 0.76 0.86

274

R. Collins et al. r The Science of the Total En¨ ironment 228 (1999) 259]274

cation upon stream N concentrations to be assessed. A doubling of atmospheric and fertiliser N input significantly increases predicted stream N concentrations. Concentrations, however, are predicted to remain relatively low and such levels are unlikely to lead to future problems of eutrophication. Gaseous loss of N from the terrace system is likely to be the most significant factor in maintaining low stream N concentrations. If true, this implies a current economic loss to farmers and suggests that methods and timing of fertiliser applications can be improved.

Acknowledgements This work was funded in part by the UK Overseas Development Administration and was supported in the field by Land Rover. Staff of the Central Soil Science Division, NARC, HMG Nepal contributed to fieldwork and chemical analysis was undertaken by the staff of the Groundwater Project, Department of Hydrology and Meteorology, HMG Nepal. References Bouldin DR. The chemistry and biology of flooded soils in relation to the nitrogen economy in rice fields. Fertilizer Res 1986;9:1]14. Cassman KG, Kropff MJ, Gaunt J, Peng S. Nitrogen use efficiency of rice reconsidered: what are the key constraints? In: Barrow NJ, editor. Plant nutrition } from genetic engineering to field practice. Kluwer Academic Publishers, 1993:471]474. Collins RP, Jenkins A. The impact of agricultural land use on stream chemistry in the Middle Hills of the Himalayas, Nepal. J Hydrol 1996;185:71]86. Collins RP, Neal C. The hydrochemical impacts of terraced agriculture, Nepal. Sci Total Environ 1998;212:233]243. Fillery IRP, Vlek PLG. Reappraisal of the significance of ammonia volatilisation as an N loss mechanism in flooded

rice fields. In: De Datta SK, Patrick WH, editors. Nitrogen economy of flooded rice soils. Dordrecht, The Netherlands: Martinus Nijhoff Publishers, 1986. Galloway JN. Acid deposition: perspectives in time and space. Water Air Soil Pollut 1995;85:15]24. Gardner R, Jenkins A. Land use, soil conservation and water resource management in the Nepal middle hills. Commissioned report for ODA, 1995. HMG Nepal, Ministry of Forests and Soil Conservation. Master plan for forestry sector. HMG Nepal, Kathmandu, 1988. Mikkelsen DS. Nitrogen budgets in flooded soils used for rice production. Plant Soil 1987;100:71]97. Nijampurkar VN, Sarin MM, Rao DK. Chemical composition of snow and ice from Chhota Shigri glacier, Central Himalaya. J Hydrol 1993;151:19]34. Patrick WH, Wyatt R. Soil nitrogen loss as a result of alternate submergence and drying. Soil Sci Soc Am Proc 1964;28:647]653. Reddy KR, Patrick WH. Effect of alternate aerobic and anaerobic conditions on a redox potential, organic matter decomposition and nitrogen loss in a flooded soil. Soil Biol Biochem 1975;7:87]94. Sahrawat KL. Mineralization of soil organic nitrogen under waterlogged conditions in relation to other properties of tropical rice soils. Aust J Soil Res 1983;21:133]138. Venn MW, Day B. Computer aided procedure for time series analysis and identification of noisy processes ŽCAPTAIN. } User manual, Institute of Hydrology, Wallingford, Report No. 39, 1977. Watanabe I, De Datta SK, Roger PA. Nitrogen cycling in wetland rice soils. In: Wilson JR, editor. Advances in nitrogen cycling in agricultural ecosystems, Proceedings of the International Nitrogen Cycling Symposium. Wallingford: CAB International, 1987:239]256. Whitehead PG, Wilson EJ, Butterfield D. A semi-distributed integrated nitrogen model for multiple source assessment in catchments ŽINCA.: Part I } model structure and process equations. Sci Total Environ 1998a;210r 211:547]558. Whitehead PG, Wilson EJ, Butterfield D, Seed K. A semidistributed integrated flow and nitrogen model for multiple source assessment in catchments ŽINCA.: Part II } application to large river basins in South Wales and Eastern England. Sci Total Environ 1998b;210r211:559]583. Wu K. Measurement of soil moisture changes in spatially heterogeneous weathered soils using a capacitance probe. Hyd Proc 1998;12Ž1.:135]146.