Modelling and multicriteria analysis of water saving scenarios for an irrigation district in the upper Yellow River Basin

agricultural water management 94 (2007) 93–108

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Modelling and multicriteria analysis of water saving scenarios for an irrigation district in the upper Yellow River Basin J.M. Gonc¸alves a, L.S. Pereira a,*, S.X. Fang b, B. Dong c a

Agricultural Engineering Research Center, Institute of Agronomy, Technical University of Lisbon, Portugal Ningxia Water Resources Bureau, Yinchuan, Ningxia, China c Department of Irrigation and Drainage, College of Water Resources and Hydropower, University of Wuhan, China b

article info

abstract

Article history:

Water saving in irrigation is a main issue in the Yellow River basin. This paper refers to a

Received 3 May 2007

field and modelling study performed in the Huinong Irrigation District, a very large surface

Accepted 22 August 2007

irrigation system in Ningxia, upper Yellow River basin, intended to assess water saving and

Published on line 5 November 2007

improved water use issues. The decision support system SEDAM was purposefully developed to evaluate alternative scenarios of improvements of farm and off-farm irrigation

Keywords:

canal systems. It includes a demand and delivery simulation tool and adopts multicriteria

Irrigation delivery scheduling

analysis. Simulation is performed at various scales, starting at the distributor and then

Canal distribution systems

successively at the sub-branch, branch and sector scales. It uses a database built from

Demand simulation

random generation of system characteristics at these scales, and based on field surveys.

DSS modelling

Demand is built from exploring interactively the irrigation scheduling simulation model

Multicriteria analysis

ISAREG and the surface irrigation models SRFR and SIRMOD, which were previously para-

Yellow River

meterized. The first is used to generate improved irrigation schedules and the second to define improved basin irrigation scenarios. In addition, a simple paddy irrigation tool is used to simulate replacing the current deep flooding method by shallow water irrigation. Water delivery scenarios are built to match those of demand including several improved procedures that aim at controlling runoff and seepage. Results indicate that progressively adopting farm and delivery system improvements leads to reduced canal seepage and runoff, which is essential to an effective functioning of the drainage system, in addition to control diversions into the Huinong canal. Water savings amount to more than 50% of actual water use. However, results referring to the economic criteria, particularly to the farm gross margin, reveal that more stringent improvements have low impacts, i.e. the respective utilities increase little when scenarios require higher investments. The described application shows that adopting a DSS simulation model and multicriteria analysis is appropriate to assess water use improvements in large irrigation systems and that it is advantageous to perform the analysis of related impacts by combining economic and environmental criteria. The importance of adopting improved delivery systems is also evidenced. # 2007 Elsevier B.V. All rights reserved.

* Corresponding author. Tel.: +351 213653480; fax: +351 213621575. E-mail address: [email protected] (L.S. Pereira). 0378-3774/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2007.08.011

94 1.

agricultural water management 94 (2007) 93–108

Introduction

The Yellow River, the second largest river in China, is the main source of water in the Northwest and North China. It supplies water for about 130 million people in nine Provinces. Irrigation is the main water use in the basin, accounting for 81% of the total water use (Zhu et al., 2003). Irrigation is required through all the year in the arid Northwest, and for the winter crops in North China. Irrigated areas within the basin exceed 4 million ha, and nearly 2 million ha outside the basin, in the North China Plain, also rely on Yellow River water (Cai et al., 2003). The average water yield is about 58 billion m3/year, less than 500 m3 per capita (Cai et al., 2003). This extreme water scarcity is anthropogenic and influenced by climate variation (Wang et al., 2006). The demand is continuously increasing for domestic and industrial uses, as well as for hydroelectricity. In drought years, the demand largely exceeds the supply and the river dries out in its lower reaches for large periods before the monsoon rains. Forecasted scenarios on water resources allocation and use in the basin point out the need to reduce the irrigation water use (Xu et al., 2002; Yu, 2006). The Yellow River Conservancy Commission (YRCC) manages the water in the whole basin and is in charge of protection against floods in the North China plain (Zhu et al., 2003). The water allocation process results from complex negotiations between the Provinces and the YRCC, and among counties and irrigation districts in each Province. In periods when water is scarcer, priority is given to non-agricultural uses, mainly municipal and industrial uses. Water scarcity alleviation in the lower reaches is expected from the South–North transfers that will bring water from the Yangtze River to the North China Plain. However, water conservation and saving have to be implemented in response to the need for sustainable use of water and land resources in the basin area. Poor water management affects the irrigation districts in the upper basin, where waterlogging and salinity due to excess water diversions affect large irrigated areas (Fang and Chen, 2001). This contradictory condition, opposing extreme water scarcity in the middle and lower reaches with excess water diversions in the upper reaches, calls for innovative solutions in irrigation management at both the district and the farm levels, as well as for specific solutions for the upper and lower areas of the basin. With the objective of supporting further development of water conservation and saving policies and developing appropriate management tools, a research project has been developed in two case-study irrigation districts, one located in the upper basin, the Huinong Irrigation District (HID), in Ningxia Hui Autonomous Region, the other near the river delta, the Bojili Irrigation District (BID), Shandong Province (Pereira et al., 2003). Studies mainly focused on the water supply and conveyance systems, drainage and salinity problems and solutions, farm irrigation, and the delivery and distribution systems. The approach used to assess water saving and control of waterlogging and salinity through improved delivery and distribution systems included field studies and modelling adopting multicriteria analysis. The importance of considering the joint effects of delivery schedule and operation performance is well identified by Sanaee-Jahromi et al.

(2001) and Clemmens (2006) who show that without improving the delivery schedule any increase in systems efficiency may not be expected. A decision support system (DSS) model SEctor Demand And delivery Model (SEDAM) was purposefully developed to simulate the demand and delivery at the Sector level with application of multicriteria analysis to assess scenarios aimed at reducing the irrigation demand, improving delivery management and controlling related environmental impacts by considering simultaneously technical, environmental and socio-economic criteria. The multicriteria analysis is a methodology that provides ranking alternative solutions for complex problems with consideration of attributes and criteria of different and often contradictory nature, and thus is well adapted to support decision-making (Roy and Bouyssou, 1993; Pomerol and Romero, 2000). Adopting multicriteria analysis in water management allows finding satisfactory compromises among contradictory decision-making objectives (Giupponi and Rosato, 2002; Bazzani, 2003; Raju et al., 2006; Riesgo and Go´mez-Limo´n, 2006). It is convenient for assessing large scale irrigation and water management problems since it integrates different types of attributes on a trade-off analysis, allowing the comparison between environmental and economic criteria when searching for improved and sustainable irrigation and water management issues. This paper concerns the field and modelling studies aimed at assessing water management scenarios at the Huinong irrigation district level using multicriteria analysis. The DSS model developed for this application is described. A companion paper (Pereira et al., 2007) deals with the farm level studies that provided the underlying information used in the modelling approaches described herein.

2.

The study area

The Huinong Irrigation District has 74,400 ha of irrigated area and is part of the Qingtongxia Irrigation District, which covers more than 330,000 ha and includes five counties along the Yellow River (Fig. 1). This irrigated area has been developed after more than 2000 years and the Huinong Canal was reconstructed in the Qing dynasty (DRIDSC, 1991). The system was upgraded by 1949, however old gates are still used for canal regulation and control of off-takes. These gates are in poor functioning condition and cause very large operational losses. Water diversion is regulated by the Qingtongxia dam and is available for the entire crop season, from early Spring to Autumn. The intake volumes are extremely large, averaging 4460 million m3/year, i.e. about 6000 mm (Xie et al., 2003), much above crop irrigation requirements. That excessive water diversion is due to poor regulation and control in the conveyance and branch canals, which require that high water levels be maintained in the canals for the gates to work properly. As for the example in Fig. 2, relative to the diversions from the branch canal of Division 4, it is required that the discharges flowing in the canal are much higher than those to be allocated. Water not diverted into sub-mains, branch and tertiary canals flows to the drainage channels and ditches or to low lands, and seeps to the groundwater; only a part returns

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agricultural water management 94 (2007) 93–108

Fig. 1 – The Qingtongxia Irrigation District and the Huinong canal, Ningxia.

Table 1 – Irrigated areas (ha) by county in HID (2000) County

Rice

Wheat/maize

Flax

Sorghum

Beet

Other crops

Tree crops

Yongning Yinchuan Helan Pingluo Huinong

800 3067 3067 1167 0

900 3,453 3,427 23,853 18,613

60 240 240 1940 1513

0 0 0 1100 867

0 0 0 2920 2120

33 120 133 980 887

180 147 180 1953 440

1,973 7,027 7,047 33,913 24,440

Total

8101

50,246

3933

1967

5040

2153

2900

74,400

directly to the Yellow River. This produces the malfunctioning of the drainage system and causes extensive waterlogging and salinity problems (Hollanders et al., 2005), which are common to other irrigation districts in the region (Fang and Chen, 2001). Climate is arid, with an average rainfall of 190 mm, ranging from 100 to 290 mm. The temperature in the hottest month, July, averages 23.5 8C; during January it averages 7 8C. The cropping systems are basically irrigated wheat and maize, generally intercropped, and paddy rice (Table 1); these upland crops are often in rotation with rice. Wheat is planted by mid

Fig. 2 – Inflow rates required and corresponding allocated rates due to poor functioning of the regulation and control of HID main canal (recorded data for Division 4; source: Xie et al., 2003).

Total

March and maize by mid April depending on soil temperatures; wheat is harvested by early July and maize by mid September. Basin irrigation is used. Winter irrigation is generally applied in October and November, before soil freezes, and the crop season irrigation starts by April and ends early September. An analysis of the farm irrigation systems is given in a companion paper (Pereira et al., 2007). Soils are silty alluviums originated by sediments transported by the Yellow River from the upstream loess areas. The respective hydraulic properties are summarized in the companion paper (Pereira et al., 2007). Soils are naturally non-saline but induced salinity is observed in large areas where water management is poor. A remote sensing survey (Vincent, 2003) identified a total of 96,000 ha affected by salinity in the whole Qingtongxia area, from which 39,000 ha are not cropped; about 1/3 of this area is within the HID. Salinity occurs in low elevation areas where the drainage system is not functioning. However, their recovery is feasible if water management at the district level is improved and appropriate leaching is applied. Groundwater depths (GWDs) are currently monitored by the HID staff. They vary in time and space in relation to irrigation practice and drainage conditions. GWD are very small in paddy areas, in lowlands near the river and in depressions. GWD are larger during winter and rise when water starts to be diverted for irrigation by early Spring; the water table remains high until September, when it lowers until the winter irrigation raises it again for a short period. Studies

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agricultural water management 94 (2007) 93–108

Fig. 3 – Schematic representation of the Huinong canal, respective sub-mains and management Divisions.

on the groundwater dynamics and drainage have shown that the GWD may be controlled at a minimum depth of 1.0 m if less water is diverted into the canal system and delivery and distribution of irrigation water are improved to control runoff out of the canal system and seepage from canals (Wang et al., 2004; Hollanders et al., 2005). In fact, the drainage system could perform quite well with slight improvements and better maintenance if the huge amounts of excess water were not applied. Considering the above-described conditions, two GWD scenarios were considered for irrigation simulations (Pereira et al., 2007):  Present, where the GWD rises to near the surface after water diversions for irrigation start by April, and remain shallow during the crop season.  Target, corresponding to the foreseen groundwater controlled conditions, where the GWD is limited to 1 m, allowing a 0.9 m root depth for the upland crops. The Huinong Canal is 171 km long and the upstream discharge is 110 m3/s. HID operation, management and maintenance are performed through eight divisions (Fig. 3) under the direction of the Ningxia Water Resources Bureau. The downstream divisions are served by sub-mains: Guansi, Changrun, and Pang, whose discharges are respectively 5, 10, and 10 m3/s. Each division is divided into two sectors, one on each side of the canal, and consists of a distinct network of branch, sub-branch and distributor canals.

3.

Demand and delivery modelling

3.1.

General aspects

The irrigation water reaches the fields through a hierarchical network of main canals, secondary and tertiary canals (branches and sub-branches) and quaternary canals (distributors). Each distributor supplies a unit and is under the direct control of the farmers. The water demand at the distributors scale is built from that of the farmlands they serve and is aggregated to assess the irrigation demand at higher levels of the irrigation system after accounting for delivery losses. Thus, the SEDAM model uses an up-scaling approach for modelling the demand and delivery, starting at the downstream units served by the distributors, and aggregating the demand to the sub-branch and branch canals and, finally, to the sector. The approach generally follows that of model CADSM (Walker et al., 1995). The demand at the unit is

generated from using interactively the irrigation-scheduling model ISAREG (Liu et al., 1998) and the surface irrigation simulation models SIRMOD and SRFR (Walker, 1998; Strelkoff, 1993), as well as data relative to the paddies (Mao et al., 2004). Results of SEDAM simulations constitute input data for a supply system DSS model (Roost et al., 2003). Field evaluations and modelling were used for assessing the present situation on farm irrigation performance of upland crops, wheat and maize, and generating alternatives for surface irrigation improvement (Pereira et al., 2007). Field research was adopted to assess present and improved paddy rice irrigation (Mao et al., 2004) and to characterize the distribution systems (Dong et al., 2003). The socio-economic criteria and attributes used for the multicriteria analysis were built upon field socio-economic studies (Xu and Tian, 2003). To validate the demand and delivery simulation for the present scenario, data collected relative to the supply systems were used (Xie et al., 2003).

3.2.

Demand and delivery simulation scales

The irrigation system has the following components (Fig. 4): (a) the farm irrigated fields, (b) the unit irrigated area, which is the area supplied by a distributor comprising a variable number of fields, (c) the sub-sector, which is the area served by a branch and several sub-branch canals that supply a variable number of distributors, (d) the sector, grouping the areas served by several branch canals, located either on the left or the right side of the main canal, and (e) the division, which is the

Fig. 4 – Schematic representation of a distribution subsector network.

agricultural water management 94 (2007) 93–108

97

Table 2 – Data characterizing the irrigation systems at various scales and respective source Scale

Data characteristics

Data source

County or Division

   

Groundwater depths Crop distribution Crop–water–salinity–yield function Irrigation scheduling

& & & &

Sector

   

Branch inflow rate Irrigated area by sub-sector Branch canal length Branch design inflow rate

& Typical observed field data

Sub-sector

     

Sub-branches number and length Tertiary length Discharges Soil type Salinity type Type and number of units

& Randomly assigned based on records and surveys

Unit

 Crop  Parcel topography  Soil roughness and infiltration

command area located between two major hydraulic structures in the main canal where discharges are measured. The divisions have independent management and are generally divided into two sectors, on the left and right sides of the main canal. Because fields are too small, averaging 0.1 ha, the first level for demand estimation and simulation is the unit, with an area ranging from 2 to 6 ha. This requires the assumption that all fields in the same unit have the same crop, irrigation schedules, soil water holding capacity and infiltration, groundwater depth and salinity conditions. Data used for simulation at various scales are summarized in Table 2. As described above, the demand at the unit scale is built from results of the irrigation scheduling simulation model ISAREG and the surface irrigation simulation models SIRMOD and SRFR. The first generates the irrigation dates, depths, and impacts on yields, and the others produce the time duration of the irrigation and the respective performance indicators. A simplified procedure using the paddy fields experimental data (Mao et al., 2004) is developed to simulate the demand for the units cropped with rice. The demand by each unit – discharge rate, duration and timing of deliveries – is then aggregated with those of other units supplied by the same sub-branch canal and then to the area served by the corresponding branch canal, so producing the sub-sector demand (Fig. 5). Finally, the generated sub-sector demands are aggregated at the sector and the division scale.

3.3.

Generation of system characteristics

The available data describing the irrigation system is rather scarce when considering the size of the irrigation district under analysis (74,400 ha irrigated and about 750,000 fields). Data on soils were obtained from a survey (Pereira et al., 2007). Information on crops refers to their percentage distribution at county level. The percent distribution of groundwater depths and salinity has been estimated at the division scale. The characteristics of each sector –command area, and number, length and maximal discharges of the branch canals – are

Observations and target (future) Observed and randomly assigned Estimated Calculated

& Randomly assigned based on field observations and surveys

known, but only typical data could be available for the number, length, size, discharges and area served by the subbranches. Similarly, only typical data can be used to characterize the number, length, size and discharge of distributors, so the size and field characteristics of the units. The SEDAM model therefore adopts a random procedure to generate the data relative to sub-branches, distributors and units that are required for the demand and delivery simulation. The actual distribution (% of area) of soil types (water holding capacity), soil infiltration, groundwater depths and salinity in each sector are used to randomly assign these data to the different branches in a sector. The actual distribution (%) of crops and respective irrigation methods – basin irrigation for upland crops and flooding irrigation for paddy rice – are randomly assigned to the sub-branches. The characteristics of the fields in the units are set by random generation using typical data on field lengths, widths and slopes. The ratio between actual average sector inflow discharges and the design discharges of the branch canal of the sector are used to estimate the available branch canals discharges. The typical ratio discharge-area served is used to generate the actual discharges at the sub-branch canals. The typical area–length ratios are adopted to generate data on subbranches. A control is used to verify if the sum of areas and discharges generated are close to the actual ones. When this is not verified, the corresponding random generation procedures are repeated until the predicted and actual values are similar (within 5%). The assumptions made do not allow assigning results of simulations to a specific sub-branch or branch canal but to assess the performance of the distribution system at the sector scale. However, the SEDAM model could be tested by comparing the actual 10-day supply to the sectors and divisions with the corresponding model results when the simulation was performed for the present conditions. The 10-day cycle is adopted because it is the time scale used to allocate water to the sectors. After testing, the model is able to assess the impacts of alternative improvements in the farm systems (e.g. irrigation schedules, inflow rates, land levelling) and in canal system

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agricultural water management 94 (2007) 93–108

Fig. 5 – Schema and components of SEDAM model.

management. Results from simulations may help to establish new delivery schedules but not to produce real time management rules for the distribution system.

3.4.

Demand and delivery simulation at the unit scale

The simulation of the demand at the unit scale requires various steps (Fig. 6):  The inflow rates into the distributors served by any subbranch canal are randomly generated taking into consideration the area irrigated and the probability of occurrence of discharges. The inflow rates into the distributors (QT, l/s) are adjusted considering the typical data on the number of distributors operating simultaneously.  The seepage volumes are estimated from an efficiency ratio for the distributors (EfT), which is estimated as a function of the tertiary length LT (m) and a seepage factor SF (l/s/m) referred to the unit length of the tertiary canals (Li et al., 2003). The net unit inflow rate is then Q 0T ¼ Ef T Q T ðl=sÞ.  The inflow discharge at the field level qF (l/s) is randomly generated from the observed probabilities of occurrence of field inflow discharges, which allows estimation of the number of fields irrigated simultaneously, NF ¼ INTðQ 0T =qF Þ. Therefore, computations at the field level are performed using the adjusted discharge q0F ¼ Q 0T =NF .  The ISAREG model computes an irrigation schedule for each combination climate–soil–groundwater depth–salinity–

crop, i.e. the irrigation timings and depths (D, mm). These data are used with SIRMOD or SRFR models for each combination soil infiltration–groundwater depth–field length–field width–inflow rate to determine the time duration of the irrigation, ti. These models run for typical data sets and produce a database. SEDAM reads the database and interpolates the data according to the field characteristics considered.  The same field simulation models create the field irrigation attributes that are also stored in the database. SEDAM reads it, interpolates the values and then creates the unit irrigation attributes. These attributes include the demand hydrographs, percolation, runoff return flows, labour requirements and crop yield losses, which are further used to evaluate the scenarios for improvement as described hereafter.

3.5.

Demand and delivery simulation at the sector level

At present, the farmers served by any distributor order water from the branch canal manager, who then asks a given inflow rate and daily time allocation to the Division Authority. The latter decides the allocation of a certain inflow rate during a full day for each canal branch aiming at minimizing the number of days in each 10-day period when any branch canal is supplied. When knowing that water will be delivered to the branch canal, the farmers plan the water distribution adjusting the

agricultural water management 94 (2007) 93–108

Fig. 6 – Schema of the demand and delivery simulation at the unit scale.

inflow rates to distributors and fields, and the application times. However, irrigation is practiced during the daytime period only; thus, the water flowing through canals and distributors during the night-time period results in runoff into the drainage ditches. Farmers that are not able to irrigate during that delivery period have the opportunity to irrigate in a later day and the non-used water adds to runoff. Night-time runoff and non-used delivery contribute to the poor performance of the drainage system and waterlogging in HID. The water demand in each sub-sector is computed with a daily time step. The model first computes the number of days of water distribution considering some flexibility in the deliveries, and takes into consideration the practices referred above. In addition, other procedures relative to actual field practices are adopted to simulate the present delivery conditions. When simulating improved scenarios, the current delivery procedures are modified. Changes made usually involve the order of units’ irrigation, daytime duration of irrigation, branch inflow rates, and frequency of cases when non-used delivered water is added to runoff. In addition, the model allows the user to select among three water delivery alternatives: (a) giving priority to rice irrigation, (b) adopting a random units’ selection procedure, or (c) adopting a fixed rotation delivery (Gonc¸alves et al., 2003).

4.

Multicriteria analysis

4.1.

General

99

When each design or improvement alternative is characterized by a set of attributes, the selection of the best alternatives becomes a multiple objective problem which solution may be found through multicriteria analysis (Janssen, 1992). Diverse applications to irrigation are provided in the literature referring to performance assessment, irrigation planning or water demand and delivery (Raju and Pillai, 1999; Raju and Duckstein, 2003; Rao et al., 2004; Oad et al., 2006). The multicriteria analysis application to irrigation problems can be integrated in a DSS framework and can be used together with simulation tools. This is the case of applications for the design of farm irrigation systems where solutions are aimed at satisfying requirements of technical, economic and environmental nature (Gonc¸alves et al., 1998), and applications to find improved solutions for water supply and distribution in large irrigation districts (Roost et al., 2003; Roost and Musy, 2004). The multicriteria analysis also allows an up-scaling process to aggregate the attributes from the farm to district or catchments level (Bazzani, 2003) and is therefore useful when planning water deliveries. The multicriteria methodology integrates different types of attributes on a trade-off analysis, allowing the comparison between environmental and economic criteria, particularly when weights assigned to each criterion may be selected by the user or decision-maker as adopted in this study. This methodology favours a better understanding of the impacts relative to the technologies or management alternatives considered in each scenario, and allows a satisfactory compromise between contradictory decision-maker objectives. To make all decision criteria commensurable, the attributes are scaled to a measure of utility or value, which expresses the decision-maker preferences. Logistic and linear type value functions are applied for the environmental and economic criteria. To aggregate the values and ranking the design alternatives, the outranking ELECTRE III or the weighted summation methods are applied (Roy and Bouyssou, 1993). To express the decision-maker priorities, relative importance coefficients (weights) are assigned to the criteria. The relative importance coefficients of criteria may be directly evaluated or calculated by the AHP method (Saaty, 1990). In this application, the weights may be selected by the user according to the priority to be given to the adopted attributes. To explore the model, a database has to be prepared for the simulations (see Sections 3.4 and 3.5) and relative to the decision process referred hereafter. Then the user can analyze the simulation results and, after the appreciation and a first selection of alternatives, one may return to the simulation phase and search for other solutions, namely building a different set of scenarios through an interface system-user. The DSS allows learning by doing, which increases the perception of the problem by the decision-maker.

4.2.

Decision variables

The scenarios for water savings and improved crop conditions are developed in agreement with the decision-making process

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agricultural water management 94 (2007) 93–108

Table 3 – Decision-making process for improved irrigation and water saving Decision-making scales Farm system Objectives

& & & & &

Decision variables

(A) Field inflow (B) Field irrigation scheduling (C) Field levelling (D) Field rice intensity (I) Salinity control

Constraints

& & & &

a

Minimizing cost Maximizing yield Maximizing benefits Minimizing salinization Maximizing water savings

Water cost Land cultivated area Land taxes Agronomic field practices

Delivery system & & & &

Minimizing cost Maximizing yield & benefits Minimizing impact on drainage system Maximizing social benefits (employmenta and farmers income)

(E) Frequency of non-used delivered water (F) Delivery branch lining (G) Delivery schedule (H) Delivery night runoff (I) Salinity control & Canal system network & Maximum inlet discharge

Objective considered at level of township influence area.

summarized in Table 3. This process considers two levels, the farm and the delivery system; thus, decision-maker objectives, decision variables relative to considered improvements, and constraints are different. The decision variables are summarized in Table 4. They refer to design and management parameters used in demand and delivery simulation of farm and delivery systems and deal with three levels of improvement for both systems.

4.3.

Improvement and simulation scenarios

The improvement scenarios are built by combining the variables defined in Table 5 in different ways. The present condition corresponds to the combination where all decision variables are at level 1. The simulation scenarios are built by assuming that improved levels would be implemented progressively, i.e. only in part of the area in each sector and division (Table 6).

Table 4 – Decision variables to build up alternative improvement scenarios Decision variables

Level of change

A

Field inflow rate

A1 = present (0.5–3 l/s/m) A2 = optimal Q = f(L, S0, Inf, n)

B

Field irrigation scheduling

B1 = present B2 = improved, with leaching at every irrigation B3 = improved, with leaching at the winter irrigation only

C

Field land levelling

C1 = present C2 = S0  0.1% C3 = zero levelled

D

Field rice intensity

D1 = present crop pattern D2 = rice area reduced by 50% D3 = rice replaced by other crops

E

Delivery volume non-used by the farmer

E1 = present E2 = reduced by 50%

F

Delivery branch lining

F1 = present, unlined canals F2 = lined branch canals

G

Delivery schedule

G1 = random G2 = rotation among sub-branches

H

Delivery night runoff

H1 = supply 24 h/day H2 = supply 21 h/day H3 = supply 18 h/day

I

Soil salinity

I1 = present I2 = improved to allow cropping I3 = improved to reduce salinity impacts

Symbols: Q, unit inflow discharge per unit width of the basin; L, basin length; S0, basin slope; Inf, soil infiltration rate; n, hydraulic roughness of the basin.

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agricultural water management 94 (2007) 93–108

Table 5 – Improvement scenarios Scenarios

Decision variables and respective level of improvement

Scenario strategy

A

B

C

D

E

F

G

H

I

I

1

2

1

1

2

1

2

1

1

II

2

2

2

1

2

1

2

2

1

III

2

2

3

2

2

1

2

3

2

IV

2

3

3

3

2

2

2

3

3

Limited but easy to implement improvements at farm and delivery systems Improvements focusing the farm system but limited regarding delivery management More stringent improvements at the farm and off-farm Highest level of improvements

Table 6 – Simulation scenarios considering a progressive implementation of the improvement scenarios Simulation scenarios

1 2 3 4 5 6 7 8

Percentage area of the Sector/Division where the improvement scenarios are implemented I

II

III

IV

100 50 20 0 0 0 0 0

0 40 40 50 30 20 10 0

0 10 30 40 50 50 50 50

0 0 10 10 20 30 40 50

However, the effective application of the improvement scenarios implies changes in the supply system: mainly a reduction of total diversions into the Huinong canal and the improvement of the regulation, control and off-take structures. These changes should provide for more adequate water levels in the canals and for better control of the diversion discharges (Roost et al., 2003), and ultimately to favour the functioning and performance of the drainage system, thereby achieving the target groundwater depth as defined earlier in this paper. In other words, without improvements in diversions into the supply system and in drainage functioning, the foreseen improvements are not realistic.

4.4.

Multicriteria analysis

The evaluation of the results of the simulations for the eight scenarios defined in Table 6 is performed with the help of several indicators (Appendix A). The multicriteria analysis is performed by considering three groups of criteria (Table 7): the

Time for implementation (years)

1 2 3 4 5 6 7,8 9,10

expected benefits to the farmers, the foreseen costs for the farmers and the Irrigation District, and the environmental benefits due to water savings. The utility functions relative to the criteria in Table 7 are: (1) Benefits criteria (j = 1): U1 = aMX1 (2) Cost criteria (j = 2–4): Uj = 1  aMXj (3) Environmental criteria (j = 5–7): Uj = 1  aWXj where aM = 1.0  104 (Uj = 0 , Cost Xj = 1  104 Yuan/ha, j = 1–4) aW = 3.33  105 (Uj = 0 , water volume Xj = 3  104 m3/ha, j = 5–7) Adopting user-defined weights ((j) for every criteria j, a global utility value is computed with the weighted summation method: U¼

5 X l jU j j¼1

Table 7 – Criteria, attributes and weights used in the multicriteria analysis Criteria

Attributes

Units

Weights (%)

Benefits

(1) Farm gross margin

Yuan/ha

12.5

Costs

(2) Farm total water cost (3) Delivery cost (4) Drainage cost

Yuan/ha Yuan/ha Yuan/ha

12.5 12.5 12.5

Environmental

(5) Water use (6) Farm water seepage and runoff (7) Delivery water seepage and runoff

m3/ha m3/ha m3/ha

16.7 16.7 16.7

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agricultural water management 94 (2007) 93–108

Fig. 8 – Comparing the 10-day recorded supply (——) with the simulated delivery (—*—), farm water use (—^–) and farm consumptive use (—~—) in the whole HID.

Fig. 7 – Comparing the 10-day recorded supply (——) with the simulated delivery (—*—), farm water use (–^–) and farm consumptive use (—~—) for Divisions 2 and 4, (a) and (b), respectively.

simulated delivery and the farm aggregated demand represent runoff and seepage in the canal systems, while the differences between the farm demand and the farm consumption correspond mainly to field percolation and represent more than 50% of the supplied volumes (Fig. 7). These results are compatible with farm results as described by Mao et al. (2004) for rice, and Pereira et al. (2007) for wheat and maize. The operational losses, which represent more than 50% of the volumes supplied to the branch canals, are shown in Figs. 9 and 10, respectively for Divisions 2 and 4, and the whole HID.

In this application, the scenarios are ranked according to the global utility values and weights are selected in such a way that no priority is assigned to any of the criteria, i.e. establishing a balance between the economic and environmental criteria (Table 7).

5. Assessment of present irrigation demand and delivery situation The analysis of present demand and delivery conditions focuses on Divisions 2 and 4 and the whole HID for 1994, whose data records are complete. Results from the application of the SEDAM model to both Divisions and the whole HID are shown in Figs. 7 and 8. Comparing the 10-day recorded supply with the simulated delivery, results show that the model simulations approximate reasonably well the recorded data for both Divisions. Because upstream diversions into the Huinong canal are decided through a complex negotiation process involving the YRCC, various Province water management agencies and the Qingtongxia Irrigation District that cannot be considered in modelling, consequently discrepancies occur in the predicted and actual timing of peak demand. However, the total volumes recorded and simulated are similar. Results in Figs. 7 and 8 show that the recorded water supply is much higher than the aggregated farm demand, i.e., supply largely exceeds the water volumes required to perform the irrigation as it is currently practiced. Differences between the

Fig. 9 – Simulated seepage (—*—) and runoff (—&—) from branch and sub-branch canals, compared with percolation ) and runoff (—^—) at farm level, and distributors ( seepage (—*—), Divisions 2 and 4.

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Fig. 10 – Simulated present seepage (—*—) and runoff (–^-) from the branch and sub-branch canals and distributors seepage (—~—) compared with the recorded supplied (——) in the whole HID.

Operational losses in the branch and sub-branch canals far exceed those in distributors and farm systems, and runoff is larger then seepage. Simulated canal seepage and runoff are of the same order of magnitude. High seepage is due to the fact that canals are not lined and are often cleaned (due to the large amount of sediments carried with the Yellow River water that deposit each time canals operate), and to high water levels adopted for canal operation because the gate structures require high water levels in the canals. Studies on groundwater and drainage (Wang et al., 2004; Hollanders et al., 2005) show a

Fig. 12 – Simulated branch and sub-branch canal seepage (—~—) and runoff (—&—), distributors’ seepage (—*—), and farm percolation (—*—) and runoff ( ) for the simulation scenario 4, Divisions 2 and 4, (a) and (b), respectively.

nearly steady state flow from the irrigation canals to the drainage ones. Runoff volumes are very high because branch and sub-branch canals generally operate near their maximum levels for 24 h but water is used only during daytime, thus nighttime discharges flow directly into the drainage system, as well as excess water during daytime. Therefore, the drainage system is generally full, with a water level close to that of the surrounding land and thus not functioning. As analyzed above, main causes for the water wastes shown in Figs. 9 and 10 relate to poor management, largely due to poor regulation and control of the canal system. At the farm level, deep percolation is the main operational loss, from both upland crop basins and paddies. As analysed by Pereira et al. (2007), high percolation is caused by the inadequacy of the adopted irrigation scheduling due to very high water table and to poor land levelling. Farm runoff is mainly produced from the paddy rice basins because basin dykes are too small and water levels are excessive (Mao et al., 2004).

6. Improved irrigation demand and delivery scenarios Fig. 11 – The 10-day simulated delivery (—*—), farm water use (–^-) and farm consumptive use (—~—) relative to the simulation scenario 4 compared with the present recorded supply (——) for Divisions 2 and 4, (a) and (b), respectively.

6.1.

Water use

The 10-day recorded supply volumes for Divisions 2 and 4 are compared in Fig. 11 with the results of the simulation scenario

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Fig. 13 – Comparing the 10-day simulated delivery (—*—), farm water use (–^-) and farm consumptive use (—~—) in the whole HID relative to the simulation scenarios 1 and 8, respectively (0a) and (b).

4 (defined in Table 6) relative to the delivery, farm water use and farm consumptive use. For this scenario, the foreseen aggregated delivery is reduced to less than 50% of the present supply volumes but farm consumptive use increases. The reduction in delivered volumes is mainly due to the reduction in branch seepage and runoff and in farm percolation. The increase in consumptive use relates to higher evapotranspiration due to improved cropping and yield conditions, mainly due to improved water table depths as analysed by Mao et al. (2004) and Pereira et al. (2007). Results for both divisions are different due to the impact of rice irrigation in Division 2. Since the irrigation frequency of rice is much higher than that for wheat and maize, and irrigation depths are much larger for these crops, demand peaks are lower in Division 2 where the area percentage of paddies is greater than in Division 4. Results for scenario 4 in both Divisions show that operational losses in the canal system are reduced due to enhanced irrigation, and delivery schedules and control of upstream diversions into the Huinong canal. Main reductions of operational losses concern the farm systems, which are evidenced by the small differences between farm water use and farm consumptive use in Fig. 11. The reduction in operational losses in Divisions 2 and 4 is evident when comparing results in Fig. 12 with those for the present (Fig. 9), corresponding to a reduction of 80–90% relative to the current situation. However, operational losses are different in both divisions due to the referred differences in rice cropped area. In Division 2, canal runoff is larger than

Fig. 14 – The utility values for the current scenario (0) and the improved scenarios (1–8), Divisions 2 and 4, respectively (a) and (b).

seepage because canals operate more often and therefore more night-time runoff is produced; differently, in Division 4 canals seepage equals runoff. Field runoff is also higher for Division 2 due to rice irrigation. Fig. 13 shows a comparison of total delivery, farm water use and farm consumptive use for scenarios 1 and 8 for the whole HID. For both scenarios, the foreseen delivery is reduced relative to the present, by more than 50% for scenario 8, while farm consumptive use increases (cf. Fig. 10). The decreased delivered volumes for scenario 8 result from reducing upstream diversions into the branch canals, and from

Fig. 15 – The utility values for the current scenario (0) and improved scenarios (1–8) relative to the whole HID.

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Fig. 16 – Foreseen time evolution of the total water use (—*—), delivery canals seepage and runoff (—~—), and farm percolation and runoff ( ) for Divisions 2 and 4, respectively (a) and (b).

controlling canal seepage and runoff if the canal system is improved and an enhanced delivery schedule is adopted. Percolation and runoff at the farm are also reduced for scenario 8 due to improved irrigation systems when fields are land levelled and irrigation scheduling is enhanced. The slight increase in consumptive use relates to higher crop evapotranspiration due to improved cropping conditions produced by better controlled waterlogging and salinity.

6.2.

Utility values and water saving

Results from the multicriteria analysis relative to the utility values for the eight scenarios (Table 6) are given in Fig. 14. The utility value for the drainage cost does not change from scenarios 1–8 because related investments are considered at the first stage of improvement activities. The utility value relative to farm water costs is also almost invariable because water costs are low and the respective impacts on the production costs are small. Differently, the utility relative to delivery costs changes denotes the related increased costs when system improvements are considered. The environmental benefits utilities show evident improvements from scenarios 0–8, particularly concerning the control of seepage and runoff, and water saving. The farm benefits grow steadily but are small for the most improved scenario. This very low rate of increase reflects the structure of production costs and benefits in a peasant farming society. The global utility also increases, but with a relatively small rate from scenario 3–8. These results show that more costly improvements representing more stringent technological solutions may not be appropriate in a peasant’s society. These results call for further analysis, mainly by changing either the weights given to the criteria and attributes, or the decision variables when building the scenarios. Results relative to the utility values aggregated to the whole HID are given in Fig. 15 and behave similarly to those obtained for the Divisions 2 and 4. Summarizing, the utility value relative to farm water costs is almost invariable due to reduced impacts of water costs in the production costs, while that relative to delivery costs denotes the increased costs when management of the branch and distributor systems is improved. The utilities relative to environmental benefits – control of seepage and runoff, and water saving – show evident improvements from scenario 0–8. The farm benefits –

farm gross margin – show a low rate of increase that reflects the structure of production costs and benefits for peasant farmers. Overall, the utilities grow very little after the scenario 4, which indicates that more stringent improvements may not be of interest, or that a different formulation of scenarios and decision variables may have to be considered. Relative to water saving, which is the main objective of this study, the related impacts of the improved scenarios assuming their implementation over 10 years (Table 6) are shown in Fig. 16. It can be seen that the total water use in Division 2 could be reduced from more than 3000 mm at present to about half in 3 years, and to near 1000 mm if scenario 8 would apply, i.e. in 10 years time. That reduction is due to decreased operational losses, mainly runoff and seepage from the canal system. Results for Division 4 are similar but less drastic. For both cases, results also show that after implementing scenario 4, positive impacts on water saving and water use increase at a small rate, thus in agreement with the growth of the utilities analysed above. Percolation from fields should strongly decrease as the diversions to the Huinong canal would be diminished, and the drainage system could control the water table at the target level, thus allowing for a better functioning of the farm systems and scheduling. In fact, following this study, diversions to the Huinong canal have already been reduced by half, approximately.

7.

Conclusions

The studies described in this paper show that water saving and improved water use may be achieved in surface irrigation districts when a combined farm and system approach is adopted. Improvements at the farm are constrained by the functioning of the conveyance and distribution systems while upgrading these systems requires changes in farm irrigation scheduling and water application technologies. In addition, particularly for areas where hydrogeological conditions favours the built-up of high water-tables and high evaporation induces salinization of the soils, water saving and improved water use needs that drainage systems be also improved together with irrigation. The use of simulation and decision tools that combine field and modelling information at both the farm and the delivery

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scales show to be appropriate to assess present water use and alternative improvements as it happens with model SEDAM. Adopting multicriteria analysis allows assessment studies to be based upon economic and environmental criteria. The first refer to costs and benefits due to implementing the improved scenarios, and the second relate to water saving and control of operational losses that influence waterlogging and salinity. This approach requires that appropriate field data be collected and to conveniently characterize the improvements considered in the alternative scenarios. The model is particularly sensitive to the quality of the simulations produced by the irrigation scheduling and surface irrigation models, despite they do not need heavy calibration but good parameterization. Results show that economic benefits due to more stringent alternative solutions are limited. Utility values increase from one scenario to the next but very little for the scenarios more demanding than scenario 4 that concerns improved irrigation deliveries and scheduling but not heavy changes in farm and distribution systems. This relates to the structure of production costs and benefits in peasants farming conditions, which do not favour high economic returns from more stringent investments. Environmental benefits related to decreasing water use and controlling runoff, seepage, and percolation are

also more evident up to scenario 4. Overall, results evidence the need to adjust deliveries to the demand, and the advantages of considering improvements at the farm and the delivery scales together. Results also evidence the interrelations among improvements in the irrigation and drainage systems. The analysis provided in this paper requires further developments. On the one hand, it is aimed to evaluate the impacts from using different weights to the criteria used, as well as from altering the decision variables when formulating the scenarios. On the other hand, the numerous indicators derived from the model for each scenario may also be further explored to improve the understanding of environmental and economic processes in relation to the adoption of irrigation improvements.

Acknowledgements This study was performed as part of a research contract on ‘‘Water Saving Policies for the Yellow River Basin’’ funded by the European Union, DG XII, Program STD-INCO, and the Swiss Government.

Appendix A. Performance indicators: definition and units I1 = farm irrigation cost per unit irrigation area I2 = farm irrigation cost per unit irrigation volume I3 = yield of cereals per unit irrigation area I4 = yield of cereals per unit irrigation volume I5 = gross product (GP) per irrigated area I6 = gross product per irrigation water volume I7 = gross margin per unit irrigation area I8 = gross margin per unit irrigation water volume I9 = total water cost per unit yield I10 = total water cost per water used I11 = total water cost per gross product I12 = ratio total water cost to yield cost I13 = land levelling cost per irrigation area I14 = land levelling cost per GP I15 = fraction of runoff and percolation I16 = application efficiency I17 = farm water saving I18 = soil improvement (salinity) I19 = fraction of saline soils I20 = relative yield improvement I21 = relative GP improvement I22 = ratio delivery cost to water delivered I23 = ratio runoff and percolation to volume delivered I24 = delivery labour costs I25 = delivery lining costs I26 = total water savings I27 = total irrigation cost per irrigation area I28 = ratio irrigation cost to irrigation volume allocated I29 = global system application efficiency I30 = global water cost per total irrigation water allocated

I1 ¼ Cfw þ Cfl =A I2 ¼ Cfw þ Cfl =V ft I3 = Yc/A I4 = Yc/Vft I5 = GP/A I6 = GP/Vft I7 = GM/A I8 = GM/Vft I9 ¼ Cfwt =Y c I10 ¼ Cfwt =V ft I11 ¼ Cfw =GP  100 I12 ¼ Cfwt =Cfyc  100 I13 = Clandlev/A I14 = Clandlev/GP  100 I15 = Vfrp/Vft  100 I16 = Vfn/Vft  100 pre imp pre I17 ¼ Vft  Vft =Vft  100 I18 = Aimp  Apre/Apre  100 imp I19 ¼ Asalt =Apre  100 imp pre pre I20 ¼ Yc  Yc =Yc imp pre I21 = GP  GP /GPpre  100 I22 ¼ Cdwt =Vdt I23 = Vdrp/Vdt  100 imp pre pre I24 ¼ Cdlb  Cdlb =Cdlb  100 I25 = Cdli/A pre imp pre I26 ¼ Vdt  Vdt =Vdt  100 I27 = Cgirrig/A I28 = Cgirrig/Vdt I29 = Vfn/Vdt  100 I30 = Cgwater/Vdt

¥/ha ¥/m3 kg/ha kg/m3 ¥/ha ¥/m3 ¥/ha ¥/m3 ¥/kg ¥/m3 % % ¥/ha % % % % % % % % ¥/m3 % % ¥/ha % ¥/ha ¥/m3 % ¥/m3

Symbols: A, irrigated area; Asalt, saline soil area; Cdlb, delivery labour cost; Cdli, lining cost (annual); Cdwt, delivery water cost; Cfl, farm irrigation labour cost; Clandlev, land levelling cost; Cfyc, farm yield cost; Cfw, farm water cost; Cfwt, total farm water cost; Cgw, global irrigation cost; Cgwater, global water cost (total irrigation + drainage cost); GM, farm gross margin; GP, gross product; Vdrp, delivery runoff, percolation and seepage losses; Vdt, total water allocated; Vfn, farm net water use; Vfrp, farm runoff, percolation and seepage losses; Vft, total farm water use; Yc, yield of cereals.

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