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  • View in gallery

    (left) Location of EG and WG focus regions on the irrigation map (Siebert et al. 2005). (right) Location of Ganges (green) and Indus (yellow) river basins and annual mean APHRODITE (Yatagai et al. 2009) precipitation.

  • View in gallery

    Footprints of precipitation originating from MAM evaporation in the EG and WG regions using ECHAM. The scale shows millimeters of precipitation as well as a cumulative fraction over the domain. (Over the domain, the precipitation adds up to the evaporation in the source region, minus the moisture that leaves the domain.). (top) Natural run, (middle) irrigated run, and (bottom) difference between runs.

  • View in gallery

    As in Fig. 2, but for HIRHAM.

  • View in gallery

    As in Fig. 2, but for RAMS.

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    Footprints of precipitation originating from evaporation in the EG and WG regions for ERA-Interim. The scale shows millimeters of precipitation as well as a cumulative fraction over the domain. (Over the domain, the precipitation adds up to the evaporation in the source region, minus the moisture that leaves the domain.)

  • View in gallery

    Annual cycle of the fraction of evaporation from the EG region that recycles as precipitation within the Ganges River basin shown for: (top left) ECHAM, (top right) RAMS, and (bottom) HIRHAM.

  • View in gallery

    As in Fig. 6, but for WG.

  • View in gallery

    Annual cycle of Ganges basin atmospheric moisture budget (for ECHAM, HIRHAM5, and RAMS) for irrigated and natural runs (mean over 1990–2000).

  • View in gallery

    Total annual evaporation (mm) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

  • View in gallery

    Total annual precipitation (mm) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

  • View in gallery

    Total annual atmospheric moisture convergence (mm yr−1) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

  • View in gallery

    Wind direction (850 hPa) in the natural and irrigated runs and their difference per season; shown is the mean over ECHAM, RAMS, and HIRHAM5.

  • View in gallery

    Yearly (left) evaporation and (right) precipitation difference between the irrigated and natural runs (mean over 1990–2000 and over ECHAM, HIRHAM5, HadRM3, and RAMS) for (top) the differences in absolute evaporation and precipitation and (bottom) these differences as a percentage of the natural run.

  • View in gallery

    Precipitation trends over the entire CRU and APHRODITE datasets. Areas with significance of trend smaller than 0.95 are shaded gray.

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Effects of Irrigation in India on the Atmospheric Water Budget

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  • 1 Wageningen UR, Wageningen, Netherlands
  • | 2 MPI Meteorology, Hamburg, Germany
  • | 3 Met Office Hadley Centre, Reading, United Kingdom
  • | 4 University of Quebec, Montreal, Quebec, Canada
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Abstract

The effect of large-scale irrigation in India on the moisture budget of the atmosphere was investigated using three regional climate models and one global climate model, all of which performed an irrigated run and a natural run without irrigation. Using a common irrigation map, year-round irrigation was represented by adding water to the soil moisture to keep it at 90% of the maximum soil moisture storage capacity, regardless of water availability. For two focus regions, the seasonal cycle of irrigation matched that of the reference dataset, but irrigation application varied between the models by up to 0.8 mm day−1. Because of the irrigation, evaporation increased in all models, but precipitation decreased because of a strong decrease in atmospheric moisture convergence. A moisture tracking scheme was used to track individual evaporated moisture parcels through the atmosphere to determine where these lead to precipitation. Up to 35% of the evaporation moisture from the Ganges basin is recycling within the river basin. However, because of a decreased moisture convergence into the river basin, the total amount of precipitation in the Ganges basin decreases. Although a significant fraction of the evaporation moisture recycles within the river basin, the changes in large-scale wind patterns due to irrigation shift the precipitation from the eastern parts of India and Nepal to the northern and western parts of India and Pakistan. In these areas where precipitation increases, the relative precipitation increase is larger than the relative decrease in the areas where precipitation decreases. It is concluded 1) that the direct effects of irrigation on precipitation are small and are not uniform across the models; 2) that a fraction of up to 35% of any marginal evaporation increase (for example, due to irrigation) will recycle within the river basin; and 3) that when irrigation is applied on a large scale, the dominant effect will be a change in large-scale atmospheric flow that decreases precipitation in eastern India and increases it in western and northern India.

Corresponding author address: Obbe Tuinenburg, Wageningen UR, Droevendaalsesteeg 4, Wageningen 6700 AA, Netherlands. E-mail: otlmd@lmd.jussieu.fr

Abstract

The effect of large-scale irrigation in India on the moisture budget of the atmosphere was investigated using three regional climate models and one global climate model, all of which performed an irrigated run and a natural run without irrigation. Using a common irrigation map, year-round irrigation was represented by adding water to the soil moisture to keep it at 90% of the maximum soil moisture storage capacity, regardless of water availability. For two focus regions, the seasonal cycle of irrigation matched that of the reference dataset, but irrigation application varied between the models by up to 0.8 mm day−1. Because of the irrigation, evaporation increased in all models, but precipitation decreased because of a strong decrease in atmospheric moisture convergence. A moisture tracking scheme was used to track individual evaporated moisture parcels through the atmosphere to determine where these lead to precipitation. Up to 35% of the evaporation moisture from the Ganges basin is recycling within the river basin. However, because of a decreased moisture convergence into the river basin, the total amount of precipitation in the Ganges basin decreases. Although a significant fraction of the evaporation moisture recycles within the river basin, the changes in large-scale wind patterns due to irrigation shift the precipitation from the eastern parts of India and Nepal to the northern and western parts of India and Pakistan. In these areas where precipitation increases, the relative precipitation increase is larger than the relative decrease in the areas where precipitation decreases. It is concluded 1) that the direct effects of irrigation on precipitation are small and are not uniform across the models; 2) that a fraction of up to 35% of any marginal evaporation increase (for example, due to irrigation) will recycle within the river basin; and 3) that when irrigation is applied on a large scale, the dominant effect will be a change in large-scale atmospheric flow that decreases precipitation in eastern India and increases it in western and northern India.

Corresponding author address: Obbe Tuinenburg, Wageningen UR, Droevendaalsesteeg 4, Wageningen 6700 AA, Netherlands. E-mail: otlmd@lmd.jussieu.fr

1. Introduction

To meet the increasing demand for food from India’s growing population, agricultural intensity and consequential irrigation have increased in India during the last century. Water has been channeled from the rivers or pumped up from the ground to supply crops with irrigation water. Moreover, dams have been constructed to manage the water supply for agriculture as well as for human consumption. The purpose of the current study is to determine the atmospheric effects of this large-scale irrigation in India.

The effects of the large-scale land use changes in India on the atmosphere and especially on precipitation have been the subject of numerous studies. Generally, the increased moisture availability at the land surface is thought to result in two opposing atmospheric effects. On the one hand, the increased moisture influx into the atmosphere may increase the moist static energy of the atmosphere and, subsequently, the chances of convective precipitation. On the other hand, when the land surface wetness increases, the temperature contrast between the land and the sea, which drives the monsoon circulation, may decrease. When the monsoon flow decreases, less oceanic moisture is advected to the land and precipitation might decrease.

Koster et al. (2004) and Guo et al. (2006) conducted an experiment with global climate models to determine the role of the land surface in the climate. They located a hotspot of land–atmosphere coupling in India both for temperature and precipitation, although there was a significant spread among the models. Douville et al. (2001) simulated the Asian summer monsoon and found a precipitation shift from eastern to northern India with increasing soil moisture.

Several studies specifically included irrigation into atmospheric models. Douglas et al. (2009) found for a single precipitation event that irrigation can influence the regional climate by increasing the surface moisture flux, decreasing temperature, and changing regional circulations and precipitation patterns. Lohar and Pal (1995) used two-dimensional atmospheric simulations to relate decreased precipitation between 1973 and 1992 in West Bengal to an increased irrigation amount, arguing that a decreased sea breeze can reduce precipitation. Saeed et al. (2009) found increased precipitation in northern India due to irrigation using a regional climate model (RCM) for three years, while Niyogi et al. (2010) statistically related a decrease in precipitation in northern India to the increased irrigation amount.

Several studies suggest a decrease in monsoon flow due to a decreased land–sea contrast; Lee et al. (2009) analyzed the interannual differences in land surface greenness [normalized difference vegetation index (NDVI)] and found that the monsoon-related precipitation [June–August (JJA)] was weaker in years with more vegetation during the premonsoon season [March–May (MAM)].

Dirmeyer et al. (2009) performed an integrated analysis of soil moisture memory, evaporation, and atmospheric moisture recycling and noticed that during India’s pre- and postmonsoon periods [MAM and September–November (SON)], precipitation is most sensitive to soil moisture. Tuinenburg et al. (2011) used a single-column atmospheric model to classify the atmospheric situations during which soil moisture has an influence on precipitation triggering using a methodology developed by Findell and Eltahir (2003). The analysis of Tuinenburg et al. (2011) showed that during the monsoon onset and retreat seasons (MAM and SON), the atmospheric conditions allowed a positive influence of soil moisture on precipitation, while during the winter season [December–February (DJF)] the atmosphere is too dry and during the summer season (JJA) it is too wet for an influence of the land surface.

Tuinenburg (2013) analyzed two reanalysis datasets using several land–atmosphere indicators and found atmospheric conditions with the potential for strong land–atmosphere coupling in India during the summer half year. Based on these land–atmosphere interaction indicators, the increase in surface wetness can potentially lead to an increase in precipitation of 1 mm day−1 in northwestern India, with a potential decrease in precipitation of 0.5 mm day−1 in eastern India.

Apart from the atmospheric effects of irrigation, the hydrological effects have been studied within the European Union (EU) project Water and Global Change (WATCH; Harding et al. 2011) using large-scale hydrological models (Haddeland et al. 2011). In a global study on the effect of irrigation and dams on river discharge, Biemans et al. (2011) found the largest effects of irrigation in Asia with a discharge reduction of up to 5%, whereas the cumulative effects of dams and irrigation showed a 10% discharge reduction.

The large-scale hydrological models used by Haddeland et al. (2011) and Biemans et al. (2011) were not coupled to a GCM but were driven by meteorological forcing. Thus, feedbacks between irrigation and the atmospheric water budget could not be taken into account. However, on the river basin scale, the amount of moisture that is recycled within the Ganges basin varies from 5% during DJF to 60% during JJA and can differ between areas with different evaporation regimes, such as irrigated and nonirrigated areas (Tuinenburg et al. 2012). Therefore, the current research studies the effects of irrigation on the atmospheric water budget using four climate models forced with natural land surface conditions and with irrigated land surface conditions. We pose the following research questions:

  • What is the atmospheric response to irrigation in India?
  • Is additional precipitation triggered at the irrigation location?
  • How much moisture is exported from the river basin?
  • Are large-scale moisture flow patterns affected?
This paper is structured as follows. Section 2 describes the models, data, and approach of the study. Section 3 presents the results, with subsections on the local effects in two focus regions, the fate of the evaporation from the focus regions, the Ganges River basin moisture budget, and the large-scale effects of irrigation. Section 4 presents the discussion and conclusions.

2. Methods

In the current study, the atmospheric effects of irrigation in India are compared using four atmospheric models [HIRHAM, Hadley Centre Regional Climate Model (HadRM), Regional Atmospheric Modeling System (RAMS), and ECHAM] with explicit irrigation application. As a basis for irrigation, the global map of irrigated areas (Siebert et al. 2005) is used in each. With each atmospheric model, two runs are done: one with natural conditions and one with irrigation. Both runs were performed for at least the period 1990–2000. In the natural run, the land surface moisture is allowed to evolve freely, whereas in the irrigated run, the soil moisture in the top soil layer is year-round not allowed to fall below 90% of the maximum soil moisture storage capacity in irrigated areas. Although the focus of this paper is on the Ganges basin, irrigation was applied everywhere in the model domain where irrigation is present. This approach ensures that irrigation is treated the same way in all land surface schemes. Moreover, the amount of (model) irrigation needed to keep the model at 90% of the maximum soil moisture may vary seasonally, depending on atmospheric evaporation demand and precipitation regime.

The results of the atmospheric model simulations are compared in three ways: 1) from a local to subregional perspective, the effects of irrigation on local variables [evaporation, (local) precipitation, etc.] are compared; 2) from a nonlocal perspective, the evaporation from irrigated areas is tracked through the atmosphere and the downwind “precipitation footprint” of irrigation is determined; and 3) from a regional scale, the effects on large-scale circulation and monsoon flow are assessed. From the first perspective, the atmospheric effects of irrigation relevant to the local water resources are determined, while the second perspective focuses on the effects on water resources on the river basin scale. The last perspective focuses on the large-scale changes in evaporation and precipitation. The moisture tracking model used in the second perspective requires three-dimensional input from the atmospheric models. These data needed for the moisture tracking were not archived for HadRM3; the moisture tracking is therefore only executed for the other models.

This section will discuss the atmospheric models (section 2a), the moisture tracking scheme (section 2b), the study areas and variables compared (section 2c), and the datasets used (section 2d).

a. Climate models

The four climate models used in this study are summarized in Table 1 and described below. Three of these models (HIRHAM, HadRM3, and RAMS) are regional climate models, with a horizontal model resolution of about 50 km and a model domain size of around 4000 km in both east–west and north–south directions. At the edge of the model domain, the state of the regional climate models is forced with atmospheric reanalysis data. In contrast, ECHAM is a global climate model, which has a horizontal model resolution of about 200 km and a global model domain. Because of this global domain, the model does not need to be forced with boundary conditions, and circulation patterns can evolve freely.

Table 1.

Summary of model characteristics.

Table 1.

There are some implications of these differences in model resolution and domain size for the current study. Because of their higher spatial resolution, the regional climate models are expected to represent the orography better. This is important in and around India with the Himalaya Mountains in the north and the Ghats mountain range in the southwest. Because of this better representation of orography, precipitation is better modeled in regional climate models than in global climate models (Kumar et al. 2013). Furthermore, the shoreline will be better represented in the higher resolution and, as a consequence, sea breeze circulations will be better modeled.

Because the regional climate models are forced at the boundary with reanalysis data, the large-scale flow patterns resemble these reanalysis data. Thus, the regional climate models have a large-scale flow that is closer to the observations (the forcing data) than that of the global climate model (which is not forced with atmospheric data). However, because the regional climate models are forced with the same data in both simulations, the effect of irrigation on the large-scale flow may be counteracted by the boundary forcing of the regional climate models.

1) HIRHAM5 (DMI)

The first regional climate model used in this study is HIRHAM5 (Christensen et al. 2006), which is a hydrostatic RCM developed at the Danish Meteorological Institute (DMI) and is forced at its boundaries by the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim). It is based on the High Resolution Limited Area Model, version 7 (HIRLAM7), dynamics (Eerola 2006) and the ECHAM5 physics (Roeckner et al. 2003) using the Tiedtke (1989) mass flux convection scheme, with modification after Nordeng (1994), and the Sundqvist (1978) microphysics. The land surface scheme is unmodified from that used in ECHAM5 Roeckner et al. (2003), which employs the rainfall–runoff scheme described in the work of Dumenil and Todini (1992). Grid boxes exhibit uniform vegetation with prescribed leaf area index (LAI) dynamics. These LAI dynamics influence surface albedo, surface roughness, and the interception reservoir size (the capacity of the vegetation/leaf surface to intercept precipitation). When the irrigated fractional area of a grid cell is above 20%, irrigation is applied. There is no seasonal variability in irrigation. More details on HIRHAM5 as used here are available in Lucas-Picher et al. (2011).

2) HadRM3 (Hadley Centre)

HadRM3 is a regional version of the global Met Office Hadley Centre Atmosphere Model, version 3 (HadAM3) (Pope et al. 2000) coupled to the Met Office Surface Exchange Scheme, version II (MOSES II), land surface scheme (Essery et al. 2003), which explicitly represents subgrid heterogeneity. Boundary conditions (including SSTs) were provided by a flux-adjusted global Hadley Centre Coupled Model, version 3 (HadCM3), simulation. The irrigation implementation consists of an additional (to five other vegetation type tiles) irrigated C3 grass surface tile. For this tile, the soil moisture stress factor is at the critical soil moisture point, so evaporation is unconstrained by soil moisture. Any additional water demand from the unstressed irrigated tile compared with the nonirrigated tile is the irrigation demand. Irrigation is simulated year-round as demanded by soil moisture. Unfortunately, not all output needed to force the moisture tracking model (section 2b) was available for the HadRM3 run, so this specific analysis was not done for this model.

3) RAMS (WUR)

RAMS [version 6.1; Wageningen University and Research (WUR)] is forced by the ERA-Interim data from the ECMWF every 6 h, with a relaxation time at the five edge grid cells (around 200 km) of 5400 s. Monthly SSTs have been extracted from the Hadley Centre Sea Ice and Sea Surface Temperature, version 1, dataset (HadISST1; Rayner et al. 2003). The land use classes have been extracted from the U.S. Geological Survey (USGS) database (Loveland et al. 2000) with a resolution of around 1 km. Irrigation is implemented using the existing irrigated crop tile in the land surface scheme. If the soil moisture drops below 90% of field capacity, moisture is added to the top soil layer for this tile every time step.

4) ECHAM/JSBACH (MPI)

In contrast to the other models used in this study, the Max Planck Institute for Meteorology’s (MPI) ECHAM5 (Roeckner et al. 2003) is a coarse-scale global circulation model. It was applied for a climate like that of the time period 1978–99 (because it was forced with mean climatological ocean data) at a horizontal resolution of T63 (about 1.9°) with 31 vertical layers and a 10-min time step. The first two years are used for spinup and disregarded in the analysis. ECHAM5 was interactively coupled to the land surface scheme Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg (JSBACH; Raddatz et al. 2007), while the ocean was substituted by a fixed SST and sea ice climatology. The parameterization of the land surface is based on the Land Surface Parameters, version 2 (LSP2), dataset (Hagemann 2002). Among the data are albedo, surface roughness length, and soil water holding capacities as well as climatologies describing the seasonal variation of vegetation characteristics. It is compiled from a global distribution of major ecosystem types provided by the USGS and remapped to climate model resolutions. JSBACH uses a tile approach to represent different land cover types within one grid cell. For this study, a dedicated irrigated crop tile with a distinct water balance was implemented into the model, with a fractional size according to Siebert et al. (2005).

b. Moisture tracking scheme

To determine the location where the added irrigation moisture ends up after being evaporated and diverted out of the area, an atmospheric moisture tracking scheme is used. The moisture tracking scheme is based on the quasi-isentropic back-trajectory scheme by Dirmeyer and Brubaker (2007) and is the same as used in Tuinenburg et al. (2012). As in Tuinenburg et al. (2012), it is run in a forward mode, so it determines trajectories from evaporation to precipitation, instead of vice versa.

The scheme uses the output of any of the atmospheric models to calculate trajectories of evaporated moisture through the atmosphere. For this, 3D fields of wind speeds (u, υ, w) and specific humidity q, as well as surface fields of evaporation and precipitation are needed. For each time step and grid cell, evaporated moisture (parcel) from that location is tracked. This is done 10 times to get a statistical sample of the moisture trajectories that captures the variability. Both the starting location within the grid cell and the starting height of the parcel are determined randomly, but the starting height is weighted by the specific humidity profile. From the starting position, the parcel is tracked by interpolating the wind speeds in space and time to the current location and time of the parcel. Discrete time steps of about 5 min are used to determine the next position of the parcel.

At the start of the trajectory, the fraction of moisture evaporated from the source location equals the evaporated water divided by the total precipitable water. However, at each subsequent position of the parcel, there is an amount of (surface) evaporation entering the parcel as well as an amount of precipitation leaving the parcel. These two terms are the only terms affecting the moisture in the parcel. The evaporation entering the parcel reduces the fraction of tracked moisture in the parcel, so as the parcel moves farther away from its starting location, the amount of original water decreases. At each location, the precipitation out of the parcel that is allocated to the evaporation in the source area is the product of the precipitation and the fraction of original water present in the parcel. This process maps the evaporation to precipitation in locations downwind.

The moisture tracking scheme is applied to all models (except HadRM3), for the entire domain, with parcels released every six hours. For more details about the scheme, see Tuinenburg et al. (2012).

c. Irrigation, regions, and variables

The global map of irrigated areas (Siebert et al. 2005) shows heavily irrigated areas in India (see Fig. 1). This study will compare the effects of irrigation on atmospheric variables with the focus on two regions: the eastern Ganges (EG) and western Ganges (WG) regions (outlined in Fig. 1). For these regions, the model runs will be compared in terms of irrigation gift (amount of water needed to keep the soil moisture at 90% of the maximum), evaporation, 2-m temperature, precipitable water, precipitation, and moisture convergence.

Fig. 1.
Fig. 1.

(left) Location of EG and WG focus regions on the irrigation map (Siebert et al. 2005). (right) Location of Ganges (green) and Indus (yellow) river basins and annual mean APHRODITE (Yatagai et al. 2009) precipitation.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

d. Data

The global map of irrigated areas [GMIA; Siebert et al. (2005)] is used as common irrigation map by the models. The Monthly Irrigated and Rainfed Crop Areas (MIRCA2000) dataset (Portmann et al. 2010), which is based on the GMIA, describes the seasonal cycle in the irrigation amount for the situation around the year 2000. Based on the MIRCA2000 data, Siebert and Döll (2010) calculated green and blue water consumption per month using the Global Crop Water Model (GCWM). This is included as a quasi-observational reference.

Furthermore, ERA-Interim is used to boundary forcing for RAMS and HIRHAM, but also to compare the surface evaporation and precipitation, as well as atmospheric budgets. Comparing the model output to the same data source as the forcing data may not be very insightful, as there is a clear dependence. However, no other observations exist for these variables. Moreover, for these variables, it is interesting to assess the difference between the irrigated and nonirrigated runs. Surface temperatures are compared with the Climatic Research Unit (CRU), version 2.10, dataset (Mitchell and Jones 2005). Precipitation is also compared to the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, version 10 (APHRODITE) dataset (Yatagai et al. 2009).

3. Results

This section presents the annual mean results over the 10 years of the simulation.

a. Irrigation gift

The amount of irrigation that is applied in the irrigated run is not prescribed. Instead, the model soil is kept wet (at least at 90% of the maximum field capacity; if the soil moisture falls below this level, moisture is added to the soil to keep it at the 90% level) and the evaporation is determined by atmospheric demand. The irrigation gift is defined as the total moisture that is added to the soil to keep it at the 90% level of maximum field capacity. Tables 2 and 3 show the atmospheric and surface variables from the irrigated and natural run for the east Ganges and west Ganges region per season, as well as some reference values from different datasets.

Table 2.

Spatial mean results [irrigation gift, evaporation E, mean 2-m temperature Tmean, precipitation P, moisture convergence (MC), and precipitable water (PW)] for the EG focus region, per season. Units are mm day−1, except for Tmean (K) and PW (mm). Reference values from datasets are given for irrigation, evaporation, temperature, and precipitation. ERA stands for ERA-Interim and APH stands for APHRODITE.

Table 2.
Table 3.

As in Table 2, but for the WG focus region.

Table 3.

The first data column shows the irrigation gift, which has quite a range between the models. The last four rows show the irrigation amounts according to the GCWM dataset (Siebert and Döll 2010). This dataset cannot be used for comparison to the irrigation gifts in the models, because the GCWM data present actual irrigation estimates constrained by water availability, whereas the model gifts reflect maximum irrigation given the atmospheric demand. Therefore, they are only included as a reference.

For the EG region, the model gifts all are much lower than the reference irrigation, except for HadRM3, which has similar gifts during MAM and DJF, but underestimation during JJA and SON. This smaller irrigation gift compared to the GCWM data is probably due to the very moist atmosphere during the summer monsoon months (June–September), which reduces evaporative demand. This causes the model irrigation gift to be reduced, whereas this is not the case in the dataset.

For the WG region, the model gifts are closer to the GCWM data. There are some differences between the models. HIRHAM5 has almost no annual cycle while the others show a distinct seasonality. ECHAM and RAMS demand higher irrigation gifts, while HadRM3 and HIRHAM5 require lower gifts.

b. Local effects of irrigation

In the current study, the local effects of the applied irrigation are defined as the effects within the east Ganges and west Ganges focus regions. In the next two sections, the effects on model variables will be discussed for these two regions.

1) Eastern Ganges region

The effect of irrigation in the east Ganges region (first column in Table 2) on the model variables is shown in the other columns in Table 2. The most uniform effects of irrigation across the models are found for the surface variables. The additional moisture on the land surface provides cooling and reduces the mean surface temperature. All models show a decreased temperature, which is largest during MAM (1.6–1.8-K decrease). During the other seasons, the models differ more in terms of temperature decrease. Throughout all experiments conducted for this study, the irrigation always decreased the surface temperature, with the sole exception of ECHAM5/JSBACH during JJA.

Another consequence of the increased soil moisture is a larger surface evaporation. However, this effect is not reproduced as uniformly by the models as the temperature response. During MAM, the increased evaporation is largest at up to 1 mm day−1. During the monsoon season (JJA), there are differences across the models. While most models computed an increase (between 0.2 and 0.7 mm day−1) in evaporation, HadRM3 showed a 0.5-mm decrease in daily evaporation due to increased cloud cover and a decrease in incoming shortwave radiation. After the monsoon season (SON), the difference in evaporation is reduced to a small positive modulation of about 0.2 mm day−1, with the exception of ECHAM, where this modulation becomes slightly negative.

This general increase in evaporation does not necessarily lead to higher precipitation rates within the east Ganges region. Most of the time, precipitation is lower in the irrigated model runs than in the natural model runs. In most cases, the strengthened evaporative influx from the land surface is compensated by a lower atmospheric moisture convergence. The amount of precipitable water generally increases slightly, but too little to compensate the decreased precipitation efficiency (precipitation divided by the precipitable water). This precipitation efficiency decrease can be caused by different local atmospheric conditions (changed surface energy budget, CAPE, cloud cover, etc.).

2) Western Ganges region

In the drier western Ganges region (Table 3), the differences between the natural and irrigated runs are more pronounced than in the eastern Ganges region. The irrigation gift leads to a reduction of MAM surface temperatures that varies across the models from 1 to 4 K. During JJA, SON, and DJF, the effect on temperature is less pronounced and varies between 0.1 and 2 K. Similar to the eastern Ganges region, irrigation always decreases the surface temperature.

Compared to the eastern Ganges region, the evaporation response is much less uniform across the models and seasons. During MAM, all models show an increased evaporation ranging from 0.2 to 2 mm day−1. However, during JJA, HIRHAM5 and ECHAM both increase evaporation by more than 2 mm day−1, while RAMS and HadRM3 decrease evaporation by about 1 mm day−1. During the postmonsoon season of SON, HadRM3 continues to show a small negative evaporation anomaly while all other models compute an enhanced evaporation. During the winter season, all models show a small increase in evaporation.

Generally, precipitation increases in the irrigated run compared to the natural run. In MAM, all models show an increase of about 0.1–0.3 mm day−1, but the total amounts of precipitation vary distinctly. During JJA, HIRHAM5 shows an increase in precipitation (0.2 mm day−1), while the other models have slight decreases. During SON and DJF, all models show a small increase of 0–0.2 mm day−1.

c. Downwind footprints of evaporation

The moisture convergence into the eastern and western Ganges region decreases almost similarly in all models and seasons (see Tables 2 and 3), meaning that more moisture is exported from the area via the atmosphere. In this section, it will be investigated at which locations the exported moisture contributes to precipitation. The moisture tracking model described in section 3b is used to trace the evaporated moisture from the EG and WG regions through the atmosphere and determine where it leaves the atmosphere as precipitation. Figures 24 show the MAM footprints of the natural run (top) and irrigated run (middle) and their difference (bottom), while Fig. 5 shows the MAM footprint in ERA-Interim. The scales show the amount of evaporation from the focus region (eastern Ganges on the right, western Ganges on the left) that precipitates at that location. The (global) areal sum of the figures is equal to the amount of evaporation in the focus region (except for the moisture that leaves the domain or is still present in the atmosphere after 30 days of tracing; this is usually less than 10% of the evaporation). The scales also show the cumulative evaporation (percentage) from the focus regions that correspond to that particular color.

Fig. 2.
Fig. 2.

Footprints of precipitation originating from MAM evaporation in the EG and WG regions using ECHAM. The scale shows millimeters of precipitation as well as a cumulative fraction over the domain. (Over the domain, the precipitation adds up to the evaporation in the source region, minus the moisture that leaves the domain.). (top) Natural run, (middle) irrigated run, and (bottom) difference between runs.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for HIRHAM.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

Fig. 4.
Fig. 4.

As in Fig. 2, but for RAMS.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

1) Eastern Ganges region

Under nonirrigated conditions during the monsoon onset (MAM), evaporation from the EG region is transported in a northeasterly direction, toward the Himalayas. All models agree on this direction, but the exact location of the precipitation area differs slightly. ECHAM transports the majority of moisture into China, which is consistent with the moisture transport in the ERA-Interim (Fig. 5). HIRHAM5 and RAMS transport the moisture a bit more toward the east, while RAMS also transports a part of the moisture toward the south.

Fig. 5.
Fig. 5.

Footprints of precipitation originating from evaporation in the EG and WG regions for ERA-Interim. The scale shows millimeters of precipitation as well as a cumulative fraction over the domain. (Over the domain, the precipitation adds up to the evaporation in the source region, minus the moisture that leaves the domain.)

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

Figures 24 (bottom) show the differences between the footprints of the irrigated and natural run. The areas’ sums of these are equal (except for the moisture that is transported out of the domain) to the difference in evaporation in the focus regions between the irrigated and natural run. These plots reveal that, for the EG region, the additional evaporation from the irrigated run is transported into the far eastern provinces of India, Tibet, and China. Again, HIRHAM5 and RAMS transport the moisture a bit farther east than ECHAM. Most models also show a small decrease in the evaporative footprint scattered in the areas northeast and southwest of the EG region. This effect is strongest for HIRHAM5 at the eastern boundary of the plot.

2) Western Ganges region

The evaporation from the WG region is transported eastward in all models, although there is less agreement over the exact footprints compared to the moisture transport in the EG region. ECHAM transports the moisture strictly to the east, while the others also show a small footprint to the west of the WG region, which corresponds to the ERA-Interim footprint (Fig. 5). HIRHAM5 shows two precipitation regions: one close to the WG region in a band following the orography and the other in far eastern India. ECHAM transports the evaporation to a more continuous band following the orography. RAMS shows two branches: one into Tibet, toward the east, and one into India, toward the southeast.

For the WG region, the difference between the MAM evaporation in the irrigated and naturalized runs varies across the models. ECHAM transports some of the additional moisture toward India, but the majority of the models direct the moisture north of the Himalayas into Tibet. HIRHAM5 transports the additional moisture toward far eastern India and Tibet, but a significant part remains close to the WG area and is transported into Nepal, northern India, and Pakistan. The difference in WG region evaporation footprints between the two runs in RAMS is small compared to the other models.

3) Recycling within the Ganges basin

The majority of irrigated areas in India are located on the Ganges plain (see Fig. 1). Concerning the water budget of the Ganges basin, it is important to assess whether or not the evaporated moisture is transported out of the basin to get an estimate of whether the basins loses water on the long term. Figures 6 and 7 show the percentage of the evaporation from the EG and WG regions, respectively, that precipitates out within the Ganges basin. For MAM, this is equal to the part of the footprints in Figs. 25 that falls within the Ganges basin.

Fig. 6.
Fig. 6.

Annual cycle of the fraction of evaporation from the EG region that recycles as precipitation within the Ganges River basin shown for: (top left) ECHAM, (top right) RAMS, and (bottom) HIRHAM.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for WG.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

For the EG region (Fig. 6), ECHAM and RAMS produce an annual cycle that is similar to ERA-Interim, while HIRHAM5 overestimates the fraction of moisture that precipitates in the Ganges basin during the second half of the year. Generally, the fraction increases during the start of the year and peaks at about 35% during the summer monsoon period, with a decrease after the monsoon season to about 5%. The difference in the fraction between the irrigated and natural runs is not substantial, except in ECHAM and RAMS during April, May, and June, where the fraction that precipitates in the Ganges basin is about 5% higher in the irrigated run than in the nonirrigated run.

For the WG region (Fig. 7), the fractions are much lower than for the EG region. The ERA-Interim fraction shows an annual cycle that is near zero during the winter monsoon (DJF) and increases to about 6% during MAM, with a peak of around 9%–10% during July. After this peak, the fraction drops off again to about 5% during SON.

Compared to ERA-Interim, ECHAM and RAMS underestimated the fraction from May to August. During this period, the recycling fraction does not exceed 5%. HIRHAM5 does not deviate much from the fraction that is found for the ERA-Interim dataset. Again, the differences between irrigated and nonirrigated runs are not very large.

d. Ganges basin moisture budget

The previous sections discussed the effects from the two focus areas and showed that up to 40% of the evaporation precipitates in the Ganges region for both the irrigated and the natural run (for the east Ganges region). Figure 8 shows the annual cycle of the atmospheric moisture budget for the entire Ganges basin for ECHAM, HIRHAM5, and RAMS.

Fig. 8.
Fig. 8.

Annual cycle of Ganges basin atmospheric moisture budget (for ECHAM, HIRHAM5, and RAMS) for irrigated and natural runs (mean over 1990–2000).

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

All models show higher evaporation rates in the irrigated run than in the natural run from February to October. The amount of the evaporation that recycles within the Ganges basin is about equal in the irrigated and natural runs, with the exception of the 2–3 months before the monsoon onset (April, May, and June), when the recycled evaporation is only marginally larger for the irrigated run.

Although the total Ganges basin precipitation of the irrigated and natural runs are quite similar, the natural runs have a higher basin precipitation in the month before the monsoon season as well as during the monsoon season for HIRHAM5 and ECHAM. In RAMS, there is no distinct difference in basin precipitation. So, despite the fact that the total basin evaporation is larger in the irrigated run during large parts of the year and that moisture recycling rates are quite high, the total basin precipitation does not increase or sometimes decreases slightly. The increased amount of recycled evaporation cannot compensate the decreased moisture convergence into the Ganges basin.

e. Regional evaporation, precipitation, and wind patterns

As discussed in the previous section, the additional evaporation in the Ganges basin in the irrigated simulation does lead to a reduced Ganges basin precipitation. Figure 9 shows the annual mean evaporation for the entire simulated domain for HIRHAM5, ECHAM, HadRM3, and RAMS for the natural and irrigated runs and their differences. The largest difference in evaporation is found in northern India, in the Indus basin along the India–Pakistan border, where the evaporation can be enhanced by up to 3 mm day−1. Other areas with a distinctively higher evaporation due to irrigation are the Ganges basin and the southern coastal areas, although the magnitude of the evaporation increase varies across the models. There is a decrease in evaporation in the center of India, away from the coastal zones. However, this decrease of about 0.2–0.3 mm day−1 is small compared to the increase elsewhere.

Fig. 9.
Fig. 9.

Total annual evaporation (mm) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

Figure 10 is similar to Fig. 9, but shows the annual precipitation. Although it is not the goal of the current study to simulate the historical precipitation, it is noted that there are some precipitation biases across the atmospheric models. ECHAM, HIRHAM5, and HadRM3 significantly overestimate the precipitation in the Himalaya Mountains and show a maximum precipitation to the north of the APHRODITE dataset (Fig. 1). RAMS underestimates the precipitation in the mountainous areas. Furthermore, ECHAM and HIRHAM5 underestimate the precipitation in Bangladesh. In southern India, all models overestimate precipitation compared to the data.

Fig. 10.
Fig. 10.

Total annual precipitation (mm) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

HIRHAM5, ECHAM, and HadRM3 show a shift of precipitation from the eastern side of India toward the north, into the Indus basin and Pakistan. In Nepal, and especially the eastern provinces of India, precipitation decreases by about 1 mm day−1, whereas the increase in the north ranges from 0.5 to 1 mm day−1. RAMS shows a more patchy precipitation response with unrealistically low precipitation in the Himalayas. A less pronounced precipitation difference is the decrease in the center of India and the Ganges plain and an increase in the western coastal areas.

Figure 11 shows the annual mean atmospheric moisture convergence for the two runs, as well as their difference. In the natural run, moisture convergence is positive everywhere over land, but especially over areas with large annual precipitation (mountainous areas). In the irrigated run, the irrigated areas provide (evaporated) moisture to the atmosphere, which is subsequently transported downwind (as shown in Figs. 25). Thus, irrigated areas have negative moisture convergence. For the irrigated areas in the Ganges basin, this moisture is transported northward and leads to a zone of increased moisture convergences just north of the irrigated areas in all models. For the rest of India, the moisture convergence response differs across the models, mainly because of different precipitation response of the models.

Fig. 11.
Fig. 11.

Total annual atmospheric moisture convergence (mm yr−1) in the irrigated and nonirrigated runs and their difference for each of the four models. The areas where a t test showed significant changes at the 95% level are contoured green.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

The cause of this precipitation shift is a difference in atmospheric flow between the natural and irrigated runs. Figure 12 shows the multimodel (HIRHAM5, ECHAM, and RAMS) mean wind direction and speed at 850 hPa for the four seasons for both runs and their difference. While the wind patterns in HIRHAM5 and ECHAM are similar to each other, RAMS shows the same patterns, but smaller differences between natural and irrigated runs than the other models. During DJF, the wind is directed from the Ganges basin toward the Indian Ocean and shows almost no difference between the natural and irrigated runs. In the premonsoon season (MAM), the wind direction is still predominantly from the northwest, but the wind speed is reduced in the irrigated run. Thus, their difference is a net flow from the ocean to the land.

Fig. 12.
Fig. 12.

Wind direction (850 hPa) in the natural and irrigated runs and their difference per season; shown is the mean over ECHAM, RAMS, and HIRHAM5.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

In the natural run, the wind patterns during the monsoon season show a strong west-to-east flow for the southern half of India, which branches off toward the Ganges basin over the Bay of Bengal. This is still the case in the irrigated run, but the wind speeds in the Ganges basin are smaller, resulting in a reduced moisture flow from the Bay of Bengal. However, in the irrigated run, the dry atmospheric flow from continental Asia (from the northwest) toward northern India is weaker and moist air from the southeast may bring some more precipitation in there. The smaller wind speeds from the ocean in the Ganges basin might explain why the basin moisture recycling increases in the irrigated runs, despite a decrease in total precipitation due to the decrease in moisture transport from the Bay of Bengal. During the fall season (SON), the flow turns toward the south again, and the differences between the natural and irrigated runs decrease.

Figure 13 shows the mean annual differences between the irrigated and natural runs in evaporation and precipitation for all four models. Figure 13 (top) displays the absolute differences, while Fig. 13 (bottom) displays this difference relative to the natural runs. As seen in Figs. 9 and 10, the absolute evaporation difference is positive almost everywhere. The relative evaporation difference is highest in the Indus river basin and in the northern Ganges river basin, where annual evaporation increases with about 50%. The additional evaporation in India’s south translates only in a 10%–15% increase.

Fig. 13.
Fig. 13.

Yearly (left) evaporation and (right) precipitation difference between the irrigated and natural runs (mean over 1990–2000 and over ECHAM, HIRHAM5, HadRM3, and RAMS) for (top) the differences in absolute evaporation and precipitation and (bottom) these differences as a percentage of the natural run.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

The precipitation shift from the eastern Himalayas and the Ganges plain to the Indus basin and Pakistan is also clearly visible in Fig. 13. However, the relative precipitation changes show quite a different picture than the absolute changes. The precipitation decreases are in areas where precipitation is already quite high, so the precipitation decrease is only about 10%–15%. The precipitation increases in areas that are much drier, so the precipitation increases with up to 30%–40% in the northern areas of the domain. In India’s coastal areas, the precipitation increases with less than 10%.

4. Discussion and conclusions

This study compared the effect of large-scale irrigation on the atmospheric moisture budget in India using four climate models, run over 10 years. Two runs were performed: one based on a natural setup in which no irrigation was applied and another based on an irrigated setup in which moisture was applied to the top soil layer to keep the soil moisture at least at 90% of field capacity for areas prescribed by a common irrigation map.

The amount of irrigation that was required to keep the soil moisture at this level varied per model. For two regions with a high fraction of irrigated areas, the seasonal cycle of applied moisture was following that of an observation-based reference dataset, but the total differences of irrigation gift between the models varied to up to 0.8 mm day−1. Thus, choosing the perspective of the atmospheric water budget (and keeping the land surface at a certain wetness), rather than the perspective of prescribing the amount of irrigation already leads to some variation among the models.

For two focus regions within the Ganges basin, the local effects of irrigation were determined. All models agreed about the local decrease in temperature (of about 1–3 K) due to irrigation, which is similar to findings of other studies (e.g., Puma and Cook 2010; Lee et al. 2011; Douglas et al. 2009). Evaporation generally increased, and, perhaps counterintuitively, usually exceeded the amount of irrigation. This may be possible because of changes in large-scale flow that could alter the atmospheric characteristics such as amount of precipitation and surface humidity and influence evaporation. Other possible causes for this evaporation increase in excess of the irrigation gift are a nonlinear response of evaporation to soil moisture, changes in the ratio between bare soil evaporation and transpiration, and evaporation changes due to different moisture distribution in the soil profile. As this evaporation excess was common to all models studied here, it is unlikely to be due to model errors. The exact irrigation–evaporation response in these atmospheric models deserves more attention in future studies.

The local changes in precipitation in the two focus regions are small and not as uniform across the models as the temperature and evaporation changes. In the western Ganges focus region, the monsoon precipitation decreases and the nonmonsoon precipitation increases because of irrigation. In the east Ganges focus region, precipitation response changes due to irrigation varied across the models.

The downwind precipitation effects of evaporation from the two focus regions are generally directed toward the south during SON and DJF and toward the Himalayas as well as Tibet and eastern India during MAM and JJA. However, some of the evaporated moisture recycles within the area it evaporated from. The footprints of MAM evaporation (Figs. 25) are quite consistent among the models, although some models transport the moisture into a slightly different direction. Any additional moisture that is released is transported toward the eastern Himalayas.

During the winter months, all moisture is exported from the basin to the Indian Ocean. During MAM, the fraction of evaporation that recycles within the basin increases and it peaks during JJA, when up to 35% of the evaporation from the focus regions recycles within the Ganges basin. During SON, the fraction decreases again. The difference in moisture recycling between the natural and irrigated runs does not differ strongly; only during MAM is the recycling rate of the irrigated run a bit higher in some models. Therefore, a fraction of up to 35% of the additional moisture that is released into the atmosphere as a consequence of irrigation recycles in the Ganges basin.

Although the evaporation is higher in the irrigated run and more moisture recycles than in the natural run, the total precipitation in the Ganges basin decreases because of a decrease in the moisture transport into the basin. The large-scale changes at the land surface cause a change in atmospheric flow that shifts the precipitation from the eastern parts of India to the northwestern parts and Pakistan, as well as India’s southern coastal areas. The precipitation is thus shifted from wetter areas (India’s east) to drier areas (India’s north and Pakistan), where large relative precipitation changes occur. This loss of precipitation in the wetter areas may be of less importance than the increase in dry areas, where some crops might be grown because of the extra precipitation.

In this study, the local effects of irrigation on precipitation varied across the models (for some seasons), which corresponds to the findings of Pitman et al. (2012), who studied the robust effects of land cover change on climate extremes using different land surface and climate models and found no clear local precipitation signal. Their study [Land-Use and Climate, Identification of Robust Impacts (LUCID)] did not include irrigation changes, but recommended irrigation changes to be included in future studies. The current study provides the local precipitation effects of irrigation. However, in the diagnosis of land use effects on climate, precipitation remains one of variables in which the response varies most across models.

The different modeled local precipitation response to irrigation may be due to different parameterizations of model physics, resolution differences, or forcing differences. Previous studies also show different local atmospheric responses to irrigation. Using a higher-resolution model, Douglas et al. (2009) find precipitation shifts around the Ganges basin related to mesoscale circulations, which were not found in the current study. However, the decrease of sea breezes in the coastal regions in east India (Lohar and Pal 1995) is reproduced in the current study.

An uncertainty in the moisture recycling estimates derived in this study is the treatment of evaporation and precipitation processes in the moisture recycling model. For West Africa, van der Ent et al. (2013) show that the performance of the recycling model depends more on the evaporation assumptions than on the precipitation assumptions. The height at which evaporation is released just after it leaves the land surface can have a large effect on the calculated moisture recycling rates. In the current study [and in Dirmeyer and Brubaker (2007) and Tuinenburg et al. (2012)], perfect mixing was assumed for evaporation. It is more realistic to release the moisture parcels just above the land surface in West Africa (van der Ent et al. 2013). Because of different temporal and spatial resolutions of the forcing data, these conclusions cannot be directly applied to the Indian case. However, it should be noted that the evaporation release in the moisture recycling model is a consequential assumption for the current study. The effect of different assumptions regarding the evaporation release height will be a higher moisture recycling than presented in this study. The moisture recycling estimates presented here are probably the lower bounds, given the assumptions in the moisture recycling model.

The physical parameterizations and representation of the atmospheric dynamics in the models may have a large effect on the atmospheric effects of irrigation (Asharaf et al. 2012). For example, the parameterization of clouds (which varies across atmospheric models) has a direct influence on the surface radiation budget and, consequently, on the evaporation. This may result in the effect that irrigation does not lead to more evaporation (and/or plant productivity), but rather to less evaporation, as less energy is available because of enhanced cloud cover. In one of the present models (HadRM3), this effect was rather pronounced, but previous work with the Community Atmosphere Model, version 3.3 (CAM3.3), had already identified this possibility (Lobell et al. 2009). Pinpointing this apparent very sensitive feedback requires more attention. The large-scale circulation effects of irrigation, a shift in precipitation from east to northwest India, confirms the findings of Puma and Cook (2010), Lee et al. (2011), and Asharaf et al. (2012). As all models in this study and several previous studies show this large-scale effect of irrigation, the uncertainty in these findings is quite low, and they can be considered quite robust.

Despite this robustness of irrigation effects in model results, the trends in precipitation records shown in Fig. 14 show a strong difference between the trend over the entire twentieth century (CRU data) and the last 50 years (APHRODITE data). The significant trend in the CRU dataset is very similar to the modeled precipitation effects of irrigation (Fig. 13). However, the trend over the last 50 yr (APHRODITE) is almost opposite (though much smaller in magnitude). It is beyond the scope of the current study to compare the effects of irrigation to the observed precipitation trends, as the datasets are not only affected by a possible irrigation effect, but also by other factors, such as changing climate and changing aerosol loads, among others. However, it is recommended for future studies of atmospheric irrigation effects to perform transient climate simulations with realistic changing irrigation and to compare the irrigation effect to other effects. As a hypothesis for such a study, Fig. 14 suggests that the marginal irrigation effects on the atmosphere are largest when a small fraction of the land is irrigated (as it was in the first half of the twentieth century). When a large fraction of the land is irrigated, any additional irrigation may not lead to large atmospheric responses.

Fig. 14.
Fig. 14.

Precipitation trends over the entire CRU and APHRODITE datasets. Areas with significance of trend smaller than 0.95 are shaded gray.

Citation: Journal of Hydrometeorology 15, 3; 10.1175/JHM-D-13-078.1

We conclude that the atmospheric effects of irrigation as simulated by the four models in this study are threefold.

  1. Irrigation leads to lower temperatures and a higher evaporation locally, but local precipitation is not directly affected.
  2. Up to 35% of any additional evaporation is recycled within the Ganges basin. Thus, of any marginal evaporation increase, up to a third of the moisture is conserved as a water resource for the basin.
  3. If, however, irrigation is applied on a large scale, the large-scale circulation will change and shift the moisture away from the Ganges plain toward the Indus basin and Pakistan.

Acknowledgments

This research was undertaken as part of the European Union (FP6)–funded Integrated Project WATCH (Contract 036946).

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