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About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at ≈9km resolution for the 2001-2015 and we make this dataset available online.
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A new dataset of global irrigation areas from 2001 to 2015
Deepak Nagaraj1, Eleanor Proust1, Alberto Todeschini1, Maria Cristina Rulli3, Paolo D’Odorico2
1 School of Information, University of California, Berkeley, USA
2 Department of Environmental Science, Policy, and Management, University of California,
Berkeley, USA
3 Department of Civil and Environmental Engineering, Politecnico di Milano, Italy
About 40% of global crop production takes place on irrigated land, which accounts for approximately
20% of the global farmland. The great majority of freshwater consumption by human societies is
associated with irrigation, which contributes to a major modification of the global water cycle by
enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the
world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler
microclimate conditions, and altered land-atmosphere interactions. Despite the important role played
by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its
global extent remains poorly quantified. To date global maps of irrigated land are often outdated and
based on estimates from circa year 2000. Here we apply artificial intelligence methods based on
machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial
extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at
≈9km resolution for the 2001-2015 and we make this dataset available online.
The global demand for agricultural products is increasing as a result of demographic growth, shifts to
resource-intensive diets, and increasing reliance on biofuels (Godfray et al., 2010; Foley et al., 2011;
Cassidy et al., 2013). To sustain these ongoing trends, global crop production will have to more than
double by 2100 (Beltran-Pena et al., 2020), thereby dramatically increasing human pressure on the
limited land and water resources of the planet (e.g., Falkenmark et al., 2006; Ramankutty et al., 2008;
Rockstrom et al., 2009; Cassidy et al., 2013). Despite the big push for food security pathways that rely on
more efficient use of resources, reduction of food waste, and moderation of consumption (Kummu et
al., 2012; Davis et al., 2014; Springmann et al., 2018), the demand for increased agricultural production
will be unavoidable. It will require either the expansion of agriculture at the expenses of natural
ecosystems such as forests, savannas, and grasslands, or the increase in crop yields in the land that is
currently cultivated (Foley et al., 2011). Known as “agricultural intensification” the latter approach
would prevent additional losses of natural habitat and biodiversity and avoid the greenhouse gas
emissions associated with land conversions (Runyan and D’Odorico, 2016). At the same time,
intensification will require the provision of additional inputs that are needed to improve yields through
an adequate supply of fertilizers and water (Erisman et al., 2012; Rosa et al., 2018). In many regions of
the world the closure of the gap between actual and maximum potential yields requires irrigation
(Mueller et al., 2012). Previous studies have mapped the rainfed croplands where the local water
resources are sufficient to sustainably meet the local irrigation water requirements (Jägermeyr et al
2017; Rosa et al., 2018; 2020a). A major limitation in this line of research is the lack of knowledge of the
extent and distribution of irrigated land. Most studies rely on a reconstruction of the areas equipped for
agriculture around year 2000 (Portmann et al., 2010). A recent extension of these analyses has
reconstructed the global history of irrigated areas between 1900 and 2005 (Siebert et al., 2015), while of
more regional studies have mapped irrigated areas at 30m resolution for South Asia and Australia
(GFSAD30) (Salmon et al., 2015; Meier et al., 2018). Therefore, it is often reported that roughly 20% of
the cultivated land is irrigated and accounts for 40% of the global crop production (e.g., Molden, 2010).
These estimates, however, are outdated, as the spatial extent of irrigation has likely changed over the
past two decades. With agriculture contributing to 90% of human water consumption, lack of
knowledge of irrigated areas prevents an analysis of the extent to which water resources around the
world are used sustainably (i.e., without depleting local groundwater stocks and environmental flows). It
also limits our ability to investigate ongoing changes in agricultural practices around the world (Davis et
al., 2017; Jägermeyr et al 2017; D’Odorico et al., 2018; Rosa et al., 2019).
Satellite remote sensing combined with modern machine learning techniques provides unprecedented
opportunities to identify areas equipped for irrigation, where irrigation may therefore take place for at
least part of the year. Irrigated areas tend to exhibit higher productivity and “greenness” than their
rainfed counterparts in the same region. As a result, irrigation is expected to be detectable with
greenness indices such as EVI and NDVI (e.g., Kotsuki and Tanaka, 2015). Moreover, there is compelling
evidence (Muller et al., 2016; 2017; Thiery et al., 2020) that irrigated areas tend to be cooler during the
day than adjacent rainfed land as a result of the different partitioning of the incoming solar radiation
into sensible and latent heat fluxes, with irrigated soil exhibiting greater evapotranspiration and
associated latent heat fluxes (Kueppers et al., 2007; Puma et al., 2010; Lobell et al., 2008; 2009; Bonfils
and Lobell, 2007). Therefore, surface temperature, which can be detected from space, is expected to be
a good proxy for irrigation (Wei et al., 2013; Cook et al., 2020).
In this study, we develop a global assessment of irrigation and provide global annual maps of irrigated
land at ≈9km resolution for the 2001-2015 period. To that end, we apply advanced machine learning
methods to data available from satellite remote sensing, using as “label” (to train the algorithm) the
known distributions of irrigated land from Siebert et al. (2015). We make this dataset available on the
Zenodo repository .
2. Data and Methods
Irrigation maps are produced by two machine learning models: (A) a time-series model, and (B) a point-
in-time (time-stationary) model, as explained below. The results from these two models are then
combined for better overall performance.
2.1 Data sets
Both models are trained on a global dataset of irrigation extent (the time-series model is
trained only on areas identified as croplands), originating from Siebert et al.
(2015). This dataset is available at a spatial resolution of 5 arc minutes, i.e. 1 pixel of the map
corresponds to an area of 86 km2 (or 8604 ha) at the equator, which corresponds to a pixel side of
≈9.276 km. We use “area equipped for irrigation” data from the Siebert et al. (2015) dataset, specifically
the one listed as “HYDE Final” as labels to train the algorithm.
2.1.1 Time-Series Model
The four available “HYDE final” datasets from Siebert et al. (2015) for 1985-2000 were used as “labels”.
Because satellite data used in this study were not consistently available pre-1981, labels from previous
years were discarded. Given the restraints on satellite and geographic data available from 1985-2015,
we used as features the following datasets: (a) NDVI Data sourced from the AVHRR 15 day data set and
compiled in the Global Inventory Monitoring and Modeling System, GIMMS (NCAR, 2018). These data
were aggregated biannually to take the mean, variance and max of all inputs over a 6 month period; (b)
TerraClimate data were taken from the University of Idaho online archives and re-projected to the same
resolution as the NDVI data and labels (Abatzoglou, et al., 2018). For each year, monthly values of
climate variables were used to estimate annual mean, maximum, variance and minimum. The long-term
averages aggregated by TerraClimate for the years 1981-2010 were also fed to the model as a stand-in
for long term suitability for agriculture. The datasets that proved most useful to the purpose of this
study were maximum temperature, minimum temperature, vapor pressure, downward surface
shortwave radiation, wind-speed, actual evapotranspiration, climate water deficit (i.e., the difference
between potential and actual evapotranspiration), and soil moisture. These hydroclimatic variables are
known to control evapotranspiration rates, and crop water demand (e.g., Katul et al., 2012).
Given the relatively high computing requirements to train the model globally over two decades, a
decision was made to concentrate on cultivated land by filtering the data based on the 2010 GFSAD
(Global Food Security-support Analysis Data) croplands extent, including irrigated and rainfed croplands
(Massey et al., 2017). This mask was selected because it contained most of the irrigated land and was
more extensive than the MODIS land cover data set. Applying this mask reduced the required computing
time by a factor of ten and allowed for the inclusion of many more training features.
The time series model was performed in two stages. Firstly, a binary test was used to determine
whether at least 1% of land was irrigated. Secondly, any land that was detected to be irrigated was then
run through another binary test to detect whether the land was highly irrigated (defined as a pixel with
at least 20% of the area or 2000 hectares irrigated). The result was a three class feature irrigated at 1%,
irrigated between 1 to 20%, and irrigated 20% or more.
Within the GFSAD cropland area the non-irrigated land is more than four times greater than the
irrigated land (i.e., about 20% of the cultivated land is irrigated (e.g., Molden, 2010)). Decision tree
models (including ‘random forests’ and ‘extra trees’) tend to not perform well with such an imbalance
between classes. Therefore, we under-sampled the non-irrigated land at a rate of 60%. This significantly
improved the overall performance of the model.
The F1 and kappa metrics were used to evaluate the performance of the model. Given the class
imbalance, a model could deliver relatively high accuracy by only predicting non-irrigated land. The
kappa metric represents how much better the model performs with respect to a baseline model that
simply predicts based on class frequencies (or “expected accuracy”). Thus, kappa is expressed as (model
accuracy expected accuracy) / (1 expected accuracy). Kappa adjusts the accuracy score to reflect
class imbalance and therefore Kappa is a better metric when there is class imbalance (i.e., there is no
even split between classes as in our case, as we have only 11% irrigated land). The F1 score is a measure
that incorporates both recall and precision. The kappa metric of the first stage processing was 78%, with
an F1 score of 92%. On the second layer of learning, differentiating between “highly irrigated” and other
irrigation classes kappa was 76% and F1 again was 92%; this led to an overall model performance of
kappa=0.76 and accuracy (a measure of how often the model classifies correctly) of 0.89, and F1 of 0.88.
2.1.2 Time-Stationary Model
For the time-stationary model, we used labels from Siebert et al. (2015) for the year 2005 and the same
spatial resolution as in the time series model (i.e., with pixels of 8604 ha, which corresponds to a pixel
side of about 9.276 km). We ignored pixels with less than 25 hectares of land equipped for irrigation
and classified them as not irrigated. Pixels with 25 to 2000 hectares equipped for irrigation were
classified as “low or medium” irrigation. Pixels with more than 2000 hectares of land equipped for
irrigation were classified as “high” irrigation. Because the distribution is highly skewed, it drops rapidly
as we go to higher values of irrigated areas -- there is only a relatively limited amount of data at higher
levels of irrigation.
We took a random sample of 20,000 points worldwide to develop a machine learning model. To train
the model, we used features pertaining to climate, soil, vegetation and land-cover from TERRACLIMATE
(Abatzoglou et al., 2018), the Global Land Data Assimilation System, GLDAS (Rodell et al., 2004), and
MODIS datasets. Our final choice was a random forest model with 1000 trees, a bagging fraction of
0.63, and 10 variables per split. We also used a random forest algorithm to select features based on
their importance. Our final model used 11 features, including latitude and longitude (X and Y), annual
average maximum temperature (tmmx), potential evapotranspiration (pet), downward surface
shortwave radiation (srad), and wind-speed (vs) from TERRACLIMATE; annual average albedo
(Albedo_inst), direct evaporation from bare soil (ESoil_tavg), atmospheric pressure (Psurf_f_inst) from
GLDAS; land cover type (MCD12Q1 for UMD) and annual maximum EVI from MODIS. The relative
importance of these predictors is shown in Figure 1. Again, the microclimate variables chosen as
“features” are known for playing a role in determining the rates of evapotranspiration and the crop
water requirements (e.g., Katul et al, 2012).
We used the R statistical environment for model development and tuning. We then exported this
model to Google Earth Engine® and ran it there for the years 2001 through 2015. This platform
provided us with a repository to access many geospatial datasets, as well as a distributed computing
infrastructure that allowed us to process large amounts of feature data and classify the cultivated land
based on the occurrence and intensity of irrigation. The time-stationary model has accuracy of 0.89, and
a Kappa value of 0.56.
2.2 Combining the Two Models
We then combined predictions from the above two models. First, we partitioned the world’s land area
into croplands and non-croplands using the GFSAD crop dominance dataset to determine the cropland
boundaries (considering all crop classes in GFSAD). For croplands, we predict irrigation using the time-
series model. For non-croplands, we predict with the time-stationary model. Our combined map has
accuracy of 0.94, and a kappa value of 0.73. The confusion matrix and summary statistics are shown in
Tables 1 and 2.
Figure 1. Relative importance of the features used for the time-stationary model, which included including latitude
and longitude (X and Y), annual average maximum temperature (tmmx), potential evapotranspiration (pet),
downward surface shortwave radiation (srad), and wind-speed (vs) from TERRACLIMATE; annual average albedo
(Albedo_inst), direct evaporation from bare soil (ESoil_tavg), atmospheric pressure (Psurf_f_inst) from GLDAS; land
cover type (MCD12Q1 for UMD) and annual maximum EVI from MODIS.
Table 1. Confusion matrix of the combined model
↓ Predicted | Actual →
Not irrigated
Low to medium
Not irrigated
Low to medium
Table 2. Summary statistics or accuracy, precision, recall, and F1-score
Not irrigated
Low to medium irrigated
Highly irrigated
2.3 Validation
After running our model, we validated the false positives and false negatives produced by the model, by
taking a random sample of 100 points in each class. We downloaded LANDSAT 7 TOA (Top of the
Atmosphere) imagery for these points, for a buffer of 9 km and a buffer of further 9 km. We then
visually inspected each picture to validate the model classification.
Figure 2. An example of pixels exhibiting a mosaic of croplands and non-croplands (the red square wraps
a circle of 9.3km diameter)
3. Results
We developed annual global maps of irrigation (Figure 3), encompassing all continents except Antarctica
and the Pacific islands of Oceania. These maps are at a spatial resolution of 5 arc minutes (9276 m at the
equator) and are available for every year from 2001 through 2015. The maps show the extent of 3
classes of irrigation: (i) no irrigation, (ii) low-concentration irrigation (1% to 20% irrigated), and (iii) high-
concentration irrigation (>20% irrigated). The model predicts that as of 2005 11.2% of the world’s land
was used as irrigated cropland. Of this, 80.5% is in the low irrigation class, and 19.5% is in the high
irrigation class. The confusion matrix (Table 1) shows that 97% of the non-irrigated areas were correctly
predicted as non-irrigated; 73% of low-concentration irrigated areas and 80% of high concentration
irrigated areas were correctly predicted as irrigated at low or high concentrations, respectively. These
discrepancies between predictions and actual irrigation are for most part due to 22% of low irrigation
areas being mistakenly classified as non-irrigated and 16% of high-concentration irrigation areas being
mistakenly classified as low-concentration (Table 1). We use a validation process to evaluate to what
extent these discrepancies are due to limitations in our predictions or are the result of inconsistencies in
our label and do not correspond to actual misclassifications in our predictions.
The results of the validation process show that model performance is better than what is reported in
Sections 2.2 and 2.3. In fact, for points that are false positives, we found that 41 out of 100 points were
croplands (27) or a mosaic of croplands and non-croplands such as uncultivated hills, water bodies or
forests (14). This means that the model was correct in its classification for these points. The remaining
59 points were non-croplands. For points that are false negatives, we found that 47 out of 100 points
were indeed non-croplands. This means the model was correct in its classification for these points. 33
points were a mosaic, and 20 points were croplands. These numbers show some of the limitations in the
use of Siebert et al., (2015) as training labels, bring uncertainty to the model assessment metrics
reported above based solely on the training labels. These results show that our models fare poorly in
areas which have a mosaic of croplands and non-croplands. This problem occurs in the Brazilian
highlands, northern Germany, and southern China, where the terrain is a mix of hilly areas and
croplands. Our spatial resolution assigns 86 square km to each pixel, and this can be too coarse to
identify irrigation in such mosaics (Figure 2). Additionally, one of the feature datasets, GLDAS, is at even
coarser resolution, which can cause predictions to mis-classify adjoining pixels. Moreover, mosaics have
the effect of confusing our model in the low class of irrigation (Figure 2).
Globally, most irrigation occurs at low density (i.e., with less than 20% of the area being irrigated),
particularly in sub-Saharan Africa, Oceania, and South America. South Asia and East Asia, however,
exhibit a relatively large fraction of their irrigated areas at high-density irrigation (51% and 32%,
respectively), pointing to regions of the world of particularly intensified crop production (Figure 3).
Figure 3 shows a map of the irrigated areas in 2001 (top) and 2015 (bottom). Dark blue corresponds to high
density (i.e., >20%) irrigation and lighter blue to areas with low density (1-20%) irrigation.
Our predictions of irrigated and non-irrigated areas between 2001 and 2015 show some interesting
trends (Figure 4). There is a slight decreasing trend in irrigation in high levels of irrigation. But there is
an increasing trend in irrigation in low levels of irrigation (1-20% irrigated land). This may indicate
increased irrigation because of drought or reduced rainfall in regions traditionally reliant on rainfall.
Figure 4. Number of pixels with low and high-density irrigation between year 2001 and 2015 (see figure S1 in the
Appendix for the definition of these regions).
Figure 5. Difference in irrigation between 2015 and 2001. Green areas exhibited a decrease in irrigation; in orange
areas an increase in irrigation. Darker colors correspond to bigger changes (i.e. from high density irrigation to no
irrigation or vice versa); Lighter colors correspond to smaller changes in irrigation (from high to low density or from
low density to no irrigation).
Interestingly, we see that irrigation has decreased in central parts of eastern China, Thailand, Saudi
Arabia, Russia, and the Southeastern USA, Syria, Albania, Eastern Germany, Northern Italy, and certain
parts of Brazilian highlands (Figure 5). We see an increase in irrigation in central parts of the United
States (Figure 6), along the mid-low course of the Danube (Hungary, Serbia, Romania, and Bulgaria),
Northern India, and in drier regions of Central Asia. Links to these global maps and the validation areas
are provided in the online materials.
Figure 5. Example of detected decrease in Brazilian highlands: 2001 (left) vs. 2015 (right).
Figure 6. Example of detected increase of irrigation in central plains of North America: 2001 (left) vs. 2015 (right).
This study differs from previous efforts based on statistical methods (Portmann et al., 2010), in that it
uses machine learning algorithms to map irrigated areas worldwide. The dataset developed in this
research provides a global scale mapping of low and high intensity/density irrigated areas with about 9
km resolution. This analysis allows us to investigate spatiotemporal patterns of irrigation worldwide.
Specifically, we find that high density irrigated areas are found mostly in South Asia and East Asia,
followed by North America, Europe and the Middle-East/North Africa (MENA) region. Between 2001 and
2015 irrigated areas have increased across North America and South Asia, and the increase was
contributed by an expansion of low density irrigation areas. Conversely, South America and the MENA
region saw a decrease in irrigated areas as a result of a decrease in areas with low-density irrigation. The
case of East Asia is different because, while low density irrigation areas have increased, low density
irrigated areas have shrunk, leading to an overall decrease of irrigated areas. These patterns are
expected to change as an effect of climate change with an expansion of irrigated agriculture at the mid-
high latitude and a loss of irrigation suitability or the need for seasonal reservoirs in breadbasket regions
of Eurasia (Rosa et al., 2020b).
A comparison between our results for 2015 and those obtained by Meier et al (2018) for the same years
shows an overall agreement in the classification or irrigated and non-irrigated areas ranging between
81%-96% - depending on the region - with an average of 91%. When the comparison is limited to the
areas classified as irrigated in our study the agreement ranges between 59%-81% with an average of
73%. Alternatively, we can measure the agreement as a percentage of the irrigated area in Meier et al
(2018); in that case the agreement drops to 24%-77% with an average of 48%. The differences are likely
a consequence of the different methods used by the two studies. We are unable to establish, however,
which one of the two methods provides the correct classification. Most likely, both methods produce
correct or wrong results in different regions. The validation results presented in the previous section for
a sample of 100 pixels shows that for the year 2001 our classification tends to be in stronger agreement
than the “label” (Siebert et al., 2015) with the signs of irrigation detectable with Google Earth in areas
classified in this study as irrigated. Of course, a more extensive analysis would be needed to generalize
these conclusions to the entire world.
Table 2. Comparison between the irrigated areas in this study and in Meier et al. (2018) for the year
Total #
Irrigated in this
study but not in
Meier et al.
for both
Irrigated in
Meier et al.,
(2018) but not in
this study
t (% of
total area)
Agreement (%
of irrigated area
in Meier et al.,
N. America
S. America
South East
The authors thank Lorenzo Rosa (ETHZ) for providing comments and suggestions.
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Figure S1. Regions used in figure 1 and 2.
In the Zenodo archive ( the reader can find:
Prediction maps: Prediction maps for every year from 2001 to 2015, in GeoTIFF format. Class 0
represents no irrigation, class 1 is low to medium irrigation, and class 2 is high irrigation.
Assessment map: Model assessment map for the year 2005.
Difference map: Map showing cropland differences between 2001 and 2015.
... Irrigation data with vector format (each square grid representing five arc minutes) were acquired, representing the percentage of irrigation equipment in the area from 2000 to 2008 to irrigate crops ( Fig. 2e) (Siebert et al. 2013). From 2001 to 2015, 91.779% of the irrigation areas in Nepal had not changed (Nagaraj et al. 2021). Crop data from 1997 to 2003 stem from the Harvested Area and Yield for 175 crop datasets with a spatial resolution of 10 km. ...
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Accurate assessments of drought risks are essential for sustainable developments. Previous studies, though, explored drought risks often from a single perspective, e.g., hydrological and agricultural droughts, and holistic analyses have not been thoroughly investigated. To fill this knowledge gap, in this study, Nepal was chosen as the study area to assess compound drought by integrating meteorological, hydrological, agricultural, and socioeconomic droughts. Specially, precipitation, groundwater, seven crops, evaporation (ET), available water-holding capacity (AWC), gross domestic product (GDP), irrigation, international wealth index (IWI), and distance to waterways (DW) were employed as influencing factors to model drought hazard, vulnerability, and prevention. Especially, a compound drought risk assessment (CDRA) model was proposed. Moreover, the multi-scale geographically weighted regression was utilized to identify the factors influencing the drought risk under varying climate settings. Upon examination, the performance of the CDRA model was satisfactory and yielded a compelling demonstration of drought risk estimations in Nepal. Regions with high drought risks were favorably consistent with historical disaster zonings. The Pearson correlation coefficient was 0.566 compared with the standardized precipitation evapotranspiration index results. A relatively high proportion of medium- and high-hazard levels was observed in the temperature zone with a hot or warm summer, suggesting the high temperature significantly increased drought risks. A qualified total explained variance of drought risks was found in driving analyses (R² value = 0.679). In addition, the ranking of the influencing factors from high to low was IWI, GDP, AWC, irrigation, groundwater, precipitation, ET, and DW. It suggests that socioeconomic alleviations in wealth, inequality, and poverty are essential for drought relief. The results can provide a reference for drought mitigations for governments and communities.
... The geolocation, construction year, and capacity of global reservoirs and dams were retrieved from the Global Reservoir and Dam Database (GRanD). We also discuss the influences of changes in irrigation area and land cover on RC alterations based on a new global irrigation dataset from Nagaraj et al (2021) and the annual global land cover maps of the European Space Agency Climate Change Initiative (ESA CCI) project. Additionally, to examine the effects of the coverage of snow and glaciers on the RC, snow cover from the ESA CCI database and ice area from the Global Land Ice Measurements from Space product were obtained. ...
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Assessing variations in the annual runoff coefficient (RC) on a basin scale is crucial for understanding the hydrological cycle under natural and anthropogenic changes, yet a systematic global assessment remains unexamined from a water-balance perspective. Here, we combine observation-based runoff and precipitation datasets to quantify basin-averaged RC changes in 433 major global river basins during the period 1985–2014. Thereafter, the ratios of terrestrial water storage changes and evaporation to precipitation (SC and EC, respectively) are obtained to evaluate the factors driving the RC changes. The results show that 12.93% of the basins experience significant decreasing trends in RC, with slopes ranging from −0.55 ± 0.17% yr ⁻¹ to −0.05 ± 0.02% yr ⁻¹ , while 6.47% basins show increasing RCs with slopes ranging from 0.09 ± 0.04% yr ⁻¹ to 0.56 ± 0.17% yr ⁻¹ . A higher percentage (62.95%) of basins reveal decreasing RCs for the regions with considerable human intervention compared to those (58.24%) with dominant natural variability. Changes in EC dominate the RC changes over 79.68% of the basins for both increasing and decreasing trends, with a maximum contribution (53.65%) from transpiration, among other partitioned components. Corroborated inferences from explicit investigation in the Yangtze River basin highlight the robustness of our results for global water managers and policymakers.
... Several attempts have been made to estimate the long-term extent of irrigation at regional and global scales (Ketchum et al., 2020;Ozdogan et al., 2006;Peña-Arancibia et al., 2014;Sharma et al., 2018). For instance, the global irrigation maps from 2001 to 2015 with a resolution of 5 ′ (Deepak Nagaraj et al., 2021), the 250-m irrigation maps of India for 2000-2015 (Ambika et al., 2016), 30-m annual irrigation maps for the Republican River Basin in the US since 1984 and the conterminous US for 1997-2017 (Xie and Lark, 2021). However, on one hand, global time-series irrigation maps cannot accurately reflect national and regional irrigated areas due to their coarse resolution; on the other hand, moderate or high-resolution maps based on remote sensing-based classification methods often suffer from undermined accuracy in humid areas , because the signals of irrigated and rainfed crops are too similar to differentiate (Xie et al., 2019;Xu et al., 2019). ...
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Irrigation is widely implemented in China to enhance grain production and ensure food security. Spatiotemporal information on irrigation is critically important, but the existing global and regional irrigation products have a coarse spatial resolution, low accuracy, and short temporal coverage, which is a knowledge gap to be filled. Generating explicit and accurate information on the spatial and temporal extent of irrigation is essential to underpin and facilitate water resource allocation and management, as well as to understand catchment hydrology, and evaluate irrigation infrastructure investment. We proposed a two-step strategy to map annual irrigated areas at 500 m resolution in China from 2000 to 2019. We first generated initial irrigation maps using MODIS data and statistical data (MIrAD-GI). Then we combined MIrAD-GI, the existing irrigation maps, and land use/cover products with irrigation information, into an improved series of annual irrigation maps (IrriMap_Syn) with constrained statistics. Afterwards, pixel-wise accuracy assessment for IrriMap_Syn was conducted in four representative regions in three years. The resultant irrigation dataset performed well with a mean overall accuracy of 0.89 and a kappa coefficient of 0.82. According to our annual irrigation maps, the total irrigated area in China increased by 24.8%, from 52.8 million ha in 2000–65.9 million ha in 2019, at a rate of 690,000 ha per year. Spatially, irrigated croplands increased substantially in North China, especially in Xinjiang and Heilongjiang. Land reclamation largely contributed to the increase in irrigation in North China. A growing population and improved irrigation infrastructure also promoted the increase in irrigated areas. As the first of its kind in the country, our spatially explicit maps of irrigated croplands advance our understanding of the spatiotemporal pattern of irrigation dynamics in China and are expected to contribute to sustainable water resource management and irrigation strategies in the face of climate change.
Groundwater depletion threatens global water and food security. Despite local successes in reducing groundwater extraction, the potential for transitioning currently irrigated systems to rainfed systems to address both production and groundwater use problems remains poorly understood at the national level. Here, we consider wheat production in China, which strongly benefits from groundwater irrigation, to assess the potential for achieving zero groundwater extraction, while maintaining current production. Using data from 1273 counties, we assessed the area-weighted potential yield and water productivity, and established the attainable values for each county using the top 10% of producers in the county. When data were aggregated at the national level, wheat yield and water productivity improved by 34% and 26% respectively, by closing gaps between the mean and attainable values. We next mapped the county-by-county acreage allocation and calculated the attainable production capacity. The results revealed that China could maintain wheat production without groundwater extraction by reducing the yield gap. This work provides essential insights and detailed spatiotemporal information for policymakers working to achieve groundwater and food sustainability.
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The aim of this study was to clarify the distribution of irrigated drylands in arid and semi-arid areas, where complex terrain, diverse crops and staggered cultivated lands exist. This paper studied the classification methods of irrigated drylands based on temperature, precipitation, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Landsat data in the one-harvest area of the northern Loess Plateau of China by using the Google Earth Engine (GEE) platform. An extraction method was proposed for irrigated drylands in arid and semi-arid regions of northwest China. In addition, the change types of irrigated and rainfed drylands in the two periods were classified, and a method was also put forward to directly classify the change types by using the image differences between the two periods combined with the classification results of each period. It was found that combining the ratio of NDVI and NDWI with the accumulated values of temperature and precipitation of the 30 days before imaging could effectively improve the classification accuracy. Moreover, directly classifying the gaps of remote sensing factors in the time dimension before combining spatial clustering information could yield a more accurate type of change, because the accumulation of errors in the change maps obtained from the overlay analysis of distribution maps of the two periods could be avoided. The accuracy of classification could be improved by introducing the dynamic information of time dimension into the classification of historical periods. This study complements the extraction method for this type of irrigated dryland, and the classification results can improve the accuracy of existing products in terms of spatial resolution, which can fill the shortage of detailed distribution data for irrigated and rainfed drylands in this region.
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Droughts grow concurrently in space and time; however, their spatiotemporal propagation is still not fully studied. In this study, drought propagation and spatiotemporal characteristics were studied in northern, northeastern, and central Thailand (NNCT). The NNCT is an important agricultural exporter worldwide, and droughts here can lead to considerable pressure on the food supply. This study investigated meteorological drought and soil drought in northern Thailand and identified 70 meteorological drought events and 44 soil drought events over 1948–2014. Severe droughts (droughts with long trivariate return periods) mainly occurred after 1975 and were centered in northern and northeastern Thailand. Meteorological drought and soil drought that occurred during 1979–1980 had the longest trivariate return periods of 157 years and 179 years, respectively. The drought centers were mainly located in the Chao Phraya River basin and the Mun River basin. The mean propagation ratios of all drought parameters (duration, area, severity) were lower than 1, indicating that the underlying surface can serve as a buffer to alleviate water deficits. Most of the probability distribution coefficients and all drought propagation ratios of the three drought parameters were found to change significantly based on a moving-window method, indicating that the drought parameters and propagation from meteorological drought to soil drought were non-stationary. Significant increasing trends were detected in mean values of most drought parameters, ranging from 2.4%/decade to 16.6%/decade. Significant decreasing trends were detected in coefficients of skewness (Cs) of all drought parameters and coefficients of variation (Cv) of most drought parameters, ranging from −3.3 to −12.4%/decade, and from −5.5 to −19.4%/decade, respectively. The propagation ratios of all drought parameters showed significant increasing trends, indicating that the function of the underlying surface as a buffer has become weaker. The drought propagation ratios were found to be positively related to two climate indices, the phase index (PI) and the climate seasonality index (CSI). These findings will help to develop a better understanding and management of water resources in Thailand.
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Irrigation represents one of the most impactful human interventions in the terrestrial water cycle. Knowing the distribution and extent of irrigated areas as well as the amount of water used for irrigation plays a central role in modeling irrigation water requirements and quantifying the impact of irrigation on regional climate, river discharge, and groundwater depletion. Obtaining high-quality global information about irrigation is challenging, especially in terms of quantification of the water actually used for irrigation. Here, we review existing Earth observation datasets, models, and algorithms used for irrigation mapping and quantification from the field to the global scale. The current observation capacities are confronted with the results of a survey on user requirements on satellite-observed irrigation for agricultural water resources’ management. Based on this information, we identify current shortcomings of irrigation monitoring capabilities from space and phrase guidelines for potential future satellite missions and observation strategies.
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More and more attention has been paid to farmland water conservancy project (FWCP) maintenance in China, which can reallocate water resources in a more rational and efficient manner. Compared with the traditional survey such as field survey, FWCP maintenance can be improved efficiently with geospatial technology. To improve the level of FWCP maintenance in China, a collaborative sensing system framework by integrating satellite, aerial, and ground remote sensing is put forward. The structure of the system framework includes three sections, namely the data acquisition, the operational work, and the application and service. Through the construction and operation of such collaborative sensing system, it will break through the limitation of any single remote sensing platform and provide all-around and real-time information on FWCP. The collaborative monitoring schemes for the designed FWCP maintenance can engage ditch riders to maintain more effectively, which will enable them to communicate more specifically with smallholders in the process of irrigation. Only when ditch riders and farmers are fully involved, irrigation efficiency will be improved. Furthermore, the collaborative sensing system needs feasible standards for multi-source remote sensing data processing and intelligent information extraction such as data fusion, data assimilation, and data mining. In a way, this will promote the application of remote sensing in the field of agricultural irrigation and water saving. On the whole, it will be helpful to improve the traditional maintenance problems and is also the guarantee for establishing a long-term scientific management mechanism of FWCP maintenance in developing countries, especially in China.
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Climate change is expected to affect crop production worldwide, particularly in rain-fed agricultural regions. It is still unknown how irrigation water needs will change in a warmer planet and where freshwater will be locally available to expand irrigation without depleting freshwater resources. Here, we identify the rain-fed cropping systems that hold the greatest potential for investment in irrigation expansion because water will likely be available to suffice irrigation water demand. Using projections of renewable water availability and irrigation water demand under warming scenarios, we identify target regions where irrigation expansion may sustain crop production under climate change. Our results also show that global rain-fed croplands hold significant potential for sustainable irrigation expansion and that different irrigation strategies have different irrigation expansion potentials. Under a 3°C warming, we find that a soft-path irrigation expansion with small monthly water storage and deficit irrigation has the potential to expand irrigated land by 70 million hectares and feed 300 million more people globally. We also find that a hard-path irrigation expansion with large annual water storage can sustainably expand irrigation up to 350 million hectares, while producing food for 1.4 billion more people globally. By identifying where irrigation can be expanded under a warmer climate, this work may serve as a starting point for investigating socioeconomic factors of irrigation expansion and may guide future research and resources toward those agricultural communities and water management institutions that will most need to adapt to climate change. climate change | water sustainability | sustainable irrigation expansion | water scarcity | agriculture
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Meeting the increasing global demand for agricultural products without depleting the limited resources of the planet is a major challenge that humanity is facing. Most studies on global food security do not make projections past the year 2050, just as climate change and increasing demand for food are expected to intensify. Moreover, past studies do not account for the water sustainability limits of irrigation expansion to presently rainfed areas. Here we perform an integrated assessment that considers a range of factors affecting future food production and demand throughout the 21st century. We evaluate the self-sufficiency of 165 countries under sustainability, middle-of-the-road, and business-as-usual scenarios considering changes in diet, population, agricultural intensification, and climate. We find that under both the middle-of-the-road and business-as-usual trajectories global food self-sufficiency is likely to decline despite increased food production through sustainable agricultural intensification since projected food demand exceeds potential production. Contrarily, under a sustainability scenario, we estimate that there will be enough food production to feed the global population. However, most countries in Africa and the Middle East will continue to be heavily reliant on imports throughout the 21st century under all scenarios. These results highlight future hotspots of crop production deficits, reliance on food imports, and vulnerability to food supply shocks.
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There is strong evidence that the expansion and intensification of irrigation over the twentieth century has affected climate in many regions. However, it remains uncertain if these irrigation effects, including buffered warming trends, will weaken or persist under future climate change conditions. Using a 20‐member climate model ensemble simulation, we demonstrate that irrigation will continue to attenuate greenhouse gas‐forced warming and soil moisture drying in many regions over the 21st century, including Mexico, the Mediterranean, Southwest Asia, and China. Notably, this occurs without any further expansion or intensification of irrigation beyond current levels, even while greenhouse gas forcing steadily increases. However, the magnitude and significance of these moderating irrigation effects vary across regions and are highly sensitive to the background climate state and the degree to which evapotranspiration is supply (moisture) versus demand (energy) limited. Further, limitations on water and land availability may restrict our ability to maintain modern irrigation rates into the future. Nevertheless, it is likely that irrigation, alongside other components of intensive land management, will continue to strongly modulate regional climate impacts in the future. Irrigation should therefore be considered in conjunction with other key regional anthropogenic forcings (e.g., land cover change and aerosols) when investigating the local manifestation of global climate drivers (e.g., greenhouse gases) in model projections.
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Water scarcity raises major concerns on the sustainable future of humanity and the conservation of important ecosystem functions. To meet the increasing food demand without expanding cultivated areas, agriculture will likely need to introduce irrigation in croplands that are currently rain-fed but where enough water would be available for irrigation. "Agricultural economic water scarcity" is, here, defined as lack of irrigation due to limited institutional and economic capacity instead of hydrologic constraints. To date, the location and productivity potential of economically water scarce croplands remain unknown. We develop a monthly agrohydrological analysis to map agricultural regions affected by agricultural economic water scarcity. We find these regions account for up to 25% of the global croplands, mostly across Sub-Saharan Africa, Eastern Europe, and Central Asia. Sustainable irrigation of economically water scarce croplands could feed an additional 840 million people while preventing further aggravation of blue water scarcity.
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Irrigation affects climate conditions – and especially hot extremes – in various regions across the globe. Yet how these climatic effects compare to other anthropogenic forcings is largely unknown. Here we provide observational and model evidence that expanding irrigation has dampened historical anthropogenic warming during hot days, with particularly strong effects over South Asia. We show that irrigation expansion can explain the negative correlation between global observed changes in daytime summer temperatures and present-day irrigation extent. While global warming increases the likelihood of hot extremes almost globally, irrigation can regionally cancel or even reverse the effects of all other forcings combined. Around one billion people (0.79–1.29) currently benefit from this dampened increase in hot extremes because irrigation massively expanded throughout the 20th century. Our results therefore highlight that irrigation substantially reduced human exposure to warming of hot extremes but question whether this benefit will continue towards the future. How the effects of irrigation on the climate conditions compare to other anthropogenic forcings is not well known. Observational and model evidence show that expanding irrigation has dampened historical anthropogenic warming during hot days, an effect that is particularly strong over South Asia.
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Recent studies have highlighted the reliance of global food production on unsustainable irrigation practices, which deplete freshwater stocks and environmental flows, and consequently impair aquatic ecosystems. Unsustainable irrigation is driven by domestic and international demand for agricultural products. Research on the environmental consequences of trade has often concentrated on the global displacement of pollution and land use, while the effect of trade on water sustainability and the drying of over-depleted watercourses has seldom been recognized and quantified. Here we evaluate unsustainable irrigation water consumption (UWC) associated with global crop production and determine the share of UWC embedded in international trade. We find that, while about 52% of global irrigation is unsustainable, 15% of it is virtually exported, with an average 18% increase between year 2000 and 2015. About 60% of global virtual transfers of UWC are driven by exports of cotton, sugar cane, fruits, and vegetables. One third of UWC in Mexico, Spain, Turkmenistan, South Africa, Morocco, and Australia is associated with demand from the export markets. The globalization of water through trade contributes to running rivers dry, an environmental externality commonly overlooked by trade policies. By identifying the producing and consuming countries that are responsible for unsustainable irrigation embedded in virtual water trade, this study highlights trade links in which policies are needed to achieve sustainable water and food security goals in the coming decades.
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The food system is a major driver of climate change, changes in land use, depletion of freshwater resources, and pollution of aquatic and terrestrial ecosystems through excessive nitrogen and phosphorus inputs. Here we show that between 2010 and 2050, as a result of expected changes in population and income levels, the environmental effects of the food system could increase by 50–90% in the absence of technological changes and dedicated mitigation measures, reaching levels that are beyond the planetary boundaries that define a safe operating space for humanity. We analyse several options for reducing the environmental effects of the food system, including dietary changes towards healthier, more plant-based diets, improvements in technologies and management, and reductions in food loss and waste. We find that no single measure is enough to keep these effects within all planetary boundaries simultaneously, and that a synergistic combination of measures will be needed to sufficiently mitigate the projected increase in environmental pressures.
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Water is a major factor limiting crop production in many regions around the world. Irrigation can greatly enhance crop yields, but the local availability and timing of freshwater resources constrains the ability of humanity to increase food production. Innovations in irrigation infrastructure have allowed humanity to utilize previously inaccessible water resources, enhancing water withdrawals for agriculture while increasing pressure on environmental flows and other human uses. While substantial additional water will be required to support future food production, it is not clear whether and where freshwater availability is sufficient to sustainably close the yield gap in cultivated lands. The extent to which irrigation can be expanded within presently rainfed cropland without depleting environmental flows remains poorly understood. Here we perform a spatially explicit biophysical assessment of global consumptive water use for crop production under current and maximum attainable yield scenarios assuming current cropping practices. We then compare these present and anticipated water consumptions to local water availability to examine potential changes in water scarcity. We find that global water consumption for irrigation could sustainably increase by 48% (408 km3 H2O y-1) – expanding irrigation to 26% of currently rainfed cultivated lands (2.67×106 km2) and producing 37% (3.38×1015 kcal y-1) more calories, enough to feed an additional 2.8 billion people. If current unsustainable blue water consumption (336 km3 y-1) and production (1.19×1015 kcal y-1) practices were eliminated, a sustainable irrigation expansion and intensification would still enable a 24% increase in calorie (2.19×1015 kcal y-1) production. Collectively, these results show that the sustainable expansion and intensification of irrigation in selected croplands could contribute substantially to achieving food security and environmental goals in tandem in the coming decades.
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Water availability is a major factor constraining humanity's ability to meet the future food and energy needs of a growing and increasingly affluent human population. Water plays an important role in the production of energy, including renewable energy sources and the extraction of unconventional fossil fuels that are expected to become important players in future energy security. The emergent competition for water between the food and energy systems is increasingly recognized in the concept of the “food‐energy‐water nexus.” The nexus between food and water is made even more complex by the globalization of agriculture and rapid growth in food trade, which results in a massive virtual transfer of water among regions and plays an important role in the food and water security of some regions. This review explores multiple components of the food‐energy‐water nexus and highlights possible approaches that could be used to meet food and energy security with the limited renewable water resources of the planet. Despite clear tensions inherent in meeting the growing and changing demand for food and energy in the 21st century, the inherent linkages among food, water, and energy systems can offer an opportunity for synergistic strategies aimed at resilient food, water, and energy security, such as the circular economy.
Global Deforestation provides a concise but comprehensive examination of the variety of ways in which deforestation modifies environmental processes, as well as the societal implications of these changes. The book stresses how forest ecosystems may be prone to nearly irreversible degradation. To prevent the loss of important biophysical and socioeconomic functions, forests need to be adequately managed and protected against the increasing demand for agricultural land and forest resources. The book describes the spatial extent of forests, and provides an understanding of the past and present drivers of deforestation. It presents a theoretical background to understand the impacts of deforestation on biodiversity, hydrological functioning, biogeochemical cycling, and climate. It bridges the physical and biological sciences with the social sciences by examining economic impacts and socioeconomic drivers of deforestation. This book will appeal to advanced students, researchers and policymakers in environmental science, ecology, forestry, hydrology, plant science, ecohydrology, and environmental economics. © Christiane Runyan and Paolo D'Odorico 2016. All rights reserved.