Assessing irrigated agriculture's surface water and groundwater consumption by combining satellite remote sensing and hydrologic modelling

Estimating Groundwater Pumping for Irrigation: A Method Comparison

Effective groundwater management is critical to future environmental, ecological, and social sustainability and requires accurate estimates of groundwater withdrawals. Unfortunately, these estimates are not readily available in most areas due to physical, regulatory, and social challenges. Here, we compare four different approaches for estimating groundwater withdrawals for agricultural irrigation. We apply these methods in a groundwater-irrigated region in the state of Kansas, USA, where high-quality groundwater withdrawal data are available for evaluation. The four methods represent a broad spectrum of approaches: (1) the hydrologically-based Water Table Fluctuation method (WTFM); (2) the demand-based SALUS crop model; (3) estimates based on satellite-derived evapotranspiration (ET) data from OpenET; and (4) a landscape hydrology model which integrates hydrologic- and demand-based approaches. The applicability of each approach varies based on data availability, spatial and temporal resolution, and accuracy of predictions. In general, our results indicate that all approaches reasonably estimate groundwater withdrawals in our region, however, the type and amount of data required for accurate estimates and the computational requirements vary among approaches. For example, WTFM requires accurate groundwater levels, specific yield, and recharge data, whereas the SALUS crop model requires adequate information about crop type, land use, and weather. This variability highlights the difficulty in identifying what data, and how much, are necessary for a reasonable groundwater withdrawal estimate, and suggests that data availability should drive the choice of approach. Overall, our findings will help practitioners evaluate the strengths and weaknesses of different approaches and select the appropriate approach for their application.

Effects of agricultural activities on hydrochemistry in the Shiyang River Basin, China

Agricultural water accounts for more than 80% of the available water in arid areas. Agricultural activities have a great impact on surface water and groundwater. If the impact of agricultural activities on hydrochemistry is not prevented, the risk of water quality change in arid areas may be greatly intensified. Based on the hydrochemical data of the whole Shiyang River Basin from April 2014 to October 2019, this paper analyzes the impact of agricultural activities on hydrochemistry in the basin. The results show that (i) in the middle and lower reaches of farmland with high intensity of agricultural activities, the ion concentration of groundwater in summer and autumn is significantly higher than that in winter and spring due to the influence of irrigation; (ii) the runoff ion concentration in the backflow of the river reaches recharged by irrigation water is significantly higher than that of other reaches; (iii) due to strong evaporation, different types of reservoirs will lead to an overall increase in ion concentration, which is more obvious in plain reservoirs and river tail lakes. In addition, the reservoirs have a certain removal effect on nitrates.

A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.

A Review of Irrigation Information Retrievals from Space and Their Utility for Users

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.

Integrating Drone Technology into an Innovative Agrometeorological Methodology for the Precise and Real-Time Estimation of Crop Water Requirements

Precision agriculture has been at the cutting edge of research during the recent decade, aiming to reduce water consumption and ensure sustainability in agriculture. The proposed methodology was based on the crop water stress index (CWSI) and was applied in Greece within the ongoing research project GreenWaterDrone. The innovative approach combines real spatial data, such as infrared canopy temperature, air temperature, air relative humidity, and thermal infrared image data, taken above the crop field using an aerial micrometeorological station (AMMS) and a thermal (IR) camera installed on an unmanned aerial vehicle (UAV). Following an initial calibration phase, where the ground micrometeorological station (GMMS) was installed in the crop, no equipment needed to be maintained in the field. Aerial and ground measurements were transferred in real time to sophisticated databases and applications over existing mobile networks for further processing and estimation of the actual water requirements of a specific crop at the field level, dynamically alerting/informing local farmers/agronomists of the irrigation necessity and additionally for potential risks concerning their fields. The supported services address farmers’, agricultural scientists’, and local stakeholders’ needs to conform to regional water management and sustainable agriculture policies. As preliminary results of this study, we present indicative original illustrations and data from applying the methodology to assess UAV functionality while aiming to evaluate and standardize all system processes.

A Remote Sensing Diagnosis of Water Use and Water Stress in a Region with Intense Irrigation Growth in Brazil

Western Bahia, Brazil, is a classic example of a region where intense irrigation growth has led to acute water stress situations in a few small basins. The water stress problem has the potential to grow regionally. However, there are currently no systematic field measurements of water withdrawn from rivers or groundwater to supply irrigation systems. In this work, we merge remote sensing and river gauge data to assess both the amount of water used for irrigation in Western Bahia and also its consequences for regional water stress, identifying water conflict situations and assessing water security. Remote sensing products used include time series of the normalized difference vegetation index, evapotranspiration, and rainfall. Field data include time series of river discharge and calibration data for crop status and actual evapotranspiration. From calibrated remote sensing products, three-day water balances were calculated for each center pivot using computations of irrigation depth and water uptake for irrigation, both individually at the center-pivot scale and integrated regionally. From these regional integrations, a simple water-use diagnostic indicated that three sub-basins presented the most critical conditions for water conflicts. An in-depth analysis of these sub-basins shows that, despite the high water stress, water use for irrigation has been steadily increasing, pushing the water use to its limits. This work demonstrates that the use of remote sensing products together with field data is a powerful tool for diagnosing water conflict situations. The limitations of this work relate to the absence of field data to validate the water uptake estimated and to the lack of additional long-term and high-quality river flow stations to provide diagnostics for all small basins in the region.

Exploiting High-Resolution Remote Sensing Soil Moisture to Estimate Irrigation Water Amounts over a Mediterranean Region

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.

Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model’s coefficients of determination (R²) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha⁻¹ and 786.5 kg ha⁻¹. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion.

Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

Direct or Spillover Effect: The Impact of Pure Technical and Scale Efficiencies of Water Use on Water Scarcity in China

The spatial relationship between water use efficiency and water scarcity has been widely discussed, but little attention has been paid to the impact of the pure technical and scale efficiencies of water use on water scarcity. Using input-oriented data envelopment analysis (DEA) and panel spatial Durbin models (SDM), the direct and spillover effects of different water use efficiencies on water scarcity from 2007 to 2016 in China were examined at the regional scale. The results show that the water use pure technical efficiency had significantly negative direct effects on water scarcity; however, the water use scale efficiency did not have a similar effect. The improvement in water use pure technical efficiency in one region could aggravate the water scarcity in neighboring regions through spatial spillover effects, but the same effect was not observed between the water use scale efficiency and water scarcity. Finally, we propose solutions to improve the water use efficiency to reduce the water scarcity.

Simulation of regional irrigation requirement with SWAT in different agro-climatic zones driven by observed climate and two reanalysis datasets

Irrigation water is one of the most substantial water uses worldwide. Thus, global simulation studies about water availability and demand typically include irrigation. Nowadays, regional scale is of major interest for water resources management but irrigation lacks attention in many catchment modelling studies. This study evaluated the performance of the agro-hydrological model SWAT (Soil and Water Assessment Tool) for simulating streamflow, evapotranspiration and irrigation in four catchments of different agro-climatic zones at meso-scale (Baitarani/India: Subtropical monsoon; Ilmenau/Germany: Humid; Itata/Chile: Mediterranean; Thubon/Vietnam: Tropical). The models were calibrated well with Kling-Gupta Efficiency (KGE) varying from 0.74-0.89 and percentage bias (PBIAS) from 5.66-6.43%. The simulated irrigation is higher when irrigation is triggered by soil-water deficit compared to plant-water stress. The simulated irrigation scheduling scenarios showed that a significant amount of water can be saved by applying deficit irrigation (25-48%) with a small reduction in annual average crop yield (0-3.3%) in all climatic zones. Many catchments with a high share of irrigated agriculture are located in developing countries with a low availability of input data. For that reason, the application of uncorrected and bias-corrected National Centers for Environmental Prediction (NCEP) and ERA-interim (ERA) reanalysis data was evaluated for all model scenarios. The simulated streamflow under bias-corrected climate variables is close to the observed streamflow with ERA performing better than NCEP. However, the deviation in simulated irrigation between observed and reanalysis climate varies from -25.5-45.3%, whereas the relative irrigation water savings by deficit irrigation could be shown by all climate input data. The overall variability in simulated irrigation requirement depends mainly on the climate input data. Studies about irrigation requirement in data scarce areas must address this in particular when using reanalysis data.

Remote Sensing for Irrigation of Horticultural Crops

This paper reviews the literature on applications of remote sensing for monitoring soil- and crop- water status for irrigation purposes. The review is organized into two main sections: (1) sensors and platforms applied to irrigation studies and (2) remote sensing approaches for precision irrigation to estimate crop water status, evapotranspiration, infrared thermography, soil and crop characteristics methods. Recent literature reports several remote sensing (RS) approaches to monitor crop water status in the cultivated environment. Establishing the right amount of water to supply for different irrigation strategies (maximization of yield or water use efficiency (WUE)) for a large number of crops is a problem that remains unresolved. For each crop, it will be necessary to create a stronger connection between crop-water status and crop yield.

Itaipu royalties: The role of the hydroelectric sector in water resource management

For countries dependent on hydroelectricity, water scarcity poses a real risk. Hydroelectric plants are among the most vulnerable enterprises to climate change. Investing in the conservation of the hydrographic basin is a solution found by the hydropower sector. Given the importance of the Itaipu plant to the energy matrix of Brazil and Paraguay, the aim of this study is to review the current distribution of royalties from Itaipu, using the hydrographic basin as a of criterion of analysis. Approximately 98.73% of the Itaipu basin is in Brazil. The flow contributes 99% of the total electricity generated there, while the drop height of the water contributes only 1%. Under the current policy, royalties are shared equally between Brazil and Paraguay. In the proposed approach, each country would receive a percentage for their participation in the drop height and water flow in the output of the turbines, which are intrinsic factors for electricity generation. Thus, Brazil would receive 98.35% of the royalties and Paraguay, 1.65%. The inclusion of the hydrographic basin as a criterion for the distribution of royalties will promote more efficient water resource management, since the payment will be distributed throughout the basin of the plant. The methodology can be applied to hydroelectric projects worldwide.