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Map of the Haihe River Basin (HRB) containing the NCP domain. The depicted river network represents the natural drainage system. The map of mean annual NDVI is differentiated into cropland and natural vegetation based on a MODIS land cover classification. Based on this classification, urban areas are shown in gray, waterbodies in blue, and barren soil in brown. The name of the eight discharge stations corresponds to the IDs in Table 1. The top left panel indicates the HRB in dark gray and China in medium gray.
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Irrigation is the greatest human interference with the terrestrial water cycle. Detailed knowledge on irrigation is required to better manage water resources and to increase water use efficiency (WUE). This study applies a framework to quantify net irrigation at monthly timescale at a spatial resolution of 1 km² providing high spatial and temporal...
Citations
... The North China Plain (NCP) with an area of about 400 thousand square kilometers is China's agricultural base providing about 37% of the national wheat and maize production (National Bureau of Statistics of China, 2020). Irrigation contributes an essential component of agriculture in this region due to the limited annual precipitation of approximately 475 mm relative to similar locations in South China (Kang & Eltahir, 2018;Koch et al., 2020). Moreover, owing to the influence of the sub-humid temperate continental monsoon climate, the seasonal variation in precipitation is highly uneven, with approximately 70% of the annual precipitation occurring from June-September ( Figure S1 in Supporting Information S1). ...
... Moreover, owing to the influence of the sub-humid temperate continental monsoon climate, the seasonal variation in precipitation is highly uneven, with approximately 70% of the annual precipitation occurring from June-September ( Figure S1 in Supporting Information S1). Therefore, the relatively high potential evapotranspiration and low precipitation from March-May render spring irrigation more important in ensuring crop growth, accounting for a high proportion of the annual irrigation water use (Koch et al., 2020;. Furthermore, despite the considerably high precipitation from June-September, summer irrigation is still needed based on site-specific needs . ...
Plain Language Summary
Irrigation exerts a stronger impact on extreme temperatures than on mean temperatures. The North China Plain (NCP) is a typical winter wheat‐summer maize rotation planting area, where irrigation is necessary in both spring and summer, but with a higher proportion of irrigation water applied during spring. The climatic effects of spring and summer irrigation in the NCP are intertwined due to the carryover effects of soil moisture. Recently, the climatic effect of irrigation in the NCP has been extensively explored, whereas the cross‐seasonal effects of irrigation on summer extreme heat events have never been quantified. In this study, we employ the Weather Research and Forecasting model coupled with a demand‐driven irrigation algorithm to discern the effects of spring and/or summer irrigation on summer extreme heat events by means of idealized climate simulations. The results show that spring and summer irrigation significantly reduces the frequency and intensity of summer extreme heat events by approximately −6.5 days and −1.0°C, of which spring irrigation contributes about 38% and 30%, respectively. Our findings underline the importance of irrigation‐induced climate impacts in mitigating extreme heat events and emphasize that climate change adaptation planning in terms of irrigation must account for cross‐seasonal climatic effects.
... This is not surprising since the NCP is one of the food bowls of the country, and the precipitation water supply there is far less than the crop water demand (Fang et al., 2010). As a result, the NCP is also one of the global hot spots of irrigation and concerns surrounding possible freshwater depletion (Kang & Eltahir, 2018;Koch et al., 2020). ...
The reliability of irrigated area (IA) information dominates the performance of irrigation water use and crop modeling accuracy. IA is typically mapped using Food and Agriculture Organization (FAO) agricultural census and remote sensing indices. Recent advances in machine learning and sampling techniques further improve IA mapping. However, the relative performances of different IA mapping approaches and their capability in capturing long‐term IA temporal variability remain unknown. Here, 1861 county‐level IA information from Government Censored Data (GCD) during 2000–2021 are collected, cross‐validated, and employed to evaluate commonly used gridded IA data sets. Results show that IA data sets based on the direct interpolation of FAO agricultural census can accurately capture the spatial distribution of IA. However, FAO statistics are only available in a particular year, which cannot capture inter‐annual irrigation variations. In contrast, IA products solely based on vegetation indices are prone to positive biases over humid regions due to the lack of contrast in vegetation dynamics. Overall, the latest GCD‐based machine learning IA data sets are relatively more accurate, but they are also problematic in estimating IA trends due to the use of temporally static training samples. Such biases are tightly related to agricultural suitability (AS calculated using precipitation and potential evapotranspiration). This suggests that AS should be employed as an endogenous variable in future machine learning based IA mapping algorithms.
... To verify this possibility, the LAI changes during the winter wheat growing season (March to May, or MAM) are examined-since winter wheat irrigation represents the majority of NCP freshwater use (Koch et al., 2020). Figure 4 shows that NCP MAM LAI has a strong positive trend. ...
Globally, a persistent decline of freshwater availability has been identified over a number of intensively irrigated agricultural regions. Large‐scale inter‐basin water transfer (IBWT) has been suggested as a key tool for stabilizing regional terrestrial water storage (TWS). However, IBWT projects are prohibitively expensive, and their large‐scale cost effectiveness remains unclear. Here we quantify the IBWT impacts on TWS trends in the North China Plain (NCP), a global hotspot for TWS depletion and IBWT. Based on in‐situ observations, remote sensing, and water balance principles, we provide a framework to disentangle complex climate and anthropogenic impacts on NCP TWS. Results show that the NCP TWS depletion rate was significantly attenuated in 2015–2021, which is primarily attributable to recently enhanced IBWT. Otherwise, the average NCP TWS would currently be 94.9 ± 4.9 mm (or 12.2 ± 0.6 km³) lower. However, the positive effect of IBWT is partly offset by increased crop water consumption (−24.1 ± 5.2 mm or −3.1 ± 0.7 km³). IBWT and agricultural management (i.e., reducing crop density) are both necessary for stabilizing future NCP TWS. Otherwise, a TWS declining trend exceeding 100 mm/year may occur under elevated CO2 conditions. As such, this study verifies the feasibility and effectiveness of IBWT for mitigating regional water shortages, as well as the crucial role of agricultural management in stabilizing regional TWS.
... The majority of the approaches propose the assimilation of remote sensing data into a Land Surface Model (LSM) such as Noah (Nie et al., 2022;Modanesi et al., 2022, SURFEX (Escorihuela andQuintana-Seguí, 2016) and the Community Land Model (Sacks et al., 2009;Zhu et al., 2020;Yao et al., 2022). Other approaches proposed to estimate irrigation by closing the water balance equation using SM observations (Brocca et al., 2018, or comparing ET observations between irrigated pixels and non-irrigated areas (Brombacher et al., 2022), or between observed and modeled hydrological variables (Kumar et al., 2015;Zaussinger et al., 2019;Zhang and Long, 2021;Koch et al., 2020;Kragh et al., 2023;Peng et al. (2021) highlighted the need of using SM observations with a spatial resolution of at least 1 km to adequately describe irrigation practices. Brocca et al. (2018) proposed to input SM observations into a simple soil water balance equation, modified from the SM2RAIN methodology, created for rainfall amounts correction (Brocca et al., 2014;Brocca et al., 2016). ...
... The approach proved to be effective only for the case in which enough natural pixels were available in the study area, which is not always the case for intensively cultivated areas. Koch et al. (2020) and Kragh et al. (2023) proposed to compare modeled and observed ET values to estimate net irrigation at a spatial resolution of 1 km and 5 km, respectively. These approaches focused on the estimation of net irrigation, which is only a portion of the actual irrigation amount, corresponding to the evaporative loss. ...
Irrigated agriculture is the primary driver of freshwater use and is continuously expanding. Precise knowledge of irrigation amounts is critical for optimizing water management, especially in semi-arid regions where water is a limited resource. This study proposed to adapt the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM was originally conceived to correct precipitation products, assimilating Soil Moisture (SM) observations into an antecedent precipitation index (API) formula, using a particle filter scheme. This novel application of PrISM uses initial precipitation and SM observations to detect instances of water excess in the soil (not caused by precipitation) and estimates the amount of irrigation, along with its uncertainty. This newly proposed approach does not require extensive calibration and is adaptable to different spatial and temporal scales. The objective of this study was to analyze the performance of PrISM for irrigation amount estimation and compare it with current state-of-the-art approaches. To develop and test this methodology, a synthetic study was conducted using SM observations with various noise levels to simulate uncertainties and different spatial and temporal resolutions. The results indicated that a high temporal resolution (less than 3 days) is crucial to avoid underestimating irrigation amounts due to missing events. However, including a constraint on the frequency of irrigation events, deduced from the system of irrigation used at the field level, could overcome the limitation of low temporal resolution and significantly reduce underestimation of irrigation amounts. Subsequently, the developed methodology was applied to actual satellite SM products at different spatial scales (1 km and 100 m) over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer in Catalunya, Spain, where in situ irrigation amounts were available for various years. The validation resulted in a total Pearson's correlation coefficient (r) of 0.80 and a total root mean square error (rmse) of 7.19 mm∕week for the years from 2017 to 2021. Additional validation was conducted at the field level in the Segarra-Garrigues irrigation district using in situ data from a field where SM profiles and irrigation amounts were continuously monitored. This validation yielded a total biweekly r of 0.81 and a total rmse of − 9.34 mm∕14-days for the years from 2017 to 2021. Overall, the results suggested that PrISM can effectively estimate irrigation from SM remote sensing data, and the methodology has the potential to be applied on a large scale without requiring extensive calibration or site-specific knowledge.
... The rising water demand will inevitably have an impact on groundwater overexploitation in the region. Koch et al. (2020) estimated average annual net irrigation of 126 mm/yr (15.2 km 3 /yr) for NCP and annual winter wheat classifications reveal an increasing crop area with a trend of 2200 km 2 /yr. the NCP confronts significant issues in the management of water supplies, especially groundwater reserves, due to its status as a vital producing and living area in China. ...
... When compared to GRACE-derived GWSA at coarse resolution, the spatial fluctuation captured in the high-resolution GWSA data (Fig. 10) seems to be homogeneous in coarse resolution (Fig. S8). For example, in Xingtai, Handan, Anyang, Hebi, Jiaozuo, Xinxiang, and Puyang, downscaled GWSA observed strong spatial heterogeneity and temporal variations at a higher decline with primarily intensive winterwheat and summer-maize cropping system and increasing agricultural land use area in recent years Koch et al., 2020). For instance, GRACE-derived GWSA shows homogenized spatial distribution at pixel scale over the Xingtai and Handan regions (Fig. S8), while spatially distributed GWSA variations are observed by downscaled GWSA within these regions (Fig. 10). ...
Groundwater storage and depletion fluctuations in response to groundwater availability for irrigation require understanding on a local scale to ensure a reliable groundwater supply. However, the coarser spatial resolution and intermittent data gaps to estimate the regional groundwater storage anomalies (GWSA) prevent the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GARCE-FO) mission from being applied at the local scale. To enhance the resolution of GWSA measurements using machine learning approaches, numerous recent efforts have been made. With a focus on the development of a new algorithm, this study enhanced the GWSA resolution estimates to 0.05° by extensively investigating the continuous spatiotemporal variations of GWSA based on the regional downscaling approach using a regression algorithm known as the geographically weighted regression model (GWR). First, the modified seasonal decomposition LOESS method (STL) was used to estimate the continuous terrestrial water storage anomaly (TWSA). Secondly, to separate GWSA from TWSA, a water balance equation was used. Third, the continuous GWSA was downscaled to 0.05° based on the GWR model. Finally, spatio-temporal properties of downscaled GWSA were investigated in the North China Plain (NCP), China's fastest-urbanizing area, from 2003 to 2022. The results of the downscaled GWSA were spatially compatible with GRACE-derived GWSA. The downscaled GWSA results are validated (R=0.83) using in-situ groundwater level data. The total loss of GWSA in cities of the NCP fluctuated between 2003 and 2022, with the largest loss seen in Handan (-15.21 ± 7.25 mm/yr), Xingtai (-14.98 ± 7.25 mm/yr), and Shijiazhuang (-14.58 ± 7.25 mm/yr). The irrigated winter-wheat farming strategy is linked to greater groundwater depletion in several cities of NCP (e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The study's high-resolution findings can help with understanding local groundwater depletion that takes agricultural water utilization and provide quantitative data for water management.
Keywords: North China Plain; GRACE; GWSA; STL; GWR; downscaling
... ET is a critical component in the hydrological cycle. The good correlations (R > 0.94, p < 0.01) between ET and irrigation in the cropland (Fig. S5) are consistent with the previous studies which revealed that irrigation could significantly affect the ET (Qiu et al., 2008;Koch et al., 2020;Allakonon et al., 2022). Based on the results, the decrease in ET from 511 mm year − 1 to 497 mm year − 1 in the HRB cropland between Nir 0 and Nir 10 is mainly ascribed to the decrease of irrigation amount. ...
... According to the published works there are problems with the lateral synchronization of the governing systems. Case studies in south Asia, China, and New Zealand suggest that misalignment may arise from the fact that natural resource management issues are handled in isolated strategy silos (Koch et al., 2020;Saklani et al., 2020;Specht et al., 2019), there is a lack of trust between crucial policy-making entities, and there are discrepancies between the goals and objectives of different levels of Note: Table 5 provides the challenges faced by the existing studies in the area of governance studies. The table is cited based on the highest percentage of studies. ...
Rapid increases in natural resources globally during the 1970s have had far-reaching environmental consequences. The rising exploitation of environmental assets has progressively detrimental societal effects. Therefore, the current study aims to identify the nexus between natural resources and governance conflicts while assessing the role of governance in natural resource management. The results identify three significant categories of governance challenges (associated with capacity, connectivity, and knowledge) and three domains of good governance (effectiveness, involvement, and efficiency). The results highlighted that developing countries would likely need more decision-making power, financial and human resources, leadership on crucial resource challenges, and conflict resolution mechanisms. On the contrary, research into natural resource management governance structures in industrialized countries has often shown problems with policy clarity and the alignment of stakeholder institutions' goals and aims. The study adds to the existing literature by summarizing new organizational capacities and governance frameworks essential for better natural resource management.
... Integrated approaches estimate groundwater extraction using information about the hydrologic conditions and crop requirements. Examples of integrated approaches include integrated hydrologic models that simulate both hydrologic fluxes and storage and crop growth (e.g., Samaniego et al. 2010;Koch et al. 2020;Niswonger 2020). ...
... Integrated approaches have increased in use over the past several years due to increases in data availability and computational ability. For example, Koch et al. (2020) setup the multiscale Hydrologic Model (mHM, Samaniego et al. 2010) over the Haihe River Basin in China and calibrated it to discharge and ET for rainfed fields. They estimated monthly net irrigation rates (evaporative loss from irrigation water) by computing the differences between the estimated ET from the PT-JPL model using remotely sensed land surface temperature and vegetation data and the mHM forced without irrigation. ...
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. This article is protected by copyright. All rights reserved.
... Remote sensing images have provided a promising source of data for mapping ET over large areas in cost-effective ways [8][9][10][11][12] and have been increasingly used for mapping ET [13][14][15]. One advantage of remote sensing images is that they do not require knowledge of the crop growth stage, as they establish a direct link between surface radiances and energy balance components. ...
Evapotranspiration (ET) is a critical component of the water cycle, and an accurate prediction of ET is essential for water resource management, irrigation scheduling, and agricultural productivity. Traditionally, ET has been estimated using satellite-based remote sensing, which provides synoptic coverage but can be limited in spatial resolution and accuracy. Unmanned aerial vehicles (UAVs) offer improved ET prediction by providing high-resolution imagery of the Earth’s surface but are limited to a small area. Therefore, UAV and satellite images provide complementary data, but the integration of these two data for ET prediction has received limited attention. This paper presents a method that integrates UAV and satellite imagery for improved ET prediction and applies it to five crops (corn, rye grass, wheat, and alfalfa) from agricultural fields in the Walla Walla of eastern Washington State. We collected UAV and satellite data for five crops and used the combination of remote sensing models and statistical techniques to estimate ET. We show that UAV-based ET can be integrated with the Landsat-based ET with the application of integration factors. Our result shows that the Root Mean Square Error (RMSE) of daily ET for corn (Zea mays), rye grass (Lolium perenne), wheat (Triticum aestivum), peas (Pisum sativum), and alfalfa (Medicago sativa) can be improved by the application of the integration factor to the Landsat based ET in the range of (35.75–65.52%). We also explore the variability and effect of partial cloud on UAV-based ET estimation. Our findings have implications for the use of UAVs in water resource management and highlight the importance of considering multiple sources of data in ET prediction.
... Likewise, Wu et al. (2018) found that although the simulated IWUs in spring and summer are close, the irrigation cooling effects are stronger in summer than in spring owing to the larger increments of green vegetation fractions due to irrigation in summer. However, in fact, due to the uneven seasonal variations in precipitation in the NCP, the IWU in spring is significantly higher than that in summer (Koch et al., 2020;Shu et al., 2012;Yang et al., 2010), and the role of irrigation in preventing water stress and hence the promotion of crop growth in spring should be stronger and more important than that in summer. In this study, irrigation is not activated between June 1 and June 19 in view of the winter wheat harvesting in early June and summer maize planting in mid-to-late June, making the simulated seasonal variations in IWU in accordance with previous studies (Figure 6b). ...
The large‐scale irrigation is demonstrated to significantly affect regional hydroclimatic regime by changing underlying surface (e.g., crop growth and soil moisture), yet the contribution of irrigation‐induced crop “greening” to climatic effects is not well understood. In this study, we use a window‐searching algorithm to evaluate the irrigation effects on vegetation with leaf area index (LAI) as a vegetation index, and further conduct three regional climate simulations to investigate the comprehensive effects of irrigation on surface fluxes and local climate over the North China Plain. We find that irrigation leads to a more significant greening in March‐May (MAM) than in June‐September (JJAS). Especially in April and May, the irrigation‐induced change in LAI (ΔLAI) exceeds 0.4 m² m⁻² in intensively irrigated areas. Irrigation induces a cooling effect in air temperature at 2 m with decreasing magnitudes of 0.58°C in MAM and 0.43°C in JJAS, respectively, in which ΔLAI contributes about 34.5% (0.20°C) and 14.0% (0.06°C). Likewise, the irrigation‐induced changes in latent heat flux, sensible heat flux, and transpiration are all enlarged via the irrigation greening effect. Whereas the increase in soil evaporation is alleviated, because the greening‐triggered enhancement of crop root uptake reduces the increase in soil moisture. Moreover, irrigation effects on net radiation depend on the competing influences of irrigation‐triggered cooling and cloud formation. This study provides beneficial references to understand the impact of irrigation on regional energy balance and water cycle, and highlights that in modeling hydroclimatic feedback to irrigation, the greening effect induced by irrigation should be considered.
