Global energy use and carbon emissions from irrigated agriculture.

Irrigation is a land management practice with major environmental impacts. However, global energy consumption and carbon emissions resulting from irrigation remain unknown. We assess the worldwide energy consumption and carbon emissions associated with irrigation, while also measuring the potential energy and carbon reductions achievable through the adoption of efficient and low-carbon irrigation practices. Currently, irrigation contributes 216 million metric tons of CO2 emissions and consumes 1896 petajoules of energy annually, representing 15% of greenhouse gas emissions and energy utilized in agricultural operations. Despite only 40% of irrigated agriculture relies on groundwater sources, groundwater pumping accounts for 89% of the total energy consumption in irrigation. Projections indicate that future expansion of irrigation could lead to a 28% increase in energy usage. Embracing highly efficient, low-carbon irrigation methods has the potential to cut energy consumption in half and reduce CO2 emissions by 90%. However, considering country-specific feasibility of mitigation options, global CO2 emissions may only see a 55% reduction. Our research offers comprehensive insights into the energy consumption and carbon emissions associated with irrigation, contributing valuable information that can guide assessments of the viability of irrigation in enhancing adaptive capacity within the agricultural sector.

Decreasing trends of mean and extreme snowfall in High Mountain Asia.

Intense warming profoundly alters precipitation phase patterns and intensity in High Mountain Asia (HMA). While snowfall climatology and precipitation extremes have been studied, there is a lack of understanding of snowfall extremes within HMA. Here, we investigate the spatial and temporal variability of non-extreme and extreme snowfall in hydrological years 1979-2020 using multi-source meteorological data, compare weather systems during extreme and non-extreme snowfall events, and identify key circulation factors that influence fluctuations in mean annual snowfall and extreme snowfall. The snowfall amount (-0.13 d/mm), days (-0.56 d/a), and fraction (-0.0012) were significantly reduced in HMA, with a shorter snowfall season (-0.52 d/a). Some extreme snowfall metrics (maximum 1-day snowfall and maximum 3-day snowfall) were insensitive to climate change, whereas the maximum consecutive snowfall days (-0.007 d/a), snowfall amount (-0.0023 mm/a), heavy snowfall days (S95pD; 0.0087 d/a), and extremely heavy snowfall days (S99pD; -0.1019 d/a) showed significant decreases. Synthetic analyses show that extreme snowfall events were more likely to occur within a narrow temperature range (-5 °C to 3 °C) with higher relative humidity and precipitation compared to non-extreme events. A stepwise regression method was used to determine that the fluctuation in the average annual snowfall was closely related to the Atlantic Multidecadal Oscillation, whereas the variation in extreme snowfall was mainly influenced by the Southern Oscillation Index. Our research provides a reference for assessing the potential impacts of climate change on a regional scale for risk management and disaster adaptation.

Dynamic changes in water resources and comprehensive assessment of water resource utilization efficiency in the Aral Sea basin, Central Asia.

The Aral Sea Basin in Central Asia faces significant challenges in improving water utilization and treatment because of frequent transboundary river water disputes and shortages of water resources. However, the traditional water resource utilization efficiency (WRUE) assessment models generally have the defect of over-validating evaluation results. To solve this problem, this study used the Coefficient of Variation method to constrain the self-contained weights in the traditional Data Envelopment Analysis (DEA) to construct an improved CV-DEA model, and assessed the WRUE of the Aral Sea Basin countries during 2000-2018 and compared the WRUE with that of the countries in the Mekong River Basin and Northeast Asia, then explored the factors influencing water utilization. The conclusions were drawn: since 1960, the runoff from the upper Amu Darya and Syr Darya rivers increased significantly, while the runoff from the lower Amu Darya River into the Aral Sea declined. Meanwhile, the water area of the Aral Sea shrank from 2.56 × 104 km2 to 0.70 × 104 km2 in 2000-2018, with the Northern Aral Sea remaining stable while the southern part shrinking sharply. The WRUE of the Aral Sea Basin (0.599, on average) was higher than that of the Mekong River Basin (0.547) and lower than that of Northeast Asia (0.885). Kazakhstan and Uzbekistan had the highest WRUE of 0.819 and 0.685 respectively, and the WRUE in both two countries improved from 2000 to 2018. Tajikistan (0.495) and Turkmenistan (0.402) experienced decreases in WRUEs. The high input redundancy of agricultural water consumption was the main driving force affecting WRUE in the basin.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901-2021.

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Clinical and genetic characteristics of maturity-onset diabetes of the young type 13: A systematic review of the literature.

OBJECTIVE: Maturity-onset diabetes of the young type 13 (MODY13), a rare type of monogenic diabetes, is often misdiagnosed as type 1 or type 2 diabetes. To improve early diagnosis and precise treatment, we performed a systematic review and analysis of the literature about MODY13. METHODS: PubMed, Cochrane, Embase, China National Knowledge Infrastructure (CNKI), Chinese BioMedical (CBM) Literature Database, and Wanfang Database were searched using the following search terms: "MODY13," "KCNJ11 maturity-onset diabetes of the young," "KCNJ11-MODY," "maturity-onset diabetes of the young type 13," and "neonatal diabetes mellitus KCNJ11." The demography, clinical characteristics, and gene mutations of patients were expressed with descriptive statistical methods. RESULTS: A total of 33 reports were included in this study, including 75 patients and 28 types of mutations. Thirty-six patients were male. The mean onset age was 25.20 ± 15.26 years. The averages of recorded body mass index, glycated hemoglobin (HbA1c), and fasting C-peptide were 23.45 ± 4.56kg/m2 , 10.07 ± 1.96%, and 0.31 ± 0.23nmol/L, respectively. Most of the mutation sites were located in the cytosolic region of N- and C-terminal domains of Kir6.2. Seven patients were reported to have diabetic chronic complications. CONCLUSION: MODY13 was diagnosed later than other types of MODY and was associated with low fasting C-peptide. Mutation sites of MODY13 were mostly concentrated in N- and C-terminal intracellular domains. The majority of KCNJ11 gene mutations causing MODY 13 were from G to A. The incidence rates of chronic complications were lower than type 1 and type 2 diabetes.

Transcutaneous auricular vagus nerve stimulation in the treatment of disorders of consciousness: mechanisms and applications.

This review provides an in-depth exploration of the mechanisms and applications of transcutaneous auricular vagus nerve stimulation (taVNS) in treating disorders of consciousness (DOC). Beginning with an exploration of the vagus nerve's role in modulating brain function and consciousness, we then delve into the neuroprotective potential of taVNS demonstrated in animal models. The subsequent sections assess the therapeutic impact of taVNS on human DOC, discussing the safety, tolerability, and various factors influencing the treatment response. Finally, the review identifies the current challenges in taVNS research and outlines future directions, emphasizing the need for large-scale trials, optimization of treatment parameters, and comprehensive investigation of taVNS's long-term effects and underlying mechanisms. This comprehensive overview positions taVNS as a promising and safe modality for DOC treatment, with a focus on understanding its intricate neurophysiological influence and optimizing its application in clinical settings.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Minor hallucinations in Parkinson's disease with probable rapid eye movement sleep behavior disorder.

Background: Rapid eye movement sleep behavior disorder (RBD) and minor hallucinations (MHs) are prevalent nonmotor symptoms in Parkinson's disease (PD). The purpose of this study was to explore the association of MHs in PD patients with probable RBD (pRBD). Methods: This cross-sectional study included 291 patients diagnosed with PD. Patients who scored 6 or higher on the Rapid Eye Movement Behavior Disorder (RBD) Screening Questionnaire were defined as pRBD. A comprehensive evaluation was performed for all patients, including the collection of demographic information, clinical assessment, and MH features. Results: Among the 291 PD patients, 69 (23.7%) had pRBD. MHs were observed in 35 (50.7%) patients with pRBD, significantly higher than 29.7% in patients without RBD (p = 0.015). The main type of MHs in pRBD was presence hallucinations with variable content. Patients with pRBD and MHs tended to be older, had a longer disease duration, and were more likely to take levodopa or dopamine-receptor agonists. Besides, the pRBD with MHs group had higher scores on the Nonmotor Symptoms Questionnaire (NMS-Quest) and Hamilton Anxiety Scale (HAMA). Binary logistic regression analysis revealed that longer disease duration and higher NMS-Quest scores were associated with MHs in PD patients with pRBD. Conclusion: A high prevalence of MHs was observed in PD patients with pRBD. The main type of MHs in pRBD was presence hallucinations. MHs in PD with RBD are mainly associated with disease duration and severity of nonmotor symptoms. These findings provide new insights into the interaction between MHs and RBD.

Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity.

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity's response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle's response to global warming.

Sleep structure and related clinical characteristics in drug-naïve Parkinson's disease with subjectively different sleep quality.

Background: Sleep disturbance is a common non-motor symptom of Parkinson's disease (PD). Most polysomnography (PSG) studies are conducted when patients are in their "on medication" state. Our study aimed to investigate changes in the sleep structure in drug-naive PD patients with poor subjective sleep quality based on polysomnography (PSG) and to explore potential correlations between sleep structure and clinical features of the disease. Methods: A total of 44 drug-naive PD patients were included. All patients completed a standardized questionnaire to obtain demographic and clinical characteristics and underwent whole-night PSG recording. Patients with PSQI scores >5.5 were considered poor sleepers, and patients with PSQI scores <5.5 were considered to be good sleepers. Results: There were 24 (54.5%) PD patients in the good sleeper group and 20 (24.5%) PD patients in the poor sleeper group. We observed that poor sleepers had severe non-motor symptoms (NMS) and worse life quality. The PSG displayed that they had a longer wake-up time after sleep onset (WASO) and lower sleep efficiency (SE). Correlation analysis revealed that the micro-arousal index was positively associated with UPDRS-III, and the N1 sleep percentage was negatively associated with the NMS score in good sleepers. For poor sleepers, rapid eye movement (REM) sleep percentage was negatively related to the Hoehn-Yahr (H-Y) stage, WASO increased with UPDRS-III, periodic limb movement index (PLMI) increased with the NMS score, and N2 sleep percentage was negatively related to the score of life quality. Conclusion: Night awakening is the main manifestation of decreased sleep quality in drug-naive PD patients. Poor sleepers have severe non-motor symptoms and poor life quality. Additionally, the increase in nocturnal arousal events may predict the progression of motor dysfunction.

Clinical features of minor hallucinations in different phenotypes of Parkinson's disease: A cross-sectional study.

Background: Minor hallucinations (MHs) are the most common psychiatric symptom associated with Parkinson's disease (PDPsy), but little is known about their characteristics in different motor phenotypes, especially postural instability gait difficulty (PIGD). The aim of this study was to explore the clinical features of MHs in different subtypes of PD. Methods: In this cross-sectional study, 213 patients with Parkinson's disease (PD) were recruited, and the data obtained included comprehensive demographics, motor subtypes, clinical scale scores, and MH contents. Motor subtypes were classified as tremor-dominant (TD), PIGD or indeterminate according to Stebbins' method. Results: A total of 213 PD patients were included: 90 (42.3%) TD patients, 98 (46.0%) PIGD patients and 25 (11.7%) indeterminate. In total, 70 (32.9%) patients experienced MHs. Compared to patients with the TD phenotype, we found that patients with the PIGD phenotype had more severe motor and nonmotor symptoms. They also had a higher incidence of visual illusions (VIs) and a shorter MH latency. Conclusion: Our study demonstrated that compared to patients with the TD phenotype, patients with the PIGD phenotype had a higher incidence of MHs, especially VIs, which may lead to a higher incidence of visual hallucinations (VHs). They also had a shorter latency of MHs than patients with the TD phenotype, suggesting an earlier onset of MHs and a worse prognosis.

Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.

OBJECTIVES: This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS: The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS: DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS: DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.

Spatio-Temporal Development of Vegetation Carbon Sinks and Sources in the Arid Region of Northwest China.

Drylands, which account for 41% of Earth's land surface and are home to more than two billion people, play an important role in the global carbon balance. This study analyzes the spatio-temporal patterns of vegetation carbon sinks and sources in the arid region of northwest China (NWC), using the net ecosystem production (NEP) through the Carnegie-Ames-Stanford approach (CASA). It quantitatively evaluates regional ecological security over a 20-year period (2000-2020) via a remote sensing ecological index (RSEI) and other ecological indexes, such as the Normalized Difference Vegetation Index (NDVI), fraction of vegetation cover (FVC), net primary productivity (NPP), and land use. The results show that the annual average carbon capacity of vegetation in NWC changed from carbon sources to carbon sinks, and the vegetation NEP increased at a rate of 1.98 gC m-2 yr-1 from 2000 to 2020. Spatially, the annual NEP in northern Xinjiang (NXJ), southern Xinjiang (SXJ) and Hexi Corridor (HX) increased at even faster rates of 2.11, 2.22, and 1.98 gC m-2 yr-1, respectively. Obvious geographically heterogeneous distributions and changes occurred in vegetation carbon sinks and carbon sources. Some 65.78% of the vegetation areas in NWC were carbon sources during 2000-2020, which were concentrated in the plains, and SXJ, the majority carbon sink areas are located in the mountains. The vegetation NEP in the plains exhibited a positive trend (1.21 gC m-2 yr-1) during 2000-2020, but this speed has slowed since 2010. The vegetation NEP in the mountain exhibited only intermittent changes (2.55 gC m-2 yr-1) during 2000-2020; it exhibited a negative trend during 2000-2010, but this trend has reversed strongly since 2010. The entire ecological security of NWC was enhanced during the study period. Specifically, the RSEI increased from 0.34 to 0.49, the NDVI increased by 0.03 (17.65%), the FVC expanded by 19.56%, and the NPP increased by 27.44%. Recent positive trends in NDVI, FVC and NPP have enhanced the capacity of vegetation carbon sinks, and improved the eco-environment of NWC. The scientific outcomes of this study are of great importance for maintaining ecological stability and sustainable economic development along China's Silk Road Economic Belt.

Recent Tianshan warming in relation to large-scale climate teleconnections.

On the alpine areas such as Tianshan Mountains, snow and glaciers are widely distributed, which are sensitive to temperature changes. However, due to high altitude and scarcity of observed stations, the temperature changes and their causes in Tianshan are unclear. To address this issue, this study integrated Thiel-Sen trend test, Pearson correlation, and wavelet analysis methods to analyze the driving factors of temperature changes in Tianshan. We draw the following conclusions: (1) In the past 40 years, Tianshan warmed at a rate of 0.30 °C/decade. Seasonally, the temperature increased the most in spring and summer; spatially, the east Tianshan experienced the most warming. (2) Climate change has affected significant warming in the Tianshan. (3) The large-scale climate teleconnections found to be associated with warming in the Tianshan include North Pacific pattern, Atlantic Multidecadal Variability (AMV), North Atlantic Oscillation, and Western Hemisphere Warm Pool (WHWP). During the study period, the temperature changes lagged AMV and WHWP by 1.5 months, North Tropical Atlantic Index and Tropical Northern Atlantic Index by 3 months, and Arctic Oscillation by 4 months. This research contributes to understanding the response of dry mountains to global warming and atmospheric circulation changes.

Shifting in the global flood timing.

Climate change will have an impact on not only flood magnitude but also on flood timing. This paper studies the shifting in flood timing at 6167 gauging stations from 1970 to 2010, globally. The shift in flood timing and its relationship with three influential factors (maximum 7-day precipitation, soil moisture excess, and snowmelt) are investigated. There is a clear global pattern in the mean flooding date: winter (Dec-Feb) across the western Coastal America, western Europe and the Mediterranean region, summer (Jun-Aug) in the north America, the Alps, Indian Peninsula, central Asia, Japan, and austral summer (Dec-Feb) in south Africa and north Australia area. The shift in flood timing has a trend from - 22 days per decade (earlier) to 28 days per decade (delayed). Earlier floods were found extensively in the north America, Europe and northeast Australia while delayed floods were prevailing in the Amazon, Cerrado, south Africa, India and Japan. Earlier flood timing in the north America and Europe was caused by earlier snowmelt while delayed extreme soil moisture excess and precipitation have jointly led to delayed floods around the monsoon zone, including south Africa, India and Japan. This study provides an insight on the shifting mechanism of flood timing, and supports decisions on the global flood mitigation and the impact from future climate change.

The effects of landscape change on habitat quality in arid desert areas based on future scenarios: Tarim River Basin as a case study.

Human activities have caused spatiotemporal patterns of land use and land cover (LULC) change. The LULC change has directly affected habitat quality (HQ) and ecosystem functions. Assessing, simulating, and predicting spatiotemporal changes and future trends under different scenarios of LULC-influenced HQ is beneficial to land use planners and decision-makers, helping them to formulate plans in a sustainable and responsible way. This study assesses and simulates the HQ of the Tarim River Basin (TRB) using the future land use simulation model (FLUS), the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, and partial least squares regression (PLSR). Since 2000, the TRB has experienced a declining trend in HQ from 0.449 to 0.444, especially in the lower elevations (740-2000m) and on sloped land (<10°). The decline will continue unless effective and sustainable plans are implemented to halt it. Agricultural and settlement areas have a lower HQ and a higher degree of habitat degradation than native habitats. This shows that the expansion of oasis agriculture (with an annual growth rate of 372.17 km2) and settlements (with an annual growth rate of 23.50 km2) has caused a decline in native habitat and subsequent habitat fragmentation. In other words, changes in LULC have caused a decline in the HQ. Moreover, there is a significant negative correlation between HQ and urbanization rate (p<0.01), and the PLSR also indicate that number of patches (NP), area-weighted mean fractal dimension index (FRAC_AM), percentage of landscape (PLAND), and largest patch index (LPI) were also important contributors to worsening the HQ. Therefore, the TRB urgently needs appropriate strategies to preserve its natural habitats into the future, based on the ecological priority scenario (EPS) and harmonious development scenario (HDS), which can help to maintain a high-quality habitat.

Spatiotemporal variation in irrigation water requirements in the China-Pakistan Economic Corridor.

Agricultural irrigation consumes most of the fresh water in the China-Pakistan Economic Corridor (CPEC), directly affecting water resource management and allocation. Irrigation water demand is a key component of regional water resources management. We analyzed spatiotemporal variation in irrigation water requirement, irrigation demand index (IDI), and the proposed regional optimization of irrigation water use based on the Bayesian probability network. Results showed that: (1) The IDI in the study area increased slightly (trend slope = 0.028 a-1) as the effective precipitation increased by 63% during this period, and total irrigation water requirement (IR) decreased from 277.61 km3 in 2000 to 240 km3 in 2015. (2) Cotton had the highest crop IDI, followed by maize and wheat. (3) According to the comprehensive scenario analysis, improving the crop planting structure (by moderately increasing the planting proportion of maize in the CPEC) is conducive to improving regional water and food security by enhancing the grain yield (+ 9%), reducing the malnourished proportion of the population (low state + 7.2%), and bolstering water-saving irrigation technologies in Pakistan as well as water conveyance systems in Pakistan. Our results form an important baseline in determining the way forward on sustainable water resource utilization management in the CPEC.