Regional Water Resources Management Research Articles

Estimation of green and blue water evapotranspiration using machine learning algorithms with limited meteorological data: A case study in Amu Darya River Basin, Central Asia

Green-water evapotranspiration (GWET) and blue-water evapotranspiration (BWET) are much frequently discussed variables in the recent debates of water resources management and water productivity in water-scarce regions. But the deficiency of long-term, on-site records and limited observation stations is a critical challenge in determining the veracity of these variables. The GWET and BWET estimations rely considerably on extensive climate data, water fluxes data, soil parameters, crop distribution, and crop management data. However, obtaining accurate data by on-site observations or by remote sensing products is a difficult task in a data-scarce region and fewer variables are not sufficient to empirically estimate GWET and BWET. Machine learning (ML) is a modern artificial intelligence decision-making tool based on the analysis of fed-data and computer algorithms. This study reported the enormous potential of ML algorithms for estimating BWET and GWET using different sets of available climate variables. Wheat crop BWET and GWET were estimated at 114 meteorological stations in the Amu-Darya River Basin (ADRB) in Central Asia, using four most widely used ML algorithms: artificial neural network (ANN), supported vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). ML algorithms were trained with 75 % of the data, while tested and validated with 25 % of the data. A set of 24 models of different unique combinations of available variables were attempted to reasonably estimate GWET and BWET, and satisfying results were achieved. RF was found to be the most-promising ML algorithm to estimate BWET and GWET with limited available climate data. The estimated BWET and GWET can be considered in agriculture water resources policies to minimize further risks to the agroecosystem in ADRB.

The Karakoram Anomaly: Validation through Remote Sensing Data, Prospects and Implications

Millions of people rely on river water originating from snow- and ice-melt from basins in the Hindukush-Karakoram-Himalayas (HKH). One such basin is the Upper Indus Basin (UIB), where the snow- and ice-melt contribution can be more than 80%. Being the origin of some of the world’s largest alpine glaciers, this basin could be highly susceptible to global warming and climate change. Field observations and geodetic measurements suggest that in the Karakoram Mountains, glaciers are either stable or have expanded since 1990, in sharp contrast to glacier retreats that are prevalently observed in the Himalayas and adjoining high-altitude terrains of Central Asia. Decreased summer temperature and discharge in the rivers originating from this region are cited as supporting evidence for this somewhat anomalous phenomenon. This study used remote sensing data during the summer months (July–September) for the period 2000 to 2017. Equilibrium line altitudes (ELAs) for July, August and September have been estimated. ELA trends for July and September were found statistically insignificant. The August ELA declined by 128 m during 2000–2017 at a rate of 7.1 m/year, testifying to the Karakoram Anomaly concomitant with stable to mass gaining glaciers in the Hunza Basin (western Karakoram). Stable glaciers may store fresh water for longer and provide sustainable river water flows in the near to far future. However, these glaciers are also causing low flows of the river during summer months. The Tarbela reservoir reached three times its lowest storage level during June 2019, and it was argued this was due to the low melt of glaciers in the Karakoram region. Therefore, using remote sensing data to monitor the glaciers’ health concomitant with sustainable water resources development and management in the HKH region is urgently needed.

Low-Altitude Remote Sensing Inversion of River Flow in Ungauged Basins

Runoff is closely related to human production, the regional environment, and hydrological characteristics. It is also an important basis for water cycle research and regional water resource development and management. However, obtaining hydrological information for uninformed river sections is complicated by harsh environments, limited transportation, sparse populations, and a low density of hydrological observation stations in the inland arid zone. Here, low-altitude remote sensing technology was introduced to combine riverbed characteristics through unmanned aerial vehicle (UAV) inversion with classical hydraulic equations for ungauged basins in the middle and lower reaches of the Keriya River, northwest China, and investigate the applicability of this method on wide and shallow riverbeds of inland rivers. The results indicated that the estimated average error of the low-altitude remote sensing flow was 8.49% (ranging 3.26–17.00%), with a root mean square error (RMSE) of 0.59 m3·s−1 across the six selected river sections, suggesting that this method has some applicability in the study area. Simultaneously, a method for estimating river flow based on the water surface width– and water depth–flow relationship curves for each section was proposed whereas the precise relationships were selected based on actual section attributes to provide a new method for obtaining runoff data in small- and medium-scale river areas where information is lacking.

Effects of local land-use policies and anthropogenic activities on water quality in the upstream Sesan River Basin, Vietnam

Study regionThis study focuses on the upstream Sesan River Basin in the Central Highlands of Vietnam. Study focusLocal land-use policies and human activities can significantly affect hydrology and increase the magnitude of erosion and nutrients in downstream areas. The effects in terrestrial regions on water quality of the target area were evaluated during the 2000–2018 period using the SWAT (Soil and Water Assessment Tool) with updated land-use conditions following the local policy decisions and agricultural practices in different periods. New hydrological insights for the regionsThis study indicates that the implementation of the local land-use policies, along with extensive anthropogenic activities, has had significant effects on the downstream aquatic environment as compared with the period before the implementation of the land-use policies. Higher annual sediment, total nitrogen (TN), and total phosphorus (TP) loadings were found upstream from the Poko Watershed, where range land predominated, and in southern and southwestern Dakbla Watershed, where arable land and permanent cropland predominated. Arable land had the highest proportion of sediment and nutrient loadings into the reach, especially in the 2005–2009 period (conducting afforestation, agricultural expansion, and urbanization) and in the 2010–2014 period (applying crop conversion policy involving a shift from mixed forests to rubber forests). Understanding the watershed characteristics along with the combination of spatial land use, local land-use policies, and agricultural practices will support the implementation of regional land use and water resources management strategies more comprehensively.

Quantitative hydrological response to climate change and human activities in North and South Sources in upper stream of Qiantang River Basin, East China

Study regionThe North and South Sources in the upper stream of Qiantang River Basin, China. Study focusIdentifying the relative contribution of climate change and human activities to runoff variation is essential for an enhanced understanding on efficient management of regional water resources. We used multiplied abrupt change testing methods to determine the baseline period (BP) and two variation periods (VP1 and VP2) and used six Budyko-based methods to detect the quantitative hydrological response to climate change and human activities. New hydrological insights for the regionThe annual mean runoff of North Source witnessed a decrease in VP1 before it increased during VP2, while it continued to increase in South Source. The climate change dominated the runoff variation in the North Source, while it was human activities that was the main driver for South Source.The human-induced effect contributed to runoff decline in North Source, while it led to the continuous runoff increasing in South Source. It shows that the runoff change due to climate was more sensitive to precipitation than potential evapotranspiration. The land use analysis illustrates that human-induced runoff changes were composed of two aspects: 1) the increased proportion of woodland and decreased ratio of farmland reduced runoff; 2) the increased percentage of town-and-country-land increased runoff. The former influence was relatively limited compared to the latter, as well as the climate change.

Cumulative ecosystem response to Hydraulic Engineering Infrastructure Projects in an arid basin

Hydraulic Engineering Infrastructure Projects (HEIPs) typically show profound effects on hydrological systems and ecosystems. However, data restrictions have limited the exploration of the influences of compound HEIPs on ecosystems to a few studies. This study proposes a watershed-wide ecosystem assessment framework to investigate the impact of HEIPs in the Tarim River Headwaters-Hotan River Basin on the ecosystem of the arid zone. The framework includes a deep learning-meta cellular automata algorithm (DLMCAA) based on the spatiotemporal characteristics of HEIPs and hydro-meteorological and human activities. Moreover, the spatiotemporal relationships between compound HEIPs and ecosystem variances were quantified. The framework including DLMCAA showed a good performance in simulating landcover in 2020, with a Kappa coefficient of 0.89. Therefore, the DLMCAA could be used to simulate and predict ecosystem changes under the HEIPs, which suggested that the framework is effective and practical. An analysis of the spatiotemporal distribution of each ecosystem from 1980 to 2020 showed that the low shrub ecosystems changed most significantly (26.38 %) between 1980 and 2020. Also, the use of spatially driven hydrological project data from different ABC scenarios showed that ecosystems driven by HEIPs were more stable compared to those without HEIPs under future climate change. In particular, the DLMCAA indicated that compound HEIPs had a more positive impact on ecosystem oases in arid lands compared with that of single HEIPs. The results of this study can serve as a scientific reference for assessing the impact of HEIPs, as well as for understanding ecosystem changes and facilitating sustainable water resource management in the arid regions.

IMPROVEMENT OF FRESHWATER RESOURCES MANAGEMENT MODELS ON AN INNOVATIVE BASIS IN THE CONDITIONS OF SUSTAINABLE DEVELOPMENT

Effective use of freshwater resources is an extremely urgent problem in the regions of the Black Sea region due to their uneven distribution on the territory, high level of strategic needs and complex ecological situation. The realization of the above priorities requires, first of all, political will, organizational and economic reforms in the industry, as well as the establishment of a close dialogue between representatives of the state, regional governing bodies, the public and business environment, and science. That is why the improvement of freshwater resource management models on an innovative basis in conditions of sustainable development is quite urgent. The purpose of the article is the process of improving freshwater resource management models on an innovative basis in conditions of sustainable development. The regularities of the water supply of the Odesa region in the context of freshwater resource management are studied. The research is structured on the basis of water management zoning, carried out in accordance with the existing river basins (Northern, Central, Primisky (Pridnistrian), South-Western, Danube water management regions). The peculiarities of economic activity and its impact on the state of freshwater resources within the selected districts are taken into account. It was determined that the Odesa region is characterized by uneven distribution of surface water reserves. The need to determine the investment support reserves of the system of regional management of fresh water resources has been proven. An assessment of the forecast value of capital investments for environmental protection in the Odesa region for 2022, which is 73.2 million UAH, and an estimate of the forecast value for 2022 for current costs for environmental protection in the region, which is 627.0 million UAH . A model of sustainable socio-humanitarian and ecologically balanced management of freshwater resources at the regional level has been developed, which takes into account innovative forms of interaction in the field of water use (corporatization, clustering, concession, capitalization, creation of holdings and associations) and the important role of public-private partnerships. The proposed model requires the transformation of the target orientations of the functioning of the water management complex in the direction of meeting the needs of current and future generations in freshwater resources, ensuring a safe environment and protection from the natural possibilities of water, which should contribute to the necessary institutional and structural transformations at the level of regions, districts and communities. The orientations of the strategic development of the system of regional management of freshwater resources have been defined: actualization of public activity, provision of productive and balanced use of natural and economic resources, including human capital, on the basis of diversification of innovative forms of relationships in the management system itself in the context of institutional transformations.

Improving daily precipitation estimation in the data scarce area by merging rain gauge and TRMM data with a transfer learning framework

Merging the satellite and gauge precipitation has been proofed as an efficient approach to improve the accuracy of quantitative precipitation estimation. However, for areas with few and unevenly distributed rain gauges, accurate precipitation estimation still remains challenging. To address this problem, this paper proposes a framework to improve precipitation estimation for the data scarce area based on transfer learning. Taking the Qinghai-Tibet Plateau as a representative case study, we used two transfer learning methods (fine-tuning, domain-adversarial neural network (DANN)) to transfer precipitation fusion model from the source domain to the target domain. Results indicate that in comparison with the original TRMM data, the root mean square error (RMSE) and mean absolute error (MAE) of the merged precipitation in the Qinghai-Tibet Plateau during 2001–2005 are reduced by 27.6 % and 22.5 % by using the fine-tuning method, and reduced by 29.4 % and 21.5 % by using the DANN method, respectively. Meanwhile, the correlation coefficient (CC) is increased from 0.54 (TRMM data-rain gauge data) to 0.65 (merged data-rain gauge data). The performances of the proposed methods vary spatially, with CC decreased from southeast (0.80) to northwest (<0.40) of the study area. The DANN method performed well on different precipitation intensities, while Swish loss function can help DANN achieve better results on extreme precipitation estimation, with RMSE and MAE reduced by 2.5 % and 4.5 % respectively. The performances of the proposed methods are affected by various factors such as source domain selection and the length of study period. Findings imply that transfer learning provides new insights and new methods to improve precipitation estimation for the data scarce area, which would benefit regional water-related disaster defense and water resources management.

Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by &amp;gt;45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.

Spatial correlation of groundwater level with natural factors using geographically weighted regression model in the Choushui River Alluvial Fan, Taiwan

The groundwater of the Choushui River alluvial fan in Central Taiwan has been overexploited for a long time. It is essential to understand the factors governing changes in groundwater level (GWL) for the use of water resources. In this study, we first conducted a Mann–Kendall test to identify significant trends in the regional GWL and obtained its spatial characteristics using the Moran’s I index in the Choushui River alluvial fan. Furthermore, we established a geographically weighted regression (GWR) model to explore the spatial correlation between natural factors and GWL in dry and wet seasons from 1999 to 2019. The long-term trend analysis shows that the GWL of the Choushui River alluvial fan decline significantly. The Moran’s I index shows that the spatial distribution of GWL had a positive correlation in both dry and wet seasons. GWR model indicate that the GWL are affected by drainage density (Dd), slope (S), normalized difference vegetation index (NDVI), and precipitation (P) during the dry season, while Dd, S, NDVI, and wetness index (WI) have an effect on the GWL during the wet season. These results can not only describe the model applicability for exploring the relationship between natural factors and GWL but also be used as references for future regional water resource utilization and management.

Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021

Accurate understanding the spatiotemporal variability of snow cover and snow phenology over High Mountain Asia (HMA) is of great interest because of their vital impacts on the hydrological processes, global climate system and regional water resource management. This study investigates the spatiotemporal variability of snow-covered extent (SCE), snow-covered days (SCD), snow onset date (SOD), snow end date (SED) and snow duration days (SDD) of HMA using the daily cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products from the hydrological year 2002 to 2020. The vertical difference of the effect of elevation on snow phenology and the response of snow phenology to climate change are explored. The results indicate that the monthly SCE over the whole HMA exhibited a slight decreasing in most months over the study period. The variability of snow cover and snow phenology over the whole HMA generally showed decreased SCD (significant decrease accounts for 15.73%), delayed SOD (significant delay accounts for 14.35%), advanced SED (significant advance accounts for 15.46%) and shortened SDD (significant shorten accounts for 16.01%). It is worth noting that the snow cover in the West Himalaya was enhancing with its increased SCE, increased SCD and lengthened SDD. There is an elevation dependence of snow phenology changes over HMA with the high-altitude mountain ranges generally having a higher SCD, earlier SOD, later SED and longer SDD than low altitude areas. The air temperature of snow accumulation period is the dominant factor affecting the interannual variation of SOD, and the air temperature of snow melt period is the dominant factor affecting the interannual variation of SED. The increase of air temperature leads to the delayed SOD, advanced SED and shortened SDD.

Quantifying the effect of vegetation greening on evapotranspiration and its components on the Loess Plateau

As one of the largest revegetation projects in China, the Grain for Green Project has dramatically increased vegetation greenness (greening) on the Loess Plateau. Large-scale greening inevitably changed the spatiotemporal pattern of water availability on the Loess Plateau by increasing evapotranspiration (ET). Quantifying the effect of greening on ET is critical for regional water resources management and planning. This study uses the maximum entropy production (MEP) model and two attribution methods (the detrending and extending methods) to quantify the effect of greening on ET and its components (soil evaporation (Es) and transpiration (Et)) on the Loess Plateau from 2001 to 2018. The ensemble-averaged results of two attribution methods are used to reduce the potential uncertainty caused by the choice of attribution methods. The MEP model performs well in capturing the spatiotemporal variability of ET on both the site and basin scales. Greening significantly (p < 0.05) increases Et and decreases Es for the Loess Plateau, and the magnitude of increase in Et exceeds the magnitude of decrease in Es, leading to a nonsignificant (p > 0.05) increase in ET. The contribution of greening to mean annual Es, Et, and ET for the Loess Plateau is −16.6, 20.7, and 4.1 mm/yr, respectively. An increase in ET may further exacerbate water scarcity for the Loess Plateau.

Study of regional monthly precipitation based on CEEMD-BILSTM coupled model

Abstract The prediction of monthly precipitation is of great importance for regional water resources management and use. The monthly precipitation sequence is affected by various factors such as atmosphere, region and environment, and has obvious ambiguity, chance and uncertainty. CEEMD based on complementary ensemble empirical mode decomposition can effectively reduce the reconstruction error of time series, and bidirectional long short-term memory (BILSTM) model can effectively learn long-term dependencies in time series. A CEEMD-BILSTM (complementary integrated empirical mode decomposition-bidirectional long short-term memory) coupled model is constructed to predict the monthly precipitation in Zhengzhou, and the performances of the LSTM model, EEMD-LSTM model and EEMD-BILSTM model are compared. The CEEMD-BILSTM model has a maximum relative error of 7.28%, a minimum relative error of 0.00%, and an average relative error of 2.68%, with an RMS error of 2.6% and a coefficient of determination of 0.97 in predicting monthly precipitation in Zhengzhou, which is considered a good accuracy of the CEEMD-BILSTM model for predicting monthly precipitation in Zhengzhou. The model is better than the LSTM model, the EEMD-LSTM model, and the EEMD-BILSTM model and has better fitting ability. It also shows that it has strong nonlinear and complex process learning ability in the hydrological factor model of regional precipitation prediction.

Is the ‘water tower’ reassuring? Viewing water security of Qinghai-Tibet Plateau from the perspective of ecosystem services ‘supply-flow-demand’

Ecosystem service flow plays a vital role in the formation, transportation, transformation, and maintenance of ecosystem services. For ecosystem services with spatiotemporal mismatch of supply and demand, ecosystem service flow explains the integrated process of ‘supply-flow-demand’ of ecosystem services. The present study evaluates the supply and demand of ecosystem water provision services in the Qinghai-Tibet Plateau and simulates the spatial flow pattern and transmission mechanism of water provision services. Additionally, the study establishes dynamic and static water security indices and identify water security level to quantify the water resources security of Qinghai-Tibet Plateau under the changing ecological environment. The research shows: (a) the annual total water surplus were 6.71 × 1011 m3, 8.43 × 1011 m3, 7.86 × 1011 m3 and 2.91 × 1011 m3. The supply–demand relationship of water provisioning service in the Qinghai-Tibet Plateau shows an obvious oversupply condition; (b) The water security level of the plateau is high (level V and level IV), indicating that the water security status of the Qinghai- Tibet Plateau is under good condition, however, the area with low-security levels (Level I and Level II) is increasing. (c) From the perspective of ‘supply-demand-flow’ of ecosystem services, although the function of the water tower on the Qinghai Tibet Plateau has declined, it remained safe condition on the whole study area. The method of establishing spatial correlation between mismatched supply and demand of ecosystem services and evaluating regional water security based on ecosystem service flow constructed in this study presents the water security status and spatial distribution of Qinghai-Tibet Plateau more scientifically, providing a reference for water resource management in other regions.

Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China

The accurate estimation of reference crop evapotranspiration (ET0) is of great significance to improve agricultural water use efficiency and optimize regional water resources management. At present, the applicability evaluation system of ET0 models is still lacking in several climate regions in China, leading to the confusion in application of the ET0 model in some specific regions. In this study, the daily meteorological data of 84 representative stations in four climate regions of China during the past 30 years (1991–2019) were selected to evaluate the ET0 simulation results of twelve empirical models (four temperature models, five radiation models, and three hybrid models) on the daily scale, and the optimal models suitable for each climate region were screened. Whale optimization algorithm (WOA) was used to optimize the optimal model to improve the simulation accuracy, and the ET0 results were compared with those predicted by extreme learning machine (ELM). The results showed that the estimation accuracy of the hybrid model was the best throughout China, followed by the radiation model, and the temperature model was relatively poor, with R2 ranges of 0.77–0.88, 0.60–0.86, and 0.58–0.82, respectively. Among the temperature-based models, Hargreaves-Samani and Improve Baier-Robertson model had the highest accuracy, with R2 of 0.80 and 0.79. Among the radiation-based models, Priestley-Taylor and Jensen-Haise models had the best accuracy, with R2 of 0.82 and 0.79. Among the hybrid models, Penman model had the highest accuracy, with R2 of 0.84. The accuracy of Hargreaves-Samani and Improve Baier-Robertson model in SMZ climate region was higher than TCZ, TMZ, and MPZ, and the accuracy of Jensen-Haise model in TCZ was the highest. The estimation accuracy of Priestley-Taylor and Penman models was similar in SMZ, TCZ, TMZ and MPZ. Using WOA to optimize the optimal temperature, radiation, and hybrid models, the prediction accuracy was improved by 12.05 %, 11.06 %, and 10.46 %, which were higher than the result of ELM model, with R2 of 0.90, 0.91, 0.95 and 0.90, respectively. Therefore, it is recommended to adopt WOA to optimize the empirical model to estimate the ET0 all over China.

Effects of Microtopography on Runoff Generation in Plain Farmland: New Insights from an Event-Based Rainfall–Runoff Model

Plain farmland areas without significant topographic slope exhibit microtopographic features of different scales. Quantitative assessment of the effects of microtopography at different scales on runoff generation in typical farmland areas is of great significance for regional water resources management and flood disaster forecasting. The main objective of the study was to develop an event-based rainfall–runoff model based on the layered Green–Ampt model (LGAM) with the consideration of plot-scale microtopographic features in plain farmland areas. Our experimental field, located in Taihu Lake Basin, was classified into three types of topographic subunits (i.e., main field, rill, and ditch) according to the average elevation. To simplify the concentration process for three topographic subunits, the average concentration time method was employed. Here, various experimental scenarios were simulated, including two classical unsteady rainfall events in homogeneous soil, one ponding infiltration experiment, and two typical rainfall–runoff events in the experimental field. We found that the multilayered setting showed higher accuracy than the homogeneous setting for simulating infiltration in the ponding infiltration experiment in the field. The RMSE of simulated ponding water depth reduced from 0.166 cm to 0.035 cm and NSE rose from 0.988 to 0.999. The simulated hydrograph considering microtopography effects proved higher accuracy than that under unified topography assumption. After classifying topography, the RMSE and NSE of simulated hydrographs decreased and increased, respectively. The lower the topographic subunit, the earlier the outflow occurred. At the early stage, the runoff mostly originated from the relatively low topographic subunits. Infiltration-excess regime under saturated condition may initially dominate in the low-lying ditch under intense rainfall, with extremely high runoff coefficient. Concentration process in the plain farmland area was affected by both rainfall intensity and microtopography. The greater the rainfall intensity, the shorter the average concentration time. The concentration velocity under heavy rainfall was four times faster than that under light rainfall. The lower topographic subunit was characterized by shorter concentration pathway and average concentration time. Ditches reduced the peak flow and advanced the time to peak. This quantitative study provides new insights into effects of microtopography on runoff generation in plain farmland area as well as an effective alternative for plot-scale rainfall–runoff modeling.

A New Method to Map Spring Irrigated Areas Using MODIS LST Products and Ancillary Data in an Agricultural District of Northwest China

Irrigation alleviates drought in croplands and maintains or increases crop yields. The accurate monitoring of irrigated areas is important to regional water resource management, food security, climate change, drought monitoring, and emergency disaster relief. Based on field experiments that demonstrate the feasibility of irrigated area mapping using land-surface temperature, we propose a method to map spring irrigation areas using historical meteorological data, main crop phenological characteristics, irrigation regimes, and MODIS land-surface temperature (LST) products. The distribution of irrigation intensity, spring irrigated areas (SIA, considering the irrigation intensity), and total area of spring irrigation (STIA, regardless of irrigation intensity) were monitored by the proposed method for the Donglei Irrigated District (Phase II) in northwestern China from 2011 to 2018. The spring irrigation of the study area was divided into three periods (16 January–23 February, 24 February–24 March, and 25 March–31 May). Then, the temperature threshold of the irrigated area in each period was determined by the diurnal temperature range (DTR) of the rain-fed plots and precipitation data; for the three periods, this was 12 °C, 15 °C, and 11 °C, respectively. The results showed that most of the croplands in the study area were irrigated once or twice. The SIA in most years varied between 55,900 and 73,100 ha, but in 2016, the irrigation area reached 100,200 ha. The STIA accounted for 60–70% of the irrigated area except 2016. The average accuracy of SIA monitoring was satisfactory and above 94% for years when sufficient and reliable data was available.

Water consumption patterns of 110 cities in the Yangtze River Economic Belt in 2015

How to formulate scientific and effective water-saving policies is of great significance for the sustainable water consumption. As a country with the largest water consumption in the world, China has been committed to formulating scientific and effective water-saving policies. However, due to geospatial variations of water consumption in different regions, how to formulate targeted and efficient water-saving policies which suit for different local characteristics has always been a key issue to be solved. In a quest to understand the water consumption patterns, this study first adopted a dual evaluation index system combining Gini coefficient and Global Moran’s Index to classify different types of water consumption. Taking 110 cities in the Yangtze River Economic Belt as research objects, the water consumption of 110 cities was divided into nine types. According to each type, a targeted water-saving policy is given. This study provides important theoretical support for the efficient management of water resources in the Yangtze River Economic Belt, and provides reference for water resources management in other regions at the same time.

Space-time dynamics of soil moisture and groundwater in an agriculture-dominated critical zone observatory (CZO) in the Ganga basin, India

Space-time variability of soil moisture (SM) and ground water plays a fundamental role in shaping hydrology of terrestrial ecosystem, best represented as the Critical Zone (CZ), which extends from top of vegetation canopy to the bottom of groundwater table. In several parts of the world, a network of instrumented sites, known as Critical Zone Observatories (CZOs), have been set up to understand the hydrodynamics of soil–water system in particular reference to natural and anthropogenic forcings. Here, we employed the empirical orthogonal function (EOF), random combination, and temporal stability approach to understand the in-situ space–time dynamics of SM and depth to groundwater table (DTGT) over an agriculture-dominated CZO in the Ganga basin. Our results showed that both the components exhibit a constant temporal coefficient of variation, suggesting a consistent seasonal changing pattern. Around 91 % of the observed DTGT spatial variation are explained by first two spatial EOFs while the first five EOFs explain only 67 % of the total SM variability. On an annual basis, the spatial patterns of SM and DTGT are driven by topography and soil texture (% clay) while monsoon rainfall and post-monsoon crop cycle appear to be the leading factors for temporal variability of both components. Furthermore, we have demonstrated that randomly selected four sampling locations and three monitoring wells within the CZO could capture the mean spatial variability of SM (RMSE = 3 % vol/vol) and DTGT (RMSE = 0.7 mgbl) respectively. In addition, temporal stability analysis indicates that four representative sites and a single monitoring well can provide robust catchment mean with an absolute error of ±2 % vol/vol and 0.36 mgbl respectively. Overall, this study provides an insight to the hydrodynamics and controls of SM and groundwater in an agricultural landscape with significant implications for upscaling and efficient water resource management in such regions.

Climate change impacts on reference evapotranspiration in South Korea over the recent 100 years

The damage owing to climate change is increasing worldwide. In South Korea, the increase in temperature has exceeded the average global temperature increase. These temperature changes have increased the frequency and damage of droughts. To reduce drought damage, the importance of efficient water management policies and evapotranspiration (an index used for water management policies) is increasing. Generally, the potential evapotranspiration (\({ET}_{0}\)) is estimated by using the FAO-56 Penman–Monteith (PM) equation on meteorological datasets. In this study, long-term meteorological data with a maximum of 100 years were collected from 12 sites to estimate evapotranspiration. The objectives of this study were to (1) estimate the evapotranspiration based on the PM equation, (2) analyze the trends in the temperature and evapotranspiration, and (3) evaluate the relationship between the temperature and evapotranspiration through a correlational analysis. The results improve our understanding of climate change and provide a valuable reference for regional water resource management. It is found that there are generally increasing trends in spring, summer, and autumn, and generally decreasing trends in winter. The results from a seasonal Mann–Kendall test between the temperature and \({ET}_{0}\) show that the maximum temperature exhibits a distinct increase in spring and winter in certain areas. We determined the strengths of the relationships between temperature metrics and \({ET}_{0}\) using Pearson’s correlation coefficient, and the results show that the maximum temperature metric has the strongest relationship.