Comprehensive evaluation and analysis of water resource characteristics in Lixiahe Plain, China

Water is an important natural, economic, and strategic resource. Water resource evaluation analyzes the quantity, quality, spatiotemporal distribution characteristics, development and utilization conditions, current situation of development and utilization, and development trend of water resources in a certain region or basin (Wang et al. 2020; Liu et al. 2022a, 2022b). Water resource evaluation is the basis for the rational development, utilization, and conservation management of water resources and a reference for regional water resource planning and other water resource management work.

The Yangtze River Economic Belt is crucial for China's rapid, high-quality economic development (Long et al. 2020). Water resources play a vital role in the sustainable development of this region (Yan et al. 2020; Liu et al. 2022a, 2022b). Although the Yangtze River Economic Zone accounts for 45.94% of the country's water resources, the amount of water resources varies greatly between regions (Yang et al. 2022; Yuan et al. 2023). The typical Lixiahe Plain has large interannual variations in precipitation, shows an uneven distribution year by year, and faces many natural disasters, such as droughts. Therefore, a comprehensive evaluation of Lixiahe Plain's water resource characteristics needs to be conducted.

Water resource evaluation includes surface water resource evaluation and groundwater resource evaluation (Lyra et al. 2021). Surface water resource evaluation is an assessment of runoff, precipitation, evaporation, and surface water resources (Selek 2020). Groundwater resource evaluation studies the space–time distribution law of various groundwater quantities, calculates the exploitable amount of groundwater, and predicts groundwater dynamics (Chai et al. 2020; Fang et al. 2020). Many methods have been proposed for groundwater resource evaluation, and they include water balance (Bozkurt et al. 2022; Xu et al. 2023), analytical and numerical (Hsieh & Huang 2023), mining test (Chen et al. 2020a, 2020b; Xu et al. 2020), and correlation analysis (Song et al. 2020) methods. The water balance method is used to calculate various water flows in the hydrologic cycle (Chen et al. 2020a, 2020b). It is primarily employed to measure water resource utilization. Analytical and numerical methods are mainly utilized to solve groundwater-related mathematical models for thoroughly understanding the characteristics of groundwater resources (Amiri & Asgari-Nejad 2022). The mining test method is based on actual tests. It evaluates groundwater resources by calculating the recharge capacity of an area (Yuan & Han 2020). The correlation analysis method uses mathematical and statistical principles to analyze the correlation between different factors (Yang & Chen 2022; Zhou et al. 2023).

However, the evaluation of groundwater resources is complex. It involves not only the assessment of the quantity of groundwater contained in an area or basin but also the evaluation and analysis of its water quality (He et al. 2023). A sound water quality assessment provides information on water quality categories, major pollution factors, and spatiotemporal changes in water quality. Commonly used water quality evaluation methods include single-factor evaluation (SFE) (Su et al. 2022), fuzzy mathematical evaluation method (Ren et al. 2020; Wu et al. 2021), gray system evaluation method (Li et al. 2020; Zhang et al. 2023), artificial neural network method (Kulisz et al. 2021; Niu & Feng 2021; Egbueri & Agbasi 2022), and water quality labeling index evaluation method. SFE is easy to implement and provides a clear and intuitive comparison of measured and standardized values, but it ignores the overall water quality evaluation results (Han et al. 2021; Zhao et al. 2021a, 2021b). The fuzzy mathematical evaluation method is computationally complex and highly subjective in its determination of indicator weights (Zhang et al. 2020). The artificial neural network method relies on numerous parameters, lacks clear visibility into the learning process, and produces less interpretable outputs, impacting result credibility (Shi & Zhang 2021). The water quality labeling index evaluation method can be flexibly applied to different cross-section monitoring points in a given watershed or to different watersheds to analyze water quality conditions (Zhu et al. 2022). The use of different evaluation methods may produce different results due to the many complex factors that affect the evaluation of water quality and the different scopes of application of various methods. Therefore, the use of multiple evaluation methods for a comprehensive analysis is reasonable.

This study utilizes multisource data, including rainfall, evapotranspiration, and field sampling data of surface and groundwater bodies, on the Lishang River Plain region of China. Precipitation and evaporation characterization, water resource availability analysis, and water quality analysis are performed on this basis. Then, a comprehensive evaluation of the water resource characteristics of the study area is conducted. This study aims to determine the latest status and development trend of water quantity and quality in the basin and explore the water resource evaluation model applicable to the Lixiahe Plain region. The results of the study can provide technical guidance for the management and development of water resources in regional watersheds and promote the sustainable utilization of water resources in the Yangtze River Delta region. It also provides referable recommendations for the comprehensive evaluation of water resource characteristics in other global water-networked plain regions.

The most widely distributed and abundant groundwater type in the study area is loose rock pore water. In accordance with the formation era, depositional environment, burial conditions, and hydraulic characteristics of the water-bearing sand layer, the loose rock pore water in the area can be divided into five categories. These categories are the submersible aquifer (group) in the Holocene sand layer, the Ⅰ pressurized aquifer (group) in the Upper Pleistocene sand layer, the Ⅱ pressurized aquifer (group) in the Middle Pleistocene sand layer, the Ⅲ pressurized aquifer (group) in the Lower Pleistocene sand layer, and the Ⅳ pressurized aquifer (group) in the Upper Tertiary sand layer.

At present, Taizhou City has one evaporation monitoring station in each of the two zones with level-4 water resources, namely, Xinghua Station and Huangqiao Station, in the Lixiahe Abdominal Area and Tongnan Riverine Area (Yang). The evaporators at Xinghua and Huangqiao Stations are of the E601 type. In the early stages of station setup and in the mid-1970s, Φ20 and Φ80 sets of basin type were successively used. The analysis of evaporation characteristics in the study area relies on data observed at two stations using Φ20 and Φ80 basin sets and E601-type evaporators.

The amount of evaporation from the water surface measured by the E601 evaporator (Wei 2011) is a rough indication of the amount of evaporation capacity. The observations of the different evaporator models are converted and harmonized to the E601 model in the water resource evaluation work to facilitate the analysis and comparison.

Twenty rivers totaling 327 km are above the district level, 228 rivers totaling 679 km are at the township level, and 712 rivers totaling 633 km are at the village level in the study area. Three lakes and lakeshores are included in the list of lakes and lakeshores in the lower river area under provincial management; these three are Magpie Lake, Xiajiawang, and Longxi Harbor, with a total area of 5.63 km². The main water bodies, such as district-level and above rivers and Magpie Lake, are selected for water quality evaluation in accordance with the distribution characteristics of the surface water system in the study area and its current status of surface water resource utilization. The distribution of the sampling points for surface water quality assessment is shown in Figure 1(d). Water quality analysis data are collected for different periods for some of the streams in addition to the samples taken at this site.

The study area is located in the Cenozoic subsidence area of central Jiangsu. The thickness of loose sediments of alluvial and littoral facies has been 200–300 m since the Quaternary Era. Influenced by the regional geological sedimentary environment and the dissolution and leaching of groundwater in the water-bearing medium, the hydrochemical characteristics of groundwater show complex and changeable characteristics in vertical and horizontal directions. Diving water quality testing is controlled throughout the area by collecting and analyzing the results of existing groundwater quality tests to understand the distribution status and characteristics of groundwater quality in the zone. Water quality analysis samples are obtained from 39 evenly selected civil wells so that the evaluation results can accurately reflect the current situation and characteristics of diving water quality in the area. Thirty-four results of deep groundwater quality analyses are collected. The distributions of the sampling points for the water quality analysis and the collected sampling points for each town and street are shown in Figure 1(d). Diving water quality analysis data are obtained from 40 natural water samples.

This study utilized long-term precipitation monitoring data from Qintong Station and Jiangyan Station located within the research area, spanning the period from 1965 to 2020. The representativeness of a series is evaluated not only by the length of the series but also by the relative stability of the abundance structure and statistical parameters of the series. A stochastic series is generally considered to be relatively well represented when one or more complete cycles of abundant and depleted water exist in the series, which in turn contains the maximum and minimum values in a long series, and when the statistical parameters, coefficients of variation ⁠, and skewness coefficients are relatively stable (Burdun et al. 2020). Given that sampling errors occur in the analysis of statistical parameters for different length series, a series representativeness analysis should be performed to test whether the frequency analysis using synchronized series can approximate the overall distribution.

This method selects one to three long-series (n > 60) observatories within a city or county. Then, a comparative analysis of the statistical parameters of the long and short series is performed to determine the stability of the statistical parameters of the proposed series.

The water consumption coefficient is used to discount the corresponding amount of water available for one-time off-channel use on the basis of the analysis results of the maximum water supply capacity or the maximum water demand of the project.

The extraction coefficient can be categorized as good, average, and poor on the basis of hydrogeological conditions, and it includes the abundance of shallow groundwater, thickness of aquifer deposits, water supply coefficient, hydraulic conductivity, burial type of sand layers, actual extraction coefficient of current groundwater, comprehensive data from surveys on actual extraction in similar regions, and data from long-term regulation calculations.

In addition, considering the dynamic change in groundwater levels after groundwater development, the values of groundwater levels that decline by less than 1, 1–2, and more than 2 m are divided into stabilization, decline, and funnel zones, respectively. The large value of the range of extraction factors is used in the stabilization zone, and the small value is used in the funnel zone. values of 0.8–1.0, 0.6–0.8, and less than 0.6 are used for the stabilization, decline, and funnel zones, respectively. For areas lacking information, an analogous method is employed to estimate the recoverable quantity on the basis of the values of recoverable coefficients in areas with similar hydrological and hydrogeological conditions.

The diversion and drainage capacities of Yangtze River in the study area are obtained through the sluices along the river. The four medium-sized sluices of Gaogang Junction, Madian Port, Guanchuan Port, and Xiashi Port are monitored by the China National Hydrological Station, and the water diversion and drainage are measured. The five medium-sized sluices at the mouth of the bank, Kouan, Tianxing Harbor, Shangliuwei, Xialiuwei, and Shiwei Harbor are determined by the rate of inspection, and the amount of water diverted and discharged are projected. For the years with insufficient or no data, interpolation calculation is done (Kadri et al. 2022) via the correlation analysis method (Gong & Zhang 2020; Song et al. 2020; Ge et al. 2021) and the calculation method of water diversion and drainage per unit net width.

The calculation of conversion coefficient c must consider the characteristics of the work situation in different historical periods and the proportion of switching in the operation and scheduling of small- and medium-sized gates.

Water quality indicators are tested using in situ field testing and laboratory analytical testing. The in situ field test indicators include pH, dissolved oxygen, and water temperature and are sent to the Nanjing Normal University Analytical Testing Center for testing. An inductively coupled plasma atomic emission spectrometer (Thermo Scientific iCAP 7000), a UV–visible absorption spectrometer (Shimadzu UV-Vis-NIR), and an ion chromatograph (Thermo ICS-900) are utilized. The test is based on the following standards: JY/T 0567-2020 (inductively coupled plasma emission spectrometry), HJT346-2007 (determination of nitrate nitrogen by UV spectrophotometry), HJ-535-2009 (determination of ammonia nitrogen by nano reagent spectrophotometry), GB 7467-87 (determination of hexavalent chromium by dibenzoyl dihydrazide spectrophotometry), and JY/T 020-1996 (ion chromatography analysis method).

The differential-product curve graph shows that the annual rainfall in Jiangyan District decreased from the mid-1960s to the late 1960s. A reciprocal climbing trend occurred from the early to mid-1970s; the value declined first and then rose in the late 1970s. Average annual rainfall was relatively flat in the early and mid-1980s and then showed an upward trend in the late 1980s. In the early 1990s, average annual rainfall presented a remarkable increasing trend. It decreased in the middle of the period. In the latter part of the period, annual rainfall showed an increasing and then decreasing trend. A decreasing trend was observed in the 2010s, a decreasing trend was found in the early part of the 2020s, a relatively large increase occurred in the middle part of the century, and a decreasing trend was observed in the latter part of the century. The cycle of abundance and dry period changes was about 35–40 years.

The monthly distribution of average water surface evaporation from 1980 to 2020 at the representative stations of Xinghua and Huangqiao is shown in Table 1. Statistical data indicate that over the past 50–60 years, industrial pollution has contributed to the greenhouse effect, causing climate warming and an average temperature increase of 0.15 °C every decade. Although it is widely believed in the scientific community that climate warming should lead to an intensification of ocean surface evaporation, the actual evaporation from water surfaces has been decreasing year by year as global temperatures rise. Moreover, land use changes, particularly deforestation and urbanization, as well as human activities, have an impact on evapotranspiration rates. These activities can cause alterations in surface characteristics, including changes in vegetation cover, and the introduction of impermeable surfaces, such as concrete, thereby limiting water supply and reducing evapotranspiration rates.

Next, the annual evaporation of the two stations is simulated using a linear trend versus year, as shown by the red lines in Figures 5 and 6. The simulation graphs reveal a clear downward trend in evaporation at both stations. The total decrease at Xinghua Station from 1965 to 2020 was 125.0 mm, which was about 13.7% of the multiyear average evapotranspiration. The total decrease from 1968 to 2020 at Huangqiao Station was 102.5 mm, which was about 12.3% of the multiyear average evapotranspiration. In summary, water surface evapotranspiration decreased mainly from 1970 to 1990, and the changes in water surface evapotranspiration leveled off after 1990.

The available surface water resource in the study area is determined to be 0.60 billion m³ by combining inversion and positive algorithm calculations, and the utilization rate is 26.7%. The utilizable amount of surface water resources available in the Lixiahe River Abdominal Area is 0.32 hundred million m³, which accounts for 53.3% of the total amount of surface water resources available in the whole area, and the utilization rate is 26.6%. The utilizable amount of surface water resources in Tongnan Riverine District (Yang) is 0.28 hundred million m³, which accounts for 46.7% of the total, and the utilization rate is 26.9%.

The analysis of available dive-level observations in the study area reveals a decreasing trend in most areas since the beginning of the observations. Therefore, in this evaluation, the extraction coefficient should be within the range of 0.6–0.8. The reasonableness of the actual exploitation coefficient of the multiyear average is analyzed by referring to the research data of relevant departments on groundwater exploitation. The value of the exploitation factor for diving in the study area is determined to be 0.74.

The calculated diving resources in the study area amount to 125 million m³/a, including 0.62 hundred million m³/a in the Lixiahe Abdominal Area and 0.63 hundred million m³/a in the Tongnan Riverine Area (Yang). The diving availability is 0.93 hundred million m³. The amount of groundwater available in the Lixiahe Abdominal Area is 0.46 hundred million m³, which accounts for 49.5% of the total. The utilizable amount of groundwater in the Tongnan Riverine Area (Yang) is 0.47 hundred million m³, which accounts for 50.5% of the total.

The average multiyear diversion of river water from 2000 to 2020 in the Lixiahe Abdominal Area in the study area is calculated to be 298 million m³. The average multiyear diversion of river water from 1959 to 2020 in the Tongnan Riverine Area (Yang) is 198 million m³. The average multiyear inflow to the river in the whole area from 1959 to 2020 is 0.90 hundred million m³, and the average multiyear water inflow to Yangtze River from 2000 to 2020 in the Lixiahe Abdominal Area is 0.30 hundred million m³. The average multiyear water inflow to Yangtze River from 1959 to 2020 in the Tongnan Riverine Area (Yang) is 0.85 hundred million m³. The amount of water in transit in 2020 in the study area is about 1.086 billion m³. The availability rate is 10%, which means that the available amount of water in transit is 0.89 hundred million m³.

The total amount of water resources available in the whole region is 180 million m³/a. Specifically, the total amount of water resources available in Lixiahe Abdominal Area is 0.86 hundred million m³/a, accounting for 47.8% of the total amount of water resources available in the whole region. The total water resources available in Tongnan Riverine Area (Yang) is 0.94 hundred million m³/a, accounting for 52.2% of the total. The detailed results of the calculations on the availability of surface and groundwater resources and the availability of total water resources are shown in Table 2.

Two methods, namely, in situ field testing and laboratory analytical testing, are used based on water quality monitoring point fixation by the quality control requirements for surface water quality testing. The main indicators of the in situ field testing are pH, dissolved oxygen, water temperature, and water level. Water levels are measured by real time kinematic (RTK), and the other indicators are tested by laboratory analysis. The pH in the 21 surface water quality samples ranges from 7.51 to 8.89. The total hardness values range from 89.25 to 106.50 mg/L, which is considered soft water. The tested chloride (Cl⁻) values range from 11.6 to 26.4 mg/L. The sulfate (SO₄²⁻) values do not exceed the limit, and the compliance rate is 100%. The mineralization test values range from 152.3 to 353.8 mg/L. Mineralization is low, and the water is classified as low in minerals and fresh. The hydrochemical type of surface water in the study area is mainly HCO₃–Ca.Na HCO₃–Ca according to Arliekin's classification. The water in this type primarily consists of bicarbonates and contains a certain amount of sodium and potassium ions. It has low mineralization and total hardness. Table 3 shows the results of the groundwater quality analysis.

The annual maximum value of rainfall in the study area is 1,778.3 mm, and the minimum value is 490.5 mm. The ratio of maximum and minimum rainfall is 3.62. The rainfall from May to September, June to August, and October to April accounts for 66.64, 49.50, and 33.25% of the annual rainfall, respectively. The rainfall during the irrigated agricultural period from April to October accounts for 48.62% of the annual rainfall, and the rainfall during the hot and dry period from July to August accounts for 57.04% of the annual rainfall.

The study area has high summer temperatures with high evapotranspiration and low winter temperatures with low evapotranspiration. The maximum monthly evaporation during the multiyear average year at Xinghua Station occurs in August and accounts for 12.6% of the annual evaporation, and the minimum monthly evaporation occurs in January and accounts for only 2.9% of the annual evaporation. The four consecutive months with maximum evaporation are from May to August, with their evaporation totaling to 440.7 mm or 48.8% of the annual evaporation. The maximum monthly water surface evaporation during the multiyear average year at the Huangqiao Station occurs in July and accounts for 12.8% of the annual evaporation, and the minimum monthly evaporation occurs in January and accounts for 3.3% of the annual evaporation. The four consecutive months with maximum evaporation are May to August, with a total evaporation of 392.6 mm accounting for 49.2% of the annual evaporation.

The intra-annual distribution of rainfall and the uneven variability over the years lead to the intra-annual distribution of runoff and uneven variability over the years. The average monthly distribution of runoff for years 1965–2020, 1965–1979, 1965–2000, and 1980–2020 for the region is more than 90% of the annual runoff during the flood season from May to September. About 94 and 88% of the runoff is from the Lixiahe Abdominal Area and Tongnan Riverine Area (Yang), respectively. The whole region's multiyear average runoff in July accounts for about 34% of the annual runoff, and the surface runoff in December is the smallest and close to or less than zero. The runoff in January, February, and post-flood October and November is about 1% of the annual runoff. The runoff in March, April, and May before the main flood season is about 2–3% of the annual runoff. The region's runoff for the year is generated primarily from June to September.

The water quality of the major rivers and lakes in the study area is good, and the drinking water source area meets or is better than the Class III water standard. In 2020, the study area had 10 surface water quality assessment sections, and all of them met the water quality requirements of Classes III and IV with a compliance rate of 100%. The monitoring data of 39 submersible wells show that the water quality indexes of submersible water reach Class III or are even better than Class III, except for NO₃⁻ contamination that is serious. With the increase in Ⅰ, Ⅱ, Ⅲ, and Ⅳ pressurized water burial depth, the types of water quality exceeding the standard rate and the magnitude of water quality items gradually decrease. The quality of the deep pressurized water in the study area is excellent, and the water can be used as drinking water, except for the Ⅰ pressurized water and Ⅱ and Ⅲ pressurized water in the southern part of the study area that are of slightly poorer quality. The Ⅲ and Ⅳ pressurized water in the area is rich in many kinds of trace elements and could be used to create natural high-quality drinking mineral water.

As of 1 November 2020, the total population of the research area is 731,300, with a population density of 853 people/km². The sum of the available surface water resources and transboundary water amounts to 201 million m³. Comparing this with the current annual surface water supply of 341 million m³, it is evident that it falls short of meeting the current water supply demand in Jiangyan District. Comprehensive water conservation measures should be implemented. The goal is to build a water-saving society and system, promoting various engineering and policy measures for water conservation. Actively explore water sources.

In this study, we used synchronous series representativeness analysis, water quality analysis, and water resource availability estimation methods to characterize precipitation and evaporation, analyze water quality, and calculate water resource availability to achieve an integrated evaluation of water resources in the Lixiahe Plain area. The latest status of the local waters was determined, and trends were predicted. The analysis of rainfall and evaporation monitoring data in the study area over the years revealed that although the area's water conservancy projects already have a certain disaster prevention and mitigation capacity, the contradiction between floods and localized water shortage still exists. The total amount of water resources calculated for the study area in 2020 was 420 million m³, and the amount of available surface water resources was 112 million m³. The amount of water in transit in 2020 was about 1,086 million m³, and the available amount was 89 million m³. The sum of surface water resource availability and transit water availability was 201 million m³, which cannot easily meet the demand of the current water supply in the study area compared with the current annual surface water supply of 341 million m³. In response to the weak current state of water resource management regulations and standards, norms and standards should be formulated for standardized water supply, water use, water conservation, and water resource protection, ensuring the sustainable utilization of water resources.

The red line for water resource development and utilization is drawn based on the characteristics of water resources to meet the demand for water resources for socioeconomic development in the study area and to ensure sustainable development. The total water consumption is strictly controlled. In the event of sudden surface water pollution incidents or extreme weather conditions causing surface water supply interruptions, the relatively abundant groundwater resources in the area should be fully utilized through the establishment of emergency groundwater sources for emergency water supply. Groundwater is mostly of good quality and easy to extract, but it is not inexhaustible. The benefits of the limited high-quality groundwater resources should be maximized. At present, a small portion of high-quality groundwater in the study area is still used for industrial equipment cooling, resulting in a waste of high-quality groundwater resources and failure to optimize the use of high-quality water. This study systematically and completely demonstrates the whole evaluation process to lay a solid foundation for the effective management of water resources in the Lixiahe Plain area, as exemplified by the study area, and to promote the sustainable and healthy development of the Yangtze River Delta.

LH contributed to conceptualization, supervision, project administration, and funding acquisition. LH and LC contributed to the methodology and review and editing the writing of the article. LY contributed to software, investigation, resources, and writing the original draft. SZ contributed to validation of data. LC and YY contributed to formal analysis. CX contributed to data curation. LC contributed to visualization. All authors read and agreed to the published version of the manuscript.

The authors acknowledge funding support from the National Science Foundation of China (NFSC) (42301487); the General Program for Natural Science Research of Basic Disciplines in Universities of Jiangsu Province (23KJD170004); the Key Laboratory of Virtual Geographic Environment, Ministry of Education, Open Fund Project (2023VGE03); and the Nanjing Xiaozhuang University Nature Science High-level Research Project (2022NXY03).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.