Water Resource Planning Research Articles

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase—from 0.1083 W m-2μm−1sr−1 in 2001 to 0.1468 W m-2μm−1sr−1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

Changes in terrestrial water storage in the Three-North region of China over 2003–2021: Assessing the roles of climate and vegetation restoration

Large-scale vegetation restoration has markedly enhanced ecosystem services in the Three-North (TN) region, concurrently exacerbating the pre-existing water resource crisis stemming from climate change in this arid domain. However, the specific contributions of climate and vegetation changes to the evolution of water resources in the TN region have yet to be elucidated. This study leverages data from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-on mission (GRACE-FO), specifically the terrestrial water storage anomaly (TWSA) from 2003 to 2021. We identify two TWSA rising (TWSA_rⅠ and TWSA_rⅡ) and two declining (TWSA_dⅠ and TWSA_dⅡ) hotspots in the TN region. By exploring the relative importance of climate drivers and vegetation dynamics in influencing the temporal dynamics of TWSA, we found that vegetation restoration assumes a more pivotal role than climate factors in 45.2% of the total area within the identified hotspots, predominantly evident in TWSA declining hotspots. The heightened TWSA in TWSA_rⅠ and TWSA_rⅡ primarily stems from precipitation recharge, with precipitation-dominated areas constituting 19.3% of the area within the identified hotspots. Potential evapotranspiration dominates TWSA changes in only 4.6% of the region within the identified hotspots, primarily scattered in TWSA_dⅠ and TWSA_dⅡ. Furthermore, a comparative assessment of soil moisture and groundwater responses to environmental factors indicates that water storage at shallower layers is more responsive to climate factors, while the impacts of vegetation changes on TWSA are more discernibly reflected in water storage at deeper layers. These findings furnish scientific guidance for future ecological restoration planning and sustainable water resources management.

Projected Intensified Hydrological Processes in the Three‐River Headwater Region, Qinghai Tibetan Plateau

AbstractThe Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau.

Multidimensional forecasting of precipitation and potential evapotranspiration in the Paranapanema river basin using neural network time series

Spatial and temporal forecasts of the hydrological cycle compartments aiming projections of extreme drought scenarios represent a challenge for the planning, management and monitoring of water resources in order to mitigate potential impacts on the natural environment, civil society and wildlife under climate change. Machine Learning (ML) methods can help in this task, combining constant updating of model information and further scenarios evaluation. This study investigated the application of multidimensional forecast of precipitation and potential evapotranspiration at the Paranapanema River Basin (PRB) for the years 2023–2025. PRB is a region that provides hydrological, energy and agricultural resources, located in the southeast of Brazil that has suffered several problems related to water deficit and stress as well as droughts in the last 10 years. For these reasons, geospatial technologies such as remote sensing and Geographic Information Systems (GIS) were applied to generate time series between 2001 and early 2023 for a total of 22 Hydrological Planning Units (HPUs) in the PRB. Subsequently, a Neural Network Auto Regression (NNAR) was used to forecast precipitation and potential evapotranspiration of the HPUs in the period 2023–2025, finding for the months of May, June, July and August of 2024 and later in 2025 possible periods of water deficit in the central and northern regions. Finally, a comparative analysis of possible impacts on the agricultural, energy and social sectors based on the forecast developed by the NNAR network is presented, showing possible scenarios for short and mid-term water planning in the PRB.

Assessing the Spatiotemporal Variations in the Hydrological Response of the Qin River Basin in Loess Plateau, China

In recent decades, the hydrological processes on the Loess Plateau have undergone significant changes due to the combined effects of climate change and human activities. This study investigates the Qin River basin, the core area of the Loess Plateau. Between 1971 and 2000, the hydrometeorology of the flood season in the Qin basin on the Loess Plateau in China changed abruptly in 1982 and 1992. This study uses distributed hydrologic modeling and statistical analysis to distinguish the spatial and temporal impacts of climate change and human activities. The fastest dynamic changes in land use occurred in grassland and construction land, accounting for 0.36% and −0.1%, respectively, from 1982 to 1991. From 1992 to 2000, it was 0.29% and 0.3%, respectively. The increase in basin area where the Normalized Difference Vegetation Index increased was 95%. However, except for significant changes in runoff from 1981 to 1992, there were no significant changes in precipitation, runoff, and temperature during other flood seasons. During the two studied variation periods, from 1982 to 1991 and from 1992 to 2000, the contribution rate of climate change to flood season runoff (CClimate) decreased from 83.26% to 74.47%, while the contribution rate of human activity disturbance to flood season runoff (Chuman) increased from 16.74% to 25.53%. In terms of spatial distribution, from 1982 to 1991, areas with a Chuman > 50% were primarily concentrated in the upper reaches of the basin, while from 1992–2000, these areas shifted to the lower reaches. This study offers valuable insights for water resource planning and soil conservation measures in the Loess Plateau and similar arid regions worldwide, aiming to ensure water resource safety and sustainability in response to climate change and human interference.

Evolution in Patterns of Urban Water Consumption Accompanying Socio-Economic Development

To explore the evolution in patterns of urban water consumption (UWC) accompanying socio-economic development, historical data on socio-economic conditions and water consumption in developed cities worldwide were collected. stages of evolution and patterns in UWC were identified through Pettitt tests. Through correlation analysis, the main socio-economic indicators influencing UWC were identified, and their threshold values for different trends in the evolution of UWC were statistically analyzed. The use of these patterns of evolution for UWC prediction was explored taking Changsha, China as a case study. Results indicate the following: (1) UWC typically increases in the early stage of development and then stabilizes or decreases later; (2) when UWC stabilizes or decreases, socio-economic indicators, namely per capita GDP, tertiary industry’s contribution to GDP, and urbanization rate, range from [USD 10,000, 60,000], [60%, 80%], and [85%, 95%], respectively, entering or approaching the developed economy stage. (3) Using patterns in this evolution, Changsha’s UWC is predicted to increase until the turning point, which will occur between 2027 and 2035 (earlier than that predicted using genetic programming) and then decrease earlier than that of Hunan Province or the entirety of China. Predicted values are lower than those obtained using the quota method. This study provides a new perspective on relationships between UWC and socio-economic conditions, supporting water resources planning.

Assessment of the effects of climate change on water balance components in the upper Erer subbasin, Ethiopia

Eastern Ethiopia watersheds are located in transition zone from Arid to semi-humid climate and in expanding to westwards the west annual rainfall is highly declining. This paper explains future hydrological response impacts under changing climate using ensemble average of the CORDEX RCMs for historical (1979–2014) and future (2024–2070) periods. The result revels the monthly average temperature varies (0.04–6.25°C) for RCP-4.5, while it varies (0.03–6.59°oC) for RCP-8.5. The monthly average rainfall to be decline by 90.71 mm and rise by 211. 22 mm for RCP-4.5, while it is going to decline by 84.97 mm and rise by 235.62 mm for RCP-8.5. The adjusted SWAT model was used to detect the changes of projected hydrological response from reference period. Balance components of the baseline period was compared to future period. The result shows the change in decrease of annual mean surface flow (4.98 %–5.63 %), groundwater flow (5.63 %–6.68 %), evapotranspiration (2.45 %–2.57 %) and water yield (5.54 %–5.21 %) to be expected from RCP-4.5 to RCP-8.5. The findings of this paper provide valuable assistance to water resource planners by enhancing their comprehension of change in climate effects at local level.

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Projected water availability in the Tawa River Basin India in changing climate

The Tawa River is one among the major southern tributary of the Narmada River in India, exhibiting substantial variation in rainfall, altering the water availability. The variation in water availability effect the hydrological characteristics such groundwater recharge, soil moisture level, water balance. Studying rainfall-runoff conversion is necessary for proper and sustainable surface and ground water resources planning and management. In the changing climate, it becomes imperative to understand the hydrological response and project water availability for sustainable water management. In this work, the MIKE 11 NAM model is calibrated and validated to evaluate the climate change impact on availability of water. Using the outputs of downscaled, bias-corrected data of CMIP6, the future projection of runoff is done for the near century, mid-century and end century under the scenario SSP245 and SSP 585. MPI-ESM1-2-HRand EC-EARTH 3-VEG climate models were selected. Temporal analysis was performed to evaluate the impact on the availability of water at the 50%, 75% and 90% percentage dependability flow. The annual analysis revealed that scenario SSP5-8.5 has a higher increase in the runoff than SSP2- 4.5, mainly for the end century, as depicted by both the models. Monthly analysis revealed strong intra-seasonal variations and highlighted that August is projected as the most active month of the year, and wet seasons displayed a larger change than dry seasons. The findings indicate that climate change shall significantly influence hydrological processes in the Tawa watershed and potentially impact the availability of water in the Tawa River basin. Our study underscores the imperative need to adapt water resource planning and management strategies to mitigate the potential impacts of climate change on the availability of water in the Tawa River basin.

Quantifying the Impact of Geomorphic and Topographic Evolution on the Environmental Planning of Spring Water Resources

Quantifying the Impact of Geomorphic and Topographic Evolution on the Environmental Planning of Spring Water Resources

A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts

Developing precise groundwater level (GWL) forecast models is essential for the optimal usage of limited groundwater resources and sustainable planning and management of water resources. In this study, an improved forecasting accuracy for up to 3 weeks ahead of GWLs in Bangladesh was achieved by employing a coupled Long Short Term Memory (LSTM) network-based deep learning algorithm and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) data preprocessing. The coupled LSTM-MODWPT model’s performance was compared with that of the LSTM model. For both standalone LSTM and LSTM-MODWPT models, the Random Forest feature selection approach was employed to select the ideal inputs from the candidate GWL lags. In the LSTM-MODWPT model, input GWL time series were decomposed using MODWPT. The ‘Fejér-Korovkin’ mother wavelet with a filter length of 18 was used to obtain a collection of scaling coefficients and wavelets for every single input time series. Model performance was assessed using five performance indices: Root Mean Squared Error; Scatter Index; Maximum Absolute Error; Median Absolute Deviation; and an a-20 index. The LSTM-MODWPT model outperformed standalone LSTM models for all time horizons in GWL forecasting. The percentage improvements in the forecasting accuracies were 36.28%, 32.97%, and 30.77%, respectively, for 1-, 2-, and 3-weeks ahead forecasts at the observation well GT3330001. Accordingly, the coupled LSTM-MODWPT model could potentially be used to enhance multiscale GWL forecasts. This research demonstrates that the coupled LSTM-MODWPT model could generate more precise GWL forecasts at the Bangladesh study site, with potential applications in other geographic locations globally.

Seasonal influence of tropical Pacific and Atlantic sea surface temperature on streamflow variability in the patia river basin

This research presents a seasonal analysis of the variability of streamflows in the Patía River Basin (PRB) between 1984 and 2018 and the influence exerted by the large-scale climate variability using non-linear principal component analysis (NLPCA), Pearson's correlation, and composite analysis. The study was conduced during the minimum (July–August–September, JAS) and maximum (October–November–December, OND) streamflow periods. The NLPCA depicted a single significant mode of variability for each season with explained variances greater than 75%. The correlation analysis between the main mode of variability during OND and climate indices showed significant results, mainly with the Pacific Ocean and El Niño-Southern Oscillation (ENSO). In contrast, for JAS, the correlations were significant for the indices linked to the Atlantic Ocean. Finally, the composite analysis indicated that the positive (negative) events during JAS, which show the increase (decrease) of streamflow in PRB, are related to negative (positive) anomalies in the Tropical Northern Atlantic band, including the Caribbean Sea and the Gulf of Mexico. In comparison, the positive (negative) events during OND are related to negative (positive) sea surface temperature (SST) anomalies in the tropical Pacific, corresponding to La Niña (El Niño) events. The results provide evidence of the strong influence of climate indices and tropical Pacific and Atlantic SST on seasonal streamflow in the PRB and establish the foundations for seasonal streamflow modelling, relevant for prevention and risk management as well as for adequate planning and management of water resources in the region.

Integrated water-population interactions framework: An application to assess water security in Iran

While dynamic water security models related to food and livelihood security have advanced significantly in recent decades, the inclusion of demographic variables in these models is often limited to merely population growth. As many countries have either completed or are in the process of completing the demographic transition, population growth alone may no longer be the predominant demographic variable influencing water security models. Therefore, there is a discernible need for more comprehensive water security models to consider the simultaneous impact of various demographic variables on different aspects of water security. Inspired by the contemporary environmental demography perspectives, we introduce a generic integrated framework for integrated water-population interactions (IWPI) which explores the overlooked impacts of demographic transitions on different aspects of water security. Demographic shifts can impact food and water consumption and agricultural employment. Recognizing these dynamics is essential as countries advance through demographic transitions and face mounting pressures on water resources. The Integrated Water-Population Interaction (IWPI) framework was implemented in Iran through the Water-Population System Dynamics (WPSD) model. The model shows that population size, household composition and urbanization significantly affect domestic water consumption. It also reveals how changes in educational and occupational structures impact livelihood security and food self-sufficiency under water constraints. We introduce a novel population metric for water security assessment in Iran, offering policy-makers a tool to assess and address water insecurities that can affect the population in different ways. Our findings recommend realistic food self-sufficiency targets, flexible water resource planning and policies that integrate population dynamics with water, food and livelihood security to ensure sustainable outcomes in Iran.

Using Baseflow Ensembles for Hydrologic Hysteresis Characterization in Humid Basins of Southeastern China

AbstractBaseflow plays a vital role in protecting the environment and ensuring a stable water supply for farming. There are still gaps in the current understanding of baseflow convergence rates in the humid region due to the abundance of rainfall and the high‐water table. Therefore, this study focused on the evolution and hysteresis characteristics of baseflow in humid basins of southeastern China. The baseflow ensemble simulation (BES) method was established to improve the reliability and applicability of baseflow simulation. We suggest a way of differentiating the wet and dry seasons based on the multi‐year average monthly baseflow index (BFI) to determine the intra‐annual distribution of water effectively and simply. The hydrological hysteresis effect of baseflow on precipitation is revealed by characterizing baseflow response to precipitation under precipitation events during wet and dry seasons. A methodology for assessing the performance of baseflow simulation was proposed from observations of streamflow and precipitation. We found that the BES method performed better in baseflow simulation than other single separation methods. Using the BES method, the lag time of baseflow to precipitation during the wet and dry seasons was found to be 3.09 and 4.04 days after utilizing the BFI to divide the hydrological situation into wet and dry seasons. Additionally, precipitation had nearly twice as much intensity influence on baseflow during the dry season compared to the wet season. These findings have significant ramifications for the use, management, and planning of water resources in humid areas of China.

The changes in the annual distribution of mountain runoff during the period of 1965–2018 in Hexi Corridor, Northwest China

The annual distribution characteristics of river runoff in arid regions have significant implications for water resource stability and management. Based on the mountain runoff data from 1965 to 2018, this study examines the annual change characteristics of monthly runoff of the Shiyang River Basin, Heihe River Basin, and Shule River Basin in the Hexi Corridor, Northwest China. Many indexes are used and analyzed, including the coefficient of variance, the complete regulation coefficient, the concentration degree and concentration period, the magnitude of change, the skewness coefficient, and the kurtosis coefficient of the annual distribution curves. The results reveal the following: (1) The inhomogeneity of annual runoff distribution in the Taolai River and the rivers to the west of it, except the Shiyou River, show an increasing trend. Conversely, the inhomogeneity of the rivers to the east of the Taolai River generally show a downward trend, but the coefficient of variance value is still very high. (2) In the Shiyang River Basin, the annual distribution of the concentration period is characterized by a relatively discrete pattern. Conversely, the Heihe River Basin exhibits a relatively concentrated pattern, and the distribution pattern of the Shule River Basin is quite different. Notably, all concentration periods in the three basins have shifted backward after the 2000s. (3) The Shiyang River Basin exhibits disordered annual distribution curves of runoff in different years. In contrast, the Heihe River Basin presents a typical ‘single-peak’ pattern with a prominent right-skewed. The Shule River Basin has regular distribution curves, with a gradually significant ‘double-peak’ pattern from east to west. Overall, there has been a slight change in runoff in the Shiyang River Basin, while the Heihe River Basin and Shule River Basin have experienced significant increases in runoff. The annual distribution curves of runoff in the Liyuan River and the rivers to the east of it exhibit a gentle peak pattern, and the appearance probability of extreme runoff during the year is low. Conversely, the rivers to the west of the Liyuan River, excluding the Danghe River, display a sharp peak and thick tail pattern, indicating that the appearance probability of extreme runoff during the year is high. These findings have practical implications for the planning and management of water resources in the Hexi Corridor. Moreover, they provide a solid foundation for predicting future changes in regional water resources.

Assessment of aquatic ecosystem quality in Dharoi Reservoir using Sentinel-2 satellite imagery

Background: Dharoi Reservoir, located in Gujarat, India, is a vital freshwater resource supporting agriculture, industry, and local communities. Chl-a, a key indicator of water quality, reflects the trophic state and ecological balance of aquatic systems. Objective(s): This study aims to provide comprehensive insights into the water quality dynamics of Dharoi Reservoir, offering valuable information for environmental management and sustainable water resource planning. Methods: This study employs high-resolution Sentinel-2 satellite imagery to analyze Chl-a concentrations in the reservoir during October 2020. The Chl-a index, calculated by dividing Sentinel-2 bands B5 and B4, reveals a spatial distribution of Chl-a concentrations. Results: The Chl-a index ranges from 73.78 to 100. The mean Chl-a index is 91.6 with a standard deviation of 3.27, indicating elevated and variable Chl-a concentrations. Conclusions: The findings contribute to the understanding of the reservoir’s ecological health and assist in making informed decisions for water quality management. This research exemplifies the integration of remote sensing technology and environmental stewardship, promoting sustainable water management practices in the region. Policy recommendations: No policy recommendations are explicitly stated in the abstract, but they could be inferred from the conclusions. For example, one possible policy recommendation is to monitor and regulate the sources of nutrient inputs into the reservoir, such as agricultural runoff, sewage, and industrial effluents, to reduce the risk of eutrophication and algal blooms. Another possible policy recommendation is to implement adaptive management strategies that consider the seasonal and spatial variability of Chl-a concentrations and their impacts on water quality and availability.

Added value of seasonal hindcasts to create UK hydrological drought storylines

Abstract. The UK has experienced recurring periods of hydrological droughts in the past, including the drought declared in summer 2022. Seasonal hindcasts, consisting of a large sample of plausible weather sequences, can be used to create drought storylines and add value to existing approaches to water resources planning. In this study, the drivers of winter rainfall in the Anglian region in England are investigated using the ECMWF SEAS5 hindcast dataset, which includes 2850 plausible winters across 25 ensemble members and 3 lead times. Four winter clusters are defined using the hindcast winters based on possible combinations of various atmospheric circulation indices (such as the North Atlantic Oscillation, NAO; East Atlantic, EA, pattern; and El Niño–Southern Oscillation). Using the 2022 drought as a case study, we demonstrate how storylines representing alternative ways the event could have unfolded can be used to explore plausible worst-case scenarios over winter 2022/23 and beyond. The winter clusters span a range of temperature and rainfall response in the study region and represent circulation storylines that could have happened over winter 2022/23. River flow and groundwater level simulations with the large sample of plausible hindcast winters show that drier-than-average winters characterised by predominantly NAO−/EA− and NAO+/EA− circulation patterns could have resulted in the continuation of the drought with a high likelihood of below-normal to low river flows across all selected catchments and boreholes by spring and summer 2023. Catchments in Norfolk were particularly vulnerable to a dry summer in 2023 as river flows were not estimated to recover to normal levels even with wet winters characterised predominantly by NAO−/EA+ and NAO+/EA+ circulation patterns, due to insufficient rainfall to overcome previous dry conditions and the slow response nature of groundwater-dominated catchments. Through this analysis, we aim to demonstrate the added value of this approach to create drought storylines during an ongoing event. Storylines constructed in this way supplement traditional weather forecasts and hydrological outlooks, in order to explore a wider range of plausible outcomes.

Optimal allocation planning of regional water resources with multiple objectives using improved firefly algorithm

Abstract Population growth and economic development, coupled with water pollution and the frequent occurrence of extreme weather, have led to a growing contradiction between water supply and demand in some regions. To address this challenge, rational and optimal allocation of regional water resources has emerged as a crucial approach. This study focuses on creating a comprehensive model for optimizing regional water resource allocation, taking into account social, economic, and ecological factors. In addition, three innovative modifications are introduced to the firefly algorithm (FA), resulting in the development of the improved firefly algorithm (IFA). The effectiveness of IFA is validated through experiments involving nine benchmark functions. The results highlight the improved search efficiency and convergence achieved by IFA compared to other intelligent algorithms. Moreover, the application of IFA in solving the water resource allocation challenge in Shannxi Province, China, for 2020 and 2021 demonstrates a reduction in the overall water shortage rate to 4.69 and 1.72%, at a 75% guarantee rate. This reduction in water shortages contributes to addressing future scarcities. The proposed allocation scheme offers comprehensive benefits and provides crucial technical support for water resource management. Ultimately, this study offers valuable insights and guidance for addressing the issue of water supply–demand disparities.