Water Resources Management Planning Research Articles

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.

Predicting Rainfall Pattern in Kakamega County using Time Series

Rainfall patterns play a critical role in shaping various aspects of our lives. Understanding the patterns, trends and predictability of rainfall is essential for effective planning and decision making in various aspects including agriculture, water resource management, disaster preparedness and social economic planning. In agricultural activities crops require specific amount of water at the right time for growth. By understanding the rainfall patterns, farmers can adapt their farming activities, optimizing irrigation strategies and make informed decisions. In the management, it help policy makers and management authorities for planning efficient water allocations and conservations measures. Therefore, in this paper we fit a time series model that best describes rainfall patterns of Kakamega county for the general ARIMA and generated the values of (P, D, Q) to forecast average expected monthly rainfall. Also we use R software for verification and data fitting of the model. The data we have used is from the Kakamega meteorological station in Kakamega.

Operational analysis of lake Tana under climate change, Upper Blue Nile Basin, Ethiopia

The excessive abstraction of lake water affects its sustainability and is common in Lake Tana. Hence this study aims to develop operation rule curves for optimum operation of Lake Tana reservoir by considering the effects of small and large-scale irrigation water abstraction, Hydropower demand, and climate change. The data used in this study were produced by two Global Circulation Models (GCMs) (MIROC5 and MPI) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) in the 2040s (2020–2049) and 2070s (2050–2079). For RCP4.5 and RCP 8.5 scenarios of the 2040s- and 2070s-time domains, lake temperature exhibits an upward trend. The lake rainfall also will show an increase for the 2040s and 2070s under both RCP scenarios. The outcome showed that in all future scenarios, lake evaporation under RCP4.5 of 2040s and 2070s rise between 3.6 % to 7 % and 4.6 % to 19, respectively, while for RCP8.5 of 2040s and 2070s increases between 4.6 % to 9.9 % and 9.2 % to 24.7 %. The surface water inflow increased for RCP4.5 (2070s) and RCP8.5 (2040s), whereas it declined for RCP4.5 (the 2040s) and RCP8.5 (2070s. Under both scenarios, the average value of the time-based and volumetric reliability in the 2040s and 2070s is less than 80 %. The reservoir will take a very long time to recover from the shortfall as the resilience levels are below 50 %. Additionally, the dimensionless vulnerability was less than 50 %, indicating that there won't be a shortage of inflow to meet demand at the reservoir. Based on evaluation, the reservoir will not probably perform well in any way because of the rise in upstream abstractions. To guide the operation of the reservoir, operation rule curves were developed using the Hec-ResSim model. Generally, the government, decisions, and policymakers should set a water resources management plan not trigger normal functioning of the Lake system.

Trends of Sediment Resuspension and Budget in Southern Lake Michigan Under Changing Wave Climate and Hydrodynamic Environment

AbstractSediment suspension and transport driven by waves and currents play a significant role in both the ecological and physical environments of large lakes. Lake Michigan has faced a rapidly increasing water level associated with intensified wind waves in the past decade. To investigate the spatiotemporal characteristics of suspended sediment concentration (SSC) and associated coastal sediment budgets in southern Lake Michigan, a 30‐year (1991–2020) hindcast was performed using a coupled wave‐current‐sediment model (SWAN‐FVCOM‐CSTMS). We found that in southern Lake Michigan, the basin‐wide mean SSC increased, and the coastal sediment loss accelerated dramatically, corresponding with intensified waves, currents and lake water level rises over the past decade. The basin‐wide mean SSC, coastal sediment loss, wave height, wind speed, current speed, and water level in southern Lake Michigan are highly correlated. Spatially, the results reveal decreases in coastal SSC and sediment loss in the western portion of the southern basin, while the eastern sectors show an increase in both metrics. This reflects a clear shift in the wave climate and hydrodynamic environment. The alterations in long‐term coastal sediment budgets imply that considerable shoreline transformations are being influenced by modifications in the wave climate. Understanding the spatiotemporal characteristics of SSC and coastal sediment budgets is crucial for strategic water resource management and coastal infrastructure planning.

Analysing sensitivity of conditioning factors for wetland habitat quality modelling

Wetland habitat quality degradation, in addition to wetland loss, is a growing concern that demands rapid action. The purpose of the current study is to assess the sensitivity analysis of conditioning factors on the Wetland Habitat Quality State (WHQS) in the Rarh tract of Murshidabad, West Bengal. Total seventeen metrics in total, including water quality, hydrology, and landscape composition were employed to mapping WHQS. WHQS was modelled using machine learning techniques. The efficiency of the models was evaluated using statistical methods such as the Receiver operating characteristics (ROC) curve. Very strong portions of wetland area have been decreased from 32.74% in 1990 to 20.72% in 2020. According to the study, wetland fragmentation, distance from agricultural land, and trophic status factor of the wetlands are key elements that affect wetland habitat quality to a large extent. The current study's findings could serve as the foundation for developing water resource management plans for the management, conservation, and restoration of wetlands, in addition to doing extensive research on monitoring habitat quality in wetlands.

Deep Learning Approaches for Stream Flow and Peak Flow Prediction: A Comparative Study

Stream flow prediction is crucial for effective water resource management, flood prevention, and environmental planning. This study investigates the performance of various deep neural network architectures, including LSTM, biLSTM, GRU, and biGRU models, in stream flow and peak stream flow predictions. Traditional methods for stream flow forecasting have relied on hydrological models and statistical techniques, but recent advancements in machine learning and deep learning have shown promising results in improving prediction accuracy. The study compares the performance of the models using comprehensive evaluations with 1-6 input steps for both general stream flow and peak stream flow predictions. Additionally, a detailed analysis is conducted specifically for the biLSTM model, which demonstrated high performance results. The biLSTM model is evaluated for 1-4 ahead forecasting, providing insights into its specific strengths and capabilities in capturing the dynamics of stream flow. Results show that the biLSTM model outperforms other models in terms of prediction accuracy, especially for peak stream flow forecasting. Scatter plots illustrating the forecasting performances of the models further demonstrate the effectiveness of the biLSTM model in capturing temporal dependencies and nonlinear patterns in stream flow data. This study contributes to the literature by evaluating and comparing the performance of deep neural network models for general and peak stream flow prediction, highlighting the effectiveness of the biLSTM model in improving the accuracy and reliability of stream flow forecasts.

Evaluation of the Impact of Climate Change on the Water Balance of the Mixteco River Basin with the SWAT Model

Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB.

Hydrological effects of the conversion of tropical montane forest to agricultural land in the central Andes of Peru

AbstractAgricultural expansion is one of the main causes of deforestation of tropical montane forests in the central Andes of Peru. The hydrological impacts of converting forests to cropland are well known; however, an issue that is not entirely clear is related to the hydrological effects that can result from the conversion of montane forests to agroforestry systems and the recovery of the hydrological functions of montane forests after a disturbance. In this study, we compare the hydrological processes of different land covers previously modified by agricultural expansion, in order to determine the impact of the conversion of tropical montane forest to agricultural land on the ecosystem service of water provision and regulation. To achieve this, we establish study plots in four land cover types located in the central Andes of Peru (mature montane forest (BMC), natural regenerating secondary forest (BMR), coffee agroforestry systems (AF), and cropland (C)), for the purpose of measuring the vegetation structure and soil properties in them, and subsequently carry out a soil water balance in each plot to calculate the actual evapotranspiration, surface runoff, and groundwater recharge. The results revealed the following percentages (based on precipitation) for the hydrological components in the four land cover types: annual actual evapotranspiration—BMR (41.2%), AF (40.4%), BMC (40.0%), and C (38.0%); annual surface runoff—C (16.1%), BMC (8.3%), BMR (5.2%), and AF (4.6%); and annual groundwater recharge—AF (43.0%), BMR (41.6%), C (34.0%), and BMC (31.7%). Furthermore, the study also examined the relationship between vegetation structure and the hydrological components across the four land cover types. The findings indicated that the reduction of thicker and taller trees could increase surface runoff generation, whereas the presence of thinner and shorter trees could facilitate groundwater recharge. These results shed light on the complex interactions between land cover types, vegetation structure, and hydrological processes, emphasizing the importance of considering these factors in water resource management and land use planning.

Innovative Drought Classification Matrix and Acceptable Time Period for Temporal Drought Evaluation

Evaluating drought is paramount in water resources management and drought mitigation plans. Drought indices are essential tools in this evaluation, which mainly depends on the time period of the original datasets. Investigating the effects of time periods is critical for a comprehensive understanding and evaluation of drought. Also, It holds particular significance for regions facing data availability challenges. The existing literature reveals a gap in drought assessment and comparison analysis using conventional methods based on drought indices only. This research proposes an innovative drought classification matrix to compare drought indices and spatial and temporal scenarios; the proposed matrix depends on any drought classification for comparison procedure. Furthermore, it aims to investigate the differences between several time period scenarios based on the proposed matrix and several statistical metrics (R2, CC, RMSE, HH, and RB) and determine the acceptable/minimum time period. The application of the proposed matrix and selection of an acceptable/minimum time period is presented to three different climates: Durham station in the United Kingdom, Florya station in Türkiye, and Karapinar station in Türkiye. The results show that the time period scenarios are able to catch the reference time period (RTP) scenario reasonably, with strong correlation and negative relative bias. The 10-year time period is sufficient as an acceptable/minimum time period for short timescales, such as meteorological drought. Conversely, for longer timescales, such as hydrological drought, a 20-year time period is the acceptable/minimum time period. The proposed matrix demonstrates a robust and powerful framework for comparison, making it applicable to various drought assessment scenarios globally.

Assessing the impact of climate change on surface runoff of the Ubolratana Reservoir, Thailand

Assessing surface runoff in river basins is paramount for effectively managing water resources. Climate change significantly impacts the availability of water within these basins. The Ubolratana reservoir is vital for sustainable water supply, aquaculture, agricultural practices, and domestic needs. This research aims to evaluate the runoff availability of the reservoir by using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) under distinct climate change scenarios. The HEC-HMS model was calibrated based on observed rainfall and runoff data. To project future runoff dynamics, calibrated model parameters were coupled with the bias-corrected rainfall data of Can-ESM5, CESM2, and GFDL-ESM4 models for two Shared Socioeconomic Pathways (SSPs) climate scenarios (SSP 245 and SSP 585). The results show upward trends in both projected rainfall and runoff within the drainage area of the Ubolratana reservoir between 2022 and 2061. In summary, the findings of this research may be useful for regional water resources management and strategic planning endeavors.

Runoff projection in the Tibetan Plateau using a long short-term memory network-based framework under various climate scenarios

Runoff projections in the Tibetan Plateau (TP) can provide a valuable basis for making decisions regarding water resource management and facilitating the assessment of potential risks to water security. This study estimated the runoff of the Tibetan Plateau (TP) at a grid resolution of 0.5°for the near-term period (2022–2051) and the long-term period (2052–2082) under various climate scenarios. A framework based on long short-term memory (LSTM) was used to project the impact of climate change on runoff by integrating input selection, LSTM modelling and runoff projection application. This novel framework was used to determine the variables that contribute to runoff occurrence in different grid cells, analyze the relationship between these variables and runoff, and predict how runoff may change in the future due to precipitation, temperature, and antecedent runoff under various climate scenarios. The projected results showed that the runoff would be approximately 450 mm/a for the period from 2022 to 2051 and approximately 465 mm/a during the period from 2052 to 2082, representing an increase of about 46 % compared to the period from 1982 to 2012. Future runoff will increase in a decreasing pattern from the southwest to the northeast across the TP region. These findings regarding the projected increase in runoff will assist in the development of proactive adaptation strategies and support long-term economic growth through effective water resource management plans.

Historical and projected future runoff over the Mekong River basin

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

The Spatial-Temporal Matching Characteristics of Water Resources and Socio-Economic Development Factors: A Case Study of Guangdong Province

The spatial-temporal mismatch of water resources and socio-economic development in rapidly urbanized regions has been the focus of water resource management, and is one of the main limitations to sustainable development goals (SDGs). Guangdong Province is represented by a highly developed economy and society, and has been undergoing disproportionately rapid population growth during the past two decades. The uneven distribution and spatial mismatch of water resources have aggravated the contradictions between water supply demand. In this paper, we evaluate the matching degree of water resources and social economic elements, detect the spatial-temporal changing pattern of the matching degree, and reveal the changing mechanism using the combined methods of the Thiel index, the space–time Gini coefficient, and the Lorenz curve. The results show that (1) the temporal matching is relatively balanced and reasonable, while the spatial mismatch is prominent and deteriorating constantly, especially the connection between the amount of water and GDP; (2) the water volume pressure is mainly concentrated in the Peral River Delta and eastern and western Guangdong, while water consumption stress is relatively lower in northeastern Guangdong; and (3) the water volume inequality is dominated by an intercity difference and is primarily caused by regional differences. Based on the results, policy implications, such as the implementation of integrated water resource management plans, investment in the development of alternative water sources, as well as enhanced public education and the establishment of a water saving society, have been provided to alleviate the mismatch issue between water elements and socio-economic parameters, and to push the realization of water-related SDGs.

Study on multiscale-multivariate prediction and risk assessment of urban flood

Few studies have explored the impact of drivers on urban flood at multi-time scales, and few studies have assessed the urban flood risk based on accurate description and quantification of the intrinsic correlation between urban flood and its drivers. Therefore, this study develops a multiscale-multivariate prediction model for flood volume (FV) using wavelet transform, and a flood risk assessment model based on the joint distribution of FV and its key drivers using the Copula method. The downtown area of Zhengzhou City, a typical city in North China, is taken as the study area. The results reveal that land use change is the key driver for variation of urban rainstorm flood, and rapid urbanization led to the variation observed in 2005. The impact of land use change on FV primarily manifests at the long cycle scale, and the multiscale-multivariate prediction model demonstrates effective simulation (NSE = 0.957, R2 = 0.958, MAPE = 13.47%) and prediction capabilities (relative errors are all below 20%). Taking the FV exceeding the threshold corresponding to the frequency of 37.5% as an example, the maximum conditional risk probability under nine combinations of key drivers is 88.34%, while the minimum probability is only 6.83%, intuitively indicating the impact of rainstorm and urbanization on this risk. These findings can provide technical references for urban flood forecasting, urban water resources management and urban development planning.

Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India

This paper addresses the challenge of predicting erratic rainfall in Rajasthan state of India, particularly in southern regions. Reliable rainfall predictions are crucial for water resource management and agriculture planning. The research involved selecting 58 stations across seven districts of southern Rajasthan and identifying the best fit computational neural (ANN) and wavelet integrated computational neural (W-ANN) architectures based on performance metrics. Different combinations of input characters, hidden layer neurons, learning algorithms, and training cycles were tested to determine optimal models. Hybrid models, combining wavelet analysis with ANN, were explored to tackle non-stationary hydrologic signals effectively. Results showed that ANN Model C with ten input layer neurons performed best for 74% of stations, followed by Model B (21% of stations) and Model A (5% of stations). Models with increased input and hidden layer neurons performed better. Among the selected stations, 81% of stations demonstrated improved performance using W-ANN models due to effective signal decomposition and information extraction. The hybrid W-ANN models outperformed simple ANN models for rainfall prediction. Both ANN and W-ANN models accurately forecasted weekly rainfall, as observed in the comparison of actual and forecasted values.

Spatiotemporal characteristics of vegetation cover change in the Central Yunnan urban agglomeration from 2000 to 2020 based on Landsat data and its driving factors

The study of dynamic changes in vegetation coverage has significant implications for urban clusters in terms of ecological environment protection, climate change research, water resource management and urban planning. A thorough understanding of the driving mechanisms behind vegetation coverage helps provide scientific basis and policy recommendations for the ecological environment protection, sustainable development and decision-making of urban clusters. Taking the central Yunnan urban cluster as an example, this study utilizes long-term Landsat remote sensing image data from 2000 to 2020 on the Google Earth Engine platform. The fractional vegetation cover (FVC) is estimated using a pixel-based binary model. The Sen’s slope, Mann–Kendall trend analysis and Hurst index methods are employed to investigate the spatiotemporal dynamic characteristics of FVC. The impacts of climate change and human activities on vegetation coverage are explored through partial correlation analysis and residual analysis. Finally, considering natural factors such as topography and climate, as well as socioeconomic factors, the geographic detector is used to quantitatively analyze the driving factors behind FVC changes. The results indicate that: (1) From 2000 to 2020, the FVC in the central Yunnan urban cluster changed significantly, showing an overall improvement trend. The average FVC is 0.496 with a growth rate of 0.0024 per year, and it exhibits a distribution pattern of higher values in the west and lower values in the east. (2) In terms of percentage distribution, areas with low FVC and high FVC account for a relatively high proportion. The trend of FVC changes is as follows: improvement (49.6%) > degradation (26.4%) > no change (24%). The average Hurst value is 0.45, indicating that future FVC changes in the study area will be opposite to the past. (3) FVC shows a positive correlation with precipitation and a negative correlation with temperature. Human activities have a positive impact on FVC in the study area, accounting for 55.1% of the regional proportion. (4) Slope and nighttime light contribute the most to FVC. The explanatory power of the interaction between slope and precipitation/temperature is the most significant.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

Environmental DNA, hydrochemistry and stable water isotopes as integrative tracers of urban ecohydrology

Urbanization and the persistent environmental changes present a major challenge for urban freshwaters and availability of water for humans and wildlife. In order to increase understanding of urban ecohydrology, we investigated the variability of planktonic bacteria and benthic diatoms - as two key biological indicators - coupled with insights from hydrochemistry and stable water isotopes across four urban streams characterized by different dominant water sources in Berlin, the German capital, over a period of one year (2021–2022). DNA metabarcoding results show that substantial spatio-temporal variability exists across urban streams in terms of microbial diversity and richness, with clear links to abiotic factors and nutrient concentrations. Bacterial communities showed clear distinction between effluent-impacted and non-effluent impacted streams as well as clear seasonal turnover. In-stream benthic diatom assemblages also showed robust seasonal variation as well as high species diversity. Our multiple-tracer approach is relevant for emerging questions regarding the increased use of treated effluent to supplement declining baseflows, the assessment of stream restoration projects and the impact of storm drainage and surface pollution on aquatic ecosystem health. eDNA analysis allows analysis of spatial and temporal patterns not feasibly studied with traditional analyses of macroinvertebrates. This can ultimately be leveraged for future water resource management and restoration planning and monitoring of urban freshwater systems across metropolitan areas.

Large-scale mapping of existing and lost wetlands: Earth Observation data and tools to support restoration in the Sebou and Medjerda river basins

Mediterranean wetlands are the richest and most productive ecosystems in the region, and are essential for climate change adaptation and mitigation. However, despite their importance, they have suffered significant destruction over time. We estimate that half of the natural wetlands have been lost since the 1970s, and the regional trend shows no signs of slowing down. It is therefore urgent to implement concrete solutions that can preserve the remaining wetlands and restore those that have been lost. The increasing availability of free and open Earth Observation (EO) data and tools has provided a basis for mapping these ecosystems and monitoring their status and trends. In this paper, we show how EO-based data and tools can support the pre-identification of candidate sites for wetlands restoration at large scale through the mapping and delineation of existing and lost wetland habitats, their current land use status, and the estimation of the efforts needed to recreate the lost and transformed ones. We used this approach in the Sebou river basin in Morocco and the transboundary Medjerda watershed between Algeria and Tunisia. The resulting products, i.e., Potential Wetland Areas and Potentially Restorable Wetlands maps, enabled the identification of more than 7000 km2 and 1700 km2 of lost wetland habitats that could be regained in the Sebou and Medjerda basins, respectively. These results hold immense value for water resources management and land planning as they can enhance and assist prioritization efforts for wetland restoration at local, national, and regional scales. They can serve as baseline data to identify candidate sites to implement wetland restoration actions as Nature-based Solutions, regenerate their habitats, and restore the ecosystem services they provide to society.