Water Balance Model Research Articles

Scenario‐based water balance analysis of estuary reservoir for multipurpose water supply

AbstractIn the circumstance of increasing industrial and municipal water demand, estuary reservoirs are one of the critical water resources that can be a solution. Therefore, we simulated the water balance model of an estuary agricultural reservoir to evaluate the impact of additional industrial water release on its performance. We considered runoff and treated water as water inflow and irrigation requirement in paddy fields and water use in the hydrogen plant as water release. Runoff and irrigation water requirements were estimated using the Tank model and the FAO modified Penman method, respectively. Industrial water demand was surveyed at 32,000 m3 day−1 throughout the year. The results show that the reservoir's reliability increases as the water loss and irrigation area decrease with reusing the return flow, which is hardly affected by the industrial water supply. The trend of resiliency resembles reliability; however, the industrial release has the most significant impact when lower water loss is applied and the paddy area is decreased. Vulnerability gets worse when additional purpose water is released. In other words, the more water the reservoir releases, the more severe the drought gets. Therefore, the engineer and operator should consider the expected risks presented as reliability, resiliency and vulnerability in the design and management of the reservoir.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Impact Assessment of Climate Change on Water Requirements for Summer Paddy Cultivation in Raipur District, Chhattisgarh

Water is a crucial and finite resource, primarily utilized for irrigation, necessitating effective distribution for long-term sustainability. This study quantifies the water requirements for summer paddy cultivation in Raipur district, Chhattisgarh, and assesses its implications under changing climatic conditions. The FAO-CROPWAT 8.0 model was applied to assess irrigation parameters using historical meteorological data from 2000 to 2020 and projected meteorological data from 2040 to 2100. Reference evapotranspiration (ET₀) was estimated using Penman-Monteith method, incorporating crop coefficient (Kc) values to determine crop evapotranspiration (ETc). Irrigation water requirements were estimated by accounting for various losses and efficiencies of crop and field. Comprehensive water balance models were developed across the century, focusing on a 20-year average time span, including the historical period as 2000-2020 and the predicted time span from 2040 to 2100 with two socio-economic scenarios: SSP2-4.5 and SSP5-8.5, along with soil, meteorological, and phenological factors. Over the baseline period, the crop water requirement (CWR) for summer paddy was calculated as 751.4 mm, with net irrigation water requirement (NIWR) and gross irrigation water requirement (GIWR) as 1094.7 mm and 1563.8 mm, respectively. Under SSP2-4.5 scenario, the minimum and maximum temperatures are projected to rise by 1.3 °C and 1 °C respectively with rainfall increasing by 378.5 mm. This scenario anticipates a CWR increase of 32.6 mm by century's end, resulting in final values of CWR as 784 mm, NIWR as 1164.7 mm, and GIWR as 1663.9 mm. In the SSP5-8.5 scenario, temperatures are expected to rise by 4.4 °C and 3.1 °C as minimum and maximum temperature, leading to a CWR of 800.4 mm, with NIWR and GIWR as 1176.5 mm and 1680.7 mm, respectively. Overall, most parameters indicate an upward trend throughout the century. This study provides critical insights for efficient irrigation management in summer paddy cultivation and highlights the implications of future climate change on irrigation needs, promoting sustainable agricultural practices.

Adaptive assessment of reservoir scheduling to hydrometeorological comprehensive dry and wet condition evolution in a multi-reservoir region of southeastern China

The role of reservoirs in water resource management is becoming crucial for flood control and drought mitigation in any basin because of the frequent occurrence of extreme weather events attributed to global climate change and human activities. Therefore, evaluating the relationship between reservoir storage (discharge) and wet (dry) evolution is crucial. This study explores the time-delay effect and spatial heterogeneity of reservoir discharge and storage on dry and wet conditions in several basins of Lin’an District (LAD) in southeastern China. An integrated methodology is developed in this study to assess the relationship by a monthly streamflow simulation model, the meteorological and hydrological comprehensive drought index (CDI) using a Frank Copula function, and an eXtreme Gradient Boosting (XGBoost) model and Shapley Additive exPlanations (SHAP) framework were used to develop a model to forecast dry and wet conditions and to evaluate the key factors affecting their changes. Results from the study indicate that the monthly water balance model can simulate the monthly hydrological processes with relatively high accuracy in the LAD region. The CDI reflects the intensity of wet and dry events more precisely, thoroughly, sensitively, and consistently by combining the benefits of hydrological and meteorological drought indicators. Precipitation, evaporation, streamflow, the Pacific Decadal Oscillation (PDO), and the Indian Ocean Dipole (IOD) were the main contributing factors influencing the above 80% accuracy of the wet and dry forecast models. The average correlation between the outflow of each reservoir in LAD and CDI is 0.47, which is higher than the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI). Moreover, the delay in months of dry (wet) events based on SPI, SRI, and CDI are 0.45 (0.41), 1.07 (0.65), and 0.87 (0.60), respectively. It suggests reservoirs are less capable of adaptive scheduling for drought events than for wet events, and they respond most quickly to SPI defined events. The results can provide scientific and technological support for water safety and security in the study area.

Quantification of the Effect of Soil and Biophysical Parameters on Water Balance Modelling Using SWAT+ in Forested Catchments

ABSTRACTAccurate simulation of water balance components is crucial for effective water and land management practices. The performance of process‐based hydrological models relies on the accurate determination of input variables. The objective of this study is to quantify the magnitude of the effect of soil properties (depth and texture) and biophysical parameters on water balance simulation for a forested catchment using the Soil and Water Assessment Tool (SWAT+). Simulations were carried out for a baseline scenario using the default soil inputs, followed by extending the soil profile depth up to 15 m under three different rainfall scenarios. Sensitivity analysis of model outputs was performed using the SENSitivity ANalysis (SENSAN) programme of the Parameter ESTimation (PEST) suite, coupled with SWAT+. The results showed that increasing soil profile depth to 15 m led to around 50% increase in water yield, and around 20% reduction in percolation with slight variations across the three rainfall scenarios. Evapotranspiration rates were slightly increased in deeper soil profiles. The sensitivity of evapotranspiration, surface runoff, and percolation to LAI‐related biophysical parameters was pronounced, highlighting the need to include such parameters in SWAT+ model calibration. The water uptake from deeper soil layers by deep roots, even in rocky substrates, as documented in the literature, is not adequately captured by the SWAT+ model. Our work showed that in general, developing local soil databases with detailed information on deeper layers is needed, to improve the accuracy and reliability of hydrological models in predicting water fluxes, thereby supporting informed water resources management decisions.

The joint effect of changes in urbanization and climate on trends in floods: A comparison of panel and single-station quantile regression approaches

Estimates of annual maximum (peak) flow quantiles are needed for basins undergoing changes in both urbanization and climate. Most previous work on the effect of urbanization on peak flows has considered urbanization alone and only the spatial variation in flood quantiles or its mean temporal effect, and most work on the effect of nonstationarity in climate has focused on single-station analyses, which give uncertain results for extreme quantiles. To address these gaps, three approaches to the statistical estimation of the joint effects of changes in impervious cover and climate on the estimation of peak-flow quantiles were compared: single-station quantile regression; a fixed effect panel-quantile regression (pQR) method using a location (mean) shift to homogenize the panel; and a location-scale panel regression model (pQRmom), which accounts for both scale (variance) and location effects. The different approaches were applied to a dataset consisting of instantaneous annual peak flows from 127 minimally nested basins in the midwestern United States with at least 4 % change in imperviousness. The annual maximum daily discharge from a water-balance model was selected as the primary climate predictor; in addition, to provide a comparison of climate predictors, precipitation was also considered. The coefficients from single-station regressions were usually sufficiently certain to determine the effects of climate variation but usually too uncertain to estimate the effects of urbanization. The panel-quantile regression approaches give much more certain results, but their estimates of quantile dependence differ: although both indicate urbanization effects decreasing with decreasing annual exceedance probability (AEP), the pQRmom urbanization coefficients are insignificantly different from zero for AEPs less than 0.10, whereas the pQR coefficients remain positive and are significant except for AEP = 0.01, the smallest AEP value considered. Although the location-scale structure of the pQRmom approach has less flexible quantile dependence than the pQR approach, the pQRmom approach has somewhat lower overall error, and it is found that by subsetting the dataset to homogenize the scale effects, the pQR and pQRmom results become similar, indicating the insignificant urbanization coefficients for small AEPs of the pQRmom results are likely correct for the study dataset.

A parsimonious daily water balance model based on the proportionality hypothesis

Daily water balance modeling is important for both hydrological science and water resources management. The majority of daily water balance models express the output fluxes (including runoff and evapotranspiration) as independent functions of soil moisture store(s), and as such they involve a large number of parameters, the estimation of which is a major challenge. In this paper, following a downward approach, we develop a parsimonious daily water balance model, DPWM, based on the Proportionality Hypothesis, which assumes that a proportional relation exists among different hydrological components, which contributes to model parsimony. DPWM includes just 5 parameters, 3 of which are taken up to capture water balance partitioning and 2 for runoff routing. With daily precipitation and potential evapotranspiration as inputs DPWM invokes Proportionality Hypothesis to generate four main daily hydrological components, namely evapotranspiration, storage change, fast runoff and slow runoff. DPWM was applied to simulate daily streamflow (Q) and evapotranspiration (ET) in 671 US catchments covering a wide range of climates and landscapes, and showed catchment-average KGE(Q), KGE(1/Q) and KGE(ET) of 0.64, 0.68, 0.75 respectively. DPWM is seen to capture temporal variability of streamflow in rainfall-dominated humid catchments particularly well. DPWM shows comparable performance with the MARRMoT version of the XINANJIANG model, a traditional 12-parameter rainfall-runoff model that shows top performance amongst over 30 conceptual rainfall-runoff models. DPWM’s performance on KGE(1/Q) is even higher than that of XINANJIANG model. These results provide evidence that the Proportionality Hypothesis is useful for modeling daily water balance across a wide range of climates and landscape characteristics.

Leaching Efficiency During Autumn Irrigation in China’s Arid Hetao Plain as Influenced by the Depth of Shallow Saline Groundwater and Irrigation Depth, Using Data from Static Water-Table Lysimeters and the Hydrus-1D and SIMDualKc Models

The need for controlling salinity in arid zones is essential for sustainable agricultural production and irrigation water use. A case study performed for two years in Hetao, Inner Mongolia, China, is used herein to rethink the contradictory issues of arid lands represented by water saving and controlling soil and water salinity. Two sets of static lysimeters, where water table depths (WTDs) were fixed at 1.25, 150, 2.00, and 2.25 m, were continuously monitored, and soil water and solute data were used to calibrate and validate two models: the soil water balance model SIMDualKc and the deterministic soil water and salt dynamics model HYDRUS-1D. Once accurately calibrated, the models were used to simulate maize water use, percolation, and capillary rise, along with the observed variables for the actual WTD and the autumn irrigation applied. Simulation scenarios also considered agricultural system degradation and dynamic water table behavior. Results have shown that large leaching efficiencies (Lefs) were obtained for large irrigation depths in cases of shallow water tables, but higher Lefs corresponded to high application depths when the water table was deeper. Agricultural system degradation, particularly increased groundwater salinity, lowered Lef, regardless of WTD. Conversely, water savings were minimal and only achievable when considering the dynamic nature of groundwater. These results indicate that there is a need to define different WTDs based on soil characteristics that influence fluxes and root zone storage, as well as the impacts of newly installed drainage systems aimed at salt extraction.

Water Status Detection Method Based on Water Balance Model for High-Power Fuel Cell Systems

With the gradually accelerating pace of global decarbonization, highly efficient and clean proton exchange membrane fuel cells (PEMFCs) are considered to be an energy solution for the future. During the operation of a fuel cell, it is necessary to keep the internal proton exchange membrane in a good state of hydration, so an appropriate method of detecting the hydration state is essential. At present, fuel cell systems are rapidly developing towards high power, but methods for detecting the hydration state of high-power fuel cell systems are still relatively lacking. Therefore, this paper studies the hydration state of high-power fuel cell systems and builds a condensation tail-gas water collection device for calculating the water flow out of a fuel cell system, deriving the hydration status inside the high-power fuel cell system. To verify the proposed water balance model, a series of experiments were conducted based on controlled variables such as working temperature, air metering ratio, and load current. Experiments were conducted on a 100 KW fuel cell system to collect water flow from the fuel cell system. Finally, based on the experimental data, the change rate of the internal water content of the fuel cell system under different conditions was calculated. The results show that, under the same load current, as the working temperature and air metering ratio increase, the change rate of the internal water content of the fuel cell system gradually decreases. Therefore, at low power, it is necessary to maintain an appropriate working temperature, while at high power, maintaining an appropriate air metering ratio is more important.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

Considerations in designing climate change assessments for complex, non-linear hydrological systems

Bias correction of climate model simulations is vital to allow climate change impacts to be assessed for water resources systems. However, there has been limited research to date on the implications of system non-linearity on bias correction approaches. Here we bias correct regional climate model simulations of precipitation and evapotranspiration and use the output to force hydrological and water balance models of five small, interconnected lakes located south west of Sydney. We show that substantial, non-linear storage within the lakes amplifies biases that are not evident when the climate forcing or even the hydrological model simulations are evaluated using daily distributions of the climate variables and streamflow.The non-linearity in the stage-storage relationships of the lakes means that each lake responds differently to the same climate forcings. For example, ensemble mean projections for one lake suggest increases in water level across the full distribution of lake levels, whilst other lakes are projected to have decreasing water levels up to the median of the distribution, but increases during wetter conditions. These differences are explained by the varying influence of potential evapotranspiration increases depending on the surface area of the lakes at different depths. Using bottom up climate change assessments, we further explore these non-linear responses of the lakes to different climate forcings. We show that bottom up climate change assessments can provide information on the relative role of potential evapotranspiration changes compared to precipitation changes, providing more guidance to ecosystem managers than just using bias corrected climate model simulations alone. The paper discusses opportunities for future work to improve representation of climate attributes important for storage dominated water resource and natural ecosystems.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

Temperature‐driven convergence and divergence of ecohydrological dynamics in the ecosystems of a sky island mountain range

AbstractForest and woodland decline is predicted to be increasingly influenced by meteorological variation and climate change in the future. By determining how meteorological variation leads to similar versus differing ecohydrological dynamics of forest and woodland ecosystems, we can gain insight on how future climate‐driven declines may be realized. We characterized 23 mixed conifer forest (MC), ponderosa pine forest (PP) and piñon pine–juniper woodland (PJ) sites with different canopy covers in southern Nevada, USA. We compared meteorological variation between these sites and employed water balance modelling and information theory to estimate similarity in the density distributions of soil temperature (Ts), soil water potential (SWP) and transpiration partitioning into total evapotranspiration (T/ET) within and across ecosystems in wetter and drier seasons and in cooler and warmer decades. From 1941 to 2020, this location experienced declines in meteorological water deficit due to higher precipitation, although temperatures increased over more recent time periods (1981–2020). From 1981 to 2020, we generally found greater similarity in SWP and T/ET distributions within MC sites and PP sites in the cool season and in the warm season generally found greater similarity in Ts and T/ET distributions within and between PP and PJ sites (excepting T/ET between PJ sites and higher canopy cover PP sites). Recent warm decades promoted convergence in warm and cool season Ts dynamics, such that Ts dynamics generally became more similar between higher elevation MC sites and lower elevation PP–PJ sites. At the same time, warmer decades initiated divergence of SWP and T/ET dynamics within groups of MC–PP and PP–PJ sites that were formerly more similar to each other (excepting SWP in wet seasons). Although their dynamics will remain strongly coupled to precipitation, warming temperatures have the potential to promote divergence in the ecohydrological dynamics of ecosystems at lower and higher elevations in this sky island system and may also promote novel within‐ecosystem divergence associated with variation in vegetation structural attributes.

Intercomparison of citrus evapotranspiration among eddy covariance, OpenET ensemble models, and the Water and Energy Balance Model (BAITSSS)

Remote sensing-based surface energy balance algorithms have been used to estimate water use of various crops. However, citrus evapotranspiration (ET) estimation is challenging mainly due to evergreen leaves and a clumped canopy structure. In this study, we evaluated the performance of two methods for calculating ET: the ensemble of OpenET models, which are mostly satellite thermal-based models, and the BAITSSS water and energy balance model. Calculated ET was compared with (i.) eddy covariance (EC) ET measurements and (ii.) water received (irrigation plus precipitation) data for two citrus orchards in San Joaquin Valley, California. Polaris-based soil hydraulic properties and measured volumetric water content were used for BAITSSS parameterization and initialization, respectively. Sentinel-2 based NDVI was used for BAITSSS simulation. Results showed that annual ET based on the OpenET ensemble model (1169 mm) was on average 30 % larger (r2 ∼ 0.71, RMSE ∼ 1.16 mm) than both EC ET (908 mm) and water received (886 mm). The disparity mostly occurred in spring. BAITSSS, on the other hand, showed mixed results compared to observations (r2 ∼ 0.77, RMSE ∼ 0.94 mm). Both measured from EC and modeled ET from BAITSSS and ensemble OpenET values were below grass reference ET (ETo) for the majority of the simulation period. Soil moisture and water received data indicated the orchards may have been deficit irrigated. Overall, this study highlights the challenges of ET modeling in citrus orchards and the need for improved estimation of ET for this specialty crop.

Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain

ABSTRACT Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrological modelling for ungauged or poorly gauged basins.

Impact of Irrigation on Soil Water Balance and Salinity at the Boundaries of Cropland, Wasteland and Fishponds under a Cropland–Wasteland–Fishpond System

In order to explore the effect of fishponds on soil water, salt transport and salinization in cropland wasteland, a study on soil water balance and salt distribution pattern in a cropland–wasteland–fishpond system was carried out in 2022–2023 in a typical study area selected from the Yichang Irrigation Area of the Hetao Irrigation District. A water balance model was established for the cropland–wasteland–fishpond system to analyze the effects of irrigation on soil salinity at the boundaries of the cropland, wasteland, and fishpond. The results showed that the lateral recharge from the cropland to the wasteland during spring irrigation in 2022 was 24 mm, the lateral recharge generated by fishponds to wasteland was 18 mm, and the lateral recharge from fishponds to fishpond boundaries was 34 mm. In the fertility period of 2023, the lateral recharge from cropland to wasteland was 15 mm, the lateral recharge from fishponds to wasteland was 9 mm, and the lateral recharge from fishponds to fishpond boundaries was 21 mm. Due to the low salinity content of fishpond water, it diluted the groundwater of the wasteland, and the soil salinity at the boundary between the wasteland and the fishpond was monitored. The data show that the soil salinity at the boundary of the fishpond was smaller than that of the wasteland, which indicates that the migration of fishpond water to the wasteland will not lead to an increase in the soil salinity of the wasteland, but rather to a decrease in the soil salinity of the wasteland. Fishpond regulation has a significant impact on soil and groundwater, and when the topographic conditions of the Hetao irrigation area allow, the model of cropland–wasteland–fishpond can be appropriately adopted to solve land degradation and increase the economic income of farmers; the results of the study provide a contribution for the improvement of the management of land use and soil salinization in the Hetao irrigation area.

Unveiling the future water pulse of central asia: a comprehensive 21st century hydrological forecast from stochastic water balance modeling

This study uses a new dataset on gauge locations and catchments to assess the impact of 21st-century climate change on the hydrology of 221 high-mountain catchments in Central Asia. A steady-state stochastic soil moisture water balance model was employed to project changes in runoff and evaporation for 2011–2040, 2041–2070, and 2071–2100, compared to the baseline period of 1979–2011. Baseline climate data were sourced from CHELSA V21 climatology, providing daily temperature and precipitation for each subcatchment. Future projections used bias-corrected outputs from four General Circulation Models under four pathways/scenarios (SSP1 RCP 2.6, SSP2 RCP 4.5, SSP3 RCP 7.0, SSP5 RCP 8.5). Global datasets informed soil parameter distribution, and glacier ablation data were integrated to refine discharge modeling and validated against long-term catchment discharge data. The atmospheric models predict an increase in median precipitation between 5.5% to 10.1% and a rise in median temperatures by 1.9 °C to 5.6 °C by the end of the 21st century, depending on the scenario and relative to the baseline. Hydrological model projections for this period indicate increases in actual evaporation between 7.3% to 17.4% and changes in discharge between + 1.1% to –2.7% for the SSP1 RCP 2.6 and SSP5 RCP 8.5 scenarios, respectively. Under the most extreme climate scenario (SSP5-8.5), discharge increases of 3.8% and 5.0% are anticipated during the first and second future periods, followed by a decrease of -2.7% in the third period. Significant glacier wastage is expected in lower-lying runoff zones, with overall discharge reductions in parts of the Tien Shan, including the Naryn catchment. Conversely, high-elevation areas in the Gissar-Alay and Pamir mountains are projected to experience discharge increases, driven by enhanced glacier ablation and delayed peak water, among other things. Shifts in precipitation patterns suggest more extreme but less frequent events, potentially altering the hydroclimate risk landscape in the region. Our findings highlight varied hydrological responses to climate change throughout high-mountain Central Asia. These insights inform strategies for effective and sustainable water management at the national and transboundary levels and help guide local stakeholders.

The phenological soil water balance: a proposed model for estimating water resources for an entire watershed using crop coefficients

The aim of the research was to develop a model that would allow the application, at a watershed scale, of the hydrological soil water balance model proposed by the FAO for the dosing of irrigation water in agriculture, which uses crop coefficients (Kc) for the calculation of potential crop evapotranspiration (ETc). To be able to assess the water resources of a territory in which there are land uses other than agricultural ones, the application of the proposed model has made it necessary to determine the crop coefficients of the latter. Since crop coefficients vary according to phenological stage, this model was termed ‘phenological soil water balance’. A correction factor for precipitation and potential evapotranspiration, using an acclivity coefficient (i.e., the ratio between the actual area and the projected area), has also been proposed to obtain accurate results even in non-flat areas, which allowed us to consider the actual area of the territory instead of the projected one. The model was applied daily for 7 consecutive years (from 2013 to 2019) in the Santa Maria degli Angeli watershed (Urbino, central Italy) whose area is about 14 km2. The calibration and validation of the model were conducted by comparing the deep percolation computed by the model with baseflow values of the Santa Maria degli Angeli stream obtained by flow measurements made at the closing section of the sample watershed. The results of the model showed that the total values of deep percolation and measured baseflow only differed by 3% in the whole period considered; thus the phenological soil water balance model can be used to accurately estimate water resources and can be applied at different time intervals (daily, monthly, annual, etc.). The structure of the model makes it suitable for application in both small and large watersheds and territories.

A Monte Carlo Model for WWTP Effluent Flow Treatment through Enhanced Willow Evapotranspiration

The effectiveness of using enhanced evapotranspiration rates of willow plantation is a modern environmentally friendly practice for advanced treatment of effluent WWTP flow. The key idea is that through advanced willow evapotranspiration rates, a significant proportion of the effluent flow can be transferred into the atmosphere through the physical process of evapotranspiration. This study further discusses the concept in a real-world problem using a wide dataset consisting of a recent PET monthly remote dataset namely RASPOTION, monthly recorded rainfall gauge, and experimental willow evapotranspiration surveys across Ireland, to identify the monthly cropping pattern. A Monte Carlo water balance model has been developed for the period 2003–2016. The model was applied in an existing willow plantation at Donard WWTP co. Wicklow, Ireland to identify the exceedance probability of willow plantation runoff against estimated low flows (i.e., Q95, Q99) at the adjacent small tributary. In this case study, any failure which can lead to river quality deterioration was not assessed. The overall framework aims to provide new insights considering the multiple sources of uncertainty (i.e., monthly willow cropping pattern and WWTP effluent flow) in associated environmental engineering problems.

Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd > TWBM > HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction.