Water Balance Calculations Research Articles

High-accuracy and low-disturbance approach for identifying surface water and groundwater interactions in wetlands

Comprehending the hydrological conditions in wetlands is a critical aspect of successfully enhancing wetland conservation. The interaction between wetland surface water and groundwater is a complex process, requiring detailed onsite hydrological and soil surveys, laboratory experiments, and modeling to clarify this relationship. However, conventional investigation methods often cause significant disruptions and thus may affect the natural environment and compromise data reliability. In this study, a high-accuracy and low-environmental-disturbance (LED) approach was proposed involving modified falling head permeability and modified seepage meter tests to elucidate the groundwater characteristics in an ecological reserve. A water balance (WB) calculation method was employed to examine the performance of the proposed LED approach. The results revealed that the LED performed better than conventional methods in hydraulic conductivity and seepage velocity exploration, thereby improving the accuracy of quantifying groundwater flow. Moreover, the experimental findings and ecohydrological observations were used to assess the groundwater flow regime, and the data were consistent with the field survey results. The contradiction between conducting research and protecting ecological reserves can pose difficulties in the sustainable and effective management of wetlands. The LED approach can be applied broadly, especially in areas where significant disturbance should be avoided. The water budget model can thus be developed to help deduce the interaction between groundwater and surface water. We suggest that these innovative methods are effective tools and can assist both scientists and authorities in formulating corresponding habitat management strategies.

Water balance optimization for strategic planting patterns and calendars in paddy (Oryza sativa L.) cultivation in rainfed regions

Paddy (Oryza sativa L.) is a staple food crop in Indonesia that requires more water compared to other annual crops. Cultivating paddy in rainfed areas necessitates effective water management to prevent crop failure, making it essential to calculate water balance, planting patterns, and planting calendars. This study aims to analyze the water balance in relation to planting patterns and calendars based on water availability in the field. The research was conducted in Cukilan Village, Semarang Regency, Indonesia. A descriptive quantitative method using Cropwat 8.0 was employed to determine water balance, planting patterns, and planting calendars. The results indicate that from November to April, there is a water surplus, while from May to October, there is a water deficit. Planting can be conducted once per growing season with two possible periods: November-March or December-April. During Period I (November-March), the water requirement is 640.7 mm with effective rainfall of 1031.2 mm. In Period II (December-April), the water requirement is 638 mm with effective rainfall of 935.7 mm. Planting should begin in November or December, with harvest in March or April. From May to October, the land remains fallow due to insufficient water availability. These findings are valuable for enhancing the effectiveness of paddy cultivation in rainfed areas, assisting farmers with planting planning, and minimizing the risk of crop failure due to water scarcity.

Analysis of Land Water Balance in Various Rainfall Conditions and Its Utilization to Determine Planting Patterns of Food Crops in the Eastern Part of Seram District of Seram Island

Soil water balance calculation is one of the methods used to estimate the dynamics of soil water content during plant growth. This study aims to describe rainfall conditions and analyze soil water balance as well as determine the growing season and composition of food crop combination patterns based on the available growing season in East Seram Island. This research uses rainfall data for 30 years of observation period 1992-2021 from Geser Meteorological Station (Data analysis with the following stages: (i) rainfall analysis (ii) calculation of average rainfall (iii) determination of rainfall with a 75% chance (iv) calculation of soil water balance using the Thornthwaite and Mather method (v) determination of the growing season and cropping pattern. The results showed that the average rainfall in East Seram was 2,194 mm/year with a water deficit (D) occurring in October and November, then the rainfall had a 75% chance of being exceeded at 1,439 mm/year with a water deficit of 228 mm/year which lasted for eight months, namely August - March. The growing season under normal rainfall conditions was available throughout the year, while under high rainfall conditions, a 75% chance of the growing season was available for five months, namely May and October. The cropping patterns and crop combinations used were monoculture, polyculture, and intercropping with combinations of cassava, maize, sweet potato, groundnut, mung bean, cassava-corn, cassava-peanuts/green beans, maize-peanuts, cassava-corn-peanuts/green beans, cassava-corn-horticultural crops.

Development of a laboratory complex for measuring the condensation of atmospheric water vapors

This article presents the results of an experimental study of multiphase flows focusing on the influence of convective condensation of atmospheric water vapour on heat and mass transfer. The primary emphasis is on developing a laboratory setup capable of directly measuring the condensation rate of atmospheric water vapour on a water surface. Such an approach provides the opportunity to obtain more precise and reliable data on convective condensation and its impact on the water balance under various conditions. The experimental results revealed a nonlinear relationship between the condensation rate and the moisture excess, which differs from the linear relationship described by Dalton's law. This deviation was thoroughly examined, and a linear relationship between downward molecular flows and the forces induced by differences in relative humidity concentrations in the convective layer was proposed. The estimated average values of the condensation rate obtained in the experiments were 0.017 L/m²·hour, which, according to estimations, accounts for the condensation flux of water from the atmosphere to the ocean, consistent with the precipitation-evaporation balance in the land-ocean water balance. This information helps to more accurately determine the role of convective condensation in the water balance, contributing to refining assessments of water balance components in various watersheds. The results of this study have practical significance for hydrologists, climatologists, and experts in thermohydraulics and energy engineering. The proposed approaches and methods for measuring convective condensation of atmospheric water vapour can be applicable in various engineering systems and technologies to enhance the efficiency and accuracy of water balance calculations and forecasts.

A large-scale evaluation of machine learning algorithms in mid-term water demand forecasting

ABSTRACT Water utilities currently face challenges in accurately measuring user-end consumption as they have to visit every conventional meter that is installed in their service area. This study explores the use of accessible machine learning models to forecast water demand at two crucial temporal scales, addressing practical needs such as customer billing. To draw meaningful conclusions, these models were tested and trained from actual user-end water consumption data from over 2.1 million customers over a 10-year period. These data were provided by the Water and Sewage Company of Greece (EYDAP). The large-scale experiment included the use of statistical models (ARIMA and SARIMA), deep learning [long short-term memory (LSTM)], and clustering techniques (k-NN algorithm). The model with the best performance was ARIMA, outperforming all the other models including the more complex ones. The LSTM model did not perform as expected in predicting water consumption, it excelled only in predicting the total amount of water consumed. These forecasts can be valuable tools for water utility companies, aiding in tasks such as customer billing and water balance calculations. This paper covers all the necessary steps that must be taken to achieve meaningful user-end water consumption forecasts, from raw data preprocessing to hyperparameter tuning for machine learning algorithms.

APPLICATION OF GEODETIC AND HYDROMETRIC MEASUREMENTS FOR THE PURPOSES OF DEFINING THE WATER BALANCE – THE EXAMPLE OF “DEDIN MLIN” NEAR SVETI ĐURĐ ON THE PLITVICA RIVER, CROATIA

<p>This paper presents the application of geodetic and hydrological measurements to define the water balance in the example of a lake in an alluvial medium. The purpose of the above is to provide insight into parts of the lake's water balance since evaporation from the water surface (evaporation) and precipitation, along with infiltration and surface inputs and outputs from the lake, represent input data for the water balance calculation. An example of a modern approach to geodetic and hydrometric measurements on a lake created by damming the Plitvice watercourse for water inflow to the former mill for grinding grain, called "Dedin mlin", is presented.The emphasis of the work is on the field part, which includes measuring the flow and speed of the Plitvice River on the profile at the exit from the lake, as well as determining the lake's surface using an unmanned aerial vehicle. Using a mini-submarine, insight was obtained into the lake's shape, i.e., the conditions at the bottom and along the edges of the bed. Quality analysis of the water balance is essential for calculating the volume of new reservoirs that will be designed and built and for analyzing the water balance of existing reservoirs and natural lakes.</p>

ANALYSIS OF THE INFLUENCE OF ENVIRONMENTAL BEHAVIOR ON THE WATER BALANCE OF KALIGARANG WATERSHED, CENTRAL JAVA

The increasing population and land needs have led to changes in people's behavior towards the watershed, resulting in indications of disruption of the water balance. Therefore, it is necessary to investigate how community behavior affects the water balance of the Kaligarang watershed. This research is a combination of quantitative and qualitative approaches. The method of withdrawing respondents was purposive sampling. Data collection was done through observation, documentation, questionnaire distribution to respondents and interviews with key persons. Calculation of water balance using the Thorntwaite-Mather method. The results of the water balance calculation show that in periods 1 and 2 there was a surplus from December to April and a deficit from May to November. Deficits tend to increase while surpluses tend to decrease. From period 1 to period 2 the deficit increased by 142 mm and the surplus decreased by 316 mm. The tabulation results show that there are community behaviors that have a negative influence on water balance. This is reinforced by the results of interviews with key figures (key person) stating that environmental behavior has a negative influence on water balance.

Enhancing water balance assessment in urban areas through high-resolution land cover mapping: Case study of Debrecen, Hungary

Land cover (LC) mapping in urban areas is one of the core applications in remote sensing (RS), and it plays an important role in modern urban planning and management. In order to support up-to-date simplified water balance calculation, LC modelling needs to be developed for precise and high-resolution operations to monitor rapidly expanding urban areas. This study provides a spatial decision support tool for urban water balance calculations considering hydrological parameters at pixel scale. The study is aimed to create a high-resolution LC map for more precise water balance calculations to identify and link the most important parameters, such as crop coefficient, for estimating crop evapotranspiration, runoff coefficients, and infiltration rate. Meteorological data from 2016 to 2019 were involved in evapotranspiration estimation. The study site is Debrecen, a city in northeast Hungary with a population of about 200,000. By integrating Landsat 8 imagery, Google Earth (GE), and Corine Land Cover (CLC), a LC map of 30 m resolution was created with 81.2 % overall accuracy (OA) and a coefficient of kappa greater than 0.78. For the classification of Landsat 8, seven classes were assigned (forests, sealed surfaces, areas with crop cover, grassland, semi-sealed surfaces, bare ground and surface water bodies). Classification results were validated by 101 ground truth samples using other satellite images and aerial photographs. Water balance parameters (i.e. surface runoff, infiltration and evapotranspiration) were then defined and calculated at a pixel scale and for larger areas. The technique developed in this study can also be utilized in other urban areas.

Evaluation of Soil Hydraulic Properties in Northern and Central Tunisian Soils for Improvement of Hydrological Modelling

The hydrological cycle is strongly affected by climate changes causing extreme weather events with long drought periods and heavy rainfall events. To predict the hydrological functioning of Tunisian catchments, modelling is an essential tool to estimate the consequences on water resources and to test the sustainability of the different land uses. Soil physical properties describing water flow are essential to feed the models and must therefore be determined all over the watershed. A simple but robust ring infiltration method combined with particle size distribution (PSD) analysis (BEST method) was used to evaluate and derive the retention properties and the hydraulic conductivities. Physically based and statistical pedotransfer functions based on PSD were compared to test their potential use for different types of Tunisian soils. The functional sensitivity of these parameters was assessed by employing the Hydrus-1D software (PC Progress, Prague, Czech Republic) for water balance computations. This evaluation process involved testing the responsiveness and accuracy of the parameters in simulating various water balance components within the model. The evaluation of soil hydraulic parameters across the three used models highlighted significant variations, demonstrating distinct characteristics in each model. While notable differences were evident overall, intriguing similarities emerged, particularly regarding saturated hydraulic conductivity between BEST and Rosetta, and the shape parameter (n) between Arya–Paris and Rosetta. These parallels indicate shared hydraulic properties among the models, underscoring areas of agreement amid their diverse results. Significant differences were shown for scale parameter α for the various methods employed. Marginal differences in evaporation and drainage were observed between the BEST and Arya–Paris methods, with Rosetta distinctly highlighting a disparity between physically based models and statistical models.

Hydrological dynamics of snowmelt induced streamflow in a high mountain catchment of the Pyrenees under contrasting snow accumulation and duration years

AbstractSnowmelt drives a large portion of streamflow in many mountain areas of the world. However, the water paths from snowmelt to the arrival of the water in the streams are still largely unknown. This work analyzes for first time the influence of snowmelt on spring streamflow with different snow accumulation and duration, in an alpine catchment of the central Spanish Pyrenees. This study presents the water balance of the main melting months (May and June). Piezometric values, water temperature, electrical conductivity and isotope data (δ18O) allow a better understanding of the hydrological functioning of the basin during these months. Results of the water balance calculations showed that snow represented on average 73% of the water available for streamflow in May and June while precipitation during these months accounted for only 27%. However, rainfall during the melting period was important to determine the shape of the spring hydrographs. On average, 78% of the sum of both the snow water equivalent (SWE) accumulated at the beginning of May and the precipitation in May and June converted into runoff during the May–June melting period. The average evaporation‐sublimation during the 2 months corresponded to 8.4% of the accumulated SWE and rainfall, so that only a small part of the water input was ultimately available for soil and groundwater storage. When snow cover disappeared from the catchment, soil water storage and streamflow showed a sharp decline. Consequently, streamflow electrical conductivity, temperature and δ18O showed a marked tipping point towards higher values. The fast hydrological response of the catchment to snow and meteorological fluctuations, as well as the marked diel fluctuations of streamflow δ18O during the melting period, strongly suggests short meltwater transit times. As a consequence of this hydrological behaviour, independently of the amount of snow accumulated and of melting date, summer streamflow remained always low, with only small runoff peaks driven by rainfall events.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Analisis Neraca Air Lahan untuk Perencanaan Tanaman Padi Gogo dan Jagung pada Sub Das Wolasi

Infomation climate data of a place plays an important role in the development of agriculture in the region. This can be used to find out about the relationship between plants and climate, estimates of planting time, harvest time, drought (water deficit), flooding (water surplus) can be made, and determining the types of plants used. In accordance. This study aims to determine the level of groundwater availability based on land water balance analysis in the Wolasi sub-watershed and to find out how to plan planting patterns of upland rice and corn according to groundwater availability in the Wolasi sub-watershed. This study uses monthly rainfall data for 10 years (2012-2021) and other climate data (air temperature) for 10 years (2012-20121). Calculation of land water balance using the Throntwaite-Mather method. The result of land water balance calculation under conditions of monthly average rainfall distribution of water availability in the Wolasi sub-watershed are classified as very adequate where a surplus occurs from January to July and December. While the water deficit occurs from August to October. At a 75% chance of rainfall, the distribution of water availability is classified as very adequate,where a surplus occurs from January to July and November to December and a water deficit occurs from July to October. In the Wolasi sub-watershed, the montly average rainfall for upland rice can be planted in January and the final harvest in April, while for corn, the initial planting can be done in May and the final harvest in July, upland rice and corn can be planted twice in a row. a year and can also be planted in intercropping. With a 75% chance of rainfall in the Wolasi sub-watershed, the initial planting of upland rice can be done in January and the final harvest in April, while corn plants can be intercropped, planting upland rice and corn can be done once a year.

Seasonal dynamics and spatial patterns of soil moisture in a loess catchment

Abstract. The spatial and seasonal patterns in soil moisture and the processes controlling them in semi-arid landscapes are not well understood. Loess landscapes minimize any confounding effects of variation in soil characteristics and are thus ideal for studying topographic influences on soil moisture in drylands. In this study, volumetric soil moisture was monitored monthly for 5.5 years at 20 cm intervals between the surface and 5 m depth at 89 sites across a small (0.43 km2) catchment on the Chinese Loess Plateau. The median soil moisture was computed for each month and depth for each monitoring site as a measure of the typical soil moisture conditions. Seasonal changes in soil moisture were mainly concentrated in the shallow (0–100 cm) soil, with a clear seasonal separation between wet conditions in October–March and dry conditions in May–July, even though precipitation is highest in July–August. Soil moisture was higher on the northwest-facing slopes due to increased drying from solar radiation on the southeast-facing slopes. This effect of slope aspect was greater between October and March, when the zenith angle of the sun was lower and the aspect-dependent difference in solar radiation reaching the surface was larger. The wetter, northwest-facing slopes were also characterized by larger annual soil moisture storage changes. Soil texture was nearly uniform across both slopes, and soil moisture was not correlated with the topographic wetness index, suggesting that variations in evapotranspiration dominated the spatial pattern of soil moisture in shallow soils under both wet and dry conditions. Water balance calculations indicate that over 90 % of the annual precipitation was seasonally cycled in the soil between 0 and 300 cm, suggesting that only a minor fraction infiltrates to groundwater and becomes streamflow. Our findings may be broadly applicable to loess regions with monsoonal climates and may have practical implications for catchment-scale hydrologic modeling and the design of soil moisture monitoring networks.

A multi‐method approach for the identification and quantification of water balance components of two lentic small water bodies

AbstractThe protection of the globally widespread lentic small water bodies (LSWB) must be based on detailed knowledge about their hydrological connectivity and water balance. The study aimed to identify and quantify water balance components as well as surface‐groundwater interaction of two LSWB in a characteristic lowland region with a combination of different methods. This includes the collection of hydrological data and the use of bromide and water stable isotopes (δ2H and δ18O) as tracers. With their help, mixing models were established, and daily water balances were assessed. The results show a strong bidirectional interaction of both LSWB systems with shallow groundwater. Bromide and stable isotope tracers allowed for the identification of the most relevant in‐ and outflow sources and pathways. Thereby, isotope data revealed isotopic enrichment typical for open‐water bodies and only minor precipitation inputs mainly relevant at the end of the dry season. Water balance calculations suggested accentuated seasonal dynamics that were strongly influenced by shallow groundwater, which represented large inputs into both LSWB. By that, different phases could be identified, with high inflow rates in winter and spring and decreasing fluxes in summer. In one LSWB, a drainage system was found to have a major impact next to the shallow groundwater interaction. The findings of this research provide detailed insights into the influence and importance of shallow groundwater for LSWB in lowland regions. This impacts the diffuse input of agricultural pollutants into these ecologically important landscape features.

SPATIAL ANALYSIS OF WATER BALANCE DISTRIBUTION FOR SIMULATION OF TIMING AND PATTERNS OF RICE AND CROP PLANTING IN THE REGION OF TYPE D RAINFALL IN THE SOUTH KONAWE REGENCY

Water availability plays a crucial role in the environmental hydrological cycle, where the concept states that the amount of water in a specific area on the Earth’s surface is influenced by the input and output of water over a certain period. Water imbalance can lead to excess (surplus) and deficit (water shortage), impacting various sectors, including the agricultural sector, particularly in food crop cultivation, causing seasonal shifts and changes in planting patterns. These impacts can be minimized through adequate land and environmental management, achieved by calculating the water balance conditions in a given region. This research aims to (1) analyze the spatial distribution of land water balance in a type D rainfall region and (2) determine the timing and crop planting patterns based on the water balance of rice and secondary crops in the type D rainfall region. The study employs survey methods and analysis using the Thornthwaite water balance calculation. The results indicate water surplus in the Palangga and Baito subdistricts with an 8-month surplus period, reaching 192.40 mm month-1 in June and a minimum of 2.04 mm month-1, covering various sampling points and administrative areas such as Kiaea, Watudemba, Watumerembe, Wawonggura, Eewa, Onembute, Anggondara, Mekar Sari, Wawouru, Aosole, Sanggi-sanggi, Palangga, Tolihe, Sambahule, Matabubu, Mekarjaya, Wonua Raya, Ahuangguluri, Amasara, Wawouru, Mekarsari, Anggondara, Aosole, Eewa, and Onembute. Water deficit in the Palangga and Baito subdistricts occurs for four months, peaking at 58.59 mm month-1 in June at point 6 and reaching a minimum of 4.68 mm month-1 in February at point 2. The Palangga and Baito subdistricts exhibit a cropping pattern of corn + mung bean - irrigated rice - fallowing/vegetables; soybean/irrigated rice - fallowing/vegetables; corn + peanuts - irrigated rice - fallowing/grass for animal feed.

IMPROVING CROPS BY IRRIGATION MANAGEMENT AND SOIL CONDITIONERS: A REVIEW

Deficit irrigation strategies would require an accurate assessment of growth stage-specific stress tolerances for vegetable crops and optimal water management supported by advanced irrigation systems; i.e., able to promptly cope with crop water requirements at sensitive phenological stages However, in semiarid environments, the fulfillment of leaching requirements significantly limits the possibility of applying deficit irrigation criteria and reduces the WUE in irrigated vegetable production. The adoption of methods to control the level of plant water stress (using soil tensiometers or other soil water sensors or calculation of water balance) can reduce the amount of water applied to the crop up to 53%. Use of subsurface drip irrigation has progressed from being a novelty used only in experimental fields to an accepted method of irrigation for both tree and vegetable crops mentioned that subsurface drip irrigation on lettuce, tomato and sweet corn (Z. mays) has significantly increased yield and WUE in all these crops. Subsurface drip irrigation may increase WUE in semiarid environment under saline conditions by increasing yield. Our research goal is to suggest a framework could be utilized from natural and synthetic materials that could be used as soil and/or plant conditioners to maximize water use efficiency under drought conditions

Land Cover Modelling with Sentinel 2 in Water Balance Calculations of Urban Sites

This study investigates how a land cover map is produced using Sentinel 2 satellite images and how the resulting map is integrated with the calculation of hydrological parameters. Due to its capacity to deliver high-resolution spatial data, satellite imagery is increasingly being used for land cover mapping (1). The process involves several stages, including pre-processing of the Sentinel 2 imagery, image classification using machine learning algorithms, and post-processing of the classified image to generate a land cover map. The resulting land cover map is then integrated with hydrological parameters such as precipitation, evapotranspiration, and soil characteristics to calculate various hydrological parameters. Debrecen, north-east Hungary is the study area. This study's method is suitable for usage in other cities. Understanding the relationship between land cover and water availability requires the integration of the land cover map with hydrological parameters (2). The study demonstrates the potential of using remote sensing data for hydrological studies and highlights the importance of integrating various data sources for accurate estimation of hydrological parameters. The results show that the land cover classes have a significant impact on the water balance of urban sites. This study outlines the key steps involved in creating a land cover map using Sentinel 2 satellite imagery and integrating it with hydrological parameters calculation. The application of this strategy supports sustainable water management techniques and offers insightful information about a region's hydrological processes.

A Complete Water Balance of a Rain Garden

AbstractA bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a 5‐min timeseries resolution and with instrumentation that would enable a water balance calculation. This 20‐year data set allows for an in‐depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real‐time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed. Analysis shows the rain garden captures 73.5% of runoff, resulting in a post‐implementation management of 86.2% of all rainfall in its watershed. In comparison to the hydrology of other land covers, implementing the rain garden resulted in the management of 37.6% more rainfall than pre‐implementation, producing a hydrological signature similar to that of cultivated land or low development levels (e.g., 30% impervious). Additionally, with the long data record, several statistical techniques were applied to determine the amount of monitoring needed for a certain level of precision in system performance assessment. For 5% uncertainty, approximately 3 years of continuous data is needed to assess performance. This analysis not only facilitates understanding the function of rain garden systems, but also provides conclusions and methodology for understanding the uncertainty associated with the extent of monitoring performed on these green stormwater infrastructure systems. These findings provide practical knowledge as monitoring of stormwater management infrastructures is becoming a more standard part of their operation.

How to measure field soil water balance parameters?

The article provides a methodological, computational, and instrumental assignment that aids in identifying the various elements that makeup soil water balance. Reputable publications have published a large number of studies in the last ten years that discuss the computation of the soil water balance under various cropping patterns using various establishment procedures and Resource Conservation Technologies (RCTs). However, the computational and instrumental aspects of this work have received little attention. Because they lack the necessary fundamental (instrumental/methodological) competence, prospective scientists and students are unwilling to take on soil water balance calculation problems at the master's or doctoral levels, which generates misunderstandings. A thorough analysis of all the elements of soil water balance-irrigation, precipitation, evaporation, transpiration, drainage, seepage, and changes in the soil moisture profile-was included in this collection. To determine the soil water balance, all the factors are important. Although the instrumental part is well known, the computational and methodological parts are equally important. Scientists are currently attempting to find ways to save water and increase water productivity, as well as to better understand current patterns of water usage and the repercussions of activities. If one is fully versed in the instrumental, methodological, and computational aspects required to ascertain the components of soil water balance, one may also discuss the effects of a particular RCT on raising water and land productivity in a region with differing agroclimatic conditions.

Global evapotranspiration models and their performance at different spatial scales: Contrasting a latitudinal gradient against global catchments

Actual evapotranspiration (AET) is a key variable in the global water balance, driving agricultural production and ecosystem health. It is a complex hydrologic process that depends on vegetation, climate, and available water conditions. Different moderate resolution global AET models have been developed to quantify water resources at large scales. In this work we evaluate five of these products, including MODIS, PML, SSEBop, TerraClimate, and a Synthesis AET using point and catchment-scale datasets based on flux towers. We also contrast water balance changes with total water storage (TWS) products. These comparisons cover different radiation and precipitation regimes over catchments around the world and along a strong climatic gradient in north-central Chile. We rank the models, contrast TWS datasets, and study differences related to scale in validation and the effect of rainfall and radiation on simulated values. Additionally, we use a Budyko framework to evaluate the AET products in terms of their agreement with expected water budgets. At different evaluation scales, AET estimates and observations agreed reasonably well, with the largest mean R2 of about 0.7 and errors of approximately 15% of the magnitude of the observed variables. MODIS and Synthesis AET had the highest R2 at the point (0.62) and at the catchment scales (0.71 and 0.59 for regional and global catchments), respectively, but were closely followed by PML. PML and TerraClimate led to the lowest magnitude errors at the point (RMSE = 0.78 mm day−1) and catchment scales (mean RMSE = 1.5 mm day−1), respectively. The rainfall gradient is reflected in a performance gradient. PML, MODIS, and TerraClimate gave consistent behaviour based on the Budyko curve, with a few arid catchments exceeding the water limit. The major conclusion is that remotely sensed AET outperforms flux tower AET extrapolation for water balance calculations at the catchment scale, which means that errors in satellite-based AET products tend to cancel out at larger spatial scales, which makes them viable alternatives for regional water balance studies. However, flux data integrated into AET models, such as the FluxCom model, leads to the lowest errors. The assimilation and downscaling of Gravity Recovery and Climate Experiment (GRACE) into the Global Land Data Assimilation System (GLDAS) leads to an improvement in regional results compared with other TWS products.