Effective Planning Of Water Resources Research Articles

Analysis of trends and abrupt changes in groundwater and meteorological droughts in the United Kingdom

In recent years, the escalating impact of climate change, coupled with growing concerns regarding groundwater drought, has underscored the critical importance of analyzing hydrological trends. This is a foundational step in advancing effective water resource planning and management. This study presents a regional investigation of groundwater and meteorological droughts in the UK, utilizing the Standardized Groundwater Index (SGI) and the Standardized Precipitation Index (SPI). The analysis encompasses drought duration and an evaluation of the correlation between the SGI and SPI. Subsequently, the seasonal Mann-Kendall (MK) test and the Bayesian Changepoint Detection and Time Series Decomposition (BEAST) algorithm were employed to assess overall trends for both SGI and SPI. Additionally, these methods were utilized to pinpoint trend and seasonal abrupt changes within the time series of both indices.The seasonal MK test revealed statistically significant trends in many SGI stations across the UK, spanning from Northern Scotland down to the South of England, encompassing both increasing and decreasing trends. Additionally, the MK test showed slightly positive trends at the low SPI time scale, though these were not statistically significant. In contrast, more consistent and significant increasing trends were observed throughout the entire UK, except for Northern Scotland, which exhibited negative trends. Despite indications of a progressively more humid UK, the BEAST analysis enabled the detection of decreasing abrupt changes in trends. These changes result in sudden transitions from wet to dry conditions, which can be challenging to identify using the MK test alone. However, BEAST revealed both positive and negative abrupt changes across the entire UK, as well as positive or negative variations in seasonality. This suggests a high degree of variability in seasonal patterns. In summary, this study provides a detailed assessment of groundwater and meteorological drought trends and abrupt changes, considering the impact of climate change on various regions of the UK.

The Impact of Preprocessing Approaches on Neural Network Performance: A Case Study on Evaporation in Adana, a Mediterranean Climate

The application of artificial intelligence (AI) technologies is quickly expanding in water management. Additionally, the artificial neural network methodology has an advantage over traditional statistical approaches in that it does not need assumptions about the distribution of data and variables. These methods can be used if there is a large enough data collection and criteria relevant to the nature of the problem. Preprocessing data before utilizing it improves the performance of the AI model. Evaporation matters in water management, agriculture processes and soil science. It is critical to ensure proper estimation of evaporation losses for effective water resource planning and management particularly in drought-prone areas such as Adana. Artificial intelligence approaches can be applied successfully in evaporation calculation. In this research, we used the Standard scaler, power transformer, robust scaler quantile transformer (Uniform) and quantile transformer (Normal), and min-max scaler preprocessing techniques to preprocess the multilayer perceptron neural network (MLPNN). We also trained the MLPNN using unprocessed data and compared it to the results of the preprocessed model. In the setup of the model, daily temperature, pressure, wind, sunny hours, and humidity parameters covering the years 2018-2021 were presented as input to the MLPNN model. Consequently, we pinpoint that all preprocessing approaches produce better outcomes than unscaled. Although all models produced statistically high accuracy predictions according to statistical criteria, the MLPNN model established without transformation (test phase: r2: 0.93, NSE : 0.927, SMAPE: 10.77, RMSE: 1.2, MAE: 0.9) exhibited the lowest accuracy. The evaporation prediction model that was developed using the MLPNN-based standard scalar optimization algorithm exhibited the highest level of accuracy (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.93, RMSE: 0.68, MAE: 0.48). Power Transformer (test phase: r2: 0.978, NSE: 0.977, SMAPE: 5.81, RMSE: 0.67, MAE: 0.49) showed second-degree promising results. It can be concluded from these results that the estimation of meteorological variables requires the scaling and presentation of data in a uniform structure. Therefore, improving efficiency and productivity in water management or agricultural processes can be enhanced by making more accurate evaporation estimates.

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

Multi-step ahead groundwater level forecasting in Grand Est, France: Comparison between stacked machine learning model and radial basis function neural network

In recent years, the increasing influence of climate change on the water cycle has emphasized the significance of analyzing and forecasting groundwater level (GWL) for effective water resource planning and management. This study proposes a comparative analysis of multi-step ahead daily GWL prediction by means of two different models. The former is an ensemble model, based on the stacking of two Machine Learning algorithms, Multilayer Perceptron (MLP) and Random Forest (RF). The second model is represented by a Radial Basis Function Neural Network (RBF-NN). For the modelling, a mid-term forecast horizon of up to 30 days was considered, while the precipitation and evapotranspiration were included as exogenous inputs. Three different wells located on the chalk aquifers of the northeastern region of France referred to as PZ, S1, and LS4, were selected for this study. The RBF-NN model demonstrated superior performance compared to the stacked MLP-RF model for wells PZ and S1. Conversely, for well LS4, both models displayed similar performance, albeit with a marginally higher accuracy observed in the stacked MLP-RF model. However, both models yielded accurate predictions of the GWL across all three wells, with R2 values exceeding 0.87 for all the wells and forecasting horizons. Furthermore, the RBF-NN model showed fewer reductions in performance as the forecasting horizon increased compared to the stacked MLP-RF model, leading to more reliable predictions even for a 30-day forecast horizon. An evident trend of decreasing Mean Absolute Percentage Error (MAPE) was observed from the 1st quartile to the 4th quartile of forecasted values. This highlights the models’ improved ability to provide accurate forecasts for deeper GWL values. The future developments of this research will be aimed at overcoming some limitations of the study, including lagged values of the exogenous variables precipitation and evaporation among the predictors, and considering aquifers with different hydrogeological characteristics.

Assessing Meteorological and Anthropogenic Factors on Streamflow Trends in the Ramganga River Basin in North India

The analysis of stream flow trends of hydro-climatic variables such as stream flow, rainfall, and temperature provides useful information for effective water resource planning, design, and management. Trends in observed stream flow at seven gauging stations in the Ramganga river basin of North India were examined from 1981 to 2012 using the Mann-Kendall and Sen's slope. Lag 1 autocorrelation analyses were used to investigate the relationships between trends in stream flow and rainfall. Annual, monsoonal, and winter seasonal stream flow (p0.05) decreased gradually from upstream to downstream of the river, as did crop seasonal and decadal moving averaged standardized anomalies of streamflow for the entire basin. The streamflow's declining trend was partly attributed to higher water withdrawal, higher air temperature, and higher population, and partly to a significantly declining post-monsoon rainfall trend, particularly upstream and downstream. The changing monsoon rainfall pattern and the growing population both contributed significantly to the development of this trend. The significant decreasing trend in stream flow at the upstream gauging station (-0.39 m3/s/year) was attributed to the significant decreasing trend in catchment rainfall (-8.40 mm/year). This study shows a strong positive correlation between stream flow and catchment rainfall (r=0.60). The Ramganga River basin is showing signs of drying up, as evidenced by the declining stream flow, post-monsoon rainfall, especially in the downstream area, and concurrently rising temperature trends. The Ramganga River basin's sustainable water management planning will be aided and made possible by the study's findings and recommendations, which will help stakeholders design strategies for stream flow restoration. This study thus highlights the urgent need for comprehensive water management planning in this basin by implementing procedures at various spatial scales that may be able to halt the trend of drying by improving river water use efficiency in a variety of sectors.

Flood Mitigation and Pollution Abatement in Kaduna Metropolis Through Engineering Assessment and Analytical Hierarchy Process Design

Pollution abatement and flood control activities require effective water resource planning, engineering assessment, sophisticated technology, and appropriate hydraulic structure designs. This paper x-rays flood mitigation and pollution abatement strategies that can be adopted in the Kaduna metropolis in Nigeria. Analytical hierarchy process of design, questionnaires, engineering assessment approach, and standard method for estimation of the runoff discharge was adopted for this research. Estimating water balance components and QSWAT Hydrological Model can be used with the QGIS interface for a greener environment. Suitable hydraulic systems were designed for long-term flood control in the River Kaduna catchment area through an analytic hierarchy Process. Statistical analysis, manning equations, and rational methods were utilized for adequate assessment and planning. The hydraulic discharge capacity of culverts, open channels, and other hydraulic structures was carefully checked. Flooding greatly impacts infrastructural development, and inadequate drainage systems contribute to it. Mitigation strategies, adequate water resource planning, and management will greatly benefit from addressing flood-related issues in the study area. This research provides information on the flood vulnerability of infrastructures and mitigation strategies that can be adopted in the study area. Viable policies and management strategies can be utilized to avert losses traceable to floods in developing and developed countries.

Comparison of Hydrological Platforms in Assessing Rainfall-Runoff Behavior in a Mediterranean Watershed of Northern Morocco

This research evaluates the applicability of different types of hydrological models to simulate discharge behavior scenarios in a northern Moroccan watershed, Oued Laou Watershed (OLW). In this context, an improved understanding of the runoff mechanisms through hydrological modeling of the OLW can assist in the hazard risk management and facilitate the effective planning of water resources. For that end, a multitude of hydrological models were used to perform a very efficient modelling, and a comparative approach was adopted. Comparison of the models allowed the determination of potential sources of uncertainty in hydrological modelling of a subhumid watershed. Three models (ATelier Hydrologique Spatialisé (ATHYS), Hydrologic Modeling System (HEC-HMS), and Soil and Water Assessment Tool (SWAT)) with different characteristics were employed for a continuous modelling approach. The models were calibrated and validated using observed daily rainfall and streamflow data for 4 years (2004–2008) and 3 years (2009–2011), respectively. The multi-criteria model comparison (R², NSE, RSR, and PBIAS) showed that all three models are capable of reproducing the observed flows. The SWAT model performed well over both periods (NSE = 0.76 for calibration), with an improvement in validation (NSE = 0.84). A good agreement was also observed in the HEC-HMS model outputs, with an approximately stable NSE of 0.77 and 0.78 for calibration and validation phases, respectively. The ATHYS model showed a NSE value of 0.67 during the calibration, with a decrease of 0.06 towards the validation period. The other performance criteria confirmed these findings. Additionally, results suggest that semi-distributed and conceptual hydrological models are particularly suitable for the OLW given their physical heterogeneity. Generally, the integration of these models may be suitable for water resources assessment in OLW.

An application of GRACE mission datasets for streamflow and baseflow estimation in the Conterminous United States basins

Runoff and baseflow estimates provide critical information on surface and groundwater flow dynamics, water resource availability, and water usage patterns for effective water resource planning and management. Developing a physical-based model at large spatial scales such as continental/global scales require extensive datasets, complex parameterization, and higher computational capacity. Recent advances in satellite datasets related to water cycle process modeling, on the other hand, involve less parameterization and more effective computation over larger spatial and finer temporal scales. Evapotranspiration (ET) is a critical flux in the water cycle that can be estimated with reasonable accuracy from satellite-based information by using complementary relationship (CR) concepts. Similarly, the Gravity Recovery and Climate Experiment (GRACE) mission satellite datasets provide useful estimates of total water storage change over large spatial extents. GRACE datasets have been extensively used in groundwater storage change and groundwater depletion studies. However, the application of GRACE data products for estimating runoff and baseflow is limited. Therefore, in this study, we used a CR approach to estimate ET and a water balance method for estimating runoff and baseflow over the Conterminous United States (CONUS). We evaluated the CR-based water balance model (CR-GRACE) with United States Geological Survey (USGS) runoff datasets based on hydrologic unit code level-2 (HUC-2) basin boundaries. The CR-GRACE model estimated annual runoff values agreed with USGS runoff values with root mean square error (RMSE) and correlation coefficient values of 14.7 mm/month and 0.81, respectively. We also evaluated the CR-GRACE model with two well-known Land Surface Models (LSM), Noah-LSM and Variable Infiltration Capacity LSM (VIC-LSM), from the Global Land Data Assimilation System (GLDAS) database. The comparison results for baseflow estimates from the CR-GRACE model have improved over Noah-LSM and VIC-LSM model estimates with RMSE values of 62.16, 101.00, and 207.32 mm/year, respectively. Furthermore, this study demonstrates the feasibility of using the CR-GRACE model to estimate runoff and baseflows with limited parameterization and datasets.

Monthly runoff forecasting via an improved extreme learning machine

Generally, monthly runoff prediction is of great importance for effective water resource planning and management. Extreme learning machine (ELM) is a novel training tool for the famous single layer feed-forward neural network. Due to the satisfying performance, ELM is chosen for monthly runoff forecasting in this research. Nevertheless, it is unfortunately found that ELM easily falls into local optima in practice because the randomly-determined computational parameters remain unchanged during the learning process. Thus, this paper tries to develop an improved extreme learning algorithm (IELM) where the evolutionary algorithm is used to search for satisfying computational parameters while the Moore-Penrose generalized inverse method is used to determine the output weights. The IELM method is applied to forecast the monthly runoff of Hongjiadu reservoir in southwest China. The results show that the proposed method outperforms several traditional algorithms with respect to the performance indicators. Thus, this paper provides a new and effective artificial intelligence approach for the monthly runoff forecasting.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.

Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

Estimation of quantitative spatial and temporal distribution for groundwater storage in agricultural basin of Korea: implications for rational water use

Assessment of spatio-temporal variability in groundwater storage (GWS) is critical for effective water resource planning and management with rational groundwater use. Haean Basin comprising 64 km2 area, mainly depends on groundwater for agricultural, domestic and other water uses. For quantification of GWS variability, sufficient field-based hydrological data of the ongoing decade (2011–2017) were interpolated in ArcGIS, to give reliable results. Despite satellite remote sensing having advantages in the field of hydrology, the limitation of accessibility at larger spatial resolution (~ 200,000 km2) is making it non-applicable for fine-scale observations. From the surface area, saturated thickness and specific yield of aquifer, a decline of 33 million cubic meters in GWS volume was indicated from 230 to 197 during the observed 6 years. An observable drop (4.6 m) in the mean water level due to increased water use (0.49 MCM) during this decade is responsible for the change in the mean groundwater level. A changing water level can change the surface area and saturated thickness of aquifer that have a positive correlation with GWS, and act as main controlling factors for GWS change. An annual recharge of 0.14 m to the total amount of GWS is near negligible, considering the long-term sustainability of groundwater resource. A shift towards more use of surface water, and limited drilling of groundwater wells is recommended for rational water use. The findings of this study will provide basic knowledge for relating studies, specifically which are based on small (local) scale distribution of groundwater resource.

Estimation of reference crop evapotranspiration of barley using cropwat model in Punjab

Reference crop evapotranspiration (ETo) of barley during Rabi 2016–17 for Gurdaspur and Ludhiana regions of Punjab was computed by CROPWAT model. Seasonal mean reference crop evapotranspiration was relatively higher for Ludhiana (2.74 mm day−1) as compared to Gurdaspur (2.17 mm day−1) region. Higher temperature, wind speed, sunshine hours and lower relative humidity at Ludhiana seemed to be the reason for higher ET rates at Ludhiana than Gurdaspur. Radiation use efficiency was also more for Ludhiana (14 MJ m−² day−1) than Gurdaspur (12.9 MJ m−² day−1), which has also been observed to be directly proportional to the ETo at both the locations. An increase in the temperature by 0.5, 1.0, 1.5 and 2.0°C from the existing temperature was found to increase the ET o by 0.72, 1.82, 2.91 and 4.01%, respectively and water requirement by 1.27, 3.10, 4.26 and 5.56% in the similar order at Ludhiana. The corresponding simulated departures for Gurdaspur were 0.92, 1.38, 2.30 and 3.33% for ET o and 2.65, 3.93, 5.43 and 7.10% for irrigation water requirement. The study concluded that global warming scenarios are likely to increase crop water requirements thus putting tremendous pressure on already over-exploited water resources, suggesting thereby the need for effective planning of water resources in the region.

Impact of climate change on hydrology of Manjalar sub basin of river Vaigai in Tamil Nadu, India

This study evaluates the impacts of possible future climate change scenarios on the hydrology of the catchment area of the Manjalar sub basin of River Vaigai, Tamil Nadu, India carried out at the department of Soil and Water Conservation Engineering, Tamil Nadu Agricultural University during the period of 2011-2014 using Soiland Water Assessment Tool (SWAT). For the climate impact assessment the hydrological model was driven with output of bias corrected Earth System Models of the Coupled Model Intercomparison Project Phase 5 (CMIP5): HadGEM2. Climate scenarios were downscaled to a grid resolution of 0.22° x 0.22°. In this study RCP 4.5 and RCP 8.5 were included for future assessment with three future periods: 2012–2039, 2040–2069, and 2070–2098. The projected increase in maximum and minimum temperature for RCP 4.5 scenario is 0.8 to 2.3 ºC and 0.7 to 1.6 ºC and for RCP 8.5 scenario is 1.1 to 4.0 ºC and 1.0 to 3.1 ºC, respectively. Rainfall is projected to an increase between 9.2 to 15.2 per cent for RCP 4.5 scenario and an increase of 13.6 to 18.8 per cent for RCP 8.5 scenario during 21st century. The soil water storage and stream flow contribution to ground water are likely to increase in RCP 4.5 scenario and it would again decline for RCP 8.5 scenario during 21st century. The increase in annual rainfall evapotranspiration and surface runoff would be more in RCP 8.5 scenario compared to RCP 4.5 scenario. The possible changes projected by the study provide a useful input to effective planning of water resources of the study area.

LOW FLOW FREQUENCY ANALYSIS BY L-MOMENTS METHOD (CASE STUDY: IRANIAN CENTRAL PLATEAU RIVER BASIN)

Knowledge about low flow statistics is essential for effective water resource planning and management in ungauged orpoorly gauged catchment areas, especially in arid and semi-arid regions such as Iran. We employed a data set of 20 riverflow time-series from the Iranian Central Plateau River Basin, Iran to evaluate the low-flow series using several frequencyanalysis methods and compared the result of these methods in their ability to set low flows for the conservation ofinstream water uses. Theoretical frequency distributions including the log-normal, three-parameter lognormal, Gumbel,Pearson type III, and log Pearson type III were applied with the low-flow series. Goodness-of-fit tests including Lmomentand conventional moment methods for the observed data were applied to identify the best distributions. For eachdistribution, the calculated values of the residual sum of squares (RSS) was applied to compare between the conventionalmoment and L-moment methods, and the best method was selected to determine the most appropriate probabilitydistribution. The lowest RSS values were used to select the best distribution for each station. Then, T-year low-flow serieswas estimated using the best probability distribution. Our results suggested that, for annual low flows, based on thecomputed RSS, Pearson type III, log Pearson type III, Gumbel distributions, and L-moment method were suitablydistinguished for 85%, 10%, and 5% of the stations, respectively. Finally, Compared to the conventional moment method,L-moments method was found to be more adequate to identify low-flow series probability distributions in the IranianCentral Plateau River Basin, while Pearson type III was found to be the best probability distribution for modelingminimum flow series in the study area.

Aplicação e avaliação dos modelos swap e hydrus 1d em diferentes cenários agrícolas

The understanding of water flow and storage processes in the soil is extremely important once it provides means for an effective water resource planning and management within soil-crop-atmosphere system. Several agro hydrologic models are used for this purpose, irrespective of whether they are public or commercial, simple or complex. In the present paper, two public models - SWAP and HYDRUS 1D, widely utilized in one-dimensional water flow simulations, were applied for quantifying the water balance under two significantly distinct agricultural scenarios. Based on the results, the models were then evaluated through comparisons with the observed groundwater levels and drainage through the bottom of the soil profile. The results showed good performance of both models, expressed by means of the adopted statistics. Additionally, the results provided by the two models concerning other variables not observed in the field were compared among themselves. Good adherence was found for moisture profiles as well as for the calculated evaporation values.

Development of residential water demand model for a densely populated area of Jaipur City, India

Water demand forecasting has become an essential ingredient in effective water resource planning and management. In water-scare urban areas of developing countries, this emphasis on accurate forecasting is particularly important for effective water resource planning and management. This paper presents an econometric water demand model for forecasting future residential water requirements for a densely populated area of Jaipur city. This study used an ordinary least squared (OLS) regression model to measured the impact of household income (I), age of respondent (A_R), household size (SIZE), age of home (A_H), wealth (W), asset score (AS), dwelling status (DWELL), monthly expenditure on water supply (EXP_WS), number of bathrooms (BATHR), and number of rooms (RMS) on residential water use (RWU) using data from a survey of 149 representative households in the study area. Empirical results indicate that residential water demand of the study area is characterized by I, SIZE, AS, and EXP_WS, with SIZE (0.542) and AS (0.418) having a major influence on RWU, as shown by their high standardized model coefficient values at 95% confidence intervals. Therefore major saving should be achieved by technological developments in water efficient appliances combined with education in efficient use of water.