Estimates Of Water Availability Research Articles

Analysis of Water Stress Index (WSI) for District Surrounding Ulu Sat Forest Reserve, Kelantan, Malaysia

In the present study, the Water Stress Index for the selected area surrounding the Ulu-Sat forest reserve, Kelantan, Malaysia, was calculated. Water Stress Index was derived based on the ratio of water withdrawal to availability serves as an indicator to assess the status of water scarcity in the area. Water consumption for domestic purposes includes the daily water used for drinking and cleaning for households, while the non-domestic water used includes sectors such as agriculture (paddy and non-paddy), industry, and livestock. Estimation of water availability obtained from 3 stations located around Ulu-Sat forest reserve and the climate data obtained from the year 2008 to 2018. The water stress index presented in a range of 0.0 – 1.0. The result shows that the Water Stress Index value for Ulu-Sat forest reserve surrounding area obtained at an average of 0.32 for Machang, Pasir Puteh, and Kuala Krai. The result was implying a current low level of stress in Machang and Kuala Krai but the high-stress level in the Pasir Puteh area. The study also found that moderate to high-stress level occurs during the driest period of the year while low to medium stress level during the monsoon season. However, this ratio expected to further increase soon due to increase in water consumption and application. Hence, the importance of Ulu-Sat forest reserve in maintaining the stress level ratio low viewed as the utmost concern, especially in the dry season.

The science and politics of climate risk assessment in Australia’s Murray Darling Basin

Climate change has many disruptive consequences, particularly in large river basins experiencing drying trends and declining water availability. With pressure for more adaptive policies, understanding how institutions governing water are adapting to climate risks has broad international relevance. This paper examines the challenges of climate risk assessments in Australia's Murray Darling Basin, an economically and culturally important basin where national legislation mandates a Basin Plan based on the best available science. However, despite repeated scientific warnings about a drying climate, the current Basin Plan does not reduce estimates of water resources available, and its climate risk management relies on a bricolage of policy mechanisms, which appear insufficient for the magnitude of predicted changes. This paper outlines the findings of original research exploring why this occurred. It draws on interviews with 30 senior public policy professionals and researchers with deep experience in climate and water policy. The interview data indicate that minimising political risks were the principal reasons for abandoning proposed reductions in entitlements and for relying on historical averages for estimating water availability. As the reform process evolved, decisions about water-planning techniques became increasingly politicised, and the adoption of explicit climate adaptation measures became problematic. As a result, the Basin Plan neither reflects the broad scientific consensus about climate change nor makes specific allowances for declining water availability. Climate risks are downplayed while concerns about levels of scientific uncertainties appear overstated. Importantly, lessons emerge from this examination that could contribute to the revisions of the Plan, scheduled for 2026. More generally, the research finds that proactive climate adaptation policies for large river basins require sustained and well-designed institutional reforms, particularly the adoption of legally prescribed procedures for utilising science - and accounting for its use - in controversial environmental reforms.

The Benefit of Using an Ensemble of Global Hydrological Models in Surface Water Availability for Irrigation Area Planning

Hydrological data and information on the availability of water are essential to support water allocation decisions in irrigated agriculture, especially under increasingly water scarce conditions. However, in many agricultural regions hydrological information is scarce, leading to sub-optimal water allocation decisions and crop yield reduction. Here we assess the benefit of using surface water availability estimates derived from an ensemble of global hydrological models for establishing the potential area that can be irrigated; where the size is determined based on agreed water supply reliability targets. Risky outcomes of the annual agricultural production due to the true occurrence of water scarcity are generated for both the reference and established irrigation areas, resulting in a Relative Utility Value (RUV) that expresses the utility of the information used. Results show that using an ensemble of global hydrological models provides more robust estimates of the planned area compared to using any of the single global models that constitutes the ensemble. A comparison of the information content in the ensemble shows that an ensemble with a period of record of 15 years has an information content equivalent to a single model of 30 years.

Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status, estimating water availability as well as improving ornamental and tourism values of plants. In this study, leaf color variances of the Eurasian smoke tree, Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net (GELnet) and support vector machine (SVM) algorithms. Results reveal that leaf color varied over space, and the variances were the result of geography due to its effect on solar radiation, temperature, illumination and moisture of the shrub environment, whereas the influence of climate were not obvious. The SVM and GELnet algorithm models were similar estimating leaf color indices based on geographic variables, and demonstrates that both techniques have the potential to estimate leaf color variances of C. coggygria in a shrubbery with a complex geographical environment in the absence of human activity.

Deterministic Seasonal Quantitative Precipitation Forecasts: Benchmark Skill with a GCM

Many applications, from agricultural planning (such as crop choice) to estimation of hydro-power and surface water availability require quantitative precipitation forecasts (QPF). While a large number of studies have addressed the problem of forecasting seasonal Indian summer monsoon (ISM) rainfall over the decades, the emphasis generally has been on simulation and forecasting of rainfall anomalies from long-period mean. However, given the trends in monsoon rainfall, the procedure of considering anomalies has inherent errors; thus reconstruction of actual rainfall from the anomalies does not necessarily provide accurate information. Most QPF have been attempted at short-range forecasts, with a variety of techniques like model output statistics. This work represents evaluation of an atmospheric Variable Resolution General Circulation Model (VRGCM) for QPF during monsoon season. The VRGCM, with variable grid resolution provides relatively high (~ 50 km) resolution over the ISM region, has been validated over all India as well as in different regions like Central and North India, South India, North East India for seasonal forecast of monsoon rainfall. VRGCM simulated QPF appears to provide comparable information as that of anomaly forecasts of monsoon over different regions in India. The forecast skill is appreciable, with significant correlation at all India, South India and North East India regions. The Root Mean Square Error of VRGCM in forecasting quantitative rainfall overall India and Central North India is very low and bit high over South and North East region of India. The performance of VRGCM in forecasting the rainfall in extreme (deficit or excess) monsoon years is also very high with phase synchronisation of 89% in the inter annual variability of rainfall during monsoon at all India scale while the value is about 57%, 76% and 55% over Central North, South and North East India region, respectively.

Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.

Application of Water Poverty Index (WPI) in Spatial Analysis of Water Stress in Koshi River Basin, Nepal

Water and poverty interface is strongly interconnected and a robust assessment of water stress is crucial to identify needy areas and develop appropriate intervention for poverty reduction. Water Poverty Index (WPI) provides an interdisciplinary tool to assess water stress by linking physical estimates of water availability with socio-economic drivers of poverty. This study presents an application of Water Poverty Index (WPI) to estimate and compare the level of water stress in 27 districts of Koshi River Basin in Nepal. Based on data availability, relevance to the study area and review of literatures, 12 indicators were selected under five key components outlined by WPI. The study result shows medium-low degree (WPI = 54.4) of water poverty in the Koshi River Basin in Nepal. The WPI score varies widely (from 49.75 to 69.29) along the districts and it was found that districts in Tarai regions and urban areas were more water stressed compared to the districts in mid-hill and high-hill regions. Priorities for intervention must be given to the districts in Tarai regions and urban areas with a low WPI score, explicitly on the sector regarding access to water and sanitation to address water poverty in the basin.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

A two-stage time series model for monthly hydrological projections under climate change in the Lim River basin (southeast Europe)

ABSTRACTClimate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.

Estimating Water Availability using the SCS-CN Method based on Long Term Hydrologic Simulation and the Geographic Information System

Estimating Water Availability using the SCS-CN Method based on Long Term Hydrologic Simulation and the Geographic Information System

Ecohydrologic separation alters interpreted hydrologic stores and fluxes in a headwater mountain catchment

AbstractRecent studies have demonstrated that compartmentalized pools of water preferentially supply either plant transpiration (poorly mobile water) or streamflow and groundwater (highly mobile water) in some catchments, a phenomenon referred to as ecohydrologic separation. The omission of processes accounting for ecohydrologic separation in standard applications of hydrological models is expected to influence estimates of water residence times and plant water availability. However, few studies have tested this expectation or investigated how ecohydrologic separation alters interpretations of stores and fluxes of water within a catchment. In this study, we compare two rainfall‐runoff models that integrate catchment‐scale representations of transport, one that incorporates ecohydrologic separation and one that does not. The models were developed for a second‐order watershed at the H.J. Andrews Experimental Forest (Oregon, USA), the site where ecohydrologic separation was first observed, and calibrated against multiple years of stream discharge and chloride concentration. Model structural variations caused mixed results for differences in calibrated parameters and differences in storage between reservoirs. However, large differences in catchment storage volumes and fluxes arise when considering only mobile water. These changes influence interpreted residence times for streamflow‐generating water, demonstrating the importance of ecohydrologic separation in catchment‐scale water and solute transport.

Influences of watershed characteristics on long-term annual and intra-annual water balances over India

Evaluation of the engrossment of watershed surface characteristics on partitioning of precipitation to runoff and evapotranspiration is key to inspect the availability of water at watershed scale. It is more evident in the cases of ungauged watersheds. The present study develops models using multiple linear regression method and machine learning techniques (ANN: Artificial Neural Network and RVM: Relevance Vector Machine) over 793 (25 major river basins and 768 watersheds across India) to estimate the watershed parameter ‘ω’ (in Fu’s Budyko based equation) that represents intrinsic watershed attributes. In addition, seasonality factor is incorporated in the model due to intra-annual variability in vegetation across India. The models attempt to explain the intricate relationship between vegetation alterations and regional water balance. It is seen that the ANN and RVM models have performed better in estimating ω, than the MLR (Multiple Linear Regression) models. In addition, NDVI has shown more engagement in explaining the partitioning process of water in intra-annual low NDVI period compared to high NDVI period. We have also found the present models to be more accurate than the previously developed Budyko based methods in predicting ω. The newly improved models have closely imitated the intrinsic basin attributes and enhanced the functionality of Budyko framework in estimation of water availability, which would play a crucial role in assessment of hydrology of ungauged watersheds of India.

Water availability, consumption and sufficiency in Himalayan towns: a case of Murree and Havellian towns from Indus River Basin, Pakistan

AbstractThis study attempts to analyse the status of water availability, consumption and sufficiency in two Himalayan towns – Murree and Havellian from Pakistan's Indus Basin, using the primary data collected from 350 households, 26 town level focus groups and key informants. Findings revealed that groundwater is the main source of water on which around 85% of households are heavily dependent. Estimates of water availability, consumption and water sufficiency ratio (WSR) show that available groundwater is not sufficient (0.89) to meet the requirements for consumption in Havellian. However, in the case of Murree, available water is sufficient (1.92). Taking into account the national standards of water consumption, WSR estimates show that both towns have insufficient water availability (Murree: 0.68, Havellian: 0.50). There is evidence that in both towns, water is being mismanaged at household level. In addition, one-fifth of households reported that climate change has also affected the water availability in the towns over time. Factors such as rapid urbanization and population growth are likely to result in increased requirements of water in the future. Based on the findings, the study has suggested policy actions on protection, efficient use, diversification and governance of groundwater resources.

Can global precipitation datasets benefit the estimation of the area to be cropped in irrigated agriculture?

Abstract. The area to be cropped in irrigation districts needs to be planned according to the available water resources to avoid agricultural production loss. However, the period of record of local hydro-meteorological data may be short, leading to an incomplete understanding of climate variability and consequent uncertainty in estimating surface water availability for irrigation area planning. In this study we assess the benefit of using global precipitation datasets to improve surface water availability estimates. A reference area that can be irrigated is established using a complete record of 30 years of observed river discharge data. Areas are then determined using simulated river discharges from six local hydrological models forced with in situ and global precipitation datasets (CHIRPS and MSWEP), each calibrated independently with a sample of 5 years extracted from the full 30-year record. The utility of establishing the irrigated area based on simulated river discharge simulations is compared against the reference area through a pooled relative utility value (PRUV). Results show that for all river discharge simulations the benefit of choosing the irrigated area based on the 30 years of simulated data is higher compared to using only 5 years of observed discharge data, as the statistical spread of PRUV using 30 years is smaller. Hence, it is more beneficial to calibrate a hydrological model using 5 years of observed river discharge and then to extend it with global precipitation data of 30 years as this weighs up against the model uncertainty of the model calibration.

Climate Change and Its Impact on Rainfall

This paper reviews the climate change impact on rainfall as well as extreme events occurrences. The global extreme weather contributes to the uncertainties of the climate trend and water scarcity problems to the whole world. Thus, numerical models such as General Circulation Models (GCMs) have been developed to simulate the response of the global climate system to the expected increment of the greenhouse gases concentrations. However, the GCM cannot be directly applied to climate change impact studies, hence downscaling is needed. A large number of downscaling methods are available but there is no universal method exists at present that performs best for all conditions, depending on the application and this remains a subject of investigation. Therefore, this paper compares the performances among statistical and dynamical downscaling models that have been applied by different researchers in various purposes. It can be concluded that the statistical downscaling has been widely used and able to provide reliable climate projected results especially for Malaysia’s climate variables. This review is very significant especially to the policy maker in deciding the reliable climatic methods for the long term planning and management of water resources. Besides, the reliable projected rainfall will be very beneficial in estimating water availability and water resource policy.

Geometric shape, water availability and under ice volume of Alberta lakes

Water availability information can be vital to the execution of informed management decisions. Since only a small fraction of Alberta lakes have surveyed bathymetry data, accurate estimation of lake water availability is often challenging. In this study, we analyzed available bathymetry data from 77 lakes, distributed over six major river basins and five natural regions of Alberta, and developed dimensionless relationships between volume and depth. We compared these relationships with the analytical relationship between volume and depth for five idealized lake shapes, viz. as cylindrical, pseudo-parabolic, parabolic, conic, and inverse-parabolic. Our study shows that considering the volume-depth relationship, 48% of Alberta lakes fall under parabolic shape, 29% fall under conic shape, and the rest (23%) fall under either pseudo-parabolic, inverse-parabolic, or cylindrical shape. We also developed four different models to estimate maximum lake volume (a proxy of lake water availability) and 5% under ice volume (a proxy for winter allocation limit of lake water) assuming an ice thickness of 80 cm. These models have been developed in such a way that allows the user to apply the models based on data availability and can be used in absence of site-specific data (e.g., bathymetry) to estimate volume, and subsequently water availability in lakes. Finally, we propose a formulation of percent volume reduction due to small water withdrawal, which requires only maximum depth of a lake to estimate a potential volume reduction limit for a water withdrawal.

Blue Water in Europe: Estimates of Current and Future Availability and Analysis of Uncertainty

This study presents a regional assessment of future blue water availability in Europe under different assumptions. The baseline period (1960 to 1999) is compared to the near future (2020 to 2059) and the long-term future (2060 to 2099). Blue water availability is estimated as the maximum amount of water supplied at a certain point of the river network that satisfies a defined demand, taking into account specified reliability requirements. Water availability is computed with the geospatial high-resolution Water Availability and Adaptation Policy Assessment (WAAPA) model. The WAAPA model definition for this study extends over 6 million km2 in Europe and considers almost 4000 sub-basins in Europe. The model takes into account 2300 reservoirs larger than 5 hm3, and the dataset of Hydro 1k with 1700 sub-basins. Hydrological scenarios for this study were taken from the Inter-Sectoral Impact Model Inter-Comparison Project and included simulations of five global climate models under different Representative Concentration Pathways scenarios. The choice of method is useful for evaluating large area regional studies that include high resolution on the systems´ characterization. The results highlight large uncertainties associated with a set of local water availability estimates across Europe. Climate model uncertainties for mean annual runoff and potential water availability were found to be higher than scenario uncertainties. Furthermore, the existing hydraulic infrastructure and its management have played an important role by decoupling water availability from hydrologic variability. This is observed for all climate models, the emissions scenarios considered, and for near and long-term future. The balance between water availability and withdrawals is threatened in some regions, such as the Mediterranean region. The results of this study contribute to defining potential challenges in water resource systems and regional risk areas.

Remotely Sensed Methodologies for Crop Water Availability and Requirements in Precision Farming of Vulnerable Agriculture

Agriculture is mainly impacted by water availability. Differences in climate conditions and the appearance of severe events, like droughts, has a significant imprint on local, regional and global agricultural productivity. The goal of this paper is to present remotely sensed approaches for water availability and requirements in vulnerable agriculture. Earth Observation (EO) data contribute to precision agriculture for efficient crop monitoring and irrigation management. A drought susceptible region considered as vulnerable farming was chosen, in the Thessaly prefecture in Central Greece. Water availability is measured by means of precipitation frequency examination and drought estimation. Crop water requirements are measured by assessing crop evapotranspiration (ET) with the synergistic use of WV-2 satellite images and ground-truth data. The remote-based ETcsat is assessed by utilizing the reference ETo derived from Food and Agriculture Organization (FAO) methodology, while the meteorological data and Kc are evolved from Normalized Difference Vegetation Index (NDVI). According to the rainfall frequency studies, indicators demonstrate a significant precipitation decrease. The results reveal the importance of water availability estimation for facing agriculture water needs and the necessity for monitoring of drought conditions in a vulnerable Mediterranean area in order to plan an integrated strategy for climate adaptation. Moreover, the conclusions clarify the usefulness of collaborating innovative very high spatial and sperctral resolution EO images along with ground-truth data for crop ET monitoring and also the assimilation into the precision agriculture methodology which is valuable for optimal agricultural production.

A county‐level estimation of renewable surface water and groundwater availability associated with potential large‐scale bioenergy feedstock production scenarios in the United States

AbstractThis study examines fresh renewable water resources available for bioenergy feedstock production in the United States. The impacts of feedstock irrigation on surface and groundwater resources available to nonbioenergy sectors were quantified using a pair of water availability indexes: streamflow availability index and percolation flow availability index. The two metrics were applied to both historical (2008) and three possible future biomass production scenarios from the 2016 U.S. Billion‐Ton Report at the county level. For both historical and future scenarios, we found that the consumptive irrigation requirements for bioenergy feedstock account for <0.01% of annual streamflow in all but three counties in Nebraska. Results suggest that the irrigation demand of future biomass production could be supplied by annual renewable groundwater flow in about 94% of feedstock‐growing counties that use groundwater for irrigation, representing about 92% of production tonnage. Counties that require irrigation from nonrenewable groundwater resources are mostly located in the Northern Plains and Pacific regions. We also evaluated the sensitivity of crop water footprint estimation to soil moisture carryover by comparing blue water estimates from six different empirical and process‐based methods. Our findings suggest that accounting for preseason soil moisture is critical for representative blue water estimation, so that the irrigation water consumption is not overestimated. This is especially true in the Corn Belt region, where blue water estimates with and without preseason soil moisture would be about 1.9 versus 45.5 billion m3/year under the historical scenario. This difference is smaller in semiarid regions like the High Plains, but the blue water estimate can still triple if soil moisture is not considered. From the perspective of renewable surface water and groundwater resources, scaling feedstock production up in the High Plains and California will require careful planning integrated with water management strategies to improve water resource conservation.

Aquifer Responses to Rainfall through Spectral and Correlation Analysis

AbstractThe response time of hydrological components is an important feature for establishing the connections within a hydrological system and for characterizing it. This study proposes a quantitative and qualitative assessment on the response times of river discharge components, especially the baseflow, regarding the impulse provided by precipitation, using time series analyses of time and frequency domains. To this end, a set of complementary methods including correlation and spectral approaches was used, which are: autocorrelation, cross correlation, continuous wavelet transform, and cross wavelet transform. Such framework was applied in multiple and nested basin scales (53, 1,867, and 3,519 km2), located in the Jacaré‐Guaçu River Basin (Southeastern Brazil). Based on a digital filtering technique, it was found the baseflow contributes more than 80% of the annual streamflow, revealing a major role of the aquifer in regulating the river discharge in all three studied basins. In addition, we found baseflow represents a range of 24%–27% of the annual precipitation. The uniform results are mainly due to the similar physical characteristics found in those areas. Overall, our framework indicates a baseflow response time for a precipitation of approximately two years for the smaller basins and three years for the biggest one. Such results can support the water allocation in conjunctive water management, by providing estimates of future surface water availability.