Water Resources Applications Research Articles

Solutions and case studies for thermally driven reactive transport and porosity evolution in geothermal systems (reactive Lauwerier problem)

Abstract. Subsurface non-isothermal fluid injection is a ubiquitous scenario in energy and water resource applications, which can lead to geochemical disequilibrium and thermally driven solubility changes and reactions. Depending on the nature of the solubility of a mineral, the thermal change can lead to either mineral dissolution or precipitation (due to undersaturation or supersaturation conditions). Here, by considering this thermo-hydro-chemical (THC) scenario and by calculating the temperature-dependent solubility using a non-isothermal solution (the so-called Lauwerier solution), thermally driven reactive transport solutions are derived for a confined aquifer. The coupled solutions, hereafter termed the “reactive Lauwerier problem”, are developed for axisymmetric and Cartesian symmetries and additionally provide the porosity evolution in the aquifer. The solutions are then used to study two common cases: (I) hot CO2-rich water injection into a carbonate aquifer and (II) hot silica-rich water injection into a sandstone aquifer, leading to mineral dissolution and precipitation, respectively. We discuss the timescales of such fluid–rock interactions and the changes in hydraulic system properties. The solutions and findings contribute to the understanding and management of subsurface energy and water resources, such as aquifer thermal energy storage, aquifer storage and recovery and reinjection of used geothermal water. The solutions are also useful for developing and benchmarking complex coupled numerical codes.

Heterogeneous reservoir seepage characterized by geophysical, hydrochemical, and hydrologic methods

Seepage characterization from reservoirs is challenging for various environmental and water resource applications. Nonintrusive geophysical methods can achieve excellent detection results, but they cannot provide a quantitative description of the amount of seepage. Integrating geophysical methods with traditional hydrochemical and hydrologic methods enables simultaneous localization and quantitative analysis of heterogeneous reservoir seepage. Ground and waterborne electrical resistivity tomography are used to scan a reservoir under a typical cutoff wall antiseepage system, and the measurements reveal the northern preferential infiltration area. The hydrochemical results reveal that the seepage bypass of the cutoff wall has led to evident ion concentration variations in groundwater near the dam. Based on the geologic stratum information, the typical cutoff wall antiseepage system is divided into three scenarios for numerical simulation to calculate the seepage. The total seepage of the reservoir over 15 months is approximately 7.15 × 106 m3, which is consistent with traditional water budget methods. However, in the northern part of the reservoir, only 27% of the entire dam contributes 77% of the total seepage. All methods have confirmed that the heterogeneous seepage is caused by the absence of an antiseepage seal in the northern region, which should be a primary focus for future monitoring and operational efforts. With the geophysical results undergoing extensive cross-validation, the results from the preceding methods are comprehensively leveraged, effectively lowering uncertainties and overcoming the limitations of geophysical methods in quantitatively characterizing seepage.

Real-Time Optimal Scheduling of a Water Diversion System Using an Improved Wolf-Pack Algorithm and Scheme Library

A water diversion system (WDS) with cascade pumping stations (CPSs) plays an important role in the application of water resources. However, high energy consumption is reported due to unreasonable scheduling schemes and long decision times. Herein, this paper presents a new method to achieve optimal scheduling schemes effectively, including the head allocation of CPSs, the number of running pumps, and pump blade angles. A double-layer mathematical model for a WDS was established with the goal of achieving minimal energy consumption, considering the constraints of flow rate, water level, and the characteristics of pump units. The inner-layer model was used to obtain scheduling schemes of single-stage pumping stations, as well as the water levels and flow rates of water channels, while the outer-layer model was used to optimize inter-stage head allocation. An improved wolf-pack algorithm (IWPA) was proposed to solve the model, using a Halton sequence to obtain the uniform initial population distribution and introducing simulated annealing (SA) to improve the global searchability. Moreover, an idea for a pre-established scheme library was suggested for inner-layer models to obtain the solutions in real time with less calculation workload. Taking an actual project as a case, in contrast with the actual schemes, the optimal scheduling method could result in energy savings of 14.37–20.39%, a CO2 emission reduction of 13–32 tons per day, and water savings of 0.14–18.34%. Moreover, the time complexity decreased to square order, and the CPU time of the optimal method was about 1% that of the traditional method. This study provides an efficient method for the high-value utilization of energy and water resources for a WDS.

A Photonic Immunosensor Detection Method for Viable and Non-Viable E. coli in Water Samples.

Detection and enumeration of coliform bacteria using traditional methods and current molecular techniques against E. coli usually involve long processes with less sensitivity and specificity to distinguish between viable and non-viable bacteria for microbiological water analysis. This approach involves developing and validating an immunosensor comprising ring resonators functionalized with specific antibodies surrounded by a network of microchannels as an alternative method for detecting and indirectly enumerating Escherichia coli in samples of water for consumption. Different ELISA assays were conducted to characterize monoclonal and polyclonal antibodies selected as detection probes for specific B-galactosidase enzymes and membrane LPS antigens of E. coli. An immobilization control study was performed on silicon nitride surfaces used in the immunosensor, immobilized with the selected antibodies from the ELISA assays. The specificity of this method was confirmed by detecting as few as 10 CFU/mL of E. coli from viable and non-viable target bacteria after applying various disinfection methods to water samples intended for human consumption. The 100% detection rate and a 100 CFU/mL Limit of Quantification of the proposed method were validated through a comprehensive assessment of the immunosensor-coupled microfluidic system, involving at least 50 replicates with a concentration range of 10 to 106 CFU/mL of the target bacteria and 50 real samples contaminated with and without disinfection treatment. The correlation coefficient of around one calculated for each calibration curve obtained from the results demonstrated sensitive and rapid detection capabilities suitable for application in water resources intended for human consumption within the food industry. The biosensor was shown to provide results in less than 4 h, allowing for rapid identification of microbial contamination crucial for ensuring water monitoring related to food safety or environmental diagnosis and allowing for timely interventions to mitigate contamination risks. Indeed, the achieved setup facilitates the in situ execution of laboratory processes, allowing for the detection of both viable and non-viable bacteria, and it implies future developments of simultaneous detection of pathogens in the same contaminated sample.

Remote sensing of mountain snow from space: status and recommendations

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snow water equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Time series analysis and development of simulation model for monthly rainfall using ARIMA model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregre ssive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting monthly rainfall in Junagadh Station, Gujarat. Utilizing 50 years of historical data (1968 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (1,0,0)(3,1,1)12 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Stochastic Time Series Analysis, Modeling, and Forecasting of Weekly Rainfall Using Sarima Model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregressive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting weekly rainfall in Junagadh Station, Gujarat. Utilizing 53 years of historical data (1963 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (0,0,4)(0,1,1)52 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Capturing the Onset of Mountain Snowmelt Runoff Using Satellite Synthetic Aperture Radar

AbstractThe timing of snowmelt runoff is critical for water resource applications, but its spatiotemporal evolution remains poorly understood. We present a scalable approach to map snowmelt runoff onset using Sentinel‐1 synthetic aperture radar data for the past 8 years with 10 m spatial resolution and a median temporal resolution of 3.9 days. A systematic analysis of stratovolcanoes in the Western United States showed that snowmelt runoff onset is strongly dependent on elevation (r = 0.81), with a median runoff onset lapse rate of 4.9 days per 100 m of elevation gain. During the 2015 snow drought, we observed snowmelt runoff onset 25 days early relative to the 2015–2022 median. We document a median shift in snowmelt runoff onset of +2.0 days later in the year per year between 2016 and 2022. Our open‐source tools can be used to create snowmelt runoff onset maps anywhere on Earth.

Mass‐Conserving Downscaling of Climate Model Precipitation Over Mountainous Terrain for Water Resource Applications

AbstractA mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined.

To what extent does river routing matter in hydrological modeling?

Abstract. Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration.

Quantitative Precipitation Estimation in the Tianshan Mountains Based on Machine Learning

Precipitation in the Tianshan Mountains is abundant, and the quantitative estimation of precipitation in mountainous areas is important to the application and evaluation of regional water resources. With remote sensing technology, satellite inversion of precipitation can estimate precipitation in mountainous areas. However, the Tianshan Mountain terrain is complex, and the spatiotemporal variation in precipitation is large, so the accuracy of satellite precipitation inversion is not high. Here, precipitation data from around 1000 automatic weather stations in the Tianshan Mountains are used to study the correction technology of the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (GPM) mission’s (IMERG) monthly precipitation products using stepwise regression (STEP), geographically weighted regression (GWR), and random forest (RF). First, geographic information system technology was used to extract topographic variables from a digital elevation model, and vegetation indexes, which are important precipitation indicators, were introduced as explanatory factors to correct satellite precipitation data. Second, GPM IMERG precipitation was corrected by establishing the stepwise regression, the geographically weighted regression model, and the random forest model. The three correction methods can improve the GPM IMERG in terms of relative bias, root mean square error, correlation coefficient, and Nash–Sutcliffe efficiency, while the random forest method shows better corrections than the two traditional methods. For dense rainfall stations, the geographically weighted regression method is as effective as random forest. For different altitudes, the results show that RF has the best correction effect in the first three zones, but the correction effect in the last zone (over 3000 m) is worse than STEP. This study provides a practical reference method for estimating precipitation data in the non-rainfall observation area, which helps to deepen the scientific understanding of the water resource distribution in the Tianshan Mountains and provide scientific data support for regional hydrological and meteorological research.

Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era

Hydrology and land surface and models (HM and LSM) are essential tools for estimating global terrestrial water storage (TWS), an important component of the global water budget for assessing the accessibility and long-term variability of water supplies. With the expansion of open-source and open-data policies, the community can now perform model TWS simulation from source codes as well as directly exploit end-user hydrologic products for water resource applications. Regardless of the model effectiveness and usability, an accuracy assessment is necessary to quantify the model’s global and regional strengths, weaknesses, and reliability. This paper compares the most recent global TWS estimates from six models, namely the PCRaster Global Water Balance (PCR-GLOBWB), Noah, Noah-Multiparameterization (Noah-MP), Catchment LSM, and Variable Infiltration Capacity (VIC), and Community Atmosphere Biosphere Land Exchange (CABLE)—the latter of which is cross validated for the first time. TWS observations from the Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions between 2002 and 2021 are used to validate the model. The analyses show that Noah-MP outperforms other models in terms of global average correlations and root mean square errors. PCR-GLOBWB performance is superior in irrigated regions because of the inclusion of human intervention components in the model. CABLE, a core LSM of the Australian climate model, significantly outperforms all others in Australia. CLSM performs reasonably well, but the TWS long-term trend appears to be incorrect due to an overestimated groundwater component. Noah performs similarly (but inferiorly) to Noah-MP, most likely due to model physics sharing. VIC has the least agreement with GRACE and GRACE-FO. The evaluation also sheds some light on the role of forcing data in model performance, particularly for ready-to-use products such as GLDAS, where incorporating MERRA-2 or ERA5 data into GLDAS Noah simulations may potentially improve its TWS accuracy, which has previously been overlooked due to limited modeling capacity. Despite each model’s unique strength, the ensemble mean TWS, particularly when Noah-MP and PCR-GLOBWB are included, yields better TWS estimates than an individual model result. The findings of this study could serve as a benchmark for future model development and the data published in this paper could aid in the scientific advancement and discoveries of the hydrology community.

Do Derived Drought Indices Better Characterize Future Drought Change?

AbstractCurrent methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments.

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

Low complexity single-layer neural network for enhanced rainfall estimation using microwave links

AbstractA low complexity accurate model for precipitation estimation is crucial for monitoring several hydrological and water resource applications. Based on the R-k empirical power-law relation described by the P.838-3 ITU recommendation, rainfall rate can be predicted based on specific attenuation of microwave links. The accuracy of this method is impacted by several ambiguities and errors. In order to overcome these limitations, numerous highly complex pre-treatment and post-processing methods should be used. As an alternative method of low complexity, a supervised learning algorithm using a single-layer neural network (the perceptron) is suggested in this paper. Optimal weights parameters were obtained based on the minimization of the mean square error (MSE). A case study was carried out using 40 days of data gathered from two commercial microwave links (CMLs) and one rain gauge. Experimental results showed that this machine learning-supervised approach performed better than the R-k-based method. The mean square error of the path-averaged rainfall rate was reduced from 0.13 mm2 h-1 to 0.08 mm2 h-1 for training data, and from 0.2 mm2 h-1 to 0.1 mm2 h-1 for test data. This promising alternative method for rainfall estimation could considerably improve the efficiency of many applications, such as those developed for real-time urban flood risk management.

An overview of streamflow prediction using random forest algorithm

Since the first application of Artificial Intelligence in the field of hydrology, there has been a great deal of interest in exploring aspects of future enhancements to hydrology. This is evidenced by the increasing number of relevant publications published. Random forests (RF) are supervised machine learning algorithms that have lately gained popularity in water resource applications. It has been used in a variety of water resource research domains, including discharge simulation. Random forest could be an alternate approach to physical and conceptual hydrological models for large-scale hazard assessment in various catchments due to its inexpensive setup and operation costs. Existing applications, however, are usually limited to the implementation of Breiman's original algorithm for extrapolation and categorization issues, even though several developments could be useful in handling a variety of practical challenges in the water sector. In this section, we introduce RF and its variants for working water scientists, as well as examine related concepts and techniques that have earned less attention from the water science and hydrologic communities. In doing so, we examine RF applications in water resources, including streamflow prediction, emphasize the capability of the original algorithm and its extensions, and identify the level of RF exploitation in a variety of applications.

Changes in Meteorological Dry Conditions across Water Management Zones in Uganda

This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen's method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP's CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought.

Research on the Application of Water Resources Optimal Allocation Model Based on Fuzzy Optimization Theory

Research on the Application of Water Resources Optimal Allocation Model Based on Fuzzy Optimization Theory