Search Results (36)

Reliable rainfall regionalization underpins hydrological design and the transfer of rainfall information to ungauged or short-record sites, yet it is especially difficult in arid regions with strong spatial and temporal variability and short, uneven records. This study develops a multivariate, stability-validated framework for classifying rain gauges into candidate homogeneous rainfall regions across Saudi Arabia. A national database of 274 stations was screened for 2015–2023, and 202 stations were retained. Ten descriptors representing annual rainfall, interannual variability, L-skewness of annual maxima, within-day rainfall concentration, short-duration intensity, elevation, and distance from the coast were constructed from 5 min records; correlated concentration and intensity descriptors were compressed by block-wise principal component analysis into a seven-variable, robustly scaled feature matrix. Partitions from K = 2 to 10 were evaluated using internal-validity indices, the gap statistic, minimum cluster size, repeated-subsample stability, cross-algorithm agreement, and sensitivity to alternative feature representations. The diagnostics supported several low-order structures: the four-region K-means solution showed the highest subsample stability (median adjusted Rand index: 0.977) and best consensus rank, whereas Ward clustering and Gaussian mixture modeling favored a parsimonious three-region solution. At four regions, cross-algorithm agreement was moderate (adjusted Rand index: 0.526–0.663) and the partition was strongly reproduced under the robust original-variable and global principal component analysis-(PCA)PCA representations (0.804). The regions comprised a widespread arid interior, a small near-coastal group, a wetter western–southwestern group, and a coastal-foothill group, with distance from the coast, elevation, and short-duration intensity providing the strongest contrasts. The framework offers a reproducible basis for regional rainfall-frequency analysis, station pooling, and hydrological transfer, pending verified completeness data and formal homogeneity testing. Full article

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

Hydrologic models are instrumental in understanding the behavior of the Nile River Basin (NRB), yet their effectiveness is often limited by the basin’s complex hydrology and sparse observational records. This study applies a basin-scale hydrological modeling approach to simulate near-natural, pre-reservoir flow conditions in the NRB, while incorporating lake and wetland submodels. The basin was discretized into 34 sub-watersheds with an outlet at Aswan. The conceptual GR4J rainfall–runoff model was implemented within the Raven modeling framework, chosen for its parsimony and suitability for data-limited conditions. Multi-objective calibration used discharge data from the Global Runoff Data Centre (GRDC), supplemented by digitized historical records to improve spatial and temporal coverage. A stepwise calibration strategy was applied at 18 sites, focusing on pre-reservoir periods to capture natural flow dynamics. The calibrated model reproduces observed discharges with high skill. At the Aswan outlet, Nash–Sutcliffe Efficiency (NSE) values were 0.87 (calibration) and 0.80 (validation), with percent bias (PBIAS) values of 6.1% and 5.0%, respectively. Model performance was strongest in the Blue Nile, White Nile headwaters, and the Nile main stem. The model also successfully simulated the hydrological step-change observed in Lake Victoria during the 1960s, underscoring its robustness in simulating regional hydroclimate disruptions. This calibrated model enables reconstruction of historical Nile discharge and simulation of past hydrologic disturbances, including those driven by major volcanic eruptions over the past millennia. Full article

Understanding how water-quality models perform across different hydrological and biogeochemical contexts is essential for managing nutrient losses in agricultural catchments. This study evaluated SimplyP, a parsimonious phosphorus model, adapted to better represent Irish agricultural catchments and implemented within the flexible Mobius2 framework. Long-term, high-frequency monitoring data from the Agricultural Catchments Programme (ACP) were used for two sites: Ballycanew, a grassland catchment dominated by surface runoff, and Castledockrell, an arable, groundwater-driven catchment. Model calibration and validation were performed for streamflow (Q), suspended sediment (SS), and multiple phosphorus (P) fractions, with performance assessed using Kling–Gupta efficiency (KGE). In Ballycanew, the model reproduced Q, SS, and total P load well, with weaker agreement for total reactive phosphorus (TRP), likely reflecting unaccounted point sources during low flows. In Castledockrell, performance was moderate for Q and SS, but TRP and other P fractions were not adequately captured, highlighting the need for more detailed representation of subsurface P pathways in groundwater-dominated systems. Overall, SimplyP is well-suited to surface-runoff-dominated catchments with conventional phosphorus mobilisation. Its flexible implementation in Mobius2 allows relatively straightforward modifications, such as including groundwater-mediated P processes, to extend applicability to more complex systems. High-resolution ACP datasets were crucial for identifying model strengths and limitations, supporting refinement for improved nutrient management across diverse agricultural landscapes. Full article

Accurate streamflow forecasting is vital for sustainable water resource management but remains challenging due to pronounced spatiotemporal variability. This study evaluates two process-based models, the SWAT (comprehensive) and the GWLF (parsimonious), and a data-driven random forest (RF) model for monthly streamflow simulations in two contrasting Chinese basins: the humid southern basin (SSB) and the semi-arid northern basin (SRB). Using four statistical metrics (NSE, R², MAE, RMSE), we assess model accuracy, robustness in capturing extremes, and sensitivity to hydrological characteristics and data availability. The results reveal consistently superior performance in the SSB across all models, with SWAT demonstrating the highest overall accuracy—especially for peak flows—due to its physically based structure. The GWLF provides acceptable simulations with minimal data requirements, offering a practical alternative in data-limited regions, like the SRB. RF performs well in the SSB under zero-lag conditions but requires hydrologically informed lag structures in the SRB. However, it consistently underestimates high flows due to its lack of physical constraints. The findings underscore that model selection must, therefore, be guided not only by predictive performance but also by the underlying hydrological context, data availability, and the need for physical realism in decision-making. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article

Groundwater is a vital source of water for humanity, with up to 50% of global drinking water and 43% of irrigation water being derived from such sources. Quantitative assessment and accounting of groundwater is essential to ensure its sustainable management and use. TopNet-GW is a parsimonious groundwater model that was developed to provide groundwater simulation at national, regional, and local scales across New Zealand. At a national scale, the model can help local government authorities estimate groundwater resource reliability within and between regions. However, as many catchments are ungauged, the model cannot be calibrated to local conditions against observed data. This paper, therefore, describes a method to derive an a priori, reach-scale groundwater model parameter set from national-scale hydrogeological datasets. The parameter set includes coefficients of lateral (k_l) and vertical (k_r) conductivity and effective aquifer storage (S). When the parameter set was used with the TopNet-GW model in the Wairau catchment in the Marlborough region (South Island, New Zealand), it produced a poor representation of peak river flows but a more accurate representation of low flows (overall NSE 0.64). The model performance decreased in the smaller Opawa catchment (NSE 0.39). It is concluded that the developed a priori parameter set can be used to provide national groundwater modeling capability in ungauged catchments but should be used with caution, and model performance would benefit greatly from local scale calibration. The parameter derivation method is repeatable globally if analogous hydrological and geological information is available and thus provides a basis for the parameterization of groundwater models in ungauged catchments. Future research will assess the spatial variability of parameter performance at a national scale in New Zealand. Full article

Nowadays, changes in precipitation patterns together with the increasing water demand impose a sustainable management where the budget between water availability and demand is positively closed. A parsimonious hydrogeological modelling approach coupled with a soil water balance is developed and applied in order to quantify the hydrological and hydrogeological dynamics in a semi-arid region of the Mediterranean basin. In particular, the present work focuses on the hydrogeological dynamics of the catchment areas of Siedi, Foggia di Rau, Pigonati, and the Palmarini channels located in the Brindisi Plain, Southern Italy. In the last decades, in the Brindisi Plain the anthropization processes as well as the industrial and agricultural development have generated an intensive exploitation of both shallow and deep groundwater resources as well as their qualitative deterioration. A dry hydrologic year (2019–2020) caused a recharge deficit, resulting in a lowering of the groundwater level of the shallow aquifer compared to the expected seasonal value. The results evidence a sensitive natural system, where the variability of the rainfall regime combined with water withdrawal leads to a system that is very vulnerable to climate change impacts, such as the presence of erratic rainfall patterns affecting aquifer recharge. This study represents the first approach to couple a soil moisture balance model and groundwater flow model to assess the impact of changes in rainfall patterns on groundwater recharge for the Brindisi Plain aquifer. The developed integrated hydrogeological model can be applied to other sites with similar hydrogeological features and represents an important tool in order to evaluate the effectiveness of cost-effective sustainable actions for the management of the groundwater resources with respect to land-use practices and socio-economic aspects. Full article

Many applications of water resources planning and management depend on continuous streamflow predictions. A lack of data sources makes it difficult to predict stream flows in many world regions, including Saudi Arabia. Therefore, using simple, parsimonious models is more attractive in areas where data is scarce since they contain few parameters and require minimal input data. This study investigates the ability of simple, parsimonious water balance model models to simulate monthly time series of stream flows for poorly gauged catchments. The modified Schreiber’s empirical model and SIXPAR monthly water balance model were applied to simulate monthly streamflow in six mountainous watersheds located southwest of Saudi Arabia. The SIXPAR model was calibrated on one single gauged catchment where adequate hydrological data were available. The calibrated parameters were then transferred to the ungauged catchments based on transferring information using a physical similarity approach to regionalization. The results show that the simplified Schreiber’s model was found to consistently underestimates the monthly discharge, especially at low and moderate flow. The monthly water balance model SIXPAR based on the regionalization approach was found more capable of producing the monthly streamflow at the ungauged site under all flow conditions. This study’s finding agrees with other studies conducted in the same area using different modeling approaches. Full article

In the western United States, snow accumulation, storage, and ablation affect seasonal runoff. Thus, the prediction of snowmelt is essential to improve the reliability of water supply forecasts to guide water allocation and operational decisions. The current method used at the Colorado Basin River Forecast Center (CBRFC) couples the SNOW-17 temperature-index snow model and the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model in a lumped approach. Limitations in parameter transferability and calibration requirements for changing conditions with the temperature-index model motivated this research, in which new avenues were investigated to assess and prototype the application of an energy-balance snow model in a distributed modeling approach. The Utah Energy Balance (UEB) model was chosen to compare with the SNOW-17 model because it is simple and parsimonious, making it suitable for distributed application with the potential to improve water supply forecasts. Each model was coupled with the SAC-SMA model and the Rutpix7 routing model to simulate basin snowmelt and discharge. All the models were applied on grids over watersheds using the Research Distributed Hydrologic Model (RDHM) framework. Case studies were implemented for two study sites in the Colorado River basin over a period of two decades. The model performance was evaluated by comparing the model output with observed daily discharge and snow-covered area data obtained from remote sensing sources. Simulated evaporative components of sublimation and evapotranspiration were also evaluated. The results showed that the UEB model, requiring calibration of only a snow drift factor, achieves a comparable performance to the calibrated SNOW-17 model, and both provided reasonable basin snow and discharge simulations in the two study sites. The UEB model had the additional advantage of being able to explicitly simulate sublimation for different land types and thus better quantify evaporative water balance components and their sensitivity to land cover change. UEB also has a better transferability potential because it requires calibration of fewer parameters than SNOW-17. The majority of the parameters for UEB are physically based and regarded as constants characterizing spatially invariant properties of snow processes. Thus, the model remains valid for different climate and terrain conditions for multiple watersheds. Full article

Quantitative assessment of the frequency and magnitude of drought events plays an important role in preventing drought disasters and ensuring water security in river basins. In this paper, we modified a parsimonious two-parameter monthly water balance (TPMWB) model by incorporating the generalized proportionality hypothesis with precipitation and potential evapotranspiration as input variables. The modified TPMWB was then used to simulate the monthly hydrological processes of 30 sub-basins in the Han River basin. It is shown that the water balance model can satisfactorily simulate the hydrological regimes in the selected sub-basins. We derived the probability distribution functions of monthly runoff using the principle of maximum entropy to calculate the Standardized Runoff Index (SRI), and assessed the historical hydrological drought conditions. By investigating the correlation between four major drought characteristics (i.e., drought duration, drought severity, drought intensity, and drought inter-arrival time) and four dimensionless parameters representing the climatic and underlying properties of the basin, a conclusion can be drawn that the formation and development of hydrological drought in the Han River basin is mainly controlled by watershed storage factors, and the influence of climatic factors is also significant. The proposed approach provides a potential alternative for regional drought early warning and under changing environmental conditions. Full article

Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R² > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas. Full article

Modeling pollutant transport in heterogeneous media is an important task of hydrology. Pollutant transport in a non-homogeneous environment typically exhibits non-local transport dynamics, whose efficient characterization requires a parsimonious model with the non-local feature. This study encapsulates the non-local transport characteristic of pollutants into the peridynamic differential operator (PDDO) and develops a PDDO-based model for quantifying the observed pollutant non-local transport behavior. The simulation results show that the proposed model can describe pollutant non-local transport behavior in various heterogeneous media. The non-local nature of pollutant transport can be adjusted by pre-defined weight function $w\left(\right|\xi \left|\right)$ and horizon $H_x$. Applications show that the PDDO-based model can better capture pollutant non-local transport behavior than the classical advection–diffusion equation (ADE) model, especially for quantifying the tail of the experimental data late. Analyses further reveal that the PDDO-based model can characterize both normal (Fickian) and anomalous (Lévy) diffusion regimes. Full article

Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13,700 km² water district of Greece characterized by varying topography and hydrometeorological properties. Full article

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m³). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%. Full article