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This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

Hydrometric data poverty compounds the challenge of accounting for uncertainties in non-stationary stage–discharge relationships. This paper builds on three methods to explore the integration of a dynamic approach to rating curve assessment and a physically based Bayesian framework for quantifying discharge amid geomorphologically induced rating shifts in a sparsely gauged alluvial river. The Modified GesDyn–FlowAM–BaRatin method entails sequentially segmenting gaugings according to residual indicators of riverbed instability and channel conveyance variability, leveraging cross-sectional surveys to augment calibration data, and eliciting hydraulic priors for probabilistic rating curve estimation. This method is applied to a Philippine watershed, where quarrying near the gauging station has ostensibly caused morphodynamic adjustments. Time-variable credible intervals for discharge are computed. The optimal estimates root mean square error (RMSE = 2.96 m3/s) from maximum a posteriori rating curves outperform the hydrographer’s benchmark (RMSE = 5.00 m3/s), whose systematic errors from the gauged flows arise from lapses in shift detection. [ABSTRACT FROM AUTHOR]

Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a climate change hotspot, where the temperature is increasing faster than the global annual mean surface temperature, with the largest reduction of precipitation. Consequently, freshwater availability is expected to decrease substantially. In this context, freshwater systems are especially vulnerable in terms of meeting the water demands and ecosystem requirements we know today. In this paper, we present an extension of the eco-engineering decision scaling (EEDS) method to explore trade-offs in agricultural and ecologic metrics at the catchment scale across a range of unknown future hydrological and climate states. The extended EEDS method evaluates current water resource management rules focusing on agricultural and ecologic objectives, identifies climate hazards that make the system fail and assesses climate risk in three time horizons for the design of adaptation measures. The case study is the Serpis River basin, Spain, where 72% of available water is used for agricultural purposes. [ABSTRACT FROM AUTHOR]

Natural streamflow data is required in many hydrological applications. However, many basins are located in data-scarce regions or are impacted by human construction and activities. In this paper, we explore three machine learning algorithms, namely artificial neural networks, random forest and light gradient boosting machine, to simultaneously estimate all the parameters of the coupled modèle du Génie Rural à 4 paramètres Journaliers (GR4J) and snow accounting routine called CemaNeige model. A database of 675 basins in the USA and Quebec is used to train and test ensembles. After using the estimated parameters in GR4J, the resulting naturalized streamflow series are compared with those obtained by the established drainage area ratio and spatial proximity transfer methods in 11 test basins. The results indicate that the machine learning algorithms outperform the drainage area ratio and spatial proximity transfer methods. Among machine learning algorithms, random forests obtain lower (better) continuous ranked probability scores than the other methods for 10 out of 11 test basins. [ABSTRACT FROM AUTHOR]

The increasing soil erosion (SE) and the associated problems for society, economy, and environment sparked a lot of interest in estimating and mapping SE at different basin scales. The estimation of SE exhibits that SE ranges from 10 to 50 t ha-1 yr-1, with a mean SE of 20 t ha-1 yr-1. The very steep slopes account for 54.21% of total soil loss. The SRB areas where soil loss rates are >10 t ha-1 yr-1 are considered the target areas which account for 27% of the study area and 96% of the soil loss). The high SY is concentrated only in the first-order basins located in a higher slope zone in the northern part of the river. Besides, basin morphometry (basin shape, relative relief) and anthropogenic activities (agricultural land) are retained in the PSLR model as significant factors contributing to SY in the entire river basin. [ABSTRACT FROM AUTHOR]

Landscape change is intricately linked to natural resource utilization. Landscape dynamics are closely tied to land use and land cover (LULC), serving as a representation of ecosystems and human activities. In the Citarum River Basin, Indonesia, a comprehensive approach is necessary to comprehend landscape dynamics as a manifestation of human interaction with the environment. This research aims to analyze landscape dynamics and its factors that can significantly drive changes. We focused on the Cirasea Watershed, which serves as an upper region of the Citarum River Basin. Data was acquired from Landsat-series imageries from 1993 to 2023, and LULC analyses were conducted using classification and regression trees (CART), random forest (RF), and support vector machine (SVM). We analyzed seven independent variables, including slope (X1), elevation (X2), main river (X3), population (X4), central business district (X5), distance from the past settlements (X6), and accessibility (X7) using multivariate analysis. This research found that RF stands out as the optimal choice for LULC analysis over CART and SVM because it had the highest overall accuracy and overall kappa (0.91-0.92, 0.88-0.89). Notably, there was a substantial 273.43% increase in built-up areas, coupled with significant declines in plantations and horticultures. LULC changes was more pronounced in the lower areas near Bandung City. LR model highlighted X1, X3 and X6 as the significant driving forces for built-up areas expansion (r-square 0.44 with p-value < 0.01 and 95% confidence level). Without effective spatial planning, flat areas near rivers and past settlements have the greatest potential for LULC changes. [ABSTRACT FROM AUTHOR]

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61-51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions. [ABSTRACT FROM AUTHOR]

Geomorphological mapping of glaciers and glacial landforms helps describe the current environment and investigate past glacial changes. Here we present the first detailed 1:80,000 glacial geomorphological map of the Vanch River basin (about 2100 km²), the western Pamirs, Tajikistan [38.6667°N, 72.0000°E]. Mapping was performed using DEM (30 m), Landsat 8 imagery (15 m), and Google Earth images (~1 m). The resultant map provides the spatial distribution of glaciers and associated erosional and depositional landforms such as cirques, glacial valleys, rock glaciers, and moraines. Morphology of the lower section of the main valley outlines the limits of Vanch Glacier in the Middle Pleistocene. Comparison with former glacier inventories indicates that the glaciers have generally been stable, but the proportion of debris-covered areas has increased since 2007. The identified 184 intact rock glaciers represent hidden water storage, providing a new basis for estimating water resources in the basin. [ABSTRACT FROM AUTHOR]