Rainfall-runoff modeling in ungauged basins continues to be a great hydrological research challenge. A novel approach is the Long-Short-Term-Memory neural network (LSTM) from the Deep Learning toolbox, which few works have addressed its use for rainfall-runoff regionalization. This work aims to discuss the application of LSTM as a regional method against traditional neural network (FFNN) and conceptual models in a practical framework with adverse conditions: reduced data availability, shallow soil catchments with semiarid climate, and monthly time step. For this, the watersheds chosen were located on State of Ceará, Northeast Brazil. For streamflow regionalization, both LSTM and FFNN were better than the hydrological model used as benchmark, however, the FFNN were quite superior. The neural network methods also showed the ability to aggregate process understanding from different watersheds as the performance of the neural networks trained with the regionalization data were better with the neural networks trained for single catchments.

Three change-point methodologies were used to detect changes in the mean value of annual streamflow series and analyse simultaneous changes in large-scale global sea surface temperature (SST) oscillations. To verify the relationship between the variables we used wavelet coherence analysis. A preliminary detection skill test was performed using asynthetic series and Pruned Exact Linear Time (PELT) presented the best results among the methods used (Pettitt test, Bai and Perron algorithm) when combined with a penalty selection via the Changepoints for a Range of Penalties (CROPS) method. However, the use of classical penalty functions resulted in a poor performance of PELT. The three methods showed an extremely high convergence rate (> 90%) for the correct change points and a smaller rate for false positives (< 24%). Changes in the streamflow mean value coincided with phase shift of the low-frequency indices Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), also corroborated by the wavelet results. Most of the changes can be associated with phase shift impacts in the South Atlantic Convergence Zone.

Predicting in ungauged basins (PUB) depends on the modelling uncertainties in the donor catchments (DCs). However, PUB is normally limited to a unique outcome, which may be quite uncertain, mainly in semi-arid basins where streamflow variability is high. Our goal is to assess the uncertainty in the parameter regionalization for streamflow prediction in semi-arid ungauged basins (UB). We used Differential Evolution Adaptive Metropolis (DREAM) for a parameter calibration that considers its intrinsic uncertainty. A basin similarity regionalization was performed to transfer the parameters to UB, considering one, three and five DCs. Leave-one-out cross-validation was applied for 28 gauged catchments. Regionalization performance had average Nash-Sutcliffe efficiency above 0.50. The approach considering one DC performed better than the others. The model of a poorly monitored catchment performed better using the transferred parameters from long-recorded, similar catchments than using those calibrated in the catchment itself. The developed regionalization may be a relevant tool for water management in drylands.

Os riscos de extremos hidrológicos para um local ou região estão associados aos modos de variação do clima, em suas diversas escalas temporais. A compreensão da variabilidade de baixa frequência ganha uma elevada importância em regiões onde eventos de seca são frequentes, por estar associada a longos períodos de secas consecutivas. O presente artigo analisou a relação entre a precipitação média da bacia a montante da estação fluviométrica de Iguatu, com os índices PDO e AMO (Pacific Decadal Oscillation e Atlantic Multidecadal Oscillation) através das metodologias de Análise de changepoint, Transformada de Ondeletas (TO), Transformada de Ondeletas Cruzadas (XTC) e Análise da Coerência das Ondeletas (WTC). Essa estação mensura as vazões afluente ao reservatório de Orós, um dos principais do Estado do Ceará (Brasil). A precipitação média da bacia foi obtida a partir de dados de pluviômetros. Os resultados permitiram estabelecer uma relação entre a precipitação da região e os índices PDO e AMO, indicando que um modelo baseado nos índices pode ter alguma capacidade preditiva do comportamento da precipitação local. Nota-se também que períodos com fases simultaneamente positivas (negativas) da PDO e da AMO resultam em um comportamento mais previsível das precipitações da região, com valores abaixo (acima) do normalmente esperado.

Socio-hydrology has recently garnered attention as a promising research paradigm among hydrologists. In this paper, we argue that viewing the socio-hydrological systems as a network of causal factors can assist in these efforts by revealing emergent properties of socio-hydrological systems. These factors, which are neither actors nor affiliations but symbolic representations of miscellaneous phenomena, represent the key variables affecting socio-hydrological system behaviour. Despite being crucial for understanding system dynamics, these factors are not captured by most equations of coupled human–water coevolutionary models. To demonstrate our method, we use qualitative case study research to analyse the Jaguaribe Basin in Ceará State-Brazil as a network of factors, i.e. mental conceptions, and representations of water resource management experts. We show that most results are unsurprising, exhibiting expected causal relationships. However, there was at least one unexpected result, which showed that the variable “conflict” was the single most sensitive variable to systemic evolution.

Streamflow forecasting plays an important role in ensuring the reliable supply of electricity in countries heavily reliant on hydropower. This paper proposes a novel framework that integrates various hydrological models, climate models, and observational data to develop a comprehensive forecasting system. Three families of models were employed: seasonal forecasting climate models integrated with hydrological rainfall-runoff models; stochastic or machine learning models utilizing endogenous variables, and stochastic or machine learning models that consider exogenous variables. The hyper-multimodel framework could successfully increase the overall performance of the scenarios generated through the use of the individual models. The quality of the final scenarios generated was directly connected to the performance of the individual models. Therefore, the proposed framework has potential to improve hydrological forecast for the Brazilian electricity sector with the use of more refined and calibrated individual models.