Detection and prediction of land use change impact on stream flow regime in Sahelian river basin, northwestern Nigeria

Detecting and predicting the impact of land use/land cover (LULC) changes on streamflow are crucial sources of information for the effective management and protection of land and water resources in Sahelian ecosystems such as the Hadejia river basin. In this study, LULC change detection was performed using ENVI, while the LULC modeling was conducted using the cellular automata (CA)–Markov in the IDRISI environment. However, the streamflow trend and variation were assessed using the Mann–Kendall (MK) trend test and the inverse distance weightage (IDW). Before the LULC modeling and projection (2030), the LULC was classified for 1990, 2000, and 2010 using supervise classification. Model output revealed a strong relationship between LULC and streamflow trend, thus, the decade 1990–2000, was the decade with high forest clearance and streamflow output, consequently severe floods. However, the decade 2000–2010 witnessed land use expansion mainly via construction (3.4%). Meanwhile, the scenario will slightly change in the future as agriculture is projected to expand by about 9.3% from 2010 to 2030 due to the increased human population. Thus, food insecurity aggravated by climate change should be anticipated, and measures to avert/reduce their effects must be initiated.

STREAMFLOW AND SUSPENDED SEDIMENT LOAD TRENDS IN TROTUȘ DRAINAGE BASIN: GEOMORPHIC IMPLICATIONS

The research focusing on the trends of the streamflow and suspended sediment, as well as on the controls involved in these changes, are currently topics of significant interest in fluvial geomorphology. In this context, the aim of this study is to analyze the trends of the streamflow discharge and the sediment load based on a geomorphological approach of the relations between the two variables. These trends were assessed based on three testing methods frequently employed in hydro-geomorphological studies (Mann–Kendall trend test, Șen's slope estimator and the innovative trend method) applied on data from 10 gauging stations located on major tributaries of Trotuș River. Results reveal an overall downward trend of the mean annual streamflow and an increasing trend of the mean annual suspended sediment load. Whereas the decreasing streamflow trend was linked to the diminishing precipitation amounts, the upward trend of suspended sediment load was related to the changes occurring within river channels as a result of flood events.

Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

The objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

Assessment of streamflow decrease due to climate vs. human influence in a semiarid area

This paper uses the Budyko method to investigate mean annual streamflow changes, due to climate variation and human influence, in the important Karkheh River Basin in western Iran. To validate the results, hydrological modelling (HBV model) and Landsat 5 Thematic Mapper (TM) images were used for the study period between 1980 and 2012. The recently developed DBEST (Detecting Breakpoints and Estimating Segments in Trend) method identified an abrupt negative change in the streamflow trend in 1994–5. The results show that the observed streamflow decrease in the Karkheh River is associated with both climate variation and human influence. The combination of increased irrigated area (from 9 to 19 % of the total basin area), reduction of forests (from 11 to 3 %), and decreasing annual precipitation has significantly reduced streamflow in the basin. Moreover, the results show that the streamflow reduction in the Karkheh Basin is more sensitive to the change in precipitation than temperature.

How good are hydrological models for gap-filling streamflow data?

Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1 % missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

How good are hydrological models for gap-filling streamflow data?

Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood and drought analysis and hydrological response variable estimates and predictions. However, there is lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing rate is less than 10 %, the gap-filled streamflow data obtained using calibrated hydrological models perform almost as same as the benchmark data (less than 1 % missing) for estimating annual trends for 217 unregulated catchments widely spread in Australia. Furthermore, the relative streamflow trend bias caused by the gap-filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap-filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

Natural regime of streamflow trends in Macedonia

This study investigates the annual and seasonal trends of minimum, mean and maximum streamflow, analyzed on 13 gauges/streams with natural regime, predominantly mountainous and homogeneously distributed in the studied area. The varying period of at least 40 years is used in the analysis. After the pre-whitening TFPW method was applied, the Mann-Kendall and Sen’s slope tests were used for trend testing. The analysis detects significant decreasing trends in the country (according to a = 0.1 significance level). In general, the streamflow shows levels of decrease in almost all streams with lower or higher magnitude (from 0.1 to 0.01). The results provide a unique assessment of streamflow trends in the country and the current findings are consistent with those in other regions of Europe, especially in Southern Europe. Significant trends of decrease have been found in each of the 13 streamflow gauges throughout Macedonia without a single positive significant trend. The test confirmed the general decreasing streamflow trend in the country; even the stations without any significant decreasing results are generally heading downward.

Attribution of high resolution streamflow trends in Western Austria – an approach based on climate and discharge station data

The results of streamflow trend studies are often characterized by mostly insignificant trends and inexplicable spatial patterns. In our study region, Western Austria, this applies especially for trends of annually averaged runoff. However, analysing the altitudinal aspect, we found that there is a trend gradient from higher-altitude to lower-altitude stations, i.e. a pattern of mostly positive annual trends at higher stations and negative ones at lower stations. At mid-altitudes, the trends are mostly insignificant. Here we hypothesize that the streamflow trends are caused by the following two main processes: on the one hand, melting glaciers produce excess runoff at higher-altitude watersheds. On the other hand, rising temperatures potentially alter hydrological conditions in terms of less snowfall, higher infiltration, enhanced evapotranspiration, etc., which in turn results in decreasing streamflow trends at lower-altitude watersheds. However, these patterns are masked at mid-altitudes because the resulting positive and negative trends balance each other. To support these hypotheses, we attempted to attribute the detected trends to specific causes. For this purpose, we analysed trends of filtered daily streamflow data, as the causes for these changes might be restricted to a smaller temporal scale than the annual one. This allowed for the explicit determination of the exact days of year (DOYs) when certain streamflow trends emerge, which were then linked with the corresponding DOYs of the trends and characteristic dates of other observed variables, e.g. the average DOY when temperature crosses the freezing point in spring. Based on these analyses, an empirical statistical model was derived that was able to simulate daily streamflow trends sufficiently well. Analyses of subdaily streamflow changes provided additional insights. Finally, the present study supports many modelling approaches in the literature which found out that the main drivers of alpine streamflow changes are increased glacial melt, earlier snowmelt and lower snow accumulation in wintertime.