The fate of seasonal river ecosystem habitats under climate change essentially depends on the changes in annual recharge of the river, which are related to alterations in precipitation and evaporation over the river basin. Therefore, the change in climate conditions is expected to significantly affect hydrological and ecological components, particularly in fragmented ecosystems. This study aims to assess the impacts of climate change on the streamflow in the Dinder River basin (DRB) and to infer its relative possible effects on the Dinder National Park (DNP) ecosystem habitats in Sudan. Four global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 5 and two statistical downscaling approaches combined with a hydrological model (SWAT – the Soil and Water Assessment Tool) were used to project the climate change conditions over the study periods 2020s, 2050s, and 2080s. The results indicated that the climate over the DRB will become warmer and wetter under most scenarios. The projected precipitation variability mainly depends on the selected GCM and downscaling approach. Moreover, the projected streamflow is quite sensitive to rainfall and temperature variation, and will likely increase in this century. In contrast to drought periods during the 1960s, 1970s, and 1980s, the predicted climate change is likely to affect ecosystems in DNP positively and promote the ecological restoration for the habitats of flora and fauna.

Irrigated production around the world has significantly increased over the last decade. However, climate change is a new threat that could seriously aggravate the irrigation water supplies and request. In this study, the data is derived from the IPCC Fifth Assessment Report (AR5). For the climate change scenarios, five Global Climate Models (GCMs) have been used. By using the CROPWAT approach of Smith, the net irrigation water requirement (IRnet) was calculated. For the estimation of the potential evapotranspiration (Epot), the method in Raziei and Pereira was used. According to representative concentration pathway (RCP) 4.5, these increases vary between 0.74% (North America) and 20.92% (North America) while the RCP 8.5 predict increases of 4.06% (sub-Saharan Africa) to more than 68% (North America). The results also show that the region of Latin America is the region with the large amount of IRnet with coprime value between 1.39 km³/yr (GFDL 4.5) and 1.48 km³/yr (CSIRO 4.5) while sub-Saharan Africa has the smallest IRnet amount between 0.13 km³/yr (GFDL 8.5) and 0.14 km³/yr (ECHAM 8.5). However, the most affected countries by this impact are those in sub-Saharan Africa. This study will probably help decision-makers to make corrections in making their decision.

An accurate and real-time flood forecast is a crucial nonstructural step to flood mitigation. A support vector machine (SVM) is based on the principle of structural risk minimization and has a good generalization capability. The ensemble Kalman filter (EnKF) is a proven method with the capability of handling nonlinearity in a computationally efficient manner. In this paper, a type of SVM model is established to simulate the rainfall–runoff (RR) process. Then, a coupling model of SVM and EnKF (SVM + EnKF) is used for RR simulation. The impact of the assimilation time scale on the SVM + EnKF model is also studied. A total of four different combinations of the SVM and EnKF models are studied in the paper. The Xinanjiang RR model is employed to evaluate the SVM and the SVM + EnKF models. The study area is located in the Luo River Basin, Guangdong Province, China, during a nine-year period from 1994 to 2002. Compared to SVM, the SVM + EnKF model substantially improves the accuracy of flood prediction, and the Xinanjiang RR model also performs better than the SVM model. The simulated result for the assimilation time scale of 5 days is better than the results for the other cases.

In the semiarid loess region of northwest China, the use of gravel and sand as a surface-mulching material has been an indigenous farming technique for crop production for over 300 years. A field and modeling study was carried out to quantify the effect of a gravel-sand mulch material on soil water and soil temperature in the semiarid loess region of northwest China. The field experiment (April 4, 2001–July 12, 2001) consisted of two treatments: (1) no surface mulching as a control, and (2) gravel-sand surface-mulching. The numerical model HYDRUS-1D was used in the research reported in this paper. The results showed that compared with the nonmulched condition the gravel-sand mulched field had a more favorable soil-water and temperature environment for plant growth. In the initial stage of watermelon growth, there was larger soil-water storage (3.8 cm versus 1.9 cm) and soil temperature (10.8°C versus 6.2°C) in the 0–20 cm soil layer with the gravel-sand mulch. The mulch provided very beneficial wa...